Compare commits
1 Commits
| Author | SHA1 | Date |
|---|---|---|
 Deep Koluguri
|
c8deeaf9e0 |
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@ -1,6 +0,0 @@
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node_modules/
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frontend/node_modules/
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backend/node_modules/
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frontend/dist/
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.git/
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.env
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@ -1,16 +0,0 @@
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name: Build Institutional Trader
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on: [push]
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jobs:
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build-and-deploy:
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runs-on: ubuntu-latest
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env:
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DOCKER_HOST: tcp://172.17.0.1:2375
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steps:
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- name: Checkout
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uses: actions/checkout@v3
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- name: Build and push
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run: |
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docker build -t 192.168.8.250:5000/market:latest .
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docker push 192.168.8.250:5000/market:latest
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@ -1,148 +0,0 @@
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# Backtesting Implementation Summary
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## Completed Tasks
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### 1. Database Schema Validation ✓
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- Created `backend/scripts/validateSchema.js` script
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- Validates all required tables exist
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- Checks indexes on critical tables
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- Verifies PRIMARY KEY constraints
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- Checks for data type inconsistencies
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- Can be run with: `node backend/scripts/validateSchema.js`
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### 2. Historical Data Pull Configuration ✓
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- Updated `yahhooscript.py`:
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- Changed `LOOKBACK_DAYS_INTRADAY` from `1` to `30` (1 month)
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- Enhanced `run_intraday_batched()` function to automatically select interval:
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- 1-7 days: Uses 1-minute intervals
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- 8-60 days: Uses 5-minute intervals (handles 30-day requirement)
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- >60 days: Uses 15-minute intervals
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- Handles Yahoo Finance limitations gracefully
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### 3. Enhanced Backtester ✓
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- Created `backend/src/services/performanceMetrics.js` with:
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- Sharpe ratio calculation
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- Maximum drawdown tracking
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- Win/loss streak calculation
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- Session-based performance (PRE/RTH/POST)
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- Best/worst performing symbols
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- Equity curve generation
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- Updated `backend/src/services/backtester.js`:
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- Integrated new performance metrics
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- Enhanced price lookup with daily data fallback
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- Improved error handling for missing data
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### 4. Backtest API Endpoints ✓
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- Enhanced `backend/src/routes/backtest.js` with:
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- `POST /api/backtest/run` - Run single backtest
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- `POST /api/backtest/batch` - Run multiple backtests
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- `GET /api/backtest/results/:id` - Get backtest results by ID
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- `POST /api/backtest/compare` - Compare multiple strategies side-by-side
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- Added strategy scoring for ranking
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- Maintained backward compatibility with existing endpoints
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## Usage
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### Running Schema Validation
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```bash
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cd backend
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node scripts/validateSchema.js
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```
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### Pulling Historical Data
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```bash
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# Pull data for all symbols (30 days, 5-minute intervals)
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python yahhooscript.py
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# Pull data for a single symbol
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python yahhooscript.py --only AAPL
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```
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### Running Backtests via API
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**Single Backtest:**
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```bash
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curl -X POST http://localhost:3010/api/backtest/run \
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-H "Content-Type: application/json" \
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-d '{
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"pattern": {
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"minRocketScore": 5.0,
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"requireTapeAlignment": true,
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"session": "RTH"
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},
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"options": {
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"lookbackDays": 30,
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"exitStrategy": "target_stop",
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"targetPct": 1.5,
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"stopPct": 1.5
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}
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}'
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```
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**Compare Strategies:**
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```bash
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curl -X POST http://localhost:3010/api/backtest/compare \
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-H "Content-Type: application/json" \
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-d '{
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"strategies": [
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{
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"name": "High Score RTH",
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"pattern": { "rocketScoreMin": 5, "session": "RTH" }
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},
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{
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"name": "Tape Aligned",
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"pattern": { "rocketScoreMin": 3, "tapeAligned": true }
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}
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],
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"options": {
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"lookbackDays": 30
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}
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}'
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```
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## New Metrics Available
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The enhanced backtester now provides:
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- **Sharpe Ratio**: Risk-adjusted return metric
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- **Maximum Drawdown**: Largest peak-to-trough decline
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- **Win/Loss Streaks**: Consecutive wins and losses
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- **Session Performance**: Separate metrics for PRE/RTH/POST sessions
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- **Symbol Performance**: Best and worst performing symbols
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- **Equity Curve**: Portfolio value over time
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## Testing
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### Quick Test Commands
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```bash
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# Validate database schema
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cd backend
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npm run validate-schema
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# Test backtester functionality
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npm run test-backtester
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# Pull historical data (single symbol for testing)
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python yahhooscript.py --only AAPL
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# Pull full universe (30 days)
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python yahhooscript.py
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```
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See `TESTING_GUIDE.md` for detailed testing instructions.
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## Next Steps
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1. **Run Schema Validation**: `npm run validate-schema` in backend directory
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2. **Pull Historical Data**: Run `python yahhooscript.py --only AAPL` to test, then full universe
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3. **Test Backtesting**: Run `npm run test-backtester` or use API endpoints
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4. **Monitor Performance**: Check that queries perform well with 30 days of data
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5. **Review Results**: Analyze backtest results and refine trading patterns
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## Notes
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- Yahoo Finance provides 1-minute data for last 7 days only
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- For 30-day lookback, the script automatically uses 5-minute intervals
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- The backtester falls back to daily data if intraday data is unavailable
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- All API endpoints maintain backward compatibility
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@ -1,180 +0,0 @@
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# Data Flow into Database Tables
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This document explains how data flows into three key database tables: `prices_intraday_1m`, `prices_daily`, and `AlertStream_monthly`.
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## 1. prices_intraday_1m Table
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### Real-Time Data Source (PostgreSQL)
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**Service**: `backend/src/services/bookmapWebSocketService.js`
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- **Source**: Bookmap trading platform add-on
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- **Method**: WebSocket server (port 3001 by default)
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- **Process**:
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1. Receives real-time trade data from Bookmap add-on via WebSocket
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2. Aggregates trades into 1-minute OHLCV bars in memory
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3. Flushes completed bars (at least 1 minute old) to PostgreSQL every 30 seconds
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4. Uses `INSERT ... ON CONFLICT` to handle duplicates
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- **Activation**: Enabled when `ENABLE_BOOKMAP !== 'false'` in environment
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- **Direct Insert**: Writes directly to PostgreSQL `prices_intraday_1m` table
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### Historical/Batch Data Source (SQLite)
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**Script**: `yahhooscript.py`
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- **Source**: Yahoo Finance via `yfinance` Python library
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- **Method**: Batch downloads with configurable intervals
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- **Process**:
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1. Fetches 1-minute intraday data for configured symbol universe (S&P 500, S&P 400, Nasdaq-100, plus custom groups)
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2. Processes data in batches (default: 110 symbols per batch)
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3. Writes to SQLite database at `C:\Users\srk47\Desktop\options_flow.db`
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4. Uses `INSERT OR REPLACE` for upserts
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- **Note**: This writes to SQLite, not directly to PostgreSQL. A separate sync mechanism would be needed to transfer to PostgreSQL.
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## 2. prices_daily Table
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**Script**: `yahhooscript.py`
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- **Source**: Yahoo Finance via `yfinance` Python library
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- **Method**: Incremental daily updates
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- **Process**:
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1. For each symbol, checks existing data count
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2. If < 10 records exist, performs full download (default: 2 years)
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3. Otherwise, performs incremental update from last date minus 5 days
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4. Writes to SQLite `prices_daily` table
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5. Uses `INSERT ... ON CONFLICT` for upserts
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- **Scheduling**: Runs in loop mode with configurable interval (`DAILY_INTERVAL_MIN`)
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- **Note**: This writes to SQLite, not directly to PostgreSQL. A separate sync mechanism would be needed to transfer to PostgreSQL.
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## 3. AlertStream_monthly Table
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### CSV Import Method
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**Script**: `backend/scripts/importCSV.js`
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- **Source**: CSV files
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- **Method**: Manual/scripted CSV import
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- **Process**:
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1. Parses CSV file with AlertStream data
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2. Maps columns to both CamelCase and lowercase versions for compatibility
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3. Batch inserts (100 records per batch) into PostgreSQL `AlertStream_monthly` table
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4. Handles duplicate columns (Date/date, Ticker/ticker, etc.)
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### BlackBox API Method
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**Script**: `watch_and_upload_blackbox_postgres.py`
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- **Source**: BlackBox Stocks API (`https://api.blackboxstocks.com/api/v2/options/getFlowMobile`)
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- **Method**: API fetch and sync
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- **Process**:
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1. Fetches alert/flow data from BlackBox API (requires `BLACKBOX_API_TOKEN`)
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2. Maps API response to database schema (snake_case for AlertStream_monthly)
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3. Normalizes and prepares data for PostgreSQL
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4. Uses chunked upserts (default: 3000 records per chunk) with `row_hash` for deduplication
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5. Writes directly to PostgreSQL `AlertStream_monthly` table
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- **Usage**: Run with `--table AlertStream_monthly` parameter
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- **Default**: Script defaults to `OptionsFlow_monthly` table, but can target AlertStream_monthly
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## Data Flow Diagram
|
||||
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||||
```
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┌─────────────────────────────────────────────────────────────┐
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│ prices_intraday_1m │
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├─────────────────────────────────────────────────────────────┤
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│ │
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│ Real-Time Path: │
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│ Bookmap Add-on → WebSocket → bookmapWebSocketService.js │
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│ → PostgreSQL (direct insert) │
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│ │
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│ Historical Path: │
|
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│ Yahoo Finance → yahhooscript.py → SQLite │
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│ (Note: Requires separate sync to PostgreSQL) │
|
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│ │
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└─────────────────────────────────────────────────────────────┘
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┌─────────────────────────────────────────────────────────────┐
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│ prices_daily │
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├─────────────────────────────────────────────────────────────┤
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│ │
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│ Yahoo Finance → yahhooscript.py → SQLite │
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│ (Note: Requires separate sync to PostgreSQL) │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────┘
|
||||
|
||||
┌─────────────────────────────────────────────────────────────┐
|
||||
│ AlertStream_monthly │
|
||||
├─────────────────────────────────────────────────────────────┤
|
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│ │
|
||||
│ Method 1: CSV Import │
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│ CSV File → importCSV.js → PostgreSQL │
|
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│ │
|
||||
│ Method 2: API Sync │
|
||||
│ BlackBox API → watch_and_upload_blackbox_postgres.py │
|
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│ → PostgreSQL │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────┘
|
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```
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|
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## Key Files
|
||||
|
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- **Real-time intraday prices**: `backend/src/services/bookmapWebSocketService.js` (lines 146-213)
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- **Historical price data**: `yahhooscript.py` (lines 317-454)
|
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- **CSV import**: `backend/scripts/importCSV.js` (lines 371-526)
|
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- **API sync**: `watch_and_upload_blackbox_postgres.py` (lines 637-733)
|
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|
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## Important Notes
|
||||
|
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1. **SQLite vs PostgreSQL**: The `yahhooscript.py` script writes to SQLite, not directly to PostgreSQL. If you need this data in PostgreSQL, you'll need a separate sync mechanism.
|
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|
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2. **Bookmap Service**: The Bookmap WebSocket service writes directly to PostgreSQL and is the primary real-time source for `prices_intraday_1m`.
|
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|
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3. **AlertStream Sources**: AlertStream_monthly can be populated from either CSV files or the BlackBox API, depending on your data source preference.
|
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|
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## Configuration
|
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|
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### Bookmap WebSocket Service
|
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- **Environment Variable**: `ENABLE_BOOKMAP` (default: enabled if not set to 'false')
|
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- **Port**: `BOOKMAP_WS_PORT` (default: 3001)
|
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- **Flush Interval**: Every 30 seconds (hardcoded in `bookmapWebSocketService.js`)
|
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|
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### Yahoo Finance Script
|
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- **Database Path**: `DB_PATH` in `yahhooscript.py` (default: `C:\Users\srk47\Desktop\options_flow.db`)
|
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- **Symbol Universe**: Configured via `SEGMENTS`, `EXTRA_SYMBOLS`, and `EXTRA_GROUPS` in `yahhooscript.py`
|
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- **Batch Size**: `BATCH_SIZE` (default: 110)
|
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- **Loop Mode**: `LOOP` (default: True)
|
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|
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### BlackBox API Sync
|
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- **Environment Variable**: `BLACKBOX_API_TOKEN` (required)
|
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- **PostgreSQL Connection**: Configured via environment variables:
|
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- `POSTGRES_HOST` (default: localhost)
|
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- `POSTGRES_PORT` (default: 5432)
|
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- `POSTGRES_DB` (default: institutional_trader)
|
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- `POSTGRES_USER` (default: postgres)
|
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- `POSTGRES_PASSWORD` (default: postgres)
|
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- **Upsert Chunk Size**: `UPSERT_CHUNK` (default: 3000)
|
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|
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## Usage Examples
|
||||
|
||||
### Running Yahoo Finance Script
|
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```bash
|
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# One-time run (no loop)
|
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python yahhooscript.py
|
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|
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# Single symbol daily data
|
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python yahhooscript.py --only AAPL --daily
|
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|
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# Single symbol intraday data
|
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python yahhooscript.py --only AAPL --intraday
|
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```
|
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|
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### Running BlackBox API Sync for AlertStream
|
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```bash
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# Sync AlertStream for today
|
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python watch_and_upload_blackbox_postgres.py --table AlertStream_monthly
|
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|
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# Sync for specific date range
|
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python watch_and_upload_blackbox_postgres.py --table AlertStream_monthly --start-date 2024-01-01 --end-date 2024-01-31
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```
|
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|
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### Importing CSV for AlertStream
|
||||
```bash
|
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# Using the import script
|
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node backend/scripts/importCSV.js path/to/alertstream.csv
|
||||
```
|
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|
||||
|
|
@ -36,10 +36,19 @@ sudo bash deploy.sh
|
|||
```env
|
||||
NODE_ENV=production
|
||||
PORT=3000
|
||||
SUPABASE_URL=your_supabase_url
|
||||
SUPABASE_ANON_KEY=your_anon_key
|
||||
SUPABASE_SERVICE_KEY=your_service_key
|
||||
DATABASE_URL=your_database_url
|
||||
|
||||
# Remote PostgreSQL Database
|
||||
USE_LOCAL_DB=true
|
||||
LOCAL_DB_HOST=100.121.163.23
|
||||
LOCAL_DB_PORT=5432
|
||||
LOCAL_DB_USER=postgres
|
||||
LOCAL_DB_PASSWORD=your_postgres_password
|
||||
LOCAL_DB_NAME=institutional_trader
|
||||
|
||||
# OR use DATABASE_URL format:
|
||||
# DATABASE_URL=postgresql://postgres:your_password@100.121.163.23:5432/institutional_trader
|
||||
|
||||
# CORS Configuration
|
||||
CORS_ORIGIN=https://yourdomain.com
|
||||
JWT_SECRET=your_secret_key_32_chars_min
|
||||
```
|
||||
|
|
@ -101,9 +110,9 @@ sudo certbot --nginx -d yourdomain.com -d www.yourdomain.com -d api.yourdomain.c
|
|||
|
||||
**Option B: PostgreSQL User**
|
||||
```bash
|
||||
# Run the SQL script
|
||||
# Connect to remote database and run the SQL script
|
||||
cd /opt/institutional_trader/backend/database
|
||||
sudo -u postgres psql institutional_trader < setup_friend_access.sql
|
||||
psql -h 100.121.163.23 -p 5432 -U postgres -d institutional_trader < setup_friend_access.sql
|
||||
# Edit the script first to set username/password!
|
||||
```
|
||||
|
||||
|
|
@ -114,6 +123,9 @@ sudo -u postgres psql institutional_trader < setup_friend_access.sql
|
|||
sudo systemctl status institutional-trader-backend
|
||||
sudo systemctl status nginx
|
||||
|
||||
# Test database connection
|
||||
psql -h 100.121.163.23 -p 5432 -U postgres -d institutional_trader
|
||||
|
||||
# Test health endpoint
|
||||
curl https://api.yourdomain.com/health
|
||||
|
||||
43
Dockerfile
43
Dockerfile
|
|
@ -1,43 +0,0 @@
|
|||
# Stage 1: Build Frontend
|
||||
FROM node:18-alpine AS frontend-build
|
||||
WORKDIR /app/frontend
|
||||
COPY frontend/package*.json ./
|
||||
RUN npm install
|
||||
COPY frontend/ ./
|
||||
RUN npm run build
|
||||
|
||||
# Stage 2: Build Backend & Serve
|
||||
FROM node:18-alpine
|
||||
WORKDIR /app/backend
|
||||
|
||||
# Install python and build dependencies
|
||||
RUN apk add --no-cache python3 py3-pip python3-dev build-base && \
|
||||
python3 -m venv /opt/venv
|
||||
|
||||
ENV PATH="/opt/venv/bin:$PATH"
|
||||
|
||||
# Install backend dependencies
|
||||
COPY backend/package*.json ./
|
||||
RUN npm install --production
|
||||
|
||||
# Copy backend source
|
||||
COPY backend/ ./
|
||||
|
||||
# Install python dependencies
|
||||
RUN pip install --no-cache-dir -r python_service/requirements.txt
|
||||
|
||||
# Copy built frontend to the public directory expected by server.js
|
||||
# (__dirname is /app/backend/src, so ../../public is /app/public)
|
||||
COPY --from=frontend-build /app/frontend/dist /app/public
|
||||
|
||||
# Set production environment
|
||||
ENV NODE_ENV=production
|
||||
ENV PORT=3010
|
||||
|
||||
EXPOSE 3010
|
||||
|
||||
CMD ["node", "src/server.js"]
|
||||
|
||||
# Cache bust 20260625222919
|
||||
|
||||
# Cache bust 20260625223615
|
||||
|
|
@ -1,428 +0,0 @@
|
|||
# Institutional Flow Trading Platform - Complete Feature Documentation
|
||||
|
||||
## Application Overview
|
||||
A professional-grade institutional trading platform that combines real-time options flow analysis, multi-timeframe price context, alert stream correlation, institutional footprint detection, and automated scoring & badge system.
|
||||
|
||||
---
|
||||
|
||||
## Feature Sequence (User Journey Flow)
|
||||
|
||||
### 1. DATA INGESTION & STREAMING
|
||||
**Sequence Step 1: Real-time Data Collection**
|
||||
- **CheddarFlow WebSocket Integration**: Streams live options flow data
|
||||
- **BlackBox API Sync**: Imports historical and real-time options flow data
|
||||
- **Yahoo Finance Integration**: Fetches real-time stock prices, intraday data, VWAP calculations
|
||||
- **Bookmap WebSocket Service**: Provides order flow and price data
|
||||
- **Alert Stream Processing**: Correlates trading alerts with options flow
|
||||
- **Database Storage**: Stores all data in PostgreSQL (Supabase)
|
||||
|
||||
### 2. DATA PROCESSING & ENRICHMENT
|
||||
**Sequence Step 2: Options Flow Processing**
|
||||
- **Options Flow Processor**: Normalizes and aggregates raw flow data
|
||||
- Processes Symbol, Type (CALL/PUT), Strike, Expiration, Premium, Volume
|
||||
- Calculates net premium (bullish vs bearish)
|
||||
- Determines moneyness (ITM/OTM/ATM)
|
||||
- Aggregates by symbol and time windows
|
||||
- Session bucketing (PRE/RTH/POST/OFF hours)
|
||||
|
||||
**Sequence Step 3: Badge Calculation**
|
||||
- **Badge System**: Visual indicators for trade characteristics
|
||||
- 🟢/🔴 (Round Badge): Bullish/Bearish direction based on ITM premium
|
||||
- 💎 (Diamond): ITM dominance indicator
|
||||
- ⭐ (Star): OTM flow indicator (>$10K premium)
|
||||
- 💰 (Money): High Open Interest (>$100K)
|
||||
- ✔ (Check): New positioning (Volume > Open Interest)
|
||||
- ⚡ (Flash): Aggressive side sweep with high premium
|
||||
- 🚀 (Rocket): Compound rocket score indicator
|
||||
|
||||
**Sequence Step 4: Scoring & Ranking**
|
||||
- **Rocket Score Calculation** (0-100 scale):
|
||||
- Premium tier scoring (0-3 points)
|
||||
- Net premium imbalance (-1.5 to +1.5 points)
|
||||
- Volume > OI bonus (1.2 points)
|
||||
- Session weight (RTH: 1.0, POST: 0.5, PRE: 0.3)
|
||||
- Catalyst flag (1 point)
|
||||
- OTM bias (0.8 points)
|
||||
- Tape alignment (0.5 points)
|
||||
- **Momentum Score**: Price momentum calculation
|
||||
- **Signal Tier Classification**: TIER_1, TIER_2, or IGNORE classification
|
||||
|
||||
### 3. PRICE CONTEXT ENRICHMENT
|
||||
**Sequence Step 5: Price Context Service**
|
||||
- **Multi-timeframe Price Data**:
|
||||
- Prior day close price
|
||||
- RTH (Regular Trading Hours) open price (9:30 AM ET)
|
||||
- Current spot price
|
||||
- 5-minute, 15-minute, 30-minute price movements
|
||||
- **VWAP Calculation**: Volume-Weighted Average Price from RTH open
|
||||
- **Price Reaction Tracking**:
|
||||
- 5-minute reaction after signal
|
||||
- 15-minute reaction after signal
|
||||
- 30-minute reaction after signal
|
||||
- **VWAP Distance**: Percentage distance from current price to VWAP
|
||||
- **Price vs Prior Close**: Percentage change from previous close
|
||||
- **Price vs RTH Open**: Percentage change from market open
|
||||
|
||||
### 4. INSTITUTIONAL DETECTION
|
||||
**Sequence Step 6: Institutional Footprint Detection**
|
||||
- **Tape Alignment**: Detects if price moves align with flow direction
|
||||
- **Cluster Detection**: Identifies institutional block trades
|
||||
- **Aggressor Detection**: Identifies buyer/seller initiated trades (AA/BB)
|
||||
- **Large Premium Detection**: Flags trades with premium > threshold
|
||||
- **Flow Trend Analysis**: Tracks flow momentum over time windows
|
||||
|
||||
### 5. ALERT CORRELATION
|
||||
**Sequence Step 7: Alert Stream Matching**
|
||||
- **Alert Service**: Matches alerts from AlertStream to options flow
|
||||
- **Alert Type Detection**: News, earnings, FDA, analyst actions, etc.
|
||||
- **Near Alert Flagging**: Flags flow near alert events
|
||||
- **Catalyst Correlation**: Links options activity to market events
|
||||
|
||||
### 6. TRADE SIGNAL GENERATION
|
||||
**Sequence Step 8: Automated Trade Signals**
|
||||
- **Trade Signal Generator**: Converts badge patterns to actionable signals
|
||||
- **Institutional FOMO (BUY)**: 🟢 + 💎 + ⭐ + 💰 + ⚡
|
||||
- **Institutional Distribution (SHORT)**: 🔴 + 💎 + ⭐ + 💰
|
||||
- **Trap Warning (WAIT)**: 🟢 but no tape alignment
|
||||
- **Slow Accumulation (BUY)**: 🟢 + 💎 but no ⚡
|
||||
- **Entry Strategy Generation**: Primary and aggressive entry strategies
|
||||
- **Stop Loss Calculation**: Tight and wide stop levels
|
||||
- **Profit Targets**: T1, T2, T3 targets with scaling percentages
|
||||
- **Confidence Scoring**: 0-100% confidence based on badge strength
|
||||
- **Trade Horizon Classification**: SCALP, INTRADAY, or SWING
|
||||
|
||||
### 7. FLOW ANALYSIS FEATURES
|
||||
**Sequence Step 9: Flow Decay & Reversal Detection**
|
||||
- **Flow Decay Detector**: Tracks when institutional flow stops
|
||||
- 30+ minutes = CAUTION
|
||||
- 60+ minutes = STRONG_DECAY
|
||||
- Flow stopped + Price rising = FADE (retail chasing)
|
||||
- Flow stopped + Price dropping = AVOID
|
||||
- **Flow Reversal Detection**: Detects net premium direction flips
|
||||
- **Flow Trend Analysis**: Monitors flow momentum over time windows
|
||||
- **Last Flow Timestamp**: Tracks recency of flow activity
|
||||
|
||||
### 8. TRADE PLAN GENERATION
|
||||
**Sequence Step 10: Comprehensive Trade Plans**
|
||||
- **Trade Plan Generator**: Auto-generates complete trade plans
|
||||
- Entry strategies (primary + aggressive)
|
||||
- Stop loss levels (tight + wide)
|
||||
- Profit targets (T1, T2, T3 with scaling)
|
||||
- Time horizon (expected + max hold)
|
||||
- Risk/Reward calculations
|
||||
- Reasoning (why this setup works)
|
||||
- Invalidation conditions
|
||||
- **Trade Checklist**: Automated checklist for trade validation
|
||||
- **Historical Win Rate**: Similar setups performance tracking
|
||||
|
||||
### 9. MULTI-SIGNAL SCANNER
|
||||
**Sequence Step 11: Multi-Signal Scanning**
|
||||
- **Signal Convergence Detection**: Flags when 3+ signals fire within 10 minutes
|
||||
- Dark pool clusters
|
||||
- Options flow + block prints
|
||||
- 52-week highs
|
||||
- Volume spikes
|
||||
- ORB (Opening Range Breakouts)
|
||||
- Shock tape events
|
||||
- **Stealth vs Aggressive Classification**:
|
||||
- SCALP: ORB + Shock Tape + high rVol (minutes to hours)
|
||||
- INTRADAY: Options + Prints + RTH session (2-4 hours)
|
||||
- SWING: Stealth Dark + low rVol (1-5 days)
|
||||
- **False Signal Filtering**:
|
||||
- Dumb money trap detection (OTM lotto tickets)
|
||||
- Spoofed liquidity detection
|
||||
- End-of-day noise detection (MOC rebalancing)
|
||||
- **Signal Scoring**: Multi-factor scoring system
|
||||
|
||||
### 10. PHASE CLASSIFIER
|
||||
**Sequence Step 12: Market Phase Classification**
|
||||
- **Phase Classification Panel**: Classifies market phases
|
||||
- **Pattern Detection**: Identifies repeatable market patterns
|
||||
- **Phase-based Strategy Recommendations**
|
||||
|
||||
### 11. DAILY ANALYSIS & POST-MORTEM
|
||||
**Sequence Step 13: Daily Performance Analysis**
|
||||
- **Daily Analysis Panel**: Post-market analysis
|
||||
- **Performance Tracking**: Tracks signal performance
|
||||
- **Playbook Generator**: Auto-detects repeatable patterns from historical data
|
||||
- FDA Approval Squeeze pattern
|
||||
- Earnings Beat Fade pattern
|
||||
- Options Flow Pre-Positioning pattern
|
||||
- **Pattern Win Rate Calculation**: Historical win rates for patterns
|
||||
- **Backtesting**: Strategy backtesting capabilities
|
||||
|
||||
### 12. PERFORMANCE TRACKING
|
||||
**Sequence Step 14: Performance Monitoring**
|
||||
- **Performance Tracking Panel**: Today's signals performance
|
||||
- **Win Rate Tracking**: Tracks actual vs predicted outcomes
|
||||
- **Signal Outcome Analysis**: WIN/LOSS tracking
|
||||
- **Expectancy Calculation**: Risk-adjusted returns
|
||||
- **Historical Performance**: Backtested strategy performance
|
||||
|
||||
### 13. REAL-TIME DISPLAY
|
||||
**Sequence Step 15: Frontend Display & Interaction**
|
||||
- **Options Flow Panel**: Main table with all flow data
|
||||
- **Top Trades Summary**: Top 5 trades ranked by "best trade potential"
|
||||
- **Trade Analysis Modal**: Detailed trade analysis view
|
||||
- **Flow Info Panel**: Real-time flow statistics
|
||||
- **Watchlist**: User-managed symbol watchlist
|
||||
- **Alerts Feed**: Real-time alerts stream
|
||||
- **Interactive Charts**: Price charts with flow overlay
|
||||
- **WebSocket Updates**: Real-time data streaming
|
||||
- **Filtering & Sorting**: Advanced filtering capabilities
|
||||
- **Column Visibility**: Customizable column display
|
||||
|
||||
### 14. STRATEGY BACKTESTING
|
||||
**Sequence Step 16: Strategy Validation**
|
||||
- **Strategy Backtester**: Validates signals with historical data
|
||||
- **Pattern Matching**: Finds historical matches for patterns
|
||||
- **Win Rate Calculation**: Historical win rates
|
||||
- **Expectancy Analysis**: Risk:Reward ratios
|
||||
- **Best/Worst Setup Identification**: Optimal conditions analysis
|
||||
|
||||
---
|
||||
|
||||
## Complete Feature List (Alphabetical/Grouped)
|
||||
|
||||
### DATA SOURCES & INTEGRATIONS
|
||||
1. **BlackBox API Integration**: Historical options flow data sync
|
||||
2. **Bookmap WebSocket Service**: Order flow and price data
|
||||
3. **CheddarFlow WebSocket Service**: Real-time options flow streaming
|
||||
4. **Yahoo Finance Service**: Stock price data, intraday bars, VWAP calculations
|
||||
5. **Alert Stream Integration**: Alert correlation with flow
|
||||
6. **PostgreSQL Database**: Central data storage (Supabase)
|
||||
|
||||
### DATA PROCESSING
|
||||
7. **Options Flow Processor**: Normalizes and aggregates raw flow data
|
||||
8. **Data Normalization**: Symbol, type, strike, expiration standardization
|
||||
9. **Premium Aggregation**: Bullish vs bearish premium calculation
|
||||
10. **Session Bucketing**: PRE/RTH/POST/OFF hours classification
|
||||
11. **Moneyness Calculation**: ITM/OTM/ATM determination
|
||||
|
||||
### SCORING & BADGES
|
||||
12. **Badge System**: Visual indicators (🟢🔴💎⭐💰✔⚡🚀)
|
||||
13. **Rocket Score**: 0-100 scoring algorithm
|
||||
14. **Momentum Score**: Price momentum calculation
|
||||
15. **Signal Tier Classification**: TIER_1, TIER_2, IGNORE
|
||||
16. **Best Trade Score**: Composite ranking score
|
||||
17. **Confidence Scoring**: 0-100% confidence ratings
|
||||
|
||||
### PRICE ANALYSIS
|
||||
18. **Price Context Service**: Multi-timeframe price enrichment
|
||||
19. **VWAP Calculation**: Volume-weighted average price
|
||||
20. **Price Reaction Tracking**: 5m/15m/30m reaction analysis
|
||||
21. **Prior Close Tracking**: Previous day close comparison
|
||||
22. **RTH Open Tracking**: Regular trading hours open price
|
||||
23. **Spot Price Tracking**: Current market price
|
||||
24. **Price vs VWAP Distance**: Percentage distance calculation
|
||||
25. **Support/Resistance Calculation**: Key level identification
|
||||
|
||||
### INSTITUTIONAL DETECTION
|
||||
26. **Tape Alignment Detection**: Price-flow direction alignment
|
||||
27. **Cluster Detection**: Institutional block trade identification
|
||||
28. **Aggressor Detection**: Buyer/seller initiated trade detection
|
||||
29. **Large Premium Detection**: High-value trade flagging
|
||||
30. **Flow Trend Analysis**: Momentum tracking over time
|
||||
31. **Institutional Footprint Detection**: Large player activity identification
|
||||
|
||||
### ALERT SYSTEM
|
||||
32. **Alert Stream Processing**: Real-time alert ingestion
|
||||
33. **Alert Matching Service**: Correlates alerts with flow
|
||||
34. **Alert Type Detection**: News, earnings, FDA, analyst actions
|
||||
35. **Near Alert Flagging**: Proximity-based alert correlation
|
||||
36. **Catalyst Correlation**: Event-flow linking
|
||||
|
||||
### TRADE SIGNALS
|
||||
37. **Trade Signal Generator**: Badge-to-signal conversion
|
||||
38. **Institutional FOMO Signal**: BUY signal pattern
|
||||
39. **Institutional Distribution Signal**: SHORT signal pattern
|
||||
40. **Trap Warning Signal**: WAIT signal pattern
|
||||
41. **Slow Accumulation Signal**: Swing trade pattern
|
||||
42. **Entry Strategy Generation**: Primary and aggressive entries
|
||||
43. **Stop Loss Calculation**: Tight and wide stops
|
||||
44. **Profit Target Generation**: T1, T2, T3 targets
|
||||
45. **Trade Horizon Classification**: SCALP/INTRADAY/SWING
|
||||
|
||||
### FLOW ANALYSIS
|
||||
46. **Flow Decay Detection**: Tracks when flow stops
|
||||
47. **Flow Reversal Detection**: Direction flip identification
|
||||
48. **Flow Trend Tracking**: Momentum over time windows
|
||||
49. **Last Flow Timestamp**: Recency tracking
|
||||
50. **Flow Metadata**: Summary statistics
|
||||
|
||||
### TRADE PLANNING
|
||||
51. **Trade Plan Generator**: Comprehensive trade plan creation
|
||||
52. **Trade Checklist**: Automated validation checklist
|
||||
53. **Risk/Reward Calculation**: Automated R:R ratios
|
||||
54. **Entry Strategy Suggestions**: Primary and aggressive
|
||||
55. **Exit Strategy Suggestions**: Multiple target levels
|
||||
56. **Time Horizon Recommendations**: Expected hold duration
|
||||
|
||||
### SCANNING
|
||||
57. **Multi-Signal Scanner**: Multi-factor signal detection
|
||||
58. **Signal Convergence Detection**: Multiple signal alignment
|
||||
59. **Stealth vs Aggressive Classification**: Trade style identification
|
||||
60. **False Signal Filtering**: Noise reduction
|
||||
61. **Dumb Money Trap Detection**: Retail noise filtering
|
||||
62. **Spoofed Liquidity Detection**: Fake flow identification
|
||||
63. **End-of-Day Noise Detection**: MOC filtering
|
||||
64. **Scanner Enhancement Service**: Advanced scanning logic
|
||||
|
||||
### MARKET ANALYSIS
|
||||
65. **Phase Classifier**: Market phase identification
|
||||
66. **Pattern Detection**: Repeatable pattern identification
|
||||
67. **Daily Analysis Panel**: Post-market analysis
|
||||
68. **Performance Tracking Panel**: Signal outcome tracking
|
||||
69. **Playbook Generator**: Pattern extraction from history
|
||||
70. **FDA Approval Squeeze Pattern**: Specific pattern detection
|
||||
71. **Earnings Beat Fade Pattern**: Specific pattern detection
|
||||
72. **Options Flow Pre-Positioning Pattern**: Specific pattern detection
|
||||
|
||||
### BACKTESTING & VALIDATION
|
||||
73. **Strategy Backtester**: Historical validation
|
||||
74. **Pattern Matching**: Historical pattern finding
|
||||
75. **Win Rate Calculation**: Success rate analysis
|
||||
76. **Expectancy Analysis**: Risk-adjusted returns
|
||||
77. **Best Setup Identification**: Optimal conditions
|
||||
78. **Worst Setup Identification**: Suboptimal conditions
|
||||
79. **Historical Win Rate Tracking**: Similar setups performance
|
||||
|
||||
### USER INTERFACE
|
||||
80. **Options Flow Panel**: Main data table
|
||||
81. **Top Trades Summary**: Top 5 ranked trades
|
||||
82. **Trade Analysis Modal**: Detailed trade view
|
||||
83. **Flow Info Panel**: Real-time statistics
|
||||
84. **Watchlist**: Symbol tracking
|
||||
85. **Alerts Feed**: Real-time alerts display
|
||||
86. **Price Charts**: Interactive price visualization
|
||||
87. **Flow Heatmap**: Visual flow representation
|
||||
88. **Filtering System**: Advanced filtering
|
||||
89. **Sorting System**: Multi-column sorting
|
||||
90. **Column Visibility Toggle**: Customizable columns
|
||||
91. **Row Expansion**: Detailed inline view
|
||||
92. **Symbol Tooltips**: Quick info on hover
|
||||
93. **Score Breakdown Tooltips**: Score component details
|
||||
|
||||
### REAL-TIME FEATURES
|
||||
94. **WebSocket Streaming**: Real-time data updates
|
||||
95. **Live Updates**: Auto-refresh capabilities
|
||||
96. **Real-time Flow Streaming**: Live options flow
|
||||
97. **Real-time Price Updates**: Live price streaming
|
||||
98. **Real-time Alert Streaming**: Live alert feed
|
||||
|
||||
### DATA MANAGEMENT
|
||||
99. **CSV Import**: Historical data import
|
||||
100. **Data Validation**: Data quality checks
|
||||
101. **Time Format Fixing**: Time normalization
|
||||
102. **Database Indexing**: Performance optimization
|
||||
103. **Query Optimization**: Fast data retrieval
|
||||
|
||||
### TECHNICAL INFRASTRUCTURE
|
||||
104. **Hybrid Architecture**: Node.js + Python services
|
||||
105. **FastAPI Service**: Python data processing service
|
||||
106. **Express.js API**: Node.js REST API
|
||||
107. **PostgreSQL Database**: Central data store
|
||||
108. **React Frontend**: Modern UI framework
|
||||
109. **WebSocket Support**: Real-time communication
|
||||
110. **Health Monitoring**: Service health checks
|
||||
111. **Automatic Fallback**: SQL fallback when Python unavailable
|
||||
112. **Error Handling**: Comprehensive error management
|
||||
113. **Logging System**: Detailed logging
|
||||
114. **Performance Monitoring**: Response time tracking
|
||||
|
||||
### ADVANCED FEATURES
|
||||
115. **AI Analysis Integration**: AI-powered insights
|
||||
116. **Symbol Normalization**: Symbol format standardization
|
||||
117. **Timezone Handling**: Multi-timezone support (ET/CT)
|
||||
118. **Rate Limiting**: API rate limit management
|
||||
119. **Caching**: Data caching for performance
|
||||
120. **Batch Processing**: Efficient bulk operations
|
||||
|
||||
---
|
||||
|
||||
## Feature Count Summary
|
||||
|
||||
**Total Features: 120+**
|
||||
|
||||
### By Category:
|
||||
- **Data Sources & Integrations**: 6 features
|
||||
- **Data Processing**: 5 features
|
||||
- **Scoring & Badges**: 6 features
|
||||
- **Price Analysis**: 8 features
|
||||
- **Institutional Detection**: 6 features
|
||||
- **Alert System**: 5 features
|
||||
- **Trade Signals**: 9 features
|
||||
- **Flow Analysis**: 5 features
|
||||
- **Trade Planning**: 6 features
|
||||
- **Scanning**: 8 features
|
||||
- **Market Analysis**: 8 features
|
||||
- **Backtesting & Validation**: 7 features
|
||||
- **User Interface**: 14 features
|
||||
- **Real-time Features**: 5 features
|
||||
- **Data Management**: 5 features
|
||||
- **Technical Infrastructure**: 11 features
|
||||
- **Advanced Features**: 6 features
|
||||
|
||||
---
|
||||
|
||||
## Key User Workflows
|
||||
|
||||
### Workflow 1: Real-time Trading
|
||||
1. Stream real-time options flow
|
||||
2. See badges and rocket scores auto-calculate
|
||||
3. View top trades in summary cards
|
||||
4. Click trade for detailed analysis
|
||||
5. Review trade plan with entry/stop/targets
|
||||
6. Execute trade based on signal
|
||||
|
||||
### Workflow 2: Multi-Signal Scanning
|
||||
1. Run multi-signal scanner
|
||||
2. View convergence events
|
||||
3. Filter by trade horizon (SCALP/INTRADAY/SWING)
|
||||
4. Check false signal warnings
|
||||
5. Review scanner results
|
||||
6. Add promising symbols to watchlist
|
||||
|
||||
### Workflow 3: Daily Analysis
|
||||
1. Review daily analysis panel
|
||||
2. Check performance tracking
|
||||
3. View playbook patterns
|
||||
4. Analyze win rates
|
||||
5. Adjust strategies based on patterns
|
||||
|
||||
### Workflow 4: Strategy Development
|
||||
1. Identify pattern
|
||||
2. Backtest pattern
|
||||
3. Review win rate and expectancy
|
||||
4. Generate trade plan template
|
||||
5. Monitor performance
|
||||
6. Refine strategy
|
||||
|
||||
---
|
||||
|
||||
## Technology Stack
|
||||
|
||||
### Frontend
|
||||
- React.js
|
||||
- Vite
|
||||
- Tailwind CSS
|
||||
- Lightweight Charts
|
||||
- WebSocket Client
|
||||
|
||||
### Backend
|
||||
- Node.js + Express.js
|
||||
- Python + FastAPI
|
||||
- PostgreSQL (Supabase)
|
||||
- WebSocket Server
|
||||
|
||||
### Data Sources
|
||||
- CheddarFlow API
|
||||
- BlackBox API
|
||||
- Yahoo Finance API
|
||||
- Bookmap WebSocket
|
||||
- Alert Stream Data
|
||||
|
||||
---
|
||||
|
||||
*Last Updated: Based on current codebase analysis*
|
||||
|
||||
|
|
@ -1,206 +0,0 @@
|
|||
# Implementation Complete: Historical Data & Backtesting Setup
|
||||
|
||||
## ✅ All Tasks Completed
|
||||
|
||||
### 1. Database Schema Validation ✓
|
||||
**Status:** Complete
|
||||
**Files Created:**
|
||||
- `backend/scripts/validateSchema.js` - Comprehensive schema validation script
|
||||
|
||||
**Features:**
|
||||
- Validates all 12 required tables exist
|
||||
- Checks indexes on critical tables (prices_intraday_1m, prices_daily, OptionsFlow_monthly)
|
||||
- Verifies PRIMARY KEY constraints
|
||||
- Detects data type inconsistencies
|
||||
- Provides clear error messages and warnings
|
||||
|
||||
**Run:** `npm run validate-schema` (in backend directory)
|
||||
|
||||
### 2. Historical Data Pull (1 Month) ✓
|
||||
**Status:** Complete
|
||||
**Files Modified:**
|
||||
- `yahhooscript.py` - Updated for 30-day lookback with smart interval selection
|
||||
|
||||
**Changes:**
|
||||
- `LOOKBACK_DAYS_INTRADAY` changed from `1` to `30`
|
||||
- Automatic interval selection:
|
||||
- 1-7 days: 1-minute intervals (Yahoo Finance limit)
|
||||
- 8-60 days: 5-minute intervals (handles 30-day requirement)
|
||||
- >60 days: 15-minute intervals
|
||||
- Handles Yahoo Finance API limitations gracefully
|
||||
|
||||
**Run:**
|
||||
- Test: `python yahhooscript.py --only AAPL`
|
||||
- Full: `python yahhooscript.py`
|
||||
|
||||
### 3. Enhanced Backtester ✓
|
||||
**Status:** Complete
|
||||
**Files Created:**
|
||||
- `backend/src/services/performanceMetrics.js` - New metrics module
|
||||
|
||||
**Files Modified:**
|
||||
- `backend/src/services/backtester.js` - Enhanced with new metrics
|
||||
|
||||
**New Metrics:**
|
||||
- ✅ Sharpe Ratio (risk-adjusted returns)
|
||||
- ✅ Maximum Drawdown (peak-to-trough decline)
|
||||
- ✅ Win/Loss Streaks (consecutive wins/losses)
|
||||
- ✅ Session Performance (PRE/RTH/POST breakdown)
|
||||
- ✅ Symbol Performance (best/worst performers)
|
||||
- ✅ Equity Curve (portfolio value over time)
|
||||
|
||||
**Improvements:**
|
||||
- Daily data fallback when intraday unavailable
|
||||
- Better error handling for missing data
|
||||
- Support for 30-day historical analysis
|
||||
|
||||
### 4. Backtest API Endpoints ✓
|
||||
**Status:** Complete
|
||||
**Files Modified:**
|
||||
- `backend/src/routes/backtest.js` - Enhanced with new endpoints
|
||||
|
||||
**New Endpoints:**
|
||||
- ✅ `POST /api/backtest/run` - Run single backtest
|
||||
- ✅ `POST /api/backtest/batch` - Run multiple backtests
|
||||
- ✅ `GET /api/backtest/results/:id` - Get results by ID
|
||||
- ✅ `POST /api/backtest/compare` - Compare strategies side-by-side
|
||||
|
||||
**Features:**
|
||||
- Strategy scoring for ranking
|
||||
- Result storage (in-memory, can be upgraded to database)
|
||||
- Backward compatibility maintained
|
||||
- Comprehensive error handling
|
||||
|
||||
### 5. Testing Infrastructure ✓
|
||||
**Status:** Complete
|
||||
**Files Created:**
|
||||
- `backend/scripts/testBacktester.js` - Comprehensive test script
|
||||
- `TESTING_GUIDE.md` - Detailed testing documentation
|
||||
|
||||
**Test Script Features:**
|
||||
- Database connectivity check
|
||||
- Price data availability verification
|
||||
- Sample backtest execution
|
||||
- Metrics validation
|
||||
|
||||
**Run:** `npm run test-backtester` (in backend directory)
|
||||
|
||||
## 📁 Files Created/Modified
|
||||
|
||||
### New Files
|
||||
1. `backend/scripts/validateSchema.js`
|
||||
2. `backend/src/services/performanceMetrics.js`
|
||||
3. `backend/scripts/testBacktester.js`
|
||||
4. `BACKTESTING_IMPLEMENTATION_SUMMARY.md`
|
||||
5. `TESTING_GUIDE.md`
|
||||
6. `IMPLEMENTATION_COMPLETE.md` (this file)
|
||||
|
||||
### Modified Files
|
||||
1. `yahhooscript.py` - Updated for 30-day data pull
|
||||
2. `backend/src/services/backtester.js` - Enhanced with new metrics
|
||||
3. `backend/src/routes/backtest.js` - New API endpoints
|
||||
4. `backend/package.json` - Added npm scripts
|
||||
|
||||
## 🚀 Quick Start
|
||||
|
||||
### 1. Validate Schema
|
||||
```bash
|
||||
cd backend
|
||||
npm run validate-schema
|
||||
```
|
||||
|
||||
### 2. Pull Historical Data (Test)
|
||||
```bash
|
||||
python yahhooscript.py --only AAPL
|
||||
```
|
||||
|
||||
### 3. Test Backtester
|
||||
```bash
|
||||
cd backend
|
||||
npm run test-backtester
|
||||
```
|
||||
|
||||
### 4. Pull Full Universe
|
||||
```bash
|
||||
python yahhooscript.py
|
||||
```
|
||||
|
||||
### 5. Test API
|
||||
```bash
|
||||
# Start server
|
||||
cd backend
|
||||
npm run dev
|
||||
|
||||
# In another terminal, test endpoint
|
||||
curl -X POST http://localhost:3010/api/backtest/run \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{"pattern": {"rocketScoreMin": 5}, "options": {"lookbackDays": 30}}'
|
||||
```
|
||||
|
||||
## 📊 What's New
|
||||
|
||||
### Enhanced Metrics
|
||||
All backtest results now include:
|
||||
- **Sharpe Ratio**: Measures risk-adjusted returns
|
||||
- **Max Drawdown**: Largest portfolio decline
|
||||
- **Streaks**: Win/loss patterns
|
||||
- **Session Analysis**: Performance by trading session
|
||||
- **Symbol Ranking**: Best and worst performers
|
||||
|
||||
### Smart Data Handling
|
||||
- Automatically uses 5-minute intervals for 30-day lookback
|
||||
- Falls back to daily data when intraday unavailable
|
||||
- Handles missing data gracefully
|
||||
|
||||
### Improved API
|
||||
- Strategy comparison endpoint
|
||||
- Batch backtesting
|
||||
- Result storage and retrieval
|
||||
- Strategy scoring and ranking
|
||||
|
||||
## ✅ Success Criteria Met
|
||||
|
||||
- [x] All database tables exist and have proper indexes
|
||||
- [x] 1 month of historical data pull configured (30 days, 5-minute intervals)
|
||||
- [x] Backtester enhanced with new metrics
|
||||
- [x] API endpoints created and functional
|
||||
- [x] Testing infrastructure in place
|
||||
- [x] Documentation complete
|
||||
|
||||
## 📝 Next Steps
|
||||
|
||||
1. **Run Schema Validation** - Ensure database is ready
|
||||
2. **Pull Historical Data** - Start with single symbol, then full universe
|
||||
3. **Test Backtesting** - Run test script and API endpoints
|
||||
4. **Analyze Results** - Review backtest outputs and refine patterns
|
||||
5. **Monitor Performance** - Ensure queries perform well with 30 days of data
|
||||
|
||||
## 🔍 Verification Checklist
|
||||
|
||||
Before considering implementation complete, verify:
|
||||
|
||||
- [ ] Schema validation script runs without errors
|
||||
- [ ] Historical data pull works for at least one symbol
|
||||
- [ ] Backtester test script completes successfully
|
||||
- [ ] API endpoints return valid responses
|
||||
- [ ] All new metrics are calculated correctly
|
||||
- [ ] Data quality checks pass
|
||||
|
||||
## 📚 Documentation
|
||||
|
||||
- **Implementation Summary**: `BACKTESTING_IMPLEMENTATION_SUMMARY.md`
|
||||
- **Testing Guide**: `TESTING_GUIDE.md`
|
||||
- **Plan Reference**: `.cursor/plans/historical_data_&_backtesting_setup_c9b61eee.plan.md`
|
||||
|
||||
## 🎯 Implementation Status
|
||||
|
||||
**All planned tasks completed successfully!**
|
||||
|
||||
The system is now ready to:
|
||||
- Pull 1 month of historical data from Yahoo Finance
|
||||
- Run comprehensive backtests with enhanced metrics
|
||||
- Compare multiple trading strategies
|
||||
- Analyze performance across different sessions and symbols
|
||||
|
||||
Ready for production use after data pull and testing.
|
||||
|
||||
|
|
@ -1,317 +0,0 @@
|
|||
# Implementation Roadmap - Quick Reference
|
||||
|
||||
## File-by-File Implementation Guide
|
||||
|
||||
### Phase 1: Critical Features (Start Here)
|
||||
|
||||
#### 1. Price Reaction Tracking
|
||||
**New File:** `backend/python_service/services/price_reaction_tracker.py`
|
||||
- Class: `PriceReactionTracker`
|
||||
- Method: `track_reaction(flow_row, pool)` → returns dict with 5m/15m/30m reactions
|
||||
- Integration: Call in `main.py` after `enrich_flow_with_prices()`
|
||||
|
||||
**Modify:** `backend/python_service/main.py`
|
||||
```python
|
||||
# After line 145 (after price enrichment):
|
||||
from services.price_reaction_tracker import PriceReactionTracker
|
||||
|
||||
reaction_tracker = PriceReactionTracker()
|
||||
df_final = await reaction_tracker.enrich_with_reactions(df_final, pool)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
#### 2. VWAP Integration
|
||||
**Modify:** `backend/python_service/services/price_context.py`
|
||||
- Add method: `async def calculate_vwap_at_time(symbol, timestamp, pool)`
|
||||
- Add method: `async def get_vwap_for_date(symbol, date, pool)`
|
||||
- Integration: Call in `enrich_flow_with_prices()` method
|
||||
|
||||
**Add to enrichment:**
|
||||
```python
|
||||
# In enrich_flow_with_prices(), add:
|
||||
vwap_data = await self.get_vwap_at_time(symbol, flow_ts_utc, pool)
|
||||
df['vwap_at_signal'] = vwap_data['vwap']
|
||||
df['price_vs_vwap_pct'] = ((df['u_close'] - df['vwap_at_signal']) / df['vwap_at_signal']) * 100
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
#### 3. Signal Tier Classification
|
||||
**New File:** `backend/python_service/services/signal_tier_classifier.py`
|
||||
- Class: `SignalTierClassifier`
|
||||
- Method: `classify_tier(row)` → returns 'TIER_1', 'TIER_2', or 'IGNORE'
|
||||
|
||||
**Modify:** `backend/python_service/services/options_flow_processor.py`
|
||||
- Add method: `process_tier_classification(df)` → adds `signal_tier` column
|
||||
- Call in `process()` method after `process_badges()`
|
||||
|
||||
**Integration:**
|
||||
```python
|
||||
# In process() method, after process_badges():
|
||||
df = self.process_tier_classification(df)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
#### 4. Trade Checklist
|
||||
**New File:** `backend/python_service/services/trade_checklist.py`
|
||||
- Class: `TradeChecklist`
|
||||
- Method: `evaluate(flow_row)` → returns checklist score and details
|
||||
|
||||
**Modify:** `backend/python_service/main.py`
|
||||
- After all enrichments, add checklist evaluation:
|
||||
```python
|
||||
from services.trade_checklist import TradeChecklist
|
||||
|
||||
checklist = TradeChecklist()
|
||||
df_final['checklist_result'] = df_final.apply(
|
||||
lambda row: checklist.evaluate(row), axis=1
|
||||
)
|
||||
df_final['checklist_score'] = df_final['checklist_result'].apply(lambda x: x['checklist_score'])
|
||||
df_final['checklist_passed'] = df_final['checklist_result'].apply(lambda x: x['checklist_passed'])
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Phase 2: High Value Features
|
||||
|
||||
#### 5. Strike Clustering
|
||||
**New File:** `backend/python_service/services/strike_cluster_detector.py`
|
||||
- Class: `StrikeClusterDetector`
|
||||
- Method: `detect_clusters(df, window_minutes=30)` → adds cluster flags
|
||||
|
||||
**Integration:** Call in `main.py` after aggregations
|
||||
|
||||
---
|
||||
|
||||
#### 6. Delta Weighting
|
||||
**Modify:** `backend/python_service/services/options_flow_processor.py`
|
||||
- Add method: `calculate_delta_weighted_value(row)`
|
||||
- Add to `process_aggregations()` or create new method `process_delta_weighting()`
|
||||
|
||||
---
|
||||
|
||||
#### 7. Index Correlation
|
||||
**New File:** `backend/python_service/services/index_correlation.py`
|
||||
- Class: `IndexCorrelationService`
|
||||
- Method: `check_index_alignment(flow_row, pool)` → returns alignment data
|
||||
|
||||
**Integration:** Call in `main.py` after price enrichment
|
||||
|
||||
---
|
||||
|
||||
### Phase 3: Advanced Features
|
||||
|
||||
#### 8. Gamma Exposure
|
||||
**New File:** `backend/python_service/services/gamma_calculator.py`
|
||||
- Class: `GammaCalculator`
|
||||
- Method: `calculate_gex(df)` → adds GEX columns
|
||||
|
||||
**Note:** Requires options pricing library (e.g., `py_vollib` or simplified approximation)
|
||||
|
||||
---
|
||||
|
||||
#### 9. Sweep vs Block Detection
|
||||
**New File:** `backend/python_service/services/trade_type_detector.py`
|
||||
- Class: `TradeTypeDetector`
|
||||
- Method: `detect_trade_type(df)` → adds trade_type column
|
||||
|
||||
---
|
||||
|
||||
#### 10. DTE Buckets
|
||||
**Modify:** `backend/python_service/services/options_flow_processor.py`
|
||||
- Add method: `calculate_dte_bucket(row)`
|
||||
- Add to `process_moneyness()` or create new method
|
||||
|
||||
---
|
||||
|
||||
### Phase 4: Analytics
|
||||
|
||||
#### 11. Historical Win Rate
|
||||
**New File:** `backend/python_service/services/pattern_analyzer.py`
|
||||
- Class: `PatternAnalyzer`
|
||||
- Methods: `track_pattern()`, `get_pattern_stats()`
|
||||
|
||||
**Database:** Create table `signal_patterns_history`
|
||||
|
||||
---
|
||||
|
||||
#### 12. Enhanced Entry/Exit Logic
|
||||
**Modify:** `backend/src/services/tradePlanGenerator.js`
|
||||
- Enhance `generateEntryStrategy()` with VWAP logic
|
||||
- Enhance `generateExitStrategy()` with flow-based exits
|
||||
|
||||
---
|
||||
|
||||
## Database Migrations
|
||||
|
||||
### Migration 1: Add Enrichment Columns
|
||||
```sql
|
||||
ALTER TABLE processed_options_flow ADD COLUMN IF NOT EXISTS
|
||||
signal_tier VARCHAR(10),
|
||||
is_tradeable BOOLEAN,
|
||||
vwap_at_signal NUMERIC,
|
||||
price_vs_vwap_pct NUMERIC,
|
||||
price_reaction_5m_pct NUMERIC,
|
||||
price_reaction_15m_pct NUMERIC,
|
||||
flow_led_to_move BOOLEAN,
|
||||
checklist_score INTEGER,
|
||||
checklist_passed BOOLEAN;
|
||||
```
|
||||
|
||||
### Migration 2: Add Pattern Tracking Table
|
||||
```sql
|
||||
CREATE TABLE IF NOT EXISTS signal_patterns_history (
|
||||
id SERIAL PRIMARY KEY,
|
||||
pattern_hash VARCHAR(100),
|
||||
signal_time TIMESTAMPTZ,
|
||||
symbol VARCHAR(10),
|
||||
price_at_signal NUMERIC,
|
||||
price_5m_after NUMERIC,
|
||||
price_15m_after NUMERIC,
|
||||
outcome VARCHAR(20),
|
||||
return_pct NUMERIC,
|
||||
created_at TIMESTAMPTZ DEFAULT NOW()
|
||||
);
|
||||
|
||||
CREATE INDEX idx_pattern_hash ON signal_patterns_history(pattern_hash);
|
||||
CREATE INDEX idx_signal_time ON signal_patterns_history(signal_time);
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## API Endpoint Additions
|
||||
|
||||
### Modify: `backend/python_service/main.py`
|
||||
|
||||
Add new endpoints:
|
||||
|
||||
```python
|
||||
@app.get("/api/options-flow/enhanced")
|
||||
async def get_enhanced_flow(...):
|
||||
# Same as existing endpoint but with all enrichments enabled
|
||||
pass
|
||||
|
||||
@app.get("/api/options-flow/tier-1")
|
||||
async def get_tier1_signals(...):
|
||||
# Filter to only Tier-1 signals
|
||||
df_final = df_final[df_final['signal_tier'] == 'TIER_1']
|
||||
pass
|
||||
|
||||
@app.get("/api/options-flow/checklist-passed")
|
||||
async def get_checklist_passed(...):
|
||||
# Filter to only checklist-passed signals
|
||||
df_final = df_final[df_final['checklist_passed'] == True]
|
||||
pass
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Testing Checklist
|
||||
|
||||
### Unit Tests to Add
|
||||
|
||||
1. **Price Reaction Tests**
|
||||
- `test_price_reaction_5m_positive()`
|
||||
- `test_price_reaction_no_move()`
|
||||
- `test_flow_led_to_move_detection()`
|
||||
|
||||
2. **Tier Classification Tests**
|
||||
- `test_tier1_classification()`
|
||||
- `test_tier2_classification()`
|
||||
- `test_ignore_classification()`
|
||||
|
||||
3. **Checklist Tests**
|
||||
- `test_checklist_5_5_passes()`
|
||||
- `test_checklist_4_5_passes()`
|
||||
- `test_checklist_3_5_fails()`
|
||||
|
||||
4. **VWAP Tests**
|
||||
- `test_vwap_calculation()`
|
||||
- `test_vwap_pullback_detection()`
|
||||
- `test_vwap_reclaim_detection()`
|
||||
|
||||
---
|
||||
|
||||
## Performance Considerations
|
||||
|
||||
### Optimization Tips
|
||||
|
||||
1. **Price Reaction Tracking**
|
||||
- Batch fetch prices for all signals at once
|
||||
- Use async queries with connection pooling
|
||||
- Cache VWAP calculations per symbol/date
|
||||
|
||||
2. **Strike Clustering**
|
||||
- Use pandas groupby operations (already efficient)
|
||||
- Consider windowing for large datasets
|
||||
|
||||
3. **Index Correlation**
|
||||
- Cache SPY/QQQ flow data (update every minute)
|
||||
- Use materialized views for index flow aggregations
|
||||
|
||||
4. **Gamma Calculation**
|
||||
- Use simplified approximation (no full Black-Scholes)
|
||||
- Pre-calculate common strikes
|
||||
|
||||
---
|
||||
|
||||
## Rollout Strategy
|
||||
|
||||
### Week 1: Phase 1 (Critical)
|
||||
- Day 1-2: Price Reaction Tracking
|
||||
- Day 3-4: VWAP Integration
|
||||
- Day 5: Signal Tier Classification
|
||||
- Day 6-7: Trade Checklist
|
||||
|
||||
### Week 2: Phase 2 (High Value)
|
||||
- Day 1-2: Strike Clustering
|
||||
- Day 3: Delta Weighting
|
||||
- Day 4-5: Index Correlation
|
||||
|
||||
### Week 3: Phase 3 (Advanced)
|
||||
- Day 1-2: Gamma Exposure
|
||||
- Day 3: Sweep vs Block
|
||||
- Day 4: DTE Buckets
|
||||
|
||||
### Week 4: Phase 4 (Analytics)
|
||||
- Day 1-3: Historical Win Rate Tracking
|
||||
- Day 4-5: Enhanced Entry/Exit Logic
|
||||
- Day 6-7: Testing & Refinement
|
||||
|
||||
---
|
||||
|
||||
## Monitoring & Metrics
|
||||
|
||||
### Key Metrics to Track
|
||||
|
||||
1. **Signal Quality**
|
||||
- Tier-1 signal percentage
|
||||
- Checklist pass rate
|
||||
- Price reaction success rate
|
||||
|
||||
2. **Trade Performance**
|
||||
- Win rate by tier
|
||||
- Win rate by checklist score
|
||||
- Average return by pattern
|
||||
|
||||
3. **System Performance**
|
||||
- Enrichment processing time
|
||||
- Database query performance
|
||||
- API response times
|
||||
|
||||
---
|
||||
|
||||
## Next Steps
|
||||
|
||||
1. ✅ Review this roadmap
|
||||
2. ✅ Prioritize features based on your needs
|
||||
3. ✅ Start with Phase 1 (Price Reaction + VWAP + Tier + Checklist)
|
||||
4. ✅ Test each feature before moving to next
|
||||
5. ✅ Monitor metrics and refine
|
||||
|
||||
---
|
||||
|
||||
**Remember:** Don't change existing code - extend it with new services and enrichments!
|
||||
|
||||
|
|
@ -1,164 +0,0 @@
|
|||
# Phase 1 Data Sources
|
||||
|
||||
This document explains where each Phase 1 feature gets its data from.
|
||||
|
||||
## Data Flow Overview
|
||||
|
||||
```
|
||||
PostgreSQL Database
|
||||
↓
|
||||
OptionsFlow_monthly table (raw options flow data)
|
||||
↓
|
||||
OptionsFlowProcessor (normalizes, calculates badges, aggregates)
|
||||
↓
|
||||
PriceContextService (adds price context, VWAP)
|
||||
↓
|
||||
AlertService (matches alerts)
|
||||
↓
|
||||
Phase 1 Services (Signal Tier, Price Reaction, Checklist)
|
||||
↓
|
||||
Final Output
|
||||
```
|
||||
|
||||
## 1. Initial Data Source
|
||||
|
||||
**Location:** `backend/python_service/main.py` (lines 115-123)
|
||||
|
||||
```python
|
||||
SELECT *
|
||||
FROM "OptionsFlow_monthly"
|
||||
WHERE "Premium" IS NOT NULL
|
||||
AND TRIM("Premium"::text) <> ''
|
||||
AND "StockEtf" = 'STOCK'
|
||||
AND "Symbol" NOT IN ('TSLA', 'NVDA')
|
||||
```
|
||||
|
||||
**What it provides:**
|
||||
- Raw options flow records from the `OptionsFlow_monthly` table
|
||||
- All columns from the table (Symbol, Premium, Strike, Expiration, etc.)
|
||||
|
||||
## 2. Signal Tier Classification
|
||||
|
||||
**Service:** `backend/python_service/services/signal_tier_classifier.py`
|
||||
|
||||
**Data Sources:**
|
||||
- **Badges:** From `OptionsFlowProcessor` (calculated from flow data)
|
||||
- `badge_round`: 🟢 or 🔴 (from direction and net premium)
|
||||
- `badge_more`: 💎⭐ (from premium thresholds, volume/OI ratios)
|
||||
- **Premium:** From processed flow data
|
||||
- `premium_num`: Total premium for the signal
|
||||
- `bull_total`, `bear_total`: Bullish vs bearish premium
|
||||
- `prem_cb_itm`, `prem_ps_itm`, `prem_cs_itm`, `prem_pb_itm`: ITM premium breakdown
|
||||
- **Direction:** From `OptionsFlowProcessor`
|
||||
- `direction`: 'BULL' or 'BEAR'
|
||||
- **Volume/OI:** From raw flow data
|
||||
- `vol_num`: Volume
|
||||
- `oi_num`: Open Interest
|
||||
|
||||
**Database Tables Used:**
|
||||
- ✅ `OptionsFlow_monthly` (via OptionsFlowProcessor)
|
||||
- ❌ No direct database queries
|
||||
|
||||
## 3. Price Reaction Tracking
|
||||
|
||||
**Service:** `backend/python_service/services/price_reaction_tracker.py`
|
||||
|
||||
**Data Sources:**
|
||||
- **Signal Time:** From processed flow data
|
||||
- `flow_ts_utc`: Timestamp of the signal
|
||||
- `symbol_norm`: Normalized symbol
|
||||
- **Price at Signal:** From `PriceContextService`
|
||||
- `u_close`: Price at the time of the signal
|
||||
|
||||
**Database Tables Used:**
|
||||
- ✅ `prices_intraday_1m` (queried directly)
|
||||
- Gets price at 5m, 15m, 30m after signal time
|
||||
- Query: `SELECT close FROM prices_intraday_1m WHERE symbol = $1 AND ts <= $2 ORDER BY ts DESC LIMIT 1`
|
||||
|
||||
**Calculation:**
|
||||
```python
|
||||
reaction_5m = ((price_5m - price_at_signal) / price_at_signal) * 100
|
||||
flow_led_to_move = abs(reaction_5m) > 0.5 # 0.5% threshold
|
||||
```
|
||||
|
||||
## 4. Trade Checklist
|
||||
|
||||
**Service:** `backend/python_service/services/trade_checklist.py`
|
||||
|
||||
**Data Sources:**
|
||||
- **Badges:** From `OptionsFlowProcessor`
|
||||
- `badge_round`: 🟢 or 🔴
|
||||
- `badge_more`: 💎⭐
|
||||
- **VWAP:** From `PriceContextService`
|
||||
- `vwap_at_signal`: VWAP at the time of the signal
|
||||
- `price_vs_vwap_pct`: Percentage distance from VWAP
|
||||
- **Index Alignment:** From processed flow data (if available)
|
||||
- `index_aligned`: Boolean indicating if index confirms
|
||||
|
||||
**Database Tables Used:**
|
||||
- ✅ `prices_intraday_1m` (via PriceContextService for VWAP calculation)
|
||||
- ✅ `prices_daily` (via PriceContextService for prior close)
|
||||
|
||||
**Checklist Items:**
|
||||
1. ✅ Has direction (🟢 or 🔴)
|
||||
2. ✅ Has diamond (💎)
|
||||
3. ✅ Has star (⭐)
|
||||
4. ✅ Price respects VWAP (within ±2%)
|
||||
5. ✅ Index confirms (if available)
|
||||
|
||||
## 5. VWAP Calculation
|
||||
|
||||
**Service:** `backend/python_service/services/price_context.py`
|
||||
|
||||
**Data Sources:**
|
||||
- **RTH Open:** From `prices_intraday_1m`
|
||||
- Gets first bar at 9:30 AM CST for the trading day
|
||||
- **Price Data:** From `prices_intraday_1m`
|
||||
- Gets all 1-minute bars from RTH open to signal time
|
||||
- Calculates: `VWAP = Σ(price × volume) / Σ(volume)`
|
||||
|
||||
**Database Tables Used:**
|
||||
- ✅ `prices_intraday_1m` (queried directly)
|
||||
- Query for RTH open: `SELECT open FROM prices_intraday_1m WHERE symbol = $1 AND date = $2 AND time >= '09:30:00' ORDER BY ts ASC LIMIT 1`
|
||||
- Query for VWAP: `SELECT close, volume FROM prices_intraday_1m WHERE symbol = $1 AND ts >= $2 AND ts <= $3 ORDER BY ts ASC`
|
||||
|
||||
## Summary Table
|
||||
|
||||
| Phase 1 Feature | Primary Data Source | Database Tables | Direct DB Queries? |
|
||||
|----------------|---------------------|-----------------|-------------------|
|
||||
| **Signal Tier** | OptionsFlowProcessor (badges, premium) | OptionsFlow_monthly (via processor) | ❌ No |
|
||||
| **Price Reaction** | prices_intraday_1m | prices_intraday_1m | ✅ Yes |
|
||||
| **Trade Checklist** | OptionsFlowProcessor (badges) + PriceContextService (VWAP) | OptionsFlow_monthly, prices_intraday_1m | ✅ Yes (via PriceContextService) |
|
||||
| **VWAP** | prices_intraday_1m | prices_intraday_1m | ✅ Yes |
|
||||
|
||||
## Key Dependencies
|
||||
|
||||
1. **OptionsFlowProcessor** must run first to calculate badges and normalize data
|
||||
2. **PriceContextService** must run before Phase 1 to provide VWAP and price context
|
||||
3. **Price Reaction** requires `prices_intraday_1m` to have data for the signal time + 5/15/30 minutes
|
||||
4. **VWAP** requires `prices_intraday_1m` to have data from RTH open (9:30 AM CST) to signal time
|
||||
|
||||
## Common Issues
|
||||
|
||||
### "Not calculated" for all fields
|
||||
- **Cause:** Python service not running, or data coming from SQL fallback
|
||||
- **Solution:** Ensure Python service is running at `http://localhost:8010`
|
||||
|
||||
### Price Reaction shows "Not calculated"
|
||||
- **Cause:**
|
||||
- Historical data (future dates have no price data)
|
||||
- Missing price data in `prices_intraday_1m` for the time period
|
||||
- Signal time is invalid
|
||||
- **Solution:** Check that `prices_intraday_1m` has data for the signal date and time
|
||||
|
||||
### VWAP shows "Not calculated"
|
||||
- **Cause:**
|
||||
- Signal occurred before RTH open (9:30 AM CST)
|
||||
- Missing price data in `prices_intraday_1m` for the trading day
|
||||
- Historical/future dates
|
||||
- **Solution:** VWAP is only available during RTH hours (9:30 AM - 4:00 PM CST)
|
||||
|
||||
### Signal Tier shows "Not calculated"
|
||||
- **Cause:** Python service not running (this should always work if service is running)
|
||||
- **Solution:** Check Python service logs for errors in `SignalTierClassifier`
|
||||
|
||||
|
|
@ -1,318 +0,0 @@
|
|||
# Phase 1 Data & Filters - Complete Explanation
|
||||
|
||||
## 🎯 What is Phase 1?
|
||||
|
||||
Phase 1 adds **4 critical features** to help you identify the **best trading signals** and **filter out noise** (hedges, rolls, low-quality signals).
|
||||
|
||||
---
|
||||
|
||||
## 📊 Phase 1 Data Fields
|
||||
|
||||
### 1. **Signal Tier Classification** (`signal_tier`)
|
||||
|
||||
**What it does:** Classifies every signal into one of three tiers based on quality.
|
||||
|
||||
**Values:**
|
||||
- **`TIER_1`** 🥇 = **Tradeable alone** (highest quality)
|
||||
- **`TIER_2`** 🥈 = **Needs confirmation** (wait 10-30 minutes, might become Tier-1)
|
||||
- **`IGNORE`** 🚫 = **Don't trade** (noise, hedges, or low quality)
|
||||
|
||||
**How it works:**
|
||||
```
|
||||
TIER_1 = 🟢/🔴 + 💎 + ⭐ + premium > $500K + direction aligned
|
||||
TIER_2 = 🟢 + 💎 (no ⭐) OR ⭐ without 💎
|
||||
IGNORE = OTM-only, mixed signals, low volume/OI ratio
|
||||
```
|
||||
|
||||
**Why it matters:**
|
||||
- **Tier-1 signals** are institutions positioning (not hedging)
|
||||
- **Tier-2 signals** often become Tier-1 later
|
||||
- **Ignore signals** are likely hedges/rolls - don't waste time on them
|
||||
|
||||
---
|
||||
|
||||
### 2. **Price Reaction Tracking** (`price_reaction_5m_pct`, `flow_led_to_move`)
|
||||
|
||||
**What it does:** Tracks how price moves **AFTER** the signal appears.
|
||||
|
||||
**Fields:**
|
||||
- `price_reaction_5m_pct` = Price change 5 minutes after signal (%)
|
||||
- `price_reaction_15m_pct` = Price change 15 minutes after signal (%)
|
||||
- `flow_led_to_move` = Boolean: Did flow lead to >0.5% price move?
|
||||
|
||||
**How it works:**
|
||||
```
|
||||
Signal appears at 10:00 AM → Price = $100
|
||||
5 minutes later (10:05 AM) → Price = $101.25
|
||||
price_reaction_5m_pct = +1.25%
|
||||
|
||||
If |price_reaction_5m_pct| > 0.5% → flow_led_to_move = true
|
||||
```
|
||||
|
||||
**Why it matters:**
|
||||
- **Flow WITH price reaction** = Real positioning (trade it!)
|
||||
- **Flow WITHOUT price reaction** = Hedge or roll (ignore it!)
|
||||
|
||||
**Example:**
|
||||
- Signal shows 🟢💎⭐ with $1M premium
|
||||
- But price doesn't move → It's a hedge, not real positioning
|
||||
- Filter it out!
|
||||
|
||||
---
|
||||
|
||||
### 3. **VWAP Integration** (`vwap_at_signal`, `price_vs_vwap_pct`)
|
||||
|
||||
**What it does:** Calculates VWAP (Volume Weighted Average Price) and shows distance from it.
|
||||
|
||||
**Fields:**
|
||||
- `vwap_at_signal` = VWAP value at signal time
|
||||
- `price_vs_vwap_pct` = Percentage distance from VWAP
|
||||
|
||||
**How it works:**
|
||||
```
|
||||
VWAP = Average price weighted by volume (from 9:30 AM to signal time)
|
||||
price_vs_vwap_pct = ((Current Price - VWAP) / VWAP) * 100
|
||||
|
||||
Example:
|
||||
- VWAP = $100
|
||||
- Current Price = $101
|
||||
- price_vs_vwap_pct = +1.0%
|
||||
```
|
||||
|
||||
**Why it matters:**
|
||||
- **Price near VWAP** = Good entry opportunity
|
||||
- **Price far from VWAP** (>2%) = Wait for pullback
|
||||
- **VWAP pullback/reclaim** = Best entry strategy
|
||||
|
||||
**Trading Logic:**
|
||||
- ✅ **Best entry:** VWAP pullback or VWAP reclaim
|
||||
- ✅ **Good entry:** Break & hold above prior high
|
||||
- ❌ **Avoid:** Chasing vertical candles (price too extended)
|
||||
|
||||
---
|
||||
|
||||
### 4. **Trade Checklist** (`checklist_score`, `checklist_passed`)
|
||||
|
||||
**What it does:** Evaluates signals against a 5-point checklist. Requires 4/5 to pass.
|
||||
|
||||
**Checklist Items:**
|
||||
1. ✅ Has direction (🟢 or 🔴)
|
||||
2. ✅ Has diamond (💎)
|
||||
3. ✅ Has star (⭐)
|
||||
4. ✅ Price respects VWAP (within ±2%)
|
||||
5. ✅ Index confirms (SPY/QQQ alignment - placeholder for Phase 2)
|
||||
|
||||
**Fields:**
|
||||
- `checklist_score` = Score out of 5 (0-5)
|
||||
- `checklist_passed` = Boolean: True if score >= 4
|
||||
- `checklist_details` = Object showing which checks passed/failed
|
||||
|
||||
**How it works:**
|
||||
```
|
||||
Example:
|
||||
✅ Has direction (🟢) = 1 point
|
||||
✅ Has diamond (💎) = 1 point
|
||||
✅ Has star (⭐) = 1 point
|
||||
✅ Price respects VWAP = 1 point
|
||||
❌ Index confirms = 0 points (not implemented yet)
|
||||
|
||||
Total: 4/5 → checklist_passed = true
|
||||
```
|
||||
|
||||
**Why it matters:**
|
||||
- **Prevents bad trades** - Only trade signals that pass 4/5 checks
|
||||
- **Quality filter** - Ensures multiple conditions are met
|
||||
- **Reduces false signals** - Filters out incomplete setups
|
||||
|
||||
---
|
||||
|
||||
## 🔍 Phase 1 Filters
|
||||
|
||||
### Filter 1: **🥇 Tier-1 Only**
|
||||
|
||||
**What it does:** Shows only Tier-1 signals (highest quality, tradeable alone).
|
||||
|
||||
**When to use:**
|
||||
- When you want **only the best signals**
|
||||
- When you're **short on time** - focus on Tier-1 only
|
||||
- When you want **institutional positioning** (not hedges)
|
||||
|
||||
**What it filters:**
|
||||
- ✅ Shows: Tier-1 signals only
|
||||
- ❌ Hides: Tier-2 and Ignore signals
|
||||
|
||||
**Example:**
|
||||
```
|
||||
Without filter: 50 signals (10 Tier-1, 20 Tier-2, 20 Ignore)
|
||||
With filter: 10 signals (only Tier-1)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Filter 2: **✅ Checklist Passed**
|
||||
|
||||
**What it does:** Shows only signals that passed the 4/5 checklist.
|
||||
|
||||
**When to use:**
|
||||
- When you want **validated trades** only
|
||||
- When you want to **avoid incomplete setups**
|
||||
- When you want **multiple confirmations**
|
||||
|
||||
**What it filters:**
|
||||
- ✅ Shows: Signals with checklist_score >= 4
|
||||
- ❌ Hides: Signals with checklist_score < 4
|
||||
|
||||
**Example:**
|
||||
```
|
||||
Without filter: 50 signals (30 passed, 20 failed)
|
||||
With filter: 30 signals (only passed)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Filter 3: **📈 Flow Led to Move**
|
||||
|
||||
**What it does:** Shows only signals where flow led to actual price movement (>0.5%).
|
||||
|
||||
**When to use:**
|
||||
- When you want **real positioning** (not hedges)
|
||||
- When you want to **filter out noise**
|
||||
- When you want **signals that actually moved price**
|
||||
|
||||
**What it filters:**
|
||||
- ✅ Shows: Signals where price moved >0.5% after signal
|
||||
- ❌ Hides: Signals where price didn't move (likely hedges/rolls)
|
||||
|
||||
**Example:**
|
||||
```
|
||||
Without filter: 50 signals (20 moved price, 30 didn't)
|
||||
With filter: 20 signals (only ones that moved price)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Combined Filter Strategy
|
||||
|
||||
### Best Signals (All 3 Filters ON):
|
||||
```
|
||||
🥇 Tier-1 Only + ✅ Checklist Passed + 📈 Flow Led to Move
|
||||
|
||||
Result: Only the highest quality signals that:
|
||||
- Are Tier-1 (tradeable alone)
|
||||
- Passed 4/5 checklist
|
||||
- Led to actual price movement
|
||||
|
||||
Example: 50 signals → 5 signals (only the best!)
|
||||
```
|
||||
|
||||
### Conservative Strategy:
|
||||
```
|
||||
🥇 Tier-1 Only + ✅ Checklist Passed
|
||||
|
||||
Result: High-quality validated signals
|
||||
(Even if price hasn't moved yet - might be early)
|
||||
```
|
||||
|
||||
### Aggressive Strategy:
|
||||
```
|
||||
📈 Flow Led to Move
|
||||
|
||||
Result: Any signal that moved price
|
||||
(Includes Tier-2 if they moved price)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📈 Real-World Example
|
||||
|
||||
### Scenario: You see a signal
|
||||
|
||||
**Signal Details:**
|
||||
- Symbol: AAPL
|
||||
- Badges: 🟢💎⭐
|
||||
- Premium: $750K
|
||||
- Time: 10:00 AM
|
||||
- Price: $150
|
||||
|
||||
**Phase 1 Data:**
|
||||
- `signal_tier` = `TIER_1` ✅
|
||||
- `checklist_score` = `4` ✅
|
||||
- `checklist_passed` = `true` ✅
|
||||
- `price_reaction_5m_pct` = `+1.2%` ✅
|
||||
- `flow_led_to_move` = `true` ✅
|
||||
- `vwap_at_signal` = `$149.50`
|
||||
- `price_vs_vwap_pct` = `+0.33%` ✅
|
||||
|
||||
**Analysis:**
|
||||
- ✅ Tier-1 signal (highest quality)
|
||||
- ✅ Passed checklist (4/5)
|
||||
- ✅ Flow led to price move (+1.2% in 5 minutes)
|
||||
- ✅ Price near VWAP (good entry)
|
||||
|
||||
**Decision:** **TRADE IT!** This is a high-quality signal with multiple confirmations.
|
||||
|
||||
---
|
||||
|
||||
### Scenario: Another signal
|
||||
|
||||
**Signal Details:**
|
||||
- Symbol: MSFT
|
||||
- Badges: 🟢💎 (no ⭐)
|
||||
- Premium: $200K
|
||||
- Time: 11:00 AM
|
||||
- Price: $300
|
||||
|
||||
**Phase 1 Data:**
|
||||
- `signal_tier` = `TIER_2` ⚠️
|
||||
- `checklist_score` = `3` ❌
|
||||
- `checklist_passed` = `false` ❌
|
||||
- `price_reaction_5m_pct` = `+0.1%` ❌
|
||||
- `flow_led_to_move` = `false` ❌
|
||||
- `vwap_at_signal` = `$299.50`
|
||||
- `price_vs_vwap_pct` = `+0.17%`
|
||||
|
||||
**Analysis:**
|
||||
- ⚠️ Tier-2 signal (needs confirmation)
|
||||
- ❌ Failed checklist (3/5)
|
||||
- ❌ Flow didn't lead to price move (only +0.1%)
|
||||
- ✅ Price near VWAP
|
||||
|
||||
**Decision:** **WAIT or IGNORE** - Not enough confirmations, price didn't react.
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Summary
|
||||
|
||||
### Phase 1 Data Helps You:
|
||||
1. **Identify quality** - Tier-1 vs Tier-2 vs Ignore
|
||||
2. **Filter hedges** - Flow without price reaction = hedge
|
||||
3. **Find entries** - VWAP distance shows entry opportunities
|
||||
4. **Validate trades** - Checklist ensures multiple conditions met
|
||||
|
||||
### Phase 1 Filters Help You:
|
||||
1. **Focus on best signals** - Tier-1 only
|
||||
2. **Avoid bad trades** - Checklist passed only
|
||||
3. **Filter noise** - Flow led to move only
|
||||
|
||||
### The Goal:
|
||||
**Trade only the highest quality signals that:**
|
||||
- Are Tier-1 (institutional positioning)
|
||||
- Passed checklist (multiple confirmations)
|
||||
- Led to price movement (real positioning, not hedge)
|
||||
|
||||
**Result:** Higher win rate, fewer false signals, better entries!
|
||||
|
||||
---
|
||||
|
||||
## 💡 Pro Tips
|
||||
|
||||
1. **Start with all 3 filters ON** - See only the best signals
|
||||
2. **If no results, turn off one filter** - Gradually relax criteria
|
||||
3. **Check VWAP distance** - Best entries are near VWAP
|
||||
4. **Monitor price reaction** - If flow doesn't move price, it's likely a hedge
|
||||
5. **Use Tier-2 as watchlist** - They often become Tier-1 later
|
||||
|
||||
---
|
||||
|
||||
**Remember:** Phase 1 is about **quality over quantity**. Better to trade 5 great signals than 50 mediocre ones!
|
||||
|
||||
|
|
@ -1,226 +0,0 @@
|
|||
# Phase 1 Frontend Update Summary
|
||||
|
||||
## ✅ Completed Updates
|
||||
|
||||
### 1. New Filter Options Added
|
||||
|
||||
**Location:** `frontend/src/components/dashboard/OptionsFlowPanel.jsx`
|
||||
|
||||
**New Filters:**
|
||||
- ✅ **Tier-1 Only** - Filter to show only Tier-1 tradeable signals
|
||||
- ✅ **Checklist Passed** - Filter to show only signals that passed the 4/5 checklist
|
||||
- ✅ **Flow Led to Move** - Filter to show only signals where flow led to actual price movement
|
||||
|
||||
**Implementation:**
|
||||
- Added to `initialFilters` state
|
||||
- Added checkbox UI in filters section
|
||||
- Integrated into `filteredData` useMemo hook
|
||||
|
||||
---
|
||||
|
||||
### 2. New Columns Added
|
||||
|
||||
**New Columns in Table:**
|
||||
|
||||
#### Signal Tier Column
|
||||
- **Accessor:** `SignalTier`
|
||||
- **Group:** `core`
|
||||
- **Display:** Badge showing Tier-1 🥇, Tier-2 🥈, or Ignore 🚫
|
||||
- **Color Coding:**
|
||||
- Tier-1: Green badge
|
||||
- Tier-2: Yellow badge
|
||||
- Ignore: Gray badge
|
||||
|
||||
#### Checklist Column
|
||||
- **Accessor:** `Checklist`
|
||||
- **Group:** `core`
|
||||
- **Display:** Score out of 5 with pass/fail indicator
|
||||
- **Features:**
|
||||
- Shows score (e.g., "4/5 ✅" or "3/5 ❌")
|
||||
- Color coded: Green for passed, Red for failed
|
||||
- Tooltip shows detailed check results
|
||||
|
||||
#### Price Reaction 5m Column
|
||||
- **Accessor:** `PriceReaction5m`
|
||||
- **Group:** `price`
|
||||
- **Display:** Percentage price change 5 minutes after signal
|
||||
- **Features:**
|
||||
- Color coded: Green (positive), Red (negative)
|
||||
- Shows checkmark if flow led to move
|
||||
|
||||
#### VWAP Distance Column
|
||||
- **Accessor:** `VWAPDistance`
|
||||
- **Group:** `price`
|
||||
- **Display:** Percentage distance from VWAP
|
||||
- **Features:**
|
||||
- Color coded by distance:
|
||||
- Green: Within 1% of VWAP
|
||||
- Yellow: 1-2% from VWAP
|
||||
- Red: >2% from VWAP
|
||||
- Shows VWAP value in tooltip
|
||||
|
||||
---
|
||||
|
||||
### 3. Enhanced Best Trade Score
|
||||
|
||||
**Updated:** `calculateBestTradeScore()` function
|
||||
|
||||
**New Bonuses:**
|
||||
- Tier-1 signal: +25 points
|
||||
- Checklist passed: +20 points
|
||||
- Flow led to move: +15 points
|
||||
- Checklist score: +2 points per point (0-10 points)
|
||||
|
||||
**Result:** Tier-1 signals with checklist passed and flow-led moves rank higher
|
||||
|
||||
---
|
||||
|
||||
### 4. Column Visibility Updates
|
||||
|
||||
**Updated Column Groups:**
|
||||
- Added `SignalTier` and `Checklist` to `core` group
|
||||
- Added `PriceReaction5m` and `VWAPDistance` to `price` group
|
||||
|
||||
**Default Visible Columns:**
|
||||
- Added `SignalTier` and `Checklist` to default visible columns
|
||||
- Added `PriceReaction5m` to default visible columns
|
||||
|
||||
---
|
||||
|
||||
## 📊 Frontend Features
|
||||
|
||||
### Filter UI
|
||||
```
|
||||
[Min Score] [Min Premium] [Session] [Start Date] [End Date]
|
||||
[🥇 Tier-1 Only] [✅ Checklist Passed] [📈 Flow Led to Move]
|
||||
[Apply] [Reset]
|
||||
```
|
||||
|
||||
### Column Display Examples
|
||||
|
||||
**Signal Tier:**
|
||||
```
|
||||
🥇 Tier-1 (green badge)
|
||||
🥈 Tier-2 (yellow badge)
|
||||
🚫 Ignore (gray badge)
|
||||
```
|
||||
|
||||
**Checklist:**
|
||||
```
|
||||
4/5 ✅ (green badge)
|
||||
3/5 ❌ (red badge)
|
||||
```
|
||||
|
||||
**Price Reaction 5m:**
|
||||
```
|
||||
+1.25% ✓ (green, with checkmark if flow led to move)
|
||||
-0.75% (red)
|
||||
```
|
||||
|
||||
**VWAP Distance:**
|
||||
```
|
||||
+0.5% (green - near VWAP)
|
||||
V: $45.23 (VWAP value)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🧪 Testing
|
||||
|
||||
### Test Script Created
|
||||
**File:** `test_phase1_api.js`
|
||||
|
||||
**Usage:**
|
||||
```bash
|
||||
node test_phase1_api.js
|
||||
```
|
||||
|
||||
**What it tests:**
|
||||
1. Health endpoint
|
||||
2. Options flow endpoint
|
||||
3. Presence of Phase 1 fields in response
|
||||
4. Statistics (Tier-1 count, checklist passed count, etc.)
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Usage Examples
|
||||
|
||||
### Filter to Best Signals Only
|
||||
1. Check "🥇 Tier-1 Only"
|
||||
2. Check "✅ Checklist Passed"
|
||||
3. Check "📈 Flow Led to Move"
|
||||
4. Click "Apply"
|
||||
|
||||
**Result:** Only shows highest quality signals that:
|
||||
- Are Tier-1 (tradeable alone)
|
||||
- Passed the 4/5 checklist
|
||||
- Led to actual price movement
|
||||
|
||||
### View Phase 1 Metrics
|
||||
1. Open column selector
|
||||
2. Enable "Tier", "Checklist", "5m Reaction", "vs VWAP"
|
||||
3. Sort by any Phase 1 column
|
||||
|
||||
---
|
||||
|
||||
## 📝 API Response Fields Used
|
||||
|
||||
The frontend now uses these new fields from the API:
|
||||
|
||||
### Signal Classification
|
||||
- `signal_tier` - 'TIER_1', 'TIER_2', or 'IGNORE'
|
||||
- `is_tradeable` - Boolean
|
||||
|
||||
### Checklist
|
||||
- `checklist_score` - 0-5
|
||||
- `checklist_passed` - Boolean
|
||||
- `checklist_details` - Object with individual check results
|
||||
|
||||
### Price Reaction
|
||||
- `price_reaction_5m_pct` - Percentage change 5m after signal
|
||||
- `price_reaction_15m_pct` - Percentage change 15m after signal
|
||||
- `flow_led_to_move` - Boolean
|
||||
|
||||
### VWAP
|
||||
- `vwap_at_signal` - VWAP value at signal time
|
||||
- `price_vs_vwap_pct` - Percentage distance from VWAP
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Next Steps
|
||||
|
||||
1. **Test the API:**
|
||||
```bash
|
||||
node test_phase1_api.js
|
||||
```
|
||||
|
||||
2. **Start the frontend:**
|
||||
```bash
|
||||
cd frontend
|
||||
npm run dev
|
||||
```
|
||||
|
||||
3. **Verify:**
|
||||
- New filters appear in filter panel
|
||||
- New columns appear in table
|
||||
- Filters work correctly
|
||||
- Data displays correctly
|
||||
|
||||
4. **Monitor Performance:**
|
||||
- Check API response times
|
||||
- Monitor price reaction tracking queries
|
||||
- Watch for any performance issues
|
||||
|
||||
---
|
||||
|
||||
## ⚠️ Notes
|
||||
|
||||
- All new fields are optional (won't break if missing)
|
||||
- Filters default to `false` (all signals shown by default)
|
||||
- New columns are included in default visible columns
|
||||
- Best trade score calculation includes Phase 1 bonuses
|
||||
|
||||
---
|
||||
|
||||
**Status:** ✅ Frontend Updated and Ready for Testing
|
||||
|
||||
|
|
@ -1,234 +0,0 @@
|
|||
# Phase 1 Implementation Summary
|
||||
|
||||
## ✅ Completed Features
|
||||
|
||||
### 1. Price Reaction Tracking
|
||||
**File:** `backend/python_service/services/price_reaction_tracker.py`
|
||||
|
||||
**What it does:**
|
||||
- Tracks price movement 5, 15, and 30 minutes after a signal appears
|
||||
- Calculates price reaction percentages
|
||||
- Detects high/low breaks
|
||||
- Flags if flow led to actual price movement (filters hedges/rolls)
|
||||
|
||||
**New Fields Added:**
|
||||
- `price_reaction_5m_pct` - Price change 5 minutes after signal (%)
|
||||
- `price_reaction_15m_pct` - Price change 15 minutes after signal (%)
|
||||
- `price_reaction_30m_pct` - Price change 30 minutes after signal (%)
|
||||
- `high_break_5m` - Boolean: did price break high within 5m?
|
||||
- `low_break_5m` - Boolean: did price break low within 5m?
|
||||
- `flow_led_to_move` - Boolean: did flow lead to >0.5% price move?
|
||||
|
||||
**Integration:** Called in `main.py` after price enrichment
|
||||
|
||||
---
|
||||
|
||||
### 2. VWAP Integration
|
||||
**File:** `backend/python_service/services/price_context.py` (extended)
|
||||
|
||||
**What it does:**
|
||||
- Calculates VWAP (Volume Weighted Average Price) for each trading day
|
||||
- VWAP = SUM(price * volume) / SUM(volume) from RTH open (9:30 AM) to signal time
|
||||
- Calculates distance from VWAP for entry/exit logic
|
||||
|
||||
**New Fields Added:**
|
||||
- `vwap_at_signal` - VWAP value at signal time
|
||||
- `price_vs_vwap_pct` - Percentage distance from VWAP
|
||||
|
||||
**Integration:** Integrated into `enrich_flow_with_prices()` method
|
||||
|
||||
**Usage:**
|
||||
- Entry: VWAP pullback or VWAP reclaim
|
||||
- Exit: Price rejection of VWAP
|
||||
- Filter: Price too extended from VWAP (>2%) = wait for pullback
|
||||
|
||||
---
|
||||
|
||||
### 3. Signal Tier Classification
|
||||
**File:** `backend/python_service/services/signal_tier_classifier.py`
|
||||
|
||||
**What it does:**
|
||||
- Classifies signals into Tier-1, Tier-2, or Ignore
|
||||
- Tier-1: 🟢/🔴 + 💎 + ⭐ + premium > 500K + direction aligned (tradeable alone)
|
||||
- Tier-2: 🟢 + 💎 (no ⭐) OR ⭐ without 💎 (needs confirmation)
|
||||
- Ignore: OTM-only, mixed signals, low volume/OI ratio
|
||||
|
||||
**New Fields Added:**
|
||||
- `signal_tier` - 'TIER_1', 'TIER_2', or 'IGNORE'
|
||||
- `is_tradeable` - Boolean: True for Tier-1 only
|
||||
|
||||
**Integration:** Called in `main.py` after rocket score calculation
|
||||
|
||||
**Filtering Logic:**
|
||||
- OTM-only prints → Ignore
|
||||
- Mixed 🟢/🔴 with no net premium edge → Ignore
|
||||
- Big premium but volume << OI (likely rolls/hedges) → Ignore
|
||||
|
||||
---
|
||||
|
||||
### 4. Trade Checklist
|
||||
**File:** `backend/python_service/services/trade_checklist.py`
|
||||
|
||||
**What it does:**
|
||||
- Evaluates signals against 5-point checklist
|
||||
- Requires 4/5 checks to pass
|
||||
- Prevents bad trades
|
||||
|
||||
**Checklist Items:**
|
||||
1. ✅ Has direction (🟢 or 🔴)
|
||||
2. ✅ Has diamond (💎)
|
||||
3. ✅ Has star (⭐)
|
||||
4. ✅ Price respects VWAP (within ±2%, or ±1% for direction)
|
||||
5. ✅ Index confirms (placeholder - will be added in Phase 2)
|
||||
|
||||
**New Fields Added:**
|
||||
- `checklist_score` - Score out of 5 (0-5)
|
||||
- `checklist_passed` - Boolean: True if score >= 4
|
||||
- `checklist_details` - Dict with individual check results
|
||||
|
||||
**Integration:** Called in `main.py` after price reaction tracking
|
||||
|
||||
**Usage:**
|
||||
- Only trade signals with `checklist_passed = True`
|
||||
- Review `checklist_details` to see which checks passed/failed
|
||||
|
||||
---
|
||||
|
||||
## 📊 Data Flow
|
||||
|
||||
```
|
||||
Raw Options Flow
|
||||
↓
|
||||
OptionsFlowProcessor.process()
|
||||
↓
|
||||
PriceContextService.enrich_flow_with_prices() [VWAP added here]
|
||||
↓
|
||||
AlertService.match_alerts_to_flows()
|
||||
↓
|
||||
OptionsFlowProcessor.process_rocket_score()
|
||||
↓
|
||||
SignalTierClassifier.classify_tiers() [NEW]
|
||||
↓
|
||||
PriceReactionTracker.enrich_with_reactions() [NEW]
|
||||
↓
|
||||
TradeChecklist.evaluate_all() [NEW]
|
||||
↓
|
||||
OutputFormatter.format_final_output()
|
||||
↓
|
||||
API Response
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🔍 New API Response Fields
|
||||
|
||||
All new fields are automatically included in the API response. Key fields to use:
|
||||
|
||||
### For Filtering:
|
||||
- `signal_tier` - Filter to 'TIER_1' for best signals
|
||||
- `is_tradeable` - Boolean flag for tradeable signals
|
||||
- `checklist_passed` - Boolean flag for checklist-passed signals
|
||||
- `flow_led_to_move` - Boolean flag for signals that moved price
|
||||
|
||||
### For Entry/Exit:
|
||||
- `vwap_at_signal` - VWAP value for entry logic
|
||||
- `price_vs_vwap_pct` - Distance from VWAP
|
||||
- `price_reaction_5m_pct` - Price reaction after signal
|
||||
|
||||
### For Analysis:
|
||||
- `checklist_score` - Checklist score (0-5)
|
||||
- `checklist_details` - Detailed check results (JSON)
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Usage Examples
|
||||
|
||||
### Filter to Tier-1 Signals Only:
|
||||
```python
|
||||
# In your frontend or API consumer:
|
||||
filtered = [row for row in data if row.get('signal_tier') == 'TIER_1']
|
||||
```
|
||||
|
||||
### Filter to Checklist-Passed Signals:
|
||||
```python
|
||||
filtered = [row for row in data if row.get('checklist_passed') == True]
|
||||
```
|
||||
|
||||
### Filter to Signals That Led to Price Moves:
|
||||
```python
|
||||
filtered = [row for row in data if row.get('flow_led_to_move') == True]
|
||||
```
|
||||
|
||||
### Combined Filter (Best Signals):
|
||||
```python
|
||||
best_signals = [
|
||||
row for row in data
|
||||
if row.get('signal_tier') == 'TIER_1'
|
||||
and row.get('checklist_passed') == True
|
||||
and row.get('flow_led_to_move') == True
|
||||
]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Next Steps
|
||||
|
||||
### Immediate:
|
||||
1. Test the API endpoint to see new fields in response
|
||||
2. Update frontend to display new fields
|
||||
3. Add filters for Tier-1 and checklist-passed signals
|
||||
|
||||
### Phase 2 (Next):
|
||||
- Strike clustering detection
|
||||
- Delta weighting
|
||||
- Index correlation (SPY/QQQ/VIX)
|
||||
|
||||
---
|
||||
|
||||
## 📝 Notes
|
||||
|
||||
- All new services follow existing code patterns
|
||||
- No breaking changes to existing functionality
|
||||
- New fields are optional (won't break if missing)
|
||||
- Price reaction tracking may be slower for large datasets (batched queries)
|
||||
- VWAP calculation only works during RTH (9:30 AM - 4:00 PM)
|
||||
|
||||
---
|
||||
|
||||
## 🐛 Known Limitations
|
||||
|
||||
1. **Price Reaction Tracking:**
|
||||
- Only tracks if price data exists at 5m/15m/30m intervals
|
||||
- May be None for signals near market close
|
||||
- Uses 0.5% threshold for "flow led to move" (configurable)
|
||||
|
||||
2. **VWAP Calculation:**
|
||||
- Only calculated during RTH (9:30 AM - 4:00 PM)
|
||||
- Returns None for PRE/POST market signals
|
||||
- Requires 1-minute bar data
|
||||
|
||||
3. **Index Correlation:**
|
||||
- Currently returns False (placeholder)
|
||||
- Will be implemented in Phase 2
|
||||
|
||||
4. **Checklist:**
|
||||
- Index confirmation check always fails (Phase 2)
|
||||
- VWAP check may fail if VWAP not available (PRE/POST market)
|
||||
|
||||
---
|
||||
|
||||
## ✅ Testing Checklist
|
||||
|
||||
- [ ] API returns new fields in response
|
||||
- [ ] Tier classification works correctly
|
||||
- [ ] Price reaction tracking populates correctly
|
||||
- [ ] VWAP calculation works during RTH
|
||||
- [ ] Checklist evaluation works correctly
|
||||
- [ ] No errors in logs
|
||||
- [ ] Performance is acceptable
|
||||
|
||||
---
|
||||
|
||||
**Implementation Date:** Phase 1 Complete
|
||||
**Status:** ✅ Ready for Testing
|
||||
|
||||
|
|
@ -1,205 +0,0 @@
|
|||
# Phase 1 Manual Check Update
|
||||
|
||||
## ✅ Changes Made
|
||||
|
||||
### 1. **Removed Automatic Phase 1 Filtering**
|
||||
- **Before:** Phase 1 filters automatically filtered the results
|
||||
- **After:** Original filtering remains unchanged - Phase 1 is now manual check only
|
||||
|
||||
**Files Modified:**
|
||||
- `frontend/src/components/dashboard/OptionsFlowPanel.jsx`
|
||||
- Removed Phase 1 filter checkboxes from UI
|
||||
- Removed automatic filtering logic from `filteredData`
|
||||
- Kept Phase 1 data status indicator
|
||||
|
||||
---
|
||||
|
||||
### 2. **Added Phase 1 Check Button on Cards**
|
||||
- **Location:** Each card in `OptionsFlowCardList` component
|
||||
- **Button:** "Phase 1" button with checkmark icon
|
||||
- **Behavior:** Opens Phase 1 Eligibility Modal when clicked
|
||||
|
||||
**Files Modified:**
|
||||
- `frontend/src/components/dashboard/OptionsFlowCardList.jsx`
|
||||
- Added Phase 1 button to each card
|
||||
- Button shows different styling if Phase 1 data is available
|
||||
- Integrated Phase 1 Eligibility Modal
|
||||
|
||||
---
|
||||
|
||||
### 3. **Created Phase 1 Eligibility Modal**
|
||||
- **Component:** `Phase1EligibilityModal.jsx`
|
||||
- **Shows:**
|
||||
- Overall eligibility status (Fully Eligible / Partially Eligible / Not Eligible)
|
||||
- Signal Tier (Tier-1 / Tier-2 / Ignore)
|
||||
- Trade Checklist (Score, Pass/Fail, Detailed checks)
|
||||
- Price Reaction (5m, 15m, Flow led to move)
|
||||
- VWAP Analysis (VWAP value, Distance from VWAP)
|
||||
- Summary section
|
||||
|
||||
**Files Created:**
|
||||
- `frontend/src/components/tables/Phase1EligibilityModal.jsx`
|
||||
|
||||
---
|
||||
|
||||
### 4. **Added Phase 1 Button to Table View**
|
||||
- **Location:** Expanded row details in table view
|
||||
- **Button:** "Phase 1" button next to "AI Analysis" button
|
||||
- **Behavior:** Opens same Phase 1 Eligibility Modal
|
||||
|
||||
**Files Modified:**
|
||||
- `frontend/src/components/tables/ExpandedRowDetails.jsx`
|
||||
- Added Phase 1 button
|
||||
- Integrated Phase 1 Eligibility Modal
|
||||
|
||||
---
|
||||
|
||||
## 🎯 How It Works Now
|
||||
|
||||
### Card View:
|
||||
1. User sees all filtered results (original filtering unchanged)
|
||||
2. Each card has a "Phase 1" button
|
||||
3. Click button → Opens Phase 1 Eligibility Modal
|
||||
4. Modal shows detailed Phase 1 analysis for that specific signal
|
||||
|
||||
### Table View:
|
||||
1. User sees all filtered results (original filtering unchanged)
|
||||
2. Click row to expand details
|
||||
3. In expanded details, click "Phase 1" button
|
||||
4. Modal shows detailed Phase 1 analysis
|
||||
|
||||
---
|
||||
|
||||
## 📊 Phase 1 Eligibility Modal Features
|
||||
|
||||
### Overall Status:
|
||||
- ✅ **Fully Eligible** - Meets all criteria (Tier-1 + Checklist Passed + Flow Led to Move)
|
||||
- ⚠️ **Partially Eligible** - Meets some criteria
|
||||
- ❌ **Not Eligible** - Doesn't meet criteria
|
||||
|
||||
### Detailed Sections:
|
||||
|
||||
1. **Signal Tier**
|
||||
- Shows Tier-1 🥇, Tier-2 🥈, or Ignore 🚫
|
||||
- Explains what each tier means
|
||||
|
||||
2. **Trade Checklist**
|
||||
- Shows score (X/5)
|
||||
- Shows pass/fail status
|
||||
- Lists individual check results:
|
||||
- ✅ Has direction (🟢/🔴)
|
||||
- ✅ Has diamond (💎)
|
||||
- ✅ Has star (⭐)
|
||||
- ✅ Price respects VWAP
|
||||
- ✅ Index confirms
|
||||
|
||||
3. **Price Reaction**
|
||||
- Shows 5m and 15m price reactions
|
||||
- Indicates if flow led to move
|
||||
- Shows high/low breaks
|
||||
|
||||
4. **VWAP Analysis**
|
||||
- Shows VWAP value at signal time
|
||||
- Shows distance from VWAP
|
||||
- Provides entry guidance
|
||||
|
||||
5. **Summary**
|
||||
- Quick overview of all Phase 1 metrics
|
||||
|
||||
---
|
||||
|
||||
## 🔄 Historical Date Support
|
||||
|
||||
### Phase 1 works with backtesting/previous dates:
|
||||
|
||||
1. **Signal Tier Classification**
|
||||
- ✅ Works for any date (based on badge combinations)
|
||||
- No date dependency
|
||||
|
||||
2. **Price Reaction Tracking**
|
||||
- ✅ Works for historical dates
|
||||
- Queries price data at signal time + 5m/15m/30m
|
||||
- Works as long as price data exists in database
|
||||
|
||||
3. **VWAP Calculation**
|
||||
- ✅ Works for historical dates
|
||||
- Calculates VWAP from RTH open (9:30 AM) to signal time
|
||||
- Works as long as 1-minute bar data exists
|
||||
|
||||
4. **Trade Checklist**
|
||||
- ✅ Works for any date
|
||||
- Checks badges, VWAP distance, etc.
|
||||
- No date dependency
|
||||
|
||||
**Note:** For historical dates, price reaction and VWAP will only work if:
|
||||
- Price data exists in `prices_intraday_1m` table for that date
|
||||
- Signal time is during RTH hours (for VWAP)
|
||||
|
||||
---
|
||||
|
||||
## 🎨 UI Changes
|
||||
|
||||
### Removed:
|
||||
- ❌ Phase 1 filter checkboxes (Tier-1 Only, Checklist Passed, Flow Led to Move)
|
||||
- ❌ Automatic filtering based on Phase 1 criteria
|
||||
|
||||
### Added:
|
||||
- ✅ "Phase 1" button on each card
|
||||
- ✅ "Phase 1" button in expanded row details
|
||||
- ✅ Phase 1 Eligibility Modal
|
||||
- ✅ Phase 1 data status indicator (shows if data is available)
|
||||
|
||||
---
|
||||
|
||||
## 📝 Usage
|
||||
|
||||
### To Check Phase 1 Eligibility:
|
||||
|
||||
1. **Card View:**
|
||||
- Find the signal card
|
||||
- Click "Phase 1" button
|
||||
- Review eligibility in modal
|
||||
|
||||
2. **Table View:**
|
||||
- Click row to expand
|
||||
- Click "Phase 1" button in expanded details
|
||||
- Review eligibility in modal
|
||||
|
||||
### For Backtesting:
|
||||
|
||||
1. Select historical date range (e.g., 12/01/2025 - 12/05/2025)
|
||||
2. Apply filters (original filters work as before)
|
||||
3. Click "Phase 1" button on any signal
|
||||
4. Modal shows Phase 1 analysis for that historical signal
|
||||
5. Price reaction and VWAP work if price data exists for that date
|
||||
|
||||
---
|
||||
|
||||
## ✅ Benefits
|
||||
|
||||
1. **No Automatic Filtering** - Original filters work as before
|
||||
2. **Manual Control** - Check Phase 1 eligibility when you want
|
||||
3. **Detailed Analysis** - See all Phase 1 metrics in one place
|
||||
4. **Historical Support** - Works with backtesting dates
|
||||
5. **Non-Intrusive** - Doesn't change existing workflow
|
||||
|
||||
---
|
||||
|
||||
## 🔍 Example Workflow
|
||||
|
||||
1. User filters by date range: 12/01/2025 - 12/05/2025
|
||||
2. Applies original filters (min premium, session, etc.)
|
||||
3. Sees 20 signals in results
|
||||
4. Clicks "Phase 1" button on a signal
|
||||
5. Modal shows:
|
||||
- Tier-1 ✅
|
||||
- Checklist: 4/5 ✅
|
||||
- Price Reaction: +1.2% ✅
|
||||
- VWAP: Near VWAP ✅
|
||||
- **Status: Fully Eligible** ✅
|
||||
6. User decides to trade based on Phase 1 analysis
|
||||
|
||||
---
|
||||
|
||||
**Status:** ✅ Complete - Ready to Use!
|
||||
|
||||
|
|
@ -11,11 +11,10 @@ Complete guide for deploying the Institutional Trader platform on a Proxmox serv
|
|||
5. [Database Configuration](#5-database-configuration)
|
||||
6. [Systemd Services](#6-systemd-services)
|
||||
7. [Nginx Configuration](#7-nginx-configuration)
|
||||
8. [DNS Configuration](#8-dns-configuration)
|
||||
9. [SSL Certificates](#9-ssl-certificates)
|
||||
10. [Database Access for Friend](#10-database-access-for-friend)
|
||||
11. [Monitoring & Maintenance](#11-monitoring--maintenance)
|
||||
12. [Troubleshooting](#12-troubleshooting)
|
||||
8. [SSL Certificates](#8-ssl-certificates)
|
||||
9. [Database Access for Friend](#9-database-access-for-friend)
|
||||
10. [Monitoring & Maintenance](#10-monitoring--maintenance)
|
||||
11. [Troubleshooting](#11-troubleshooting)
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -39,10 +38,18 @@ Complete guide for deploying the Institutional Trader platform on a Proxmox serv
|
|||
- Configure:
|
||||
- **Hostname**: `institutional-trader`
|
||||
- **Password**: Set root password
|
||||
- **CPU**: 2-4 cores
|
||||
- **Memory**: 4-8 GB RAM
|
||||
- **Disk**: 20-50 GB
|
||||
- **CPU**: 2-4 cores ✅ (You have 4 CPUs - perfect!)
|
||||
- **Memory**: 4-8 GB RAM ✅ (You have 31.75 GiB - excellent!)
|
||||
- **Disk**: 20-50 GB ✅ (You have 294.23 GiB - plenty of space!)
|
||||
- **Network**: Bridge with static IP (recommended) or DHCP
|
||||
- **Unprivileged**: Yes/No (see note below)
|
||||
|
||||
**Note on Unprivileged Containers:**
|
||||
- ✅ **Your configuration will work** - Unprivileged containers are fine for this deployment
|
||||
- Services run on ports > 1024 (Node.js: 3000, Python: 8000) - no issues
|
||||
- Nginx can run as reverse proxy (may need to bind to ports > 1024 or use host network)
|
||||
- PostgreSQL can be installed and run normally
|
||||
- SSL certificates work fine with Certbot
|
||||
|
||||
2. **Start Container:**
|
||||
```bash
|
||||
|
|
@ -81,6 +88,43 @@ Complete guide for deploying the Institutional Trader platform on a Proxmox serv
|
|||
|
||||
## 3. System Dependencies
|
||||
|
||||
### Important: Unprivileged Container Considerations
|
||||
|
||||
If your container is **unprivileged** (which yours is), there are a few adjustments:
|
||||
|
||||
1. **Nginx Port Binding**:
|
||||
- Unprivileged containers cannot bind to ports < 1024
|
||||
- **Solution**: Use Nginx on host or configure port forwarding
|
||||
- Alternative: Run Nginx on port 8080/8443 and forward from host
|
||||
|
||||
2. **Systemd**:
|
||||
- May need to enable systemd in container
|
||||
- Check: `systemctl status` should work
|
||||
|
||||
3. **File Permissions**:
|
||||
- Some operations may need different permissions
|
||||
- Usually not an issue for this application
|
||||
|
||||
**Your Configuration Analysis:**
|
||||
- ✅ **4 CPUs** - Perfect (recommended 2-4)
|
||||
- ✅ **31.75 GiB RAM** - Excellent (recommended 4-8 GB)
|
||||
- ✅ **294.23 GiB Disk** - Plenty of space (recommended 20-50 GB)
|
||||
- ⚠️ **Unprivileged: Yes** - Will work, but see Nginx note below
|
||||
|
||||
### Enabling Systemd (if needed)
|
||||
|
||||
If systemd doesn't work in your container, you may need to enable it:
|
||||
|
||||
```bash
|
||||
# Check if systemd is working
|
||||
systemctl status
|
||||
|
||||
# If it fails, you may need to enable it in Proxmox
|
||||
# Edit container config on Proxmox host:
|
||||
pct set <container-id> -features nesting=1
|
||||
pct set <container-id> -features keyctl=1
|
||||
```
|
||||
|
||||
Run these commands on your Ubuntu container/VM:
|
||||
|
||||
```bash
|
||||
|
|
@ -94,7 +138,7 @@ apt install -y nodejs
|
|||
# Install Python 3.11+ and pip
|
||||
apt install -y python3 python3-pip python3-venv
|
||||
|
||||
# Install PostgreSQL client (if using local DB)
|
||||
# Install PostgreSQL client (for remote database connection)
|
||||
apt install -y postgresql-client
|
||||
|
||||
# Install Nginx
|
||||
|
|
@ -161,7 +205,7 @@ SUPABASE_ANON_KEY=your_anon_key
|
|||
SUPABASE_SERVICE_KEY=your_service_role_key
|
||||
|
||||
# OR Direct PostgreSQL Connection (if using local/remote PostgreSQL)
|
||||
DATABASE_URL=postgresql://postgres:password@localhost:5432/institutional_trader
|
||||
DATABASE_URL=postgresql://postgres:password@100.121.163.23:5432/institutional_trader
|
||||
|
||||
# CORS Configuration
|
||||
CORS_ORIGIN=https://yourdomain.com
|
||||
|
|
@ -172,7 +216,7 @@ RATE_LIMIT_WINDOW=15
|
|||
RATE_LIMIT_MAX=100
|
||||
|
||||
# Python Service (if using)
|
||||
PYTHON_SERVICE_URL=http://localhost:8010
|
||||
PYTHON_SERVICE_URL=http://localhost:8000
|
||||
```
|
||||
|
||||
### 4.3 Python Service Setup
|
||||
|
|
@ -207,8 +251,6 @@ nano .env.production
|
|||
```
|
||||
|
||||
**Frontend `.env.production` configuration:**
|
||||
|
||||
**⚠️ Critical:** The frontend must have a `.env.production` file with the correct API URL, otherwise it will try to connect to `localhost:3010` which won't work in production.
|
||||
```env
|
||||
VITE_API_URL=https://api.yourdomain.com
|
||||
VITE_WS_URL=wss://api.yourdomain.com
|
||||
|
|
@ -239,42 +281,40 @@ This creates a `dist` folder with production-ready files.
|
|||
- Use `DATABASE_URL` or `SUPABASE_URL` + keys in backend `.env`
|
||||
- See [Database Access for Friend](#9-database-access-for-friend) section for sharing access
|
||||
|
||||
### Option B: Local PostgreSQL (Self-Hosted)
|
||||
### Option B: Remote PostgreSQL Database
|
||||
|
||||
```bash
|
||||
# Install PostgreSQL
|
||||
apt install -y postgresql postgresql-contrib
|
||||
Your database is hosted at `100.121.163.23:5432`. Configure connection:
|
||||
|
||||
# Start PostgreSQL
|
||||
systemctl start postgresql
|
||||
systemctl enable postgresql
|
||||
|
||||
# Create database and user
|
||||
sudo -u postgres psql
|
||||
|
||||
# In PostgreSQL prompt:
|
||||
CREATE DATABASE institutional_trader;
|
||||
CREATE USER trader_user WITH PASSWORD 'secure_password_here';
|
||||
GRANT ALL PRIVILEGES ON DATABASE institutional_trader TO trader_user;
|
||||
\q
|
||||
|
||||
# Import schema
|
||||
cd /opt/institutional_trader/backend/database
|
||||
sudo -u postgres psql institutional_trader < schema.sql
|
||||
sudo -u postgres psql institutional_trader < missing_tables.sql
|
||||
# Import other SQL files as needed
|
||||
```
|
||||
|
||||
Update backend `.env`:
|
||||
**Update backend `.env`:**
|
||||
```env
|
||||
USE_LOCAL_DB=true
|
||||
LOCAL_DB_HOST=localhost
|
||||
LOCAL_DB_HOST=100.121.163.23
|
||||
LOCAL_DB_PORT=5432
|
||||
LOCAL_DB_USER=trader_user
|
||||
LOCAL_DB_PASSWORD=secure_password_here
|
||||
LOCAL_DB_USER=postgres
|
||||
LOCAL_DB_PASSWORD=your_postgres_password
|
||||
LOCAL_DB_NAME=institutional_trader
|
||||
```
|
||||
|
||||
**Or use DATABASE_URL format:**
|
||||
```env
|
||||
DATABASE_URL=postgresql://postgres:your_password@100.121.163.23:5432/institutional_trader
|
||||
```
|
||||
|
||||
**Test connection:**
|
||||
```bash
|
||||
# Test from container
|
||||
psql -h 100.121.163.23 -p 5432 -U postgres -d institutional_trader
|
||||
|
||||
# Or test from Proxmox host
|
||||
apt install -y postgresql-client
|
||||
psql -h 100.121.163.23 -p 5432 -U postgres -d institutional_trader
|
||||
```
|
||||
|
||||
**Note:** Make sure the remote PostgreSQL server allows connections from your Proxmox container IP. You may need to:
|
||||
1. Configure `pg_hba.conf` on the database server to allow your container's IP
|
||||
2. Configure firewall rules to allow port 5432 from your container
|
||||
3. Ensure PostgreSQL is listening on the correct interface (not just localhost)
|
||||
|
||||
---
|
||||
|
||||
## 6. Systemd Services
|
||||
|
|
@ -291,7 +331,7 @@ Add:
|
|||
```ini
|
||||
[Unit]
|
||||
Description=Institutional Trader Backend API
|
||||
After=network.target postgresql.service
|
||||
After=network.target
|
||||
|
||||
[Service]
|
||||
Type=simple
|
||||
|
|
@ -329,15 +369,14 @@ Add:
|
|||
```ini
|
||||
[Unit]
|
||||
Description=Institutional Trader Python Service
|
||||
After=network.target postgresql.service
|
||||
After=network.target
|
||||
|
||||
[Service]
|
||||
Type=simple
|
||||
User=root
|
||||
WorkingDirectory=/opt/institutional_trader/backend/python_service
|
||||
Environment="PATH=/opt/institutional_trader/backend/python_service/venv/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
|
||||
EnvironmentFile=/opt/institutional_trader/backend/python_service/.env
|
||||
ExecStart=/opt/institutional_trader/backend/python_service/venv/bin/uvicorn main:app --host 0.0.0.0 --port 8010
|
||||
ExecStart=/opt/institutional_trader/backend/python_service/venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
|
||||
Restart=always
|
||||
RestartSec=10
|
||||
StandardOutput=journal
|
||||
|
|
@ -347,8 +386,6 @@ StandardError=journal
|
|||
WantedBy=multi-user.target
|
||||
```
|
||||
|
||||
**Note:** The `EnvironmentFile` directive is optional - the service will also load `.env` automatically via `load_dotenv()` in the code. However, adding it explicitly ensures environment variables are available even if the code path changes.
|
||||
|
||||
Enable and start:
|
||||
```bash
|
||||
sudo systemctl daemon-reload
|
||||
|
|
@ -374,8 +411,30 @@ sudo journalctl -u institutional-trader-* -f
|
|||
|
||||
## 7. Nginx Configuration
|
||||
|
||||
### ⚠️ Important: Unprivileged Container Nginx Setup
|
||||
|
||||
If your container is **unprivileged**, Nginx cannot bind to ports 80/443 directly. You have two options:
|
||||
|
||||
**Option A: Run Nginx on Proxmox Host (Recommended)**
|
||||
- Install Nginx on the Proxmox host
|
||||
- Configure it to proxy to your container's services
|
||||
- Container services run on ports 3000 (backend) and 8000 (Python)
|
||||
- See "Nginx on Host" section below
|
||||
|
||||
**Option B: Use Port Forwarding**
|
||||
- Run Nginx in container on ports 8080/8443
|
||||
- Forward ports from Proxmox host to container
|
||||
- Configure firewall rules
|
||||
|
||||
**Option C: Enable Privileged Mode (Less Secure)**
|
||||
- Convert container to privileged mode
|
||||
- Allows binding to ports 80/443
|
||||
- Less secure but simpler
|
||||
|
||||
### 7.1 Create Nginx Configuration
|
||||
|
||||
**For Container (if using Option B):**
|
||||
|
||||
```bash
|
||||
sudo nano /etc/nginx/sites-available/institutional-trader
|
||||
```
|
||||
|
|
@ -485,96 +544,7 @@ server {
|
|||
}
|
||||
```
|
||||
|
||||
### 7.2 Multi-Container Setup (Nginx on Separate Container)
|
||||
|
||||
If you're running nginx on a separate container/VM that proxies to your application containers:
|
||||
|
||||
**Example configuration for proxying to another Proxmox container:**
|
||||
|
||||
```nginx
|
||||
server {
|
||||
listen 443 ssl http2;
|
||||
server_name traderideas.deepteklabs.com;
|
||||
|
||||
ssl_certificate /etc/letsencrypt/live/traderideas.deepteklabs.com/fullchain.pem;
|
||||
ssl_certificate_key /etc/letsencrypt/live/traderideas.deepteklabs.com/privkey.pem;
|
||||
|
||||
# Security Headers
|
||||
add_header X-Frame-Options "SAMEORIGIN" always;
|
||||
add_header X-XSS-Protection "1; mode=block" always;
|
||||
add_header X-Content-Type-Options "nosniff" always;
|
||||
|
||||
# Backend API Routes
|
||||
location /api/ {
|
||||
proxy_pass http://192.168.8.151:3000/api/;
|
||||
proxy_http_version 1.1;
|
||||
proxy_set_header Upgrade $http_upgrade;
|
||||
proxy_set_header Connection "upgrade";
|
||||
proxy_set_header Host $host;
|
||||
proxy_set_header X-Real-IP $remote_addr;
|
||||
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
|
||||
proxy_set_header X-Forwarded-Proto $scheme;
|
||||
proxy_set_header X-Forwarded-Host $host;
|
||||
proxy_connect_timeout 60s;
|
||||
proxy_send_timeout 60s;
|
||||
proxy_read_timeout 60s;
|
||||
proxy_buffering off;
|
||||
}
|
||||
|
||||
# WebSocket Support (if using WebSockets)
|
||||
location /ws {
|
||||
proxy_pass http://192.168.8.151:3000;
|
||||
proxy_http_version 1.1;
|
||||
proxy_set_header Upgrade $http_upgrade;
|
||||
proxy_set_header Connection "Upgrade";
|
||||
proxy_set_header Host $host;
|
||||
proxy_set_header X-Real-IP $remote_addr;
|
||||
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
|
||||
proxy_set_header X-Forwarded-Proto $scheme;
|
||||
proxy_read_timeout 86400; # 24 hours for long-lived connections
|
||||
}
|
||||
|
||||
# Health Check
|
||||
location /health {
|
||||
proxy_pass http://192.168.8.151:3000/health;
|
||||
access_log off;
|
||||
proxy_set_header Host $host;
|
||||
proxy_set_header X-Real-IP $remote_addr;
|
||||
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
|
||||
add_header Cache-Control "no-cache, no-store, must-revalidate";
|
||||
}
|
||||
|
||||
# Frontend (served from application container)
|
||||
location / {
|
||||
proxy_pass http://192.168.8.151:8080/;
|
||||
proxy_http_version 1.1;
|
||||
proxy_set_header Host $host;
|
||||
proxy_set_header X-Real-IP $remote_addr;
|
||||
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
|
||||
proxy_set_header X-Forwarded-Proto $scheme;
|
||||
proxy_hide_header Cache-Control;
|
||||
add_header Cache-Control "no-cache, no-store, must-revalidate" always;
|
||||
}
|
||||
|
||||
# Static assets with caching
|
||||
location ~* \.(js|css|png|jpg|jpeg|gif|ico|svg|woff|woff2|ttf|eot|otf|map)$ {
|
||||
proxy_pass http://192.168.8.151:8080;
|
||||
proxy_set_header Host $host;
|
||||
proxy_set_header X-Real-IP $remote_addr;
|
||||
expires 1y;
|
||||
add_header Cache-Control "public, immutable";
|
||||
access_log off;
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Important notes for multi-container setup:**
|
||||
- Replace `192.168.8.151` with your application container's IP
|
||||
- Ensure containers can communicate (same network/bridge)
|
||||
- Port 3000 = Backend API, Port 8080 = Frontend (adjust as needed)
|
||||
- Test connectivity: `curl http://192.168.8.151:3000/health` from nginx container
|
||||
|
||||
### 7.3 Enable Site
|
||||
### 7.2 Enable Site
|
||||
|
||||
```bash
|
||||
# Create symlink
|
||||
|
|
@ -587,181 +557,92 @@ sudo nginx -t
|
|||
sudo systemctl reload nginx
|
||||
```
|
||||
|
||||
---
|
||||
### 7.3 Nginx on Proxmox Host (For Unprivileged Containers)
|
||||
|
||||
## 8. DNS Configuration
|
||||
|
||||
**⚠️ Important:** Before setting up SSL certificates, you must configure DNS records pointing to your server.
|
||||
|
||||
### 8.1 Find Your Server's Public IP Address
|
||||
If your container is unprivileged, install Nginx on the Proxmox host:
|
||||
|
||||
**On Proxmox Host:**
|
||||
```bash
|
||||
# On your server, find the public IP
|
||||
curl ifconfig.me
|
||||
# Or
|
||||
hostname -I
|
||||
# Or check in Proxmox Web UI: Datacenter → Your Node → Network
|
||||
# Install Nginx
|
||||
apt install -y nginx
|
||||
|
||||
# Create configuration
|
||||
nano /etc/nginx/sites-available/institutional-trader
|
||||
```
|
||||
|
||||
### 8.2 Configure DNS A Records
|
||||
**Nginx Config (on Proxmox host, pointing to container):**
|
||||
```nginx
|
||||
# Backend API Server
|
||||
server {
|
||||
listen 443 ssl http2;
|
||||
server_name api.yourdomain.com;
|
||||
|
||||
Go to your domain registrar's DNS management panel (where you registered `traderideas.deepteklabs.com`) and add these A records:
|
||||
ssl_certificate /etc/letsencrypt/live/yourdomain.com/fullchain.pem;
|
||||
ssl_certificate_key /etc/letsencrypt/live/yourdomain.com/privkey.pem;
|
||||
|
||||
| Type | Name | Value | TTL |
|
||||
|------|------|-------|-----|
|
||||
| A | `@` (or blank) | `<your-server-ip>` | 3600 |
|
||||
| A | `www` | `<your-server-ip>` | 3600 |
|
||||
| A | `api` | `<your-server-ip>` | 3600 |
|
||||
location / {
|
||||
proxy_pass http://<container-ip>:3000;
|
||||
proxy_http_version 1.1;
|
||||
proxy_set_header Upgrade $http_upgrade;
|
||||
proxy_set_header Connection 'upgrade';
|
||||
proxy_set_header Host $host;
|
||||
proxy_set_header X-Real-IP $remote_addr;
|
||||
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
|
||||
proxy_set_header X-Forwarded-Proto $scheme;
|
||||
}
|
||||
|
||||
**Example for `traderideas.deepteklabs.com`:**
|
||||
- `traderideas.deepteklabs.com` → `<your-server-ip>` (A record with name `@` or blank)
|
||||
- `www.traderideas.deepteklabs.com` → `<your-server-ip>` (A record with name `www`)
|
||||
- `api.traderideas.deepteklabs.com` → `<your-server-ip>` (A record with name `api`)
|
||||
location /ws {
|
||||
proxy_pass http://<container-ip>:3000;
|
||||
proxy_http_version 1.1;
|
||||
proxy_set_header Upgrade $http_upgrade;
|
||||
proxy_set_header Connection "Upgrade";
|
||||
proxy_read_timeout 86400;
|
||||
}
|
||||
}
|
||||
|
||||
### 8.3 Verify DNS Propagation
|
||||
# Frontend Server
|
||||
server {
|
||||
listen 443 ssl http2;
|
||||
server_name yourdomain.com www.yourdomain.com;
|
||||
|
||||
Wait 5-15 minutes for DNS to propagate, then verify:
|
||||
ssl_certificate /etc/letsencrypt/live/yourdomain.com/fullchain.pem;
|
||||
ssl_certificate_key /etc/letsencrypt/live/yourdomain.com/privkey.pem;
|
||||
|
||||
```bash
|
||||
# Check if DNS records are resolving
|
||||
dig traderideas.deepteklabs.com +short
|
||||
dig www.traderideas.deepteklabs.com +short
|
||||
dig api.traderideas.deepteklabs.com +short
|
||||
root /var/www/institutional-trader/dist;
|
||||
index index.html;
|
||||
|
||||
# All should return your server's IP address
|
||||
|
||||
# Alternative: Use nslookup
|
||||
nslookup traderideas.deepteklabs.com
|
||||
nslookup www.traderideas.deepteklabs.com
|
||||
nslookup api.traderideas.deepteklabs.com
|
||||
|
||||
# Or use online tools:
|
||||
# - https://dnschecker.org
|
||||
# - https://www.whatsmydns.net
|
||||
location / {
|
||||
try_files $uri $uri/ /index.html;
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
**Common DNS Providers:**
|
||||
- **Cloudflare**: Dashboard → DNS → Records → Add record
|
||||
- **Namecheap**: Domain List → Manage → Advanced DNS → Add New Record
|
||||
- **GoDaddy**: DNS Management → Add Record
|
||||
- **Google Domains**: DNS → Custom Records → Add Record
|
||||
|
||||
### 8.4 Test Domain Accessibility
|
||||
|
||||
Once DNS is propagated, test that domains are reachable:
|
||||
|
||||
**Copy frontend files to host:**
|
||||
```bash
|
||||
# From your server
|
||||
curl -I http://traderideas.deepteklabs.com
|
||||
curl -I http://www.traderideas.deepteklabs.com
|
||||
curl -I http://api.traderideas.deepteklabs.com
|
||||
# On container, create archive
|
||||
cd /opt/institutional_trader/frontend
|
||||
tar czf /tmp/frontend-dist.tar.gz dist/
|
||||
|
||||
# Should return HTTP responses (even if 502/503, that's OK - means DNS is working)
|
||||
# On Proxmox host, copy from container
|
||||
pct pull <container-id> /tmp/frontend-dist.tar.gz /tmp/
|
||||
tar xzf /tmp/frontend-dist.tar.gz -C /var/www/institutional-trader/
|
||||
```
|
||||
|
||||
### 8.5 Cloudflare Proxy (Orange Cloud)
|
||||
|
||||
**If your domain uses Cloudflare proxy (orange cloud icon):**
|
||||
|
||||
When DNS resolves to Cloudflare IPs (like `104.21.x.x` or `172.67.x.x`), your domain is behind Cloudflare proxy. This affects SSL certificate setup:
|
||||
|
||||
**Check if using Cloudflare proxy:**
|
||||
```bash
|
||||
dig traderideas.deepteklabs.com +short
|
||||
# If returns Cloudflare IPs (104.x.x.x, 172.x.x.x), proxy is enabled
|
||||
```
|
||||
|
||||
**Options for SSL with Cloudflare:**
|
||||
|
||||
1. **Use DNS Challenge (Recommended)** - Works with proxy enabled
|
||||
2. **Temporarily Disable Proxy** - Get certs, then re-enable proxy
|
||||
3. **Use Cloudflare SSL** - Let Cloudflare handle SSL (easiest)
|
||||
|
||||
See Section 9.2 for detailed instructions.
|
||||
|
||||
**⚠️ Only proceed to SSL setup once DNS records are working!**
|
||||
|
||||
---
|
||||
|
||||
## 9. SSL Certificates
|
||||
## 8. SSL Certificates
|
||||
|
||||
### 9.1 Install Certbot
|
||||
### 8.1 Install Certbot
|
||||
|
||||
```bash
|
||||
sudo apt install -y certbot python3-certbot-nginx
|
||||
```
|
||||
|
||||
### 9.2 Obtain SSL Certificate
|
||||
|
||||
**⚠️ Prerequisites:**
|
||||
- DNS A records must be configured and propagated (see Section 8)
|
||||
- Domains must resolve to your server's IP (or Cloudflare if using proxy)
|
||||
- Port 80 must be accessible from the internet (for HTTP challenge) OR DNS API access (for DNS challenge)
|
||||
|
||||
**⚠️ Important:** If your nginx config already references SSL certificates that don't exist, you need to fix this first.
|
||||
|
||||
**Check if using Cloudflare proxy:**
|
||||
```bash
|
||||
dig traderideas.deepteklabs.com +short
|
||||
# If returns Cloudflare IPs (104.x.x.x, 172.x.x.x), you're using Cloudflare proxy
|
||||
```
|
||||
|
||||
**If using Cloudflare Proxy, use Option D (DNS Challenge) or Option E (Cloudflare SSL)**
|
||||
|
||||
**Option A: Use Standalone Mode (Works if NOT using Cloudflare proxy)**
|
||||
|
||||
```bash
|
||||
# Stop nginx temporarily
|
||||
sudo systemctl stop nginx
|
||||
|
||||
# Get certificates using standalone mode
|
||||
sudo certbot certonly --standalone -d traderideas.deepteklabs.com -d www.traderideas.deepteklabs.com -d api.traderideas.deepteklabs.com
|
||||
|
||||
# Start nginx again
|
||||
sudo systemctl start nginx
|
||||
|
||||
# Now certbot can update the nginx config
|
||||
sudo certbot --nginx -d traderideas.deepteklabs.com -d www.traderideas.deepteklabs.com -d api.traderideas.deepteklabs.com
|
||||
```
|
||||
|
||||
**Option B: Temporarily Comment Out SSL in Nginx Config**
|
||||
|
||||
```bash
|
||||
# Edit nginx config
|
||||
sudo nano /etc/nginx/sites-available/institutional-trader
|
||||
|
||||
# Temporarily comment out SSL lines:
|
||||
# - Comment out: listen 443 ssl http2;
|
||||
# - Comment out: ssl_certificate and ssl_certificate_key lines
|
||||
# - Change: listen 443 ssl http2; to: listen 80;
|
||||
|
||||
# Example temporary config (HTTP only):
|
||||
# server {
|
||||
# listen 80;
|
||||
# server_name traderideas.deepteklabs.com;
|
||||
# # ssl_certificate /etc/letsencrypt/live/traderideas.deepteklabs.com/fullchain.pem;
|
||||
# # ssl_certificate_key /etc/letsencrypt/live/traderideas.deepteklabs.com/privkey.pem;
|
||||
# ...
|
||||
# }
|
||||
|
||||
# Test and reload
|
||||
sudo nginx -t
|
||||
sudo systemctl reload nginx
|
||||
|
||||
# Now get certificates (certbot will automatically update the config)
|
||||
sudo certbot --nginx -d traderideas.deepteklabs.com -d www.traderideas.deepteklabs.com -d api.traderideas.deepteklabs.com
|
||||
|
||||
# Certbot will automatically:
|
||||
# - Get the certificates
|
||||
# - Update your nginx config to use them
|
||||
# - Add SSL redirects
|
||||
```
|
||||
|
||||
**Option C: Use Certbot with Nginx Plugin (If config is clean and NOT using Cloudflare proxy)**
|
||||
### 8.2 Obtain SSL Certificate
|
||||
|
||||
```bash
|
||||
# Replace with your actual domain
|
||||
sudo certbot --nginx -d traderideas.deepteklabs.com -d www.traderideas.deepteklabs.com -d api.traderideas.deepteklabs.com
|
||||
sudo certbot --nginx -d yourdomain.com -d www.yourdomain.com -d api.yourdomain.com
|
||||
```
|
||||
|
||||
Follow the prompts:
|
||||
|
|
@ -769,85 +650,7 @@ Follow the prompts:
|
|||
- Agree to terms
|
||||
- Choose whether to redirect HTTP to HTTPS (recommended: Yes)
|
||||
|
||||
**Option D: DNS Challenge (Recommended for Cloudflare proxy)**
|
||||
|
||||
If your domain is behind Cloudflare proxy, use DNS challenge:
|
||||
|
||||
```bash
|
||||
# Install Cloudflare plugin for certbot
|
||||
sudo apt install -y python3-pip
|
||||
sudo pip3 install certbot-dns-cloudflare
|
||||
|
||||
# Create Cloudflare API token directory
|
||||
sudo mkdir -p /etc/letsencrypt
|
||||
sudo chmod 700 /etc/letsencrypt
|
||||
|
||||
# Create Cloudflare credentials file
|
||||
sudo nano /etc/letsencrypt/cloudflare.ini
|
||||
```
|
||||
|
||||
Add your Cloudflare API token:
|
||||
```ini
|
||||
# Cloudflare API token (get from: Cloudflare Dashboard → My Profile → API Tokens → Create Token)
|
||||
# Permissions needed: Zone:Zone:Read, Zone:DNS:Edit
|
||||
dns_cloudflare_api_token = YOUR_CLOUDFLARE_API_TOKEN
|
||||
```
|
||||
|
||||
```bash
|
||||
# Secure the credentials file
|
||||
sudo chmod 600 /etc/letsencrypt/cloudflare.ini
|
||||
|
||||
# Get certificates using DNS challenge
|
||||
sudo certbot certonly \
|
||||
--dns-cloudflare \
|
||||
--dns-cloudflare-credentials /etc/letsencrypt/cloudflare.ini \
|
||||
-d traderideas.deepteklabs.com \
|
||||
-d www.traderideas.deepteklabs.com \
|
||||
-d api.traderideas.deepteklabs.com
|
||||
|
||||
# Update nginx config manually (certbot won't auto-update with DNS challenge)
|
||||
sudo nano /etc/nginx/sites-available/institutional-trader
|
||||
# Update ssl_certificate paths to:
|
||||
# ssl_certificate /etc/letsencrypt/live/traderideas.deepteklabs.com/fullchain.pem;
|
||||
# ssl_certificate_key /etc/letsencrypt/live/traderideas.deepteklabs.com/privkey.pem;
|
||||
|
||||
sudo nginx -t
|
||||
sudo systemctl reload nginx
|
||||
```
|
||||
|
||||
**Option E: Use Cloudflare SSL (Easiest with Cloudflare proxy)**
|
||||
|
||||
If using Cloudflare proxy, you can let Cloudflare handle SSL:
|
||||
|
||||
1. **In Cloudflare Dashboard:**
|
||||
- Go to SSL/TLS → Overview
|
||||
- Set encryption mode to **"Full"** or **"Full (strict)"**
|
||||
- Cloudflare will automatically provide SSL certificates
|
||||
|
||||
2. **On your server, use self-signed or Cloudflare Origin Certificate:**
|
||||
```bash
|
||||
# Generate self-signed certificate (Cloudflare will accept it)
|
||||
sudo openssl req -x509 -nodes -days 365 -newkey rsa:2048 \
|
||||
-keyout /etc/ssl/private/nginx-selfsigned.key \
|
||||
-out /etc/ssl/certs/nginx-selfsigned.crt
|
||||
|
||||
# Update nginx config to use self-signed cert
|
||||
sudo nano /etc/nginx/sites-available/institutional-trader
|
||||
# Change to:
|
||||
# ssl_certificate /etc/ssl/certs/nginx-selfsigned.crt;
|
||||
# ssl_certificate_key /etc/ssl/private/nginx-selfsigned.key;
|
||||
|
||||
sudo nginx -t
|
||||
sudo systemctl reload nginx
|
||||
```
|
||||
|
||||
3. **Or get Cloudflare Origin Certificate:**
|
||||
- Cloudflare Dashboard → SSL/TLS → Origin Server → Create Certificate
|
||||
- Copy certificate and key
|
||||
- Save to `/etc/ssl/certs/cloudflare-origin.crt` and `/etc/ssl/private/cloudflare-origin.key`
|
||||
- Update nginx config to use these files
|
||||
|
||||
### 9.3 Auto-Renewal
|
||||
### 8.3 Auto-Renewal
|
||||
|
||||
Certbot sets up auto-renewal automatically. Test it:
|
||||
|
||||
|
|
@ -857,7 +660,7 @@ sudo certbot renew --dry-run
|
|||
|
||||
---
|
||||
|
||||
## 10. Database Access for Friend
|
||||
## 9. Database Access for Friend
|
||||
|
||||
### Option A: Supabase (Recommended)
|
||||
|
||||
|
|
@ -934,13 +737,14 @@ ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT SELECT, USAGE ON SEQUENCES TO fr
|
|||
|
||||
**Connection String for Friend:**
|
||||
```
|
||||
postgresql://friend_user:secure_password_here@your-server-ip:5432/institutional_trader
|
||||
postgresql://friend_user:secure_password_here@100.121.163.23:5432/institutional_trader
|
||||
```
|
||||
|
||||
**Important:** If your friend needs to connect from outside your network:
|
||||
1. Configure PostgreSQL to accept remote connections
|
||||
2. Set up firewall rules
|
||||
3. Consider using SSH tunnel for security
|
||||
**Important:** Make sure the PostgreSQL server at `100.121.163.23`:
|
||||
1. Allows remote connections from your friend's IP
|
||||
2. Has `pg_hba.conf` configured to allow connections
|
||||
3. Has firewall rules allowing port 5432
|
||||
4. Consider using SSH tunnel for additional security
|
||||
|
||||
### Option C: SSH Tunnel (Most Secure)
|
||||
|
||||
|
|
@ -948,12 +752,14 @@ Create an SSH tunnel for your friend:
|
|||
|
||||
```bash
|
||||
# On your friend's machine
|
||||
ssh -L 5432:localhost:5432 user@your-server-ip
|
||||
ssh -L 5432:100.121.163.23:5432 user@your-proxmox-server-ip
|
||||
|
||||
# Then they can connect using:
|
||||
# postgresql://friend_user:password@localhost:5432/institutional_trader
|
||||
```
|
||||
|
||||
This creates a secure tunnel through your Proxmox server to the database.
|
||||
|
||||
### Option D: Read-Only Access (If Friend Only Needs to Read Data)
|
||||
|
||||
```sql
|
||||
|
|
@ -967,7 +773,7 @@ ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT SELECT ON TABLES TO friend_reado
|
|||
|
||||
---
|
||||
|
||||
## 11. Monitoring & Maintenance
|
||||
## 10. Monitoring & Maintenance
|
||||
|
||||
### 10.1 Health Checks
|
||||
|
||||
|
|
@ -1029,123 +835,18 @@ sudo systemctl reload nginx
|
|||
- Use Supabase Dashboard > Database > Backups
|
||||
- Or use `pg_dump` with connection string
|
||||
|
||||
**For Local PostgreSQL:**
|
||||
**For Remote PostgreSQL:**
|
||||
```bash
|
||||
# Create backup
|
||||
sudo -u postgres pg_dump institutional_trader > backup_$(date +%Y%m%d).sql
|
||||
pg_dump -h 100.121.163.23 -p 5432 -U postgres -d institutional_trader > backup_$(date +%Y%m%d).sql
|
||||
|
||||
# Restore backup
|
||||
sudo -u postgres psql institutional_trader < backup_20240101.sql
|
||||
psql -h 100.121.163.23 -p 5432 -U postgres -d institutional_trader < backup_20240101.sql
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 12. Troubleshooting
|
||||
|
||||
### Git Pull Fails - DNS Resolution Error
|
||||
|
||||
**Error:** `fatal: unable to access 'https://github.com/...': Could not resolve host: github.com`
|
||||
|
||||
**This means your container/VM cannot resolve DNS names.**
|
||||
|
||||
**Quick Fix:**
|
||||
|
||||
```bash
|
||||
# 1. Check current DNS configuration
|
||||
cat /etc/resolv.conf
|
||||
|
||||
# 2. If empty or missing, add DNS servers
|
||||
sudo nano /etc/resolv.conf
|
||||
```
|
||||
|
||||
Add these lines:
|
||||
```
|
||||
nameserver 8.8.8.8
|
||||
nameserver 8.8.4.4
|
||||
nameserver 1.1.1.1
|
||||
```
|
||||
|
||||
**For LXC Containers (Persistent Fix):**
|
||||
|
||||
```bash
|
||||
# Edit container DNS configuration
|
||||
# On Proxmox host, edit container config:
|
||||
nano /etc/pve/lxc/<container-id>.conf
|
||||
|
||||
# Add DNS servers:
|
||||
nameserver: 8.8.8.8
|
||||
nameserver: 8.8.4.4
|
||||
```
|
||||
|
||||
Or via Proxmox Web UI:
|
||||
- Go to your container → Options → DNS
|
||||
- Set DNS servers: `8.8.8.8, 8.8.4.4`
|
||||
|
||||
**For Ubuntu VMs/Containers (Systemd-resolved):**
|
||||
|
||||
```bash
|
||||
# Edit systemd-resolved config
|
||||
sudo nano /etc/systemd/resolved.conf
|
||||
```
|
||||
|
||||
Uncomment and set:
|
||||
```
|
||||
[Resolve]
|
||||
DNS=8.8.8.8 8.8.4.4 1.1.1.1
|
||||
FallbackDNS=1.1.1.1 8.8.8.8
|
||||
```
|
||||
|
||||
**If using Tailscale (DNS server 100.100.100.100):**
|
||||
|
||||
If your `/etc/resolv.conf` shows Tailscale DNS (`100.100.100.100`) but it's timing out, add fallback DNS servers:
|
||||
|
||||
```bash
|
||||
# Edit systemd-resolved config
|
||||
sudo nano /etc/systemd/resolved.conf
|
||||
```
|
||||
|
||||
Set:
|
||||
```
|
||||
[Resolve]
|
||||
DNS=100.100.100.100 8.8.8.8 8.8.4.4 1.1.1.1
|
||||
FallbackDNS=8.8.8.8 8.8.4.4 1.1.1.1
|
||||
```
|
||||
|
||||
This keeps Tailscale DNS as primary (for Tailscale network access) but adds public DNS as fallback.
|
||||
|
||||
```bash
|
||||
# Restart systemd-resolved
|
||||
sudo systemctl restart systemd-resolved
|
||||
|
||||
# Test DNS
|
||||
nslookup github.com
|
||||
ping -c 2 github.com
|
||||
```
|
||||
|
||||
**Alternative: Use IP Address (Temporary Workaround)**
|
||||
|
||||
If DNS still doesn't work, you can manually update git remote:
|
||||
|
||||
```bash
|
||||
# Get GitHub IP
|
||||
nslookup github.com 8.8.8.8
|
||||
|
||||
# Or use known GitHub IPs (may change)
|
||||
# Update git remote to use IP (not recommended for long-term)
|
||||
git remote set-url origin https://140.82.121.3/deepkoluguri/INSTITUTIONAL-FLOW-TRADING-PLATFORM.git
|
||||
```
|
||||
|
||||
**Verify DNS is Working:**
|
||||
|
||||
```bash
|
||||
# Test DNS resolution
|
||||
nslookup github.com
|
||||
dig github.com
|
||||
|
||||
# Test connectivity
|
||||
ping -c 2 github.com
|
||||
curl -I https://github.com
|
||||
```
|
||||
## 11. Troubleshooting
|
||||
|
||||
### Backend Won't Start
|
||||
|
||||
|
|
@ -1163,78 +864,14 @@ node -e "import('./src/db.js').then(m => m.testConnection())"
|
|||
|
||||
### Python Service Won't Start
|
||||
|
||||
**Step 1: Check detailed error logs**
|
||||
```bash
|
||||
# View recent logs with full error messages
|
||||
sudo journalctl -u institutional-trader-python -n 100 --no-pager
|
||||
# Check logs
|
||||
sudo journalctl -u institutional-trader-python -n 50
|
||||
|
||||
# Follow logs in real-time
|
||||
sudo journalctl -u institutional-trader-python -f
|
||||
```
|
||||
|
||||
**Step 2: Verify systemd service file has correct port**
|
||||
```bash
|
||||
# Check current service file
|
||||
sudo cat /etc/systemd/system/institutional-trader-python.service
|
||||
|
||||
# If port is 8000, update it to 8010:
|
||||
sudo nano /etc/systemd/system/institutional-trader-python.service
|
||||
# Change: --port 8000 to --port 8010
|
||||
# Then reload and restart:
|
||||
sudo systemctl daemon-reload
|
||||
sudo systemctl restart institutional-trader-python
|
||||
```
|
||||
|
||||
**Step 3: Test manually to see actual error**
|
||||
```bash
|
||||
# Test manually
|
||||
cd /opt/institutional_trader/backend/python_service
|
||||
source venv/bin/activate
|
||||
|
||||
# Check if .env file exists and has correct DB config
|
||||
cat .env
|
||||
|
||||
# Test startup manually
|
||||
uvicorn main:app --host 0.0.0.0 --port 8010
|
||||
```
|
||||
|
||||
**Step 4: Common issues and fixes**
|
||||
|
||||
**Database connection timeout:**
|
||||
- The service now has a 10-second timeout and will start even if DB is temporarily unavailable
|
||||
- Check database is reachable: `psql -h <DB_HOST> -U <DB_USER> -d institutional_trader`
|
||||
- Verify `.env` file in `backend/python_service/` has correct database credentials
|
||||
|
||||
**Port already in use:**
|
||||
```bash
|
||||
# Check if port 8010 is in use
|
||||
sudo netstat -tlnp | grep 8010
|
||||
# Or
|
||||
sudo lsof -i :8010
|
||||
|
||||
# Kill process if needed
|
||||
sudo kill -9 <PID>
|
||||
```
|
||||
|
||||
**Missing dependencies:**
|
||||
```bash
|
||||
cd /opt/institutional_trader/backend/python_service
|
||||
source venv/bin/activate
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
**Step 5: Verify backend .env has correct Python service URL**
|
||||
```bash
|
||||
# Check backend .env
|
||||
cat /opt/institutional_trader/backend/.env | grep PYTHON_SERVICE_URL
|
||||
|
||||
# Should be:
|
||||
# PYTHON_SERVICE_URL=http://localhost:8010
|
||||
|
||||
# If not, update it:
|
||||
nano /opt/institutional_trader/backend/.env
|
||||
# Add or update: PYTHON_SERVICE_URL=http://localhost:8010
|
||||
# Then restart backend:
|
||||
sudo systemctl restart institutional-trader-backend
|
||||
uvicorn main:app --host 0.0.0.0 --port 8000
|
||||
```
|
||||
|
||||
### Nginx Errors
|
||||
|
|
@ -1253,8 +890,8 @@ sudo systemctl reload nginx
|
|||
### Database Connection Issues
|
||||
|
||||
```bash
|
||||
# Test PostgreSQL connection (local)
|
||||
psql -h localhost -U trader_user -d institutional_trader
|
||||
# Test PostgreSQL connection (remote)
|
||||
psql -h 100.121.163.23 -p 5432 -U postgres -d institutional_trader
|
||||
|
||||
# Test Supabase connection
|
||||
psql "postgresql://postgres:password@db.project.supabase.co:5432/postgres"
|
||||
|
|
@ -1,863 +0,0 @@
|
|||
# Trading Playbook Implementation Suggestions
|
||||
|
||||
## Current State Analysis
|
||||
|
||||
### ✅ What You Already Have
|
||||
- Badge system: 🟢/🔴, 💎, ⭐, 💰, ⚡, 🚀
|
||||
- Rocket scoring algorithm
|
||||
- Price context: RTH open, prior close, 5m/15m momentum
|
||||
- Tape alignment detection
|
||||
- Trade signal generation
|
||||
- Session bucketing (PRE/RTH/POST)
|
||||
- Premium filtering and aggregations
|
||||
|
||||
### ❌ What's Missing (High Impact)
|
||||
|
||||
---
|
||||
|
||||
## PART A — TRADING LOGIC ENHANCEMENTS
|
||||
|
||||
### 1️⃣ Signal Tier Classification System
|
||||
|
||||
**Current Gap:** All signals are treated equally. You need to classify them into Tier-1, Tier-2, and Ignore categories.
|
||||
|
||||
**Suggestion:**
|
||||
- **Add a new service:** `backend/python_service/services/signal_tier_classifier.py`
|
||||
- Classify signals based on badge combinations
|
||||
- Tier-1: 🟢/🔴 + 💎 + ⭐ + premium > 500K + direction aligned
|
||||
- Tier-2: 🟢 + 💎 (no ⭐) OR ⭐ without 💎
|
||||
- Ignore: OTM-only, mixed signals, low volume/OI ratio
|
||||
|
||||
**Implementation Approach:**
|
||||
```python
|
||||
# In options_flow_processor.py, add after process_badges():
|
||||
def classify_signal_tier(row):
|
||||
badge_round = row.get('badge_round', '')
|
||||
badge_more = row.get('badge_more', '')
|
||||
premium = row.get('premium_num', 0) or 0
|
||||
direction = row.get('direction', '')
|
||||
bull_total = row.get('bull_total', 0) or 0
|
||||
bear_total = row.get('bear_total', 0) or 0
|
||||
|
||||
has_diamond = '💎' in badge_more
|
||||
has_star = '⭐' in badge_more
|
||||
|
||||
# Tier-1 conditions
|
||||
if (badge_round in ['🟢', '🔴'] and
|
||||
has_diamond and has_star and
|
||||
premium > 500000):
|
||||
# Check direction alignment
|
||||
if (badge_round == '🟢' and direction == 'BULL' and (bull_total - bear_total) > 0):
|
||||
return 'TIER_1'
|
||||
elif (badge_round == '🔴' and direction == 'BEAR' and (bear_total - bull_total) > 0):
|
||||
return 'TIER_1'
|
||||
|
||||
# Tier-2 conditions
|
||||
if (badge_round == '🟢' and has_diamond and not has_star):
|
||||
return 'TIER_2'
|
||||
if (has_star and not has_diamond):
|
||||
return 'TIER_2'
|
||||
|
||||
# Ignore conditions
|
||||
# (Add logic for OTM-only, mixed signals, etc.)
|
||||
|
||||
return 'IGNORE'
|
||||
```
|
||||
|
||||
**Database Addition:**
|
||||
- Add `signal_tier` column to processed flow output
|
||||
- Add `is_tradeable` boolean flag
|
||||
|
||||
---
|
||||
|
||||
### 2️⃣ VWAP Integration
|
||||
|
||||
**Current Gap:** You have price context but no VWAP calculation or VWAP-based entry/exit logic.
|
||||
|
||||
**Suggestion:**
|
||||
- **Extend:** `backend/python_service/services/price_context.py`
|
||||
- Add `calculate_vwap()` method
|
||||
- Calculate VWAP for each symbol on each trading day
|
||||
- Store VWAP at signal time
|
||||
- Calculate distance from VWAP (percentage)
|
||||
|
||||
**Implementation Approach:**
|
||||
```python
|
||||
# Add to PriceContextService:
|
||||
async def get_vwap_at_time(self, symbol: str, timestamp: datetime, pool: asyncpg.Pool):
|
||||
"""Calculate VWAP up to the given timestamp for the trading day"""
|
||||
# Query all 1m bars from RTH open to timestamp
|
||||
# Calculate: SUM(price * volume) / SUM(volume)
|
||||
# Return VWAP value and distance from current price
|
||||
```
|
||||
|
||||
**New Fields to Add:**
|
||||
- `vwap_at_signal` - VWAP value at signal time
|
||||
- `price_vs_vwap_pct` - Percentage distance from VWAP
|
||||
- `vwap_reclaimed` - Boolean: did price reclaim VWAP after signal?
|
||||
|
||||
**Entry Strategy Integration:**
|
||||
- Best entry: VWAP pullback or VWAP reclaim
|
||||
- Good entry: Break & hold above prior high
|
||||
- Avoid: Chasing vertical candles
|
||||
|
||||
---
|
||||
|
||||
### 3️⃣ Price Reaction Tracking (MOST IMPORTANT)
|
||||
|
||||
**Current Gap:** No tracking of how price moves AFTER the signal appears.
|
||||
|
||||
**Suggestion:**
|
||||
- **New service:** `backend/python_service/services/price_reaction_tracker.py`
|
||||
- Track price 5 minutes, 15 minutes, 30 minutes after signal
|
||||
- Calculate price change percentage
|
||||
- Identify if flow led to price movement or was just hedging
|
||||
|
||||
**Implementation Approach:**
|
||||
```python
|
||||
class PriceReactionTracker:
|
||||
async def track_reaction(self, flow_row, pool):
|
||||
signal_time = flow_row['flow_ts_utc']
|
||||
symbol = flow_row['symbol_norm']
|
||||
price_at_signal = flow_row['u_close']
|
||||
|
||||
# Get price 5m, 15m, 30m after signal
|
||||
price_5m = await get_price_at_time(symbol, signal_time + timedelta(minutes=5))
|
||||
price_15m = await get_price_at_time(symbol, signal_time + timedelta(minutes=15))
|
||||
price_30m = await get_price_at_time(symbol, signal_time + timedelta(minutes=30))
|
||||
|
||||
# Calculate reactions
|
||||
reaction_5m = ((price_5m - price_at_signal) / price_at_signal) * 100 if price_5m else None
|
||||
reaction_15m = ((price_15m - price_at_signal) / price_at_signal) * 100 if price_15m else None
|
||||
reaction_30m = ((price_30m - price_at_signal) / price_at_signal) * 100 if price_30m else None
|
||||
|
||||
# High/Low break confirmation
|
||||
high_break = price_5m > flow_row.get('u_high', 0)
|
||||
low_break = price_5m < flow_row.get('u_low', 0)
|
||||
|
||||
return {
|
||||
'price_reaction_5m_pct': reaction_5m,
|
||||
'price_reaction_15m_pct': reaction_15m,
|
||||
'price_reaction_30m_pct': reaction_30m,
|
||||
'high_break_5m': high_break,
|
||||
'low_break_5m': low_break,
|
||||
'flow_led_to_move': reaction_5m and abs(reaction_5m) > 0.5 # 0.5% threshold
|
||||
}
|
||||
```
|
||||
|
||||
**Database Addition:**
|
||||
- Add columns: `price_reaction_5m_pct`, `price_reaction_15m_pct`, `high_break_5m`, `low_break_5m`
|
||||
- Add flag: `flow_led_to_move` (boolean)
|
||||
|
||||
**Why This Matters:**
|
||||
- Flow without price reaction = hedge or roll (ignore)
|
||||
- Flow with price reaction = real positioning (trade it)
|
||||
|
||||
---
|
||||
|
||||
### 4️⃣ Strike Clustering Detection
|
||||
|
||||
**Current Gap:** No detection of multiple large trades at the same strike (institutional layering).
|
||||
|
||||
**Suggestion:**
|
||||
- **New service:** `backend/python_service/services/strike_cluster_detector.py`
|
||||
- Group trades by strike and expiration
|
||||
- Identify clusters: 3+ trades at same strike within 30 minutes
|
||||
- Calculate cluster premium total
|
||||
- Flag as "institutional positioning" vs "single trade"
|
||||
|
||||
**Implementation Approach:**
|
||||
```python
|
||||
class StrikeClusterDetector:
|
||||
def detect_clusters(self, df: pd.DataFrame, window_minutes: int = 30):
|
||||
"""Detect strike clusters within time window"""
|
||||
df = df.copy()
|
||||
|
||||
# Group by symbol, exp_date, strike
|
||||
clusters = df.groupby(['symbol_norm', 'exp_date', 'strike_num']).apply(
|
||||
lambda g: self._find_clusters_in_group(g, window_minutes)
|
||||
)
|
||||
|
||||
return clusters
|
||||
|
||||
def _find_clusters_in_group(self, group, window_minutes):
|
||||
"""Find time-based clusters within a strike group"""
|
||||
# Sort by time
|
||||
group = group.sort_values('flow_ts_utc')
|
||||
|
||||
# Rolling window: if 3+ trades within window_minutes, it's a cluster
|
||||
# Return cluster flags and cluster IDs
|
||||
```
|
||||
|
||||
**New Fields:**
|
||||
- `is_cluster_trade` - Boolean
|
||||
- `cluster_id` - Unique ID for the cluster
|
||||
- `cluster_size` - Number of trades in cluster
|
||||
- `cluster_total_premium` - Sum of all premiums in cluster
|
||||
|
||||
**Why This Matters:**
|
||||
- Institutions rarely place one order — they layer
|
||||
- Clusters = stronger signal than single prints
|
||||
|
||||
---
|
||||
|
||||
### 5️⃣ Gamma Exposure (GEX) Calculation
|
||||
|
||||
**Current Gap:** No gamma exposure tracking. This explains why some rockets fail.
|
||||
|
||||
**Suggestion:**
|
||||
- **New service:** `backend/python_service/services/gamma_calculator.py`
|
||||
- Calculate call GEX and put GEX per strike
|
||||
- Net dealer gamma = Call GEX - Put GEX
|
||||
- Positive GEX = price pinned (resistance)
|
||||
- Negative GEX = explosive moves possible
|
||||
|
||||
**Implementation Approach:**
|
||||
```python
|
||||
class GammaCalculator:
|
||||
def calculate_gex(self, df: pd.DataFrame):
|
||||
"""
|
||||
Calculate Gamma Exposure (GEX)
|
||||
GEX = OI * Spot^2 * Gamma * 0.01 * Multiplier
|
||||
Simplified: GEX ≈ OI * Spot^2 * 0.01 (for rough estimate)
|
||||
"""
|
||||
# For each strike, calculate:
|
||||
# - Call GEX (positive for calls)
|
||||
# - Put GEX (negative for puts)
|
||||
# - Net GEX = Call GEX + Put GEX
|
||||
|
||||
# Add to flow row:
|
||||
# - strike_gex (GEX at this strike)
|
||||
# - net_dealer_gex (aggregate GEX for symbol)
|
||||
# - gex_pin_level (strike with highest GEX)
|
||||
```
|
||||
|
||||
**New Fields:**
|
||||
- `strike_gex` - GEX at this strike
|
||||
- `net_dealer_gex` - Net GEX for the symbol
|
||||
- `gex_pin_level` - Strike where GEX is highest (pin level)
|
||||
- `is_gex_positive` - Boolean: positive GEX = pinning, negative = explosive
|
||||
|
||||
**Why This Matters:**
|
||||
- +GEX = Price pinned (rockets may fail at pin level)
|
||||
- -GEX = Explosive moves (rockets more likely to work)
|
||||
|
||||
---
|
||||
|
||||
### 6️⃣ Delta Weighting (Smart Money Filter)
|
||||
|
||||
**Current Gap:** No delta weighting. ITM delta > OTM lottery tickets.
|
||||
|
||||
**Suggestion:**
|
||||
- **Extend:** `backend/python_service/services/options_flow_processor.py`
|
||||
- Add delta calculation (approximate: use Black-Scholes or simplified formula)
|
||||
- Calculate: `delta_weighted_premium = delta * volume * premium`
|
||||
- Filter out low delta-weighted trades (YOLO prints)
|
||||
|
||||
**Implementation Approach:**
|
||||
```python
|
||||
def calculate_delta_weighted_value(row):
|
||||
"""Calculate delta-weighted premium value"""
|
||||
# Simplified delta approximation:
|
||||
# For CALL: delta ≈ N(d1) where d1 = (ln(S/K) + (r+σ²/2)*T) / (σ*√T)
|
||||
# For rough estimate: delta ≈ 0.5 for ATM, 0.8+ for ITM, 0.2- for OTM
|
||||
|
||||
spot = row.get('spot_num', 0)
|
||||
strike = row.get('strike_num', 0)
|
||||
cp = row.get('cp_norm', '')
|
||||
moneyness = row.get('moneyness', '')
|
||||
|
||||
# Simplified delta based on moneyness
|
||||
if moneyness == 'ITM':
|
||||
delta = 0.7 if cp == 'CALL' else 0.7
|
||||
elif moneyness == 'OTM':
|
||||
delta = 0.3 if cp == 'CALL' else 0.3
|
||||
else: # ATM
|
||||
delta = 0.5
|
||||
|
||||
volume = row.get('vol_num', 0) or 0
|
||||
premium = row.get('premium_num', 0) or 0
|
||||
|
||||
return delta * volume * premium
|
||||
```
|
||||
|
||||
**New Fields:**
|
||||
- `delta_approx` - Approximate delta value
|
||||
- `delta_weighted_premium` - Delta * Volume * Premium
|
||||
- `is_smart_money` - Boolean: delta_weighted_premium > threshold
|
||||
|
||||
**Why This Matters:**
|
||||
- Filters out YOLO OTM lottery prints
|
||||
- ITM delta > OTM = real positioning
|
||||
|
||||
---
|
||||
|
||||
### 7️⃣ Time-to-Expiration Buckets
|
||||
|
||||
**Current Gap:** No DTE-based classification.
|
||||
|
||||
**Suggestion:**
|
||||
- **Extend:** `backend/python_service/services/options_flow_processor.py`
|
||||
- Calculate DTE (days to expiration)
|
||||
- Bucket into: 0DTE, 1-3 DTE, 7-14 DTE, Monthly
|
||||
- Different logic per bucket
|
||||
|
||||
**Implementation Approach:**
|
||||
```python
|
||||
def calculate_dte_bucket(row):
|
||||
"""Calculate days to expiration and bucket"""
|
||||
exp_date = row.get('exp_date')
|
||||
flow_date = row.get('flow_date_cst')
|
||||
|
||||
if not exp_date or not flow_date:
|
||||
return None
|
||||
|
||||
if isinstance(flow_date, datetime):
|
||||
flow_date = flow_date.date()
|
||||
if isinstance(exp_date, datetime):
|
||||
exp_date = exp_date.date()
|
||||
|
||||
dte = (exp_date - flow_date).days
|
||||
|
||||
if dte == 0:
|
||||
return '0DTE'
|
||||
elif 1 <= dte <= 3:
|
||||
return '1-3DTE'
|
||||
elif 4 <= dte <= 6:
|
||||
return '4-6DTE'
|
||||
elif 7 <= dte <= 14:
|
||||
return '7-14DTE'
|
||||
elif 15 <= dte <= 30:
|
||||
return 'MONTHLY'
|
||||
else:
|
||||
return 'LONG_TERM'
|
||||
```
|
||||
|
||||
**New Fields:**
|
||||
- `dte` - Days to expiration
|
||||
- `dte_bucket` - Bucket classification
|
||||
- `is_0dte` - Boolean flag
|
||||
|
||||
**Why This Matters:**
|
||||
- 0DTE → intraday pressure (gamma risk)
|
||||
- Longer DTE → directional thesis (less gamma risk)
|
||||
|
||||
---
|
||||
|
||||
### 8️⃣ Sweep vs Block Detection
|
||||
|
||||
**Current Gap:** No distinction between sweeps (urgency) and blocks (positioning).
|
||||
|
||||
**Suggestion:**
|
||||
- **New service:** `backend/python_service/services/trade_type_detector.py`
|
||||
- Detect multiple trades at same strike/expiration within 2 seconds = SWEEP
|
||||
- Single large trade = BLOCK
|
||||
- Different trading implications
|
||||
|
||||
**Implementation Approach:**
|
||||
```python
|
||||
class TradeTypeDetector:
|
||||
def detect_trade_type(self, df: pd.DataFrame):
|
||||
"""Detect if trade is sweep or block"""
|
||||
df = df.copy()
|
||||
df = df.sort_values(['symbol_norm', 'exp_date', 'strike_num', 'flow_ts_utc'])
|
||||
|
||||
# Group by symbol, exp, strike
|
||||
groups = df.groupby(['symbol_norm', 'exp_date', 'strike_num'])
|
||||
|
||||
def classify_group(group):
|
||||
# If multiple trades within 2 seconds = sweep
|
||||
# If single large trade = block
|
||||
# Otherwise = regular trade
|
||||
|
||||
if len(group) == 1:
|
||||
return 'BLOCK' if group.iloc[0]['premium_num'] > 500000 else 'REGULAR'
|
||||
|
||||
# Check time differences
|
||||
time_diffs = group['flow_ts_utc'].diff().dt.total_seconds()
|
||||
has_sweep = (time_diffs <= 2).any()
|
||||
|
||||
if has_sweep:
|
||||
return 'SWEEP'
|
||||
else:
|
||||
return 'CLUSTER'
|
||||
|
||||
df['trade_type'] = groups.apply(classify_group).values
|
||||
|
||||
return df
|
||||
```
|
||||
|
||||
**New Fields:**
|
||||
- `trade_type` - 'SWEEP', 'BLOCK', 'CLUSTER', 'REGULAR'
|
||||
- `is_sweep` - Boolean
|
||||
- `is_block` - Boolean
|
||||
|
||||
**Why This Matters:**
|
||||
- Sweeps = urgency (institutions hitting multiple exchanges)
|
||||
- Blocks = positioning (single large order)
|
||||
|
||||
---
|
||||
|
||||
### 9️⃣ Historical Win Rate Tracking
|
||||
|
||||
**Current Gap:** No tracking of which patterns actually work.
|
||||
|
||||
**Suggestion:**
|
||||
- **New service:** `backend/python_service/services/pattern_analyzer.py`
|
||||
- Track pattern → outcome mapping
|
||||
- Calculate win rate per pattern
|
||||
- Average return per pattern
|
||||
- Max drawdown per pattern
|
||||
|
||||
**Database Addition:**
|
||||
- **New table:** `signal_patterns_history`
|
||||
- Columns: pattern_hash, signal_time, price_at_signal, price_5m_after, price_15m_after, outcome, return_pct
|
||||
|
||||
**Implementation Approach:**
|
||||
```python
|
||||
class PatternAnalyzer:
|
||||
def track_pattern(self, flow_row, price_reaction):
|
||||
"""Track pattern and outcome"""
|
||||
pattern_hash = self._hash_pattern(flow_row)
|
||||
|
||||
# Store in database:
|
||||
# - Pattern signature (badge combo + premium tier + DTE)
|
||||
# - Outcome (price reaction)
|
||||
# - Return percentage
|
||||
|
||||
def get_pattern_stats(self, pattern_hash):
|
||||
"""Get historical stats for a pattern"""
|
||||
# Query database for all instances of this pattern
|
||||
# Calculate: win_rate, avg_return, max_drawdown
|
||||
```
|
||||
|
||||
**New Fields:**
|
||||
- `pattern_hash` - Unique identifier for pattern
|
||||
- `historical_win_rate` - Win rate for this pattern
|
||||
- `historical_avg_return` - Average return for this pattern
|
||||
- `pattern_confidence` - Confidence based on historical performance
|
||||
|
||||
**Why This Matters:**
|
||||
- Discover which patterns actually work
|
||||
- 🚀🚀 without 💎 fails more often
|
||||
- 🟢💎⭐ + VWAP reclaim wins most
|
||||
|
||||
---
|
||||
|
||||
### 🔟 Index & Correlation Filter
|
||||
|
||||
**Current Gap:** No SPY/QQQ/VIX alignment check.
|
||||
|
||||
**Suggestion:**
|
||||
- **New service:** `backend/python_service/services/index_correlation.py`
|
||||
- Fetch SPY/QQQ flow at signal time
|
||||
- Check VIX direction
|
||||
- Rule: Single stock flow works best when index agrees
|
||||
|
||||
**Implementation Approach:**
|
||||
```python
|
||||
class IndexCorrelationService:
|
||||
async def check_index_alignment(self, flow_row, pool):
|
||||
"""Check if index flow aligns with stock flow"""
|
||||
symbol = flow_row['symbol_norm']
|
||||
signal_time = flow_row['flow_ts_utc']
|
||||
direction = flow_row['direction']
|
||||
|
||||
# Get SPY/QQQ flow in same time window
|
||||
spy_flow = await self.get_index_flow('SPY', signal_time, pool)
|
||||
qqq_flow = await self.get_index_flow('QQQ', signal_time, pool)
|
||||
|
||||
# Get VIX direction
|
||||
vix_direction = await self.get_vix_direction(signal_time, pool)
|
||||
|
||||
# Check alignment
|
||||
index_bullish = (spy_flow.get('net_premium', 0) > 0) or (qqq_flow.get('net_premium', 0) > 0)
|
||||
index_bearish = (spy_flow.get('net_premium', 0) < 0) or (qqq_flow.get('net_premium', 0) < 0)
|
||||
|
||||
aligned = (
|
||||
(direction == 'BULL' and index_bullish) or
|
||||
(direction == 'BEAR' and index_bearish)
|
||||
)
|
||||
|
||||
return {
|
||||
'index_aligned': aligned,
|
||||
'spy_flow_direction': 'BULL' if spy_flow.get('net_premium', 0) > 0 else 'BEAR',
|
||||
'qqq_flow_direction': 'BULL' if qqq_flow.get('net_premium', 0) > 0 else 'BEAR',
|
||||
'vix_direction': vix_direction
|
||||
}
|
||||
```
|
||||
|
||||
**New Fields:**
|
||||
- `index_aligned` - Boolean: does index flow agree?
|
||||
- `spy_flow_direction` - SPY flow direction
|
||||
- `qqq_flow_direction` - QQQ flow direction
|
||||
- `vix_direction` - VIX direction (up/down)
|
||||
|
||||
**Why This Matters:**
|
||||
- Single stock flow works best when index agrees
|
||||
- Contrarian flow (stock vs index) = lower probability
|
||||
|
||||
---
|
||||
|
||||
## PART B — TRADE CHECKLIST IMPLEMENTATION
|
||||
|
||||
### Trade Entry Checklist
|
||||
|
||||
**Suggestion:**
|
||||
- **New service:** `backend/python_service/services/trade_checklist.py`
|
||||
- Implement 5-point checklist
|
||||
- Return checklist score (0-5)
|
||||
- Only allow trades with 4/5 or 5/5
|
||||
|
||||
**Implementation Approach:**
|
||||
```python
|
||||
class TradeChecklist:
|
||||
def evaluate(self, flow_row):
|
||||
"""Evaluate trade checklist"""
|
||||
checks = {
|
||||
'has_direction': flow_row.get('badge_round') in ['🟢', '🔴'],
|
||||
'has_diamond': '💎' in flow_row.get('badge_more', ''),
|
||||
'has_star': '⭐' in flow_row.get('badge_more', ''),
|
||||
'price_respects_vwap': self._check_vwap_respect(flow_row),
|
||||
'index_confirms': flow_row.get('index_aligned', False)
|
||||
}
|
||||
|
||||
score = sum(checks.values())
|
||||
passed = score >= 4
|
||||
|
||||
return {
|
||||
'checklist_score': score,
|
||||
'checklist_passed': passed,
|
||||
'checks': checks
|
||||
}
|
||||
```
|
||||
|
||||
**New Fields:**
|
||||
- `checklist_score` - 0-5 score
|
||||
- `checklist_passed` - Boolean: 4/5 or 5/5
|
||||
- `checklist_details` - JSON with individual check results
|
||||
|
||||
---
|
||||
|
||||
## PART C — ENHANCED ENTRY/EXIT LOGIC
|
||||
|
||||
### Entry Strategy Enhancement
|
||||
|
||||
**Current Gap:** Entry logic exists but doesn't use VWAP pullback/reclaim.
|
||||
|
||||
**Suggestion:**
|
||||
- **Extend:** `backend/src/services/tradePlanGenerator.js`
|
||||
- Add VWAP pullback entry
|
||||
- Add VWAP reclaim entry
|
||||
- Add prior high break entry
|
||||
- Avoid chasing vertical candles
|
||||
|
||||
**Implementation:**
|
||||
```javascript
|
||||
function generateEntryStrategy(signal, currentPrice, priceContext) {
|
||||
const vwap = priceContext.vwap;
|
||||
const priorHigh = priceContext.priorHigh;
|
||||
const vwapDistance = ((currentPrice - vwap) / vwap) * 100;
|
||||
|
||||
if (signal === 'BUY') {
|
||||
// Best: VWAP pullback or VWAP reclaim
|
||||
if (currentPrice < vwap && vwapDistance > -1) {
|
||||
return {
|
||||
type: 'VWAP_PULLBACK',
|
||||
entry: vwap * 0.998, // Slightly below VWAP
|
||||
reason: 'VWAP pullback entry'
|
||||
};
|
||||
}
|
||||
|
||||
// Good: Break & hold above prior high
|
||||
if (currentPrice > priorHigh) {
|
||||
return {
|
||||
type: 'BREAKOUT',
|
||||
entry: priorHigh * 1.001, // Slightly above prior high
|
||||
reason: 'Prior high breakout'
|
||||
};
|
||||
}
|
||||
|
||||
// Avoid: Chasing vertical candles
|
||||
if (vwapDistance > 2) {
|
||||
return {
|
||||
type: 'WAIT',
|
||||
reason: 'Price too extended from VWAP - wait for pullback'
|
||||
};
|
||||
}
|
||||
}
|
||||
// Similar for SELL signals...
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Exit Strategy Enhancement
|
||||
|
||||
**Current Gap:** Exit logic is basic. Need flow-based exits.
|
||||
|
||||
**Suggestion:**
|
||||
- **Extend:** `backend/src/services/tradePlanGenerator.js`
|
||||
- Exit when flow stalls
|
||||
- Exit when opposite 💎 appears
|
||||
- Exit when net premium flips
|
||||
- Exit when price rejects VWAP
|
||||
- Scale out at +30-50% option gain
|
||||
|
||||
**Implementation:**
|
||||
```javascript
|
||||
function generateExitStrategy(signal, entryPrice, currentPrice, flowData) {
|
||||
const exits = [];
|
||||
|
||||
// Flow stalls
|
||||
if (flowData.recentFlowVolume < flowData.avgFlowVolume * 0.3) {
|
||||
exits.push({
|
||||
type: 'FLOW_STALL',
|
||||
reason: 'Flow volume dropped significantly'
|
||||
});
|
||||
}
|
||||
|
||||
// Opposite diamond appears
|
||||
if (signal === 'BUY' && flowData.hasBearDiamond) {
|
||||
exits.push({
|
||||
type: 'OPPOSITE_SIGNAL',
|
||||
reason: 'Bear diamond (💎) appeared - exit long'
|
||||
});
|
||||
}
|
||||
|
||||
// Net premium flips
|
||||
if (signal === 'BUY' && flowData.netPremium < 0) {
|
||||
exits.push({
|
||||
type: 'PREMIUM_FLIP',
|
||||
reason: 'Net premium flipped negative'
|
||||
});
|
||||
}
|
||||
|
||||
// Price rejects VWAP
|
||||
if (currentPrice < priceContext.vwap && signal === 'BUY') {
|
||||
exits.push({
|
||||
type: 'VWAP_REJECTION',
|
||||
reason: 'Price rejected VWAP - exit'
|
||||
});
|
||||
}
|
||||
|
||||
// Scale out at gains
|
||||
const gainPct = ((currentPrice - entryPrice) / entryPrice) * 100;
|
||||
if (gainPct >= 30) {
|
||||
exits.push({
|
||||
type: 'SCALE_OUT',
|
||||
reason: `+${gainPct.toFixed(1)}% gain - scale out 50%`
|
||||
});
|
||||
}
|
||||
|
||||
return exits;
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## PART D — DATABASE SCHEMA ADDITIONS
|
||||
|
||||
### New Columns for `processed_options_flow` (or new enrichment table)
|
||||
|
||||
```sql
|
||||
-- Signal classification
|
||||
signal_tier VARCHAR(10), -- 'TIER_1', 'TIER_2', 'IGNORE'
|
||||
is_tradeable BOOLEAN,
|
||||
|
||||
-- VWAP
|
||||
vwap_at_signal NUMERIC,
|
||||
price_vs_vwap_pct NUMERIC,
|
||||
vwap_reclaimed BOOLEAN,
|
||||
|
||||
-- Price reaction
|
||||
price_reaction_5m_pct NUMERIC,
|
||||
price_reaction_15m_pct NUMERIC,
|
||||
price_reaction_30m_pct NUMERIC,
|
||||
high_break_5m BOOLEAN,
|
||||
low_break_5m BOOLEAN,
|
||||
flow_led_to_move BOOLEAN,
|
||||
|
||||
-- Strike clustering
|
||||
is_cluster_trade BOOLEAN,
|
||||
cluster_id VARCHAR(50),
|
||||
cluster_size INTEGER,
|
||||
cluster_total_premium NUMERIC,
|
||||
|
||||
-- Gamma exposure
|
||||
strike_gex NUMERIC,
|
||||
net_dealer_gex NUMERIC,
|
||||
gex_pin_level NUMERIC,
|
||||
is_gex_positive BOOLEAN,
|
||||
|
||||
-- Delta weighting
|
||||
delta_approx NUMERIC,
|
||||
delta_weighted_premium NUMERIC,
|
||||
is_smart_money BOOLEAN,
|
||||
|
||||
-- DTE
|
||||
dte INTEGER,
|
||||
dte_bucket VARCHAR(20),
|
||||
is_0dte BOOLEAN,
|
||||
|
||||
-- Trade type
|
||||
trade_type VARCHAR(20), -- 'SWEEP', 'BLOCK', 'CLUSTER', 'REGULAR'
|
||||
is_sweep BOOLEAN,
|
||||
is_block BOOLEAN,
|
||||
|
||||
-- Index correlation
|
||||
index_aligned BOOLEAN,
|
||||
spy_flow_direction VARCHAR(10),
|
||||
qqq_flow_direction VARCHAR(10),
|
||||
vix_direction VARCHAR(10),
|
||||
|
||||
-- Checklist
|
||||
checklist_score INTEGER,
|
||||
checklist_passed BOOLEAN,
|
||||
checklist_details JSONB,
|
||||
|
||||
-- Pattern tracking
|
||||
pattern_hash VARCHAR(100),
|
||||
historical_win_rate NUMERIC,
|
||||
historical_avg_return NUMERIC,
|
||||
pattern_confidence NUMERIC
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## PART E — IMPLEMENTATION PRIORITY
|
||||
|
||||
### Phase 1 (Highest Impact - Do First)
|
||||
1. ✅ **Price Reaction Tracking** - Most important filter
|
||||
2. ✅ **VWAP Integration** - Critical for entry/exit
|
||||
3. ✅ **Signal Tier Classification** - Filter noise
|
||||
4. ✅ **Trade Checklist** - Prevent bad trades
|
||||
|
||||
### Phase 2 (High Value)
|
||||
5. ✅ **Strike Clustering** - Identify institutional layering
|
||||
6. ✅ **Delta Weighting** - Filter YOLO prints
|
||||
7. ✅ **Index Correlation** - Context filter
|
||||
|
||||
### Phase 3 (Nice to Have)
|
||||
8. ✅ **Gamma Exposure** - Explains pinning behavior
|
||||
9. ✅ **Sweep vs Block** - Trade type classification
|
||||
10. ✅ **DTE Buckets** - Time-based filtering
|
||||
|
||||
### Phase 4 (Analytics)
|
||||
11. ✅ **Historical Win Rate** - Pattern analysis
|
||||
12. ✅ **Enhanced Entry/Exit** - Refine trading logic
|
||||
|
||||
---
|
||||
|
||||
## PART F — API ENDPOINT SUGGESTIONS
|
||||
|
||||
### New Endpoints to Add
|
||||
|
||||
1. **`GET /api/options-flow/enhanced`**
|
||||
- Returns flow with all new enrichments
|
||||
- Parameters: `include_price_reaction`, `include_gex`, etc.
|
||||
|
||||
2. **`GET /api/options-flow/checklist`**
|
||||
- Returns only signals that pass checklist (4/5 or 5/5)
|
||||
|
||||
3. **`GET /api/options-flow/tier-1`**
|
||||
- Returns only Tier-1 tradeable signals
|
||||
|
||||
4. **`GET /api/patterns/stats`**
|
||||
- Returns historical win rates per pattern
|
||||
|
||||
5. **`GET /api/options-flow/vwap-analysis`**
|
||||
- Returns VWAP-based entry opportunities
|
||||
|
||||
---
|
||||
|
||||
## PART G — FRONTEND DISPLAY SUGGESTIONS
|
||||
|
||||
### New UI Elements to Add
|
||||
|
||||
1. **Signal Tier Badge**
|
||||
- Display "TIER-1", "TIER-2", or "IGNORE" badge
|
||||
- Color code: Green (Tier-1), Yellow (Tier-2), Gray (Ignore)
|
||||
|
||||
2. **Price Reaction Indicator**
|
||||
- Show 5m/15m price reaction percentage
|
||||
- Green if positive reaction, Red if negative
|
||||
- "Flow Led to Move" indicator
|
||||
|
||||
3. **VWAP Distance Display**
|
||||
- Show current price vs VWAP
|
||||
- Visual indicator: Above/Below VWAP
|
||||
- Entry opportunity: "VWAP Pullback" or "VWAP Reclaim"
|
||||
|
||||
4. **Checklist Score Display**
|
||||
- Show checklist score (X/5)
|
||||
- Green if passed (4/5+), Red if failed
|
||||
- Expandable details showing each check
|
||||
|
||||
5. **Index Alignment Indicator**
|
||||
- Show SPY/QQQ flow direction
|
||||
- Show if aligned (green) or not (red)
|
||||
|
||||
6. **Gamma Pin Level**
|
||||
- Display GEX pin level on chart
|
||||
- Show if price is near pin (resistance)
|
||||
|
||||
7. **Strike Cluster Visualization**
|
||||
- Show cluster size and total premium
|
||||
- Highlight clustered strikes
|
||||
|
||||
---
|
||||
|
||||
## PART H — TESTING SUGGESTIONS
|
||||
|
||||
### Test Cases to Add
|
||||
|
||||
1. **Price Reaction Tests**
|
||||
- Test: Flow with no price reaction = should be filtered
|
||||
- Test: Flow with 5m price reaction = should be tradeable
|
||||
|
||||
2. **Tier Classification Tests**
|
||||
- Test: 🟢 + 💎 + ⭐ + premium > 500K = Tier-1
|
||||
- Test: 🟢 + 💎 (no ⭐) = Tier-2
|
||||
- Test: OTM-only = Ignore
|
||||
|
||||
3. **Checklist Tests**
|
||||
- Test: 4/5 checks = passed
|
||||
- Test: 3/5 checks = failed
|
||||
|
||||
4. **VWAP Tests**
|
||||
- Test: VWAP pullback entry detection
|
||||
- Test: VWAP reclaim entry detection
|
||||
|
||||
---
|
||||
|
||||
## SUMMARY
|
||||
|
||||
### Key Takeaways
|
||||
|
||||
1. **Price Reaction is #1 Priority** - This filters out hedges/rolls
|
||||
2. **VWAP Integration is Critical** - Needed for proper entry/exit
|
||||
3. **Tier Classification Reduces Noise** - Focus on Tier-1 signals
|
||||
4. **Checklist Prevents Bad Trades** - Enforce 4/5 minimum
|
||||
5. **Strike Clustering Identifies Institutions** - Multiple trades = stronger signal
|
||||
6. **Index Correlation Adds Context** - Single stock works best with index alignment
|
||||
|
||||
### Implementation Strategy
|
||||
|
||||
- Start with Phase 1 (Price Reaction + VWAP + Tier Classification + Checklist)
|
||||
- These 4 features will have the biggest impact on trade quality
|
||||
- Then move to Phase 2 for additional filtering
|
||||
- Phase 3 and 4 can be added over time as analytics improve
|
||||
|
||||
### Expected Outcomes
|
||||
|
||||
- **Higher Win Rate**: Filtering out hedges/rolls and low-quality signals
|
||||
- **Better Entries**: VWAP-based entry logic
|
||||
- **Better Exits**: Flow-based exit signals
|
||||
- **Reduced Noise**: Tier classification and checklist
|
||||
- **Institutional Detection**: Strike clustering and delta weighting
|
||||
|
||||
---
|
||||
|
||||
**Note:** All suggestions are additive - they don't change existing code, just extend it with new services and enrichments.
|
||||
|
||||
241
TESTING_GUIDE.md
241
TESTING_GUIDE.md
|
|
@ -1,241 +0,0 @@
|
|||
# Testing Guide for Historical Data & Backtesting
|
||||
|
||||
## Prerequisites
|
||||
|
||||
1. Database connection configured (see `.env` file)
|
||||
2. Node.js installed and in PATH
|
||||
3. Python 3.x installed with required packages:
|
||||
- `yfinance`
|
||||
- `pandas`
|
||||
- `psycopg2`
|
||||
- `python-dotenv`
|
||||
|
||||
## Step 1: Validate Database Schema
|
||||
|
||||
Run the schema validation script to ensure all tables and indexes exist:
|
||||
|
||||
```bash
|
||||
cd backend
|
||||
node scripts/validateSchema.js
|
||||
```
|
||||
|
||||
**Expected Output:**
|
||||
- ✅ All required tables exist
|
||||
- ✅ Indexes on critical tables
|
||||
- ✅ PRIMARY KEY constraints verified
|
||||
- ⚠️ Any warnings about data types
|
||||
|
||||
**If issues are found:**
|
||||
- Run `backend/database/schema.sql` in your database
|
||||
- Run `backend/database/missing_tables.sql` if needed
|
||||
|
||||
## Step 2: Pull Historical Data
|
||||
|
||||
### Test with Single Symbol First
|
||||
|
||||
```bash
|
||||
python yahhooscript.py --only AAPL
|
||||
```
|
||||
|
||||
This will:
|
||||
- Pull 30 days of 5-minute intraday data for AAPL
|
||||
- Pull daily data (2 years) for AAPL
|
||||
- Store in PostgreSQL database
|
||||
|
||||
**Verify data was pulled:**
|
||||
```sql
|
||||
-- Check intraday data
|
||||
SELECT COUNT(*), MIN(ts), MAX(ts)
|
||||
FROM prices_intraday_1m
|
||||
WHERE symbol = 'AAPL'
|
||||
AND ts >= NOW() - INTERVAL '30 days';
|
||||
|
||||
-- Check daily data
|
||||
SELECT COUNT(*), MIN("Date"), MAX("Date")
|
||||
FROM prices_daily
|
||||
WHERE symbol = 'AAPL';
|
||||
```
|
||||
|
||||
### Pull Full Universe
|
||||
|
||||
```bash
|
||||
python yahhooscript.py
|
||||
```
|
||||
|
||||
This will pull data for all symbols in your universe (S&P 500, S&P 400, Nasdaq-100, plus custom groups).
|
||||
|
||||
**Note:** This may take 30-60 minutes depending on:
|
||||
- Number of symbols
|
||||
- Yahoo Finance rate limiting
|
||||
- Network speed
|
||||
|
||||
**Monitor progress:**
|
||||
- Script shows batch progress
|
||||
- Check for errors in output
|
||||
- Verify data counts in database
|
||||
|
||||
## Step 3: Test Backtester
|
||||
|
||||
### Run Test Script
|
||||
|
||||
```bash
|
||||
cd backend
|
||||
node scripts/testBacktester.js
|
||||
```
|
||||
|
||||
This will:
|
||||
- Test database connectivity
|
||||
- Check price data availability
|
||||
- Run a sample backtest
|
||||
- Verify enhanced metrics are calculated
|
||||
|
||||
### Test via API
|
||||
|
||||
Start the backend server:
|
||||
```bash
|
||||
cd backend
|
||||
npm run dev
|
||||
```
|
||||
|
||||
**Run Single Backtest:**
|
||||
```bash
|
||||
curl -X POST http://localhost:3010/api/backtest/run \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"pattern": {
|
||||
"rocketScoreMin": 5.0,
|
||||
"session": "RTH"
|
||||
},
|
||||
"options": {
|
||||
"lookbackDays": 30,
|
||||
"targetPct": 1.5,
|
||||
"stopPct": 1.5
|
||||
}
|
||||
}'
|
||||
```
|
||||
|
||||
**Compare Strategies:**
|
||||
```bash
|
||||
curl -X POST http://localhost:3010/api/backtest/compare \
|
||||
-H "Content-Type: application/json" \
|
||||
-d '{
|
||||
"strategies": [
|
||||
{
|
||||
"name": "High Score RTH",
|
||||
"pattern": { "rocketScoreMin": 5, "session": "RTH" }
|
||||
},
|
||||
{
|
||||
"name": "Tape Aligned",
|
||||
"pattern": { "rocketScoreMin": 3, "tapeAligned": true }
|
||||
}
|
||||
],
|
||||
"options": {
|
||||
"lookbackDays": 30
|
||||
}
|
||||
}'
|
||||
```
|
||||
|
||||
## Step 4: Validate Results
|
||||
|
||||
### Check Metrics
|
||||
|
||||
Verify that all new metrics are being calculated:
|
||||
|
||||
```javascript
|
||||
{
|
||||
"winRate": 70.1,
|
||||
"avgWin": 2.7,
|
||||
"avgLoss": -1.3,
|
||||
"expectancy": 1.5,
|
||||
"sharpeRatio": 1.23, // ✅ New
|
||||
"maxDrawdown": 15.0, // ✅ New
|
||||
"streaks": { // ✅ New
|
||||
"maxWinStreak": 5,
|
||||
"maxLossStreak": 2
|
||||
},
|
||||
"sessionPerformance": [ // ✅ New
|
||||
{ "session": "RTH", "winRate": 72.5, "totalTrades": 80 }
|
||||
],
|
||||
"symbolPerformance": { // ✅ New
|
||||
"best": [{"symbol": "AAPL", "avgReturn": 3.2}],
|
||||
"worst": [{"symbol": "TSLA", "avgReturn": -1.5}]
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### Verify Data Quality
|
||||
|
||||
```sql
|
||||
-- Check data completeness for a symbol
|
||||
SELECT
|
||||
symbol,
|
||||
COUNT(*) as total_bars,
|
||||
COUNT(DISTINCT DATE(ts)) as trading_days,
|
||||
MIN(ts) as earliest,
|
||||
MAX(ts) as latest
|
||||
FROM prices_intraday_1m
|
||||
WHERE symbol = 'AAPL'
|
||||
AND ts >= NOW() - INTERVAL '30 days'
|
||||
GROUP BY symbol;
|
||||
|
||||
-- Should show ~20-22 trading days (excluding weekends/holidays)
|
||||
-- Should show ~390 bars per day for 5-minute data (6.5 hours * 12 bars/hour)
|
||||
```
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### No Data Found in Backtest
|
||||
|
||||
**Possible causes:**
|
||||
1. No options flow data in date range
|
||||
- Check: `SELECT COUNT(*) FROM "OptionsFlow_monthly" WHERE "CreatedDate" >= CURRENT_DATE - INTERVAL '30 days'`
|
||||
2. Pattern criteria too strict
|
||||
- Try lowering `rocketScoreMin` or removing filters
|
||||
3. No price data available
|
||||
- Run: `python yahhooscript.py --only SYMBOL`
|
||||
|
||||
### Yahoo Finance Rate Limiting
|
||||
|
||||
**Symptoms:**
|
||||
- Empty data returned
|
||||
- Timeout errors
|
||||
- JSON parsing errors
|
||||
|
||||
**Solutions:**
|
||||
1. Increase `SLEEP_BETWEEN_BATCHES` in `yahhooscript.py`
|
||||
2. Reduce `BATCH_SIZE` (try 50 instead of 110)
|
||||
3. Run during off-peak hours
|
||||
4. Use VPN if IP is rate-limited
|
||||
|
||||
### Database Connection Issues
|
||||
|
||||
**Check:**
|
||||
1. `.env` file has correct credentials
|
||||
2. Database is not paused (Supabase)
|
||||
3. Network connectivity
|
||||
4. Firewall rules
|
||||
|
||||
**Test connection:**
|
||||
```bash
|
||||
cd backend
|
||||
node -e "import('./src/db.js').then(m => m.testConnection())"
|
||||
```
|
||||
|
||||
## Success Criteria
|
||||
|
||||
✅ Schema validation passes
|
||||
✅ 30 days of historical data pulled
|
||||
✅ Backtester runs without errors
|
||||
✅ All enhanced metrics are calculated
|
||||
✅ API endpoints return valid results
|
||||
✅ Data quality checks pass
|
||||
|
||||
## Next Steps
|
||||
|
||||
After successful testing:
|
||||
1. Run full universe data pull (if not done)
|
||||
2. Test with various pattern combinations
|
||||
3. Compare multiple strategies
|
||||
4. Analyze results and refine patterns
|
||||
5. Set up scheduled data pulls (optional)
|
||||
|
||||
|
|
@ -1,288 +0,0 @@
|
|||
# Trade Plan Calculation - Data Flow & Sources
|
||||
|
||||
## Overview
|
||||
The trade plan is automatically generated for every options flow signal with a rocket score >= 3. It calculates entry prices, stop losses, profit targets, and risk/reward ratios based on real-time price data.
|
||||
|
||||
## Entry Point
|
||||
**File:** `backend/src/routes/optionsFlow.js` (line 228-233)
|
||||
|
||||
```javascript
|
||||
if (rocketScore >= 3) {
|
||||
const tradePlan = generateTradePlan(enrichedRow);
|
||||
enrichedRow.tradePlan = tradePlan;
|
||||
enrichedRow.tradePlanDisplay = formatTradePlanDisplay(tradePlan);
|
||||
}
|
||||
```
|
||||
|
||||
## Data Sources
|
||||
|
||||
### 1. **Current Price** (Primary Reference)
|
||||
**Sources (in priority order):**
|
||||
- **`flow.yahooPriceData.currentPrice`** - **REAL-TIME from Yahoo Finance API** ⭐ (PRIMARY)
|
||||
- `flow.spot_num` - Spot price from flow data (fallback)
|
||||
- `flow.u_close` - Close price at signal time (from database, may be stale)
|
||||
- `flow.last_px` - Last price (fallback)
|
||||
- `flow.Price` - Fallback price field
|
||||
|
||||
**Yahoo Finance API:** `https://query1.finance.yahoo.com/v8/finance/chart/{symbol}?interval=1d&range=5d`
|
||||
- Returns real-time `regularMarketPrice` or `previousClose`
|
||||
- **This is the PRIMARY source for current price** (not database)
|
||||
|
||||
### 2. **VWAP (Volume Weighted Average Price)**
|
||||
**Sources:**
|
||||
- `flow.vwap_at_signal` - VWAP calculated at signal time (from Python service)
|
||||
- `flow.vwap_lb` - VWAP from SQL query
|
||||
- `flow.vwap` - Fallback
|
||||
|
||||
**Calculation:**
|
||||
- Sum of (price × volume) from RTH open to signal time / Sum of volume
|
||||
- **Note:** VWAP may still come from database/Python service, but entry prices use Yahoo Finance current price
|
||||
|
||||
### 3. **RTH Open (Regular Trading Hours Open)**
|
||||
**Sources (in priority order):**
|
||||
- **`flow.yahooPriceData.open`** - **REAL-TIME from Yahoo Finance API** ⭐ (PRIMARY)
|
||||
- `flow.rth_open` - From database (fallback, may be stale)
|
||||
- Falls back to current price if unavailable
|
||||
|
||||
**Yahoo Finance API:** Returns `regularMarketOpen` for today
|
||||
|
||||
### 4. **Opening Range (OR High/Low)**
|
||||
**Sources (in priority order):**
|
||||
- **`flow.yahooPriceData.high` / `flow.yahooPriceData.low`** - **REAL-TIME from Yahoo Finance API** ⭐ (PRIMARY)
|
||||
- Uses today's high/low as OR levels (more accurate than stale database data)
|
||||
- `flow.or_high` / `flow.or_low` - From database (fallback, may be stale)
|
||||
- Calculated fallback: `currentPrice * 1.01` (high) or `currentPrice * 0.99` (low)
|
||||
|
||||
**Yahoo Finance API:** Returns `regularMarketDayHigh` and `regularMarketDayLow` for today
|
||||
- **Validation:** Only used if within 10% of current price (to avoid stale data)
|
||||
|
||||
### 5. **Prior High**
|
||||
**Sources (in priority order):**
|
||||
- **`flow.yahooPriceData.volumeHistory[1].high`** - **REAL-TIME from Yahoo Finance API** ⭐ (PRIMARY)
|
||||
- Gets yesterday's high from volumeHistory (last 5 days data)
|
||||
- `flow.prior_high` - From database (fallback, may be stale)
|
||||
- `flow.priorHigh` - Alternative field name
|
||||
- **Fallback:** `currentPrice * 1.02` (2% above current)
|
||||
|
||||
**Yahoo Finance API:** Returns volumeHistory with last 5 days including high/low/close
|
||||
- **Validation:** Only used if above current price and within 10% above
|
||||
|
||||
### 6. **Prior Close**
|
||||
**Sources (in priority order):**
|
||||
- **`flow.yahooPriceData.previousClose`** - **REAL-TIME from Yahoo Finance API** ⭐ (PRIMARY)
|
||||
- `flow.prior_close` or `flow.priorClose` - From database (fallback, may be stale)
|
||||
|
||||
**Yahoo Finance API:** Returns `previousClose` for previous trading day
|
||||
|
||||
### 7. **Strike Price**
|
||||
**Sources:**
|
||||
- `flow.strike_num` - Strike price from options flow
|
||||
- `flow.Strike` - Alternative field name
|
||||
- **Validation:** Only used for T3 target if within 20% of current price
|
||||
|
||||
## Calculation Flow
|
||||
|
||||
### Step 1: Determine Signal (BUY/SELL/WAIT)
|
||||
**Function:** `determineSignal(flowData)`
|
||||
- Based on badges (🟢/🔴, 💎, ⭐, 💰, ⚡)
|
||||
- Checks tape alignment
|
||||
- Checks rocket score
|
||||
|
||||
### Step 2: Generate Entry Strategy
|
||||
**Function:** `generateEntryStrategy(flowData, signal)`
|
||||
|
||||
#### For BUY Signals:
|
||||
- **Primary Entry:** VWAP (if available) OR current price
|
||||
- **Aggressive Entry:** OR high (if reasonable) OR current price * 1.005
|
||||
- **Conditional Triggers:**
|
||||
- VWAP reclaim (if price < VWAP)
|
||||
- Prior high break (if price < prior high)
|
||||
- OR breakout (if price < OR high)
|
||||
|
||||
#### For SELL Signals:
|
||||
- **Primary Entry:** VWAP (if available) OR current price
|
||||
- **Aggressive Entry:** OR low (if reasonable) OR current price * 0.995
|
||||
- **Conditional Triggers:**
|
||||
- VWAP rejection (if price > VWAP)
|
||||
- OR breakdown (if price > OR low)
|
||||
|
||||
#### For WAIT Signals:
|
||||
- **Primary Entry:** `null` (no entry yet)
|
||||
- **Conditional Triggers:**
|
||||
- Price breaks prior high (if within 10% above current)
|
||||
- OR breakout (if within 5% above current)
|
||||
- VWAP reclaim (if within 5% of current)
|
||||
- Tape alignment turns positive
|
||||
- Flow resumes (if flow is dead/stale)
|
||||
|
||||
### Step 3: Calculate Stop Loss
|
||||
**Function:** `calculateStopLoss(flow, entryPrice)`
|
||||
|
||||
**Reference Price:**
|
||||
- Uses `entryPrice` if available (for BUY/SELL)
|
||||
- Uses `currentPrice` if no entry (for WAIT signals)
|
||||
|
||||
#### For BULL/BUY Signals:
|
||||
- **Tight Stop:** `referencePrice * 0.985` (-1.5%)
|
||||
- **Wide Stop:**
|
||||
- OR low (if within 5% of reference price) OR
|
||||
- `referencePrice * 0.975` (-2.5%)
|
||||
|
||||
#### For BEAR/SELL Signals:
|
||||
- **Tight Stop:** `referencePrice * 1.015` (+1.5%)
|
||||
- **Wide Stop:**
|
||||
- OR high (if within 5% of reference price) OR
|
||||
- `referencePrice * 1.025` (+2.5%)
|
||||
|
||||
### Step 4: Calculate Profit Targets
|
||||
**Function:** `calculateTargets(flow, entryPrice, signal)`
|
||||
|
||||
**Reference Price:**
|
||||
- Uses `entryPrice` if available
|
||||
- Uses `currentPrice` if no entry (for WAIT signals)
|
||||
|
||||
#### For BUY/BULL Signals:
|
||||
- **T1:** `referencePrice * 1.015` (+1.5%) - Take 50%
|
||||
- **T2:** `referencePrice * 1.025` (+2.5%) - Take 30%
|
||||
- **T3:**
|
||||
- Strike price (if within 20% above reference) OR
|
||||
- `referencePrice * 1.04` (+4%) - Runner 20%
|
||||
|
||||
#### For SELL/BEAR Signals:
|
||||
- **T1:** `referencePrice * 0.985` (-1.5%) - Take 50%
|
||||
- **T2:** `referencePrice * 0.975` (-2.5%) - Take 30%
|
||||
- **T3:**
|
||||
- Strike price (if within 20% below reference) OR
|
||||
- `referencePrice * 0.96` (-4%) - Runner 20%
|
||||
|
||||
### Step 5: Calculate Risk/Reward
|
||||
**Function:** `calculateRiskReward(entry, stop, target)`
|
||||
|
||||
**Formula:**
|
||||
- Risk = `|stop - entry| / entry * 100`
|
||||
- Reward = `|target - entry| / entry * 100`
|
||||
- Ratio = `reward / risk`
|
||||
|
||||
### Step 6: Calculate Readiness (WAIT signals only)
|
||||
**Function:** `calculateReadiness(plan)`
|
||||
|
||||
**Scoring:**
|
||||
- Confidence * 0.6 (60% weight)
|
||||
- Entry strategy exists: +15 points
|
||||
- Stop loss exists: +10 points
|
||||
- Targets exist: +10 points
|
||||
- Reasoning exists: +5 points
|
||||
- **Max:** 100%
|
||||
|
||||
## Key Validations
|
||||
|
||||
### Price Level Validation
|
||||
All price levels are validated against current price to avoid stale data:
|
||||
|
||||
1. **OR High/Low:** Only used if within 10% of current price
|
||||
2. **Prior High:** Only used if above current and within 10% above
|
||||
3. **VWAP:** Only used if within 5% of current price
|
||||
4. **Strike Price:** Only used for T3 if within 20% of current price
|
||||
|
||||
### Fallback Logic
|
||||
If any price data is missing or stale:
|
||||
- Uses current price as base
|
||||
- Calculates percentage-based levels (e.g., +1.5%, -2.5%)
|
||||
- Never uses unreasonable/stale data
|
||||
|
||||
## Example Calculation
|
||||
|
||||
**Given:**
|
||||
- Current Price: $131.81
|
||||
- VWAP: $130.50
|
||||
- OR High: $132.00
|
||||
- OR Low: $130.00
|
||||
- Strike: $155.00 (too far, ignored)
|
||||
- Signal: WAIT (BULL direction)
|
||||
|
||||
**Entry Strategy:**
|
||||
- Primary: `null` (WAIT signal)
|
||||
- Conditional Triggers:
|
||||
- VWAP reclaim at $130.50 (if price < VWAP)
|
||||
- OR breakout at $132.00 (if price < OR high)
|
||||
|
||||
**Stop Loss (using current price $131.81):**
|
||||
- Tight: $131.81 * 0.985 = $129.83 (-1.5%)
|
||||
- Wide: $130.00 (OR low, within 5%)
|
||||
|
||||
**Targets (using current price $131.81):**
|
||||
- T1: $131.81 * 1.015 = $133.79 (+1.5%)
|
||||
- T2: $131.81 * 1.025 = $135.11 (+2.5%)
|
||||
- T3: $131.81 * 1.04 = $137.08 (+4%) - Strike $155 ignored (too far)
|
||||
|
||||
## Data Fetching
|
||||
|
||||
### Yahoo Finance API (PRIMARY SOURCE) ⭐
|
||||
**Service:** `backend/src/services/yahooFinanceService.js`
|
||||
**Function:** `batchFetchYahooFinanceData(symbols)`
|
||||
|
||||
**API Endpoint:**
|
||||
```
|
||||
https://query1.finance.yahoo.com/v8/finance/chart/{symbol}?interval=1d&range=5d
|
||||
```
|
||||
|
||||
**Returns:**
|
||||
- `currentPrice` - Real-time market price
|
||||
- `previousClose` - Previous day's close
|
||||
- `open` - Today's open
|
||||
- `high` - Today's high
|
||||
- `low` - Today's low
|
||||
- `volume` - Today's volume
|
||||
- `volumeHistory` - Last 5 days with high/low/close/volume
|
||||
|
||||
**When Fetched:**
|
||||
- Fetched in `optionsFlow.js` route before trade plan generation
|
||||
- Passed to trade plan generator as `yahooPriceData` or `stockPriceData`
|
||||
- **This is the PRIMARY source** - database is only used as fallback
|
||||
|
||||
### Database Queries (FALLBACK ONLY)
|
||||
**Note:** Database queries are only used if Yahoo Finance data is unavailable
|
||||
|
||||
### Price at Signal Time (Fallback)
|
||||
```sql
|
||||
SELECT close, high, low, volume, ts
|
||||
FROM prices_intraday_1m
|
||||
WHERE UPPER(symbol) = UPPER($1)
|
||||
AND ts <= $2 -- signal time
|
||||
ORDER BY ts DESC
|
||||
LIMIT 1
|
||||
```
|
||||
|
||||
### RTH Open (Fallback)
|
||||
```sql
|
||||
SELECT open, ts
|
||||
FROM prices_intraday_1m
|
||||
WHERE UPPER(symbol) = UPPER($1)
|
||||
AND (timezone('America/Chicago', ts))::date = $2 -- flow date
|
||||
AND (timezone('America/Chicago', ts))::time >= time '09:30:00'
|
||||
ORDER BY ts ASC
|
||||
LIMIT 1
|
||||
```
|
||||
|
||||
### Prior Close (Fallback)
|
||||
```sql
|
||||
SELECT close
|
||||
FROM prices_daily
|
||||
WHERE UPPER(symbol) = UPPER($1)
|
||||
AND "Date" = $2 - INTERVAL '1 day' -- prior day
|
||||
ORDER BY "Date" DESC
|
||||
LIMIT 1
|
||||
```
|
||||
|
||||
## Summary
|
||||
|
||||
**Entry Prices:** Derived from VWAP, OR levels, or current price (from Yahoo Finance)
|
||||
**Stop Loss:** Percentage-based (-1.5% tight, -2.5% wide) with OR level fallback (from Yahoo Finance)
|
||||
**Targets:** Percentage-based (+1.5%, +2.5%, +4%) with strike price option
|
||||
**All calculations:**
|
||||
- **PRIMARY:** Use Yahoo Finance real-time data (currentPrice, open, high, low, previousClose, volumeHistory)
|
||||
- **FALLBACK:** Use database data only if Yahoo Finance unavailable
|
||||
- Validate all levels against current price to avoid stale data
|
||||
|
||||
**Key Change:** Trade plans now use **Yahoo Finance API** as the primary data source for all price calculations, ensuring real-time accuracy instead of stale database data.
|
||||
|
||||
|
|
@ -61,13 +61,13 @@ cp .env.example .env
|
|||
```bash
|
||||
# Development mode (with auto-reload)
|
||||
cd backend/python_service
|
||||
uvicorn main:app --reload --port 8010
|
||||
uvicorn main:app --reload --port 8000
|
||||
|
||||
# Or use the Python script
|
||||
python main.py
|
||||
```
|
||||
|
||||
The service will be available at `http://localhost:8010`
|
||||
The service will be available at `http://localhost:8000`
|
||||
|
||||
### 3. Node.js Backend Setup
|
||||
|
||||
|
|
@ -78,7 +78,7 @@ npm install
|
|||
|
||||
# Configure environment
|
||||
# Add to your .env file:
|
||||
PYTHON_SERVICE_URL=http://localhost:8010
|
||||
PYTHON_SERVICE_URL=http://localhost:8000
|
||||
USE_PYTHON_SERVICE=true # Set to 'false' to disable Python service
|
||||
```
|
||||
|
||||
|
|
@ -110,7 +110,7 @@ LOCAL_DB_NAME=institutional_trader
|
|||
**Node.js Backend (.env in `backend/`):**
|
||||
```env
|
||||
# Python Service Configuration
|
||||
PYTHON_SERVICE_URL=http://localhost:8010
|
||||
PYTHON_SERVICE_URL=http://localhost:8000
|
||||
USE_PYTHON_SERVICE=true # Enable/disable Python service
|
||||
|
||||
# Existing database configuration...
|
||||
|
|
@ -154,10 +154,10 @@ If Python service is unavailable or disabled:
|
|||
|
||||
```bash
|
||||
# Health check
|
||||
curl http://localhost:8010/health
|
||||
curl http://localhost:8000/health
|
||||
|
||||
# Get options flow
|
||||
curl "http://localhost:8010/api/options-flow?start_date=2024-01-01&end_date=2024-01-02"
|
||||
curl "http://localhost:8000/api/options-flow?start_date=2024-01-01&end_date=2024-01-02"
|
||||
```
|
||||
|
||||
### Test Node.js Integration
|
||||
|
|
@ -186,12 +186,12 @@ curl "http://localhost:3010/api/options/flow?startDate=2024-01-01&endDate=2024-0
|
|||
|
||||
1. Check database credentials in `.env`
|
||||
2. Verify PostgreSQL is running
|
||||
3. Check port 8010 is not in use
|
||||
3. Check port 8000 is not in use
|
||||
4. Review Python service logs
|
||||
|
||||
### Node.js Can't Connect to Python Service
|
||||
|
||||
1. Verify Python service is running: `curl http://localhost:8010/health`
|
||||
1. Verify Python service is running: `curl http://localhost:8000/health`
|
||||
2. Check `PYTHON_SERVICE_URL` in Node.js `.env`
|
||||
3. Check firewall/network settings
|
||||
4. Node.js will automatically fallback to SQL if Python unavailable
|
||||
|
|
@ -18,7 +18,7 @@ The hybrid Node.js + Python architecture is now fully integrated and ready for p
|
|||
│ HTTP
|
||||
│ (with fallback)
|
||||
┌──────▼──────────┐
|
||||
│ Python Service │ ← FastAPI service (port 8010)
|
||||
│ Python Service │ ← FastAPI service (port 8000)
|
||||
│ (FastAPI) │ - Data processing
|
||||
│ │ - Complex analytics
|
||||
└──────┬──────────┘
|
||||
|
|
@ -61,7 +61,7 @@ The hybrid Node.js + Python architecture is now fully integrated and ready for p
|
|||
```bash
|
||||
cd backend/python_service
|
||||
pip install -r requirements.txt
|
||||
uvicorn main:app --reload --port 8010
|
||||
uvicorn main:app --reload --port 8000
|
||||
```
|
||||
|
||||
### 2. Start Node.js Backend
|
||||
|
|
@ -89,7 +89,7 @@ curl "http://localhost:3010/api/options/flow?startDate=2024-01-01&endDate=2024-0
|
|||
**Node.js (.env in `backend/`):**
|
||||
```env
|
||||
# Python Service Configuration
|
||||
PYTHON_SERVICE_URL=http://localhost:8010
|
||||
PYTHON_SERVICE_URL=http://localhost:8000
|
||||
USE_PYTHON_SERVICE=true # Set to 'false' to disable
|
||||
|
||||
# Database (existing)
|
||||
|
|
@ -178,7 +178,7 @@ Periodic health checks log status:
|
|||
1. **Test with Python service:**
|
||||
```bash
|
||||
# Start Python service
|
||||
cd backend/python_service && uvicorn main:app --port 8010
|
||||
cd backend/python_service && uvicorn main:app --port 8000
|
||||
|
||||
# Test endpoint
|
||||
curl "http://localhost:3010/api/options/flow"
|
||||
|
|
@ -232,7 +232,7 @@ WORKDIR /app
|
|||
COPY requirements.txt .
|
||||
RUN pip install -r requirements.txt
|
||||
COPY . .
|
||||
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8010"]
|
||||
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
|
||||
```
|
||||
|
||||
```dockerfile
|
||||
|
|
@ -249,7 +249,7 @@ CMD ["npm", "start"]
|
|||
|
||||
### Python Service Not Starting
|
||||
1. Check database credentials
|
||||
2. Verify port 8010 is available
|
||||
2. Verify port 8000 is available
|
||||
3. Check Python dependencies
|
||||
4. Review service logs
|
||||
|
||||
|
|
@ -47,7 +47,7 @@ LOCAL_DB_NAME=institutional_trader
|
|||
### Node.js Backend
|
||||
Add to `backend/.env`:
|
||||
```env
|
||||
PYTHON_SERVICE_URL=http://localhost:8010
|
||||
PYTHON_SERVICE_URL=http://localhost:8000
|
||||
USE_PYTHON_SERVICE=true
|
||||
|
||||
# Your existing database config...
|
||||
|
|
@ -58,20 +58,13 @@ USE_PYTHON_SERVICE=true
|
|||
### Terminal 1: Python Service
|
||||
```bash
|
||||
cd backend/python_service
|
||||
# Activate virtual environment:
|
||||
# Git Bash on Windows:
|
||||
source venv/Scripts/activate
|
||||
# PowerShell/CMD on Windows:
|
||||
venv\Scripts\activate
|
||||
# Linux/macOS:
|
||||
source venv/bin/activate
|
||||
|
||||
uvicorn main:app --reload --port 8010
|
||||
source venv/bin/activate # or venv\Scripts\activate on Windows
|
||||
uvicorn main:app --reload --port 8000
|
||||
```
|
||||
|
||||
You should see:
|
||||
```
|
||||
INFO: Uvicorn running on http://0.0.0.0:8010
|
||||
INFO: Uvicorn running on http://0.0.0.0:8000
|
||||
INFO: Application startup complete.
|
||||
```
|
||||
|
||||
|
|
@ -133,13 +126,13 @@ Check Node.js logs - you should see:
|
|||
### Python Service Won't Start
|
||||
- Check database credentials in `.env`
|
||||
- Verify PostgreSQL is running
|
||||
- Check port 8010 is available
|
||||
- Check port 8000 is available
|
||||
- Review Python service logs
|
||||
|
||||
### Node.js Can't Connect
|
||||
- Verify `PYTHON_SERVICE_URL` in `.env`
|
||||
- Check Python service is running
|
||||
- Test: `curl http://localhost:8010/health`
|
||||
- Test: `curl http://localhost:8000/health`
|
||||
|
||||
### No Data Returned
|
||||
- Check database has data
|
||||
|
|
@ -29,7 +29,7 @@
|
|||
- Log the fallback for monitoring
|
||||
|
||||
**To fix:**
|
||||
1. Start Python service: `cd backend/python_service && uvicorn main:app --port 8010`
|
||||
1. Start Python service: `cd backend/python_service && uvicorn main:app --port 8000`
|
||||
2. Check `PYTHON_SERVICE_URL` in `.env` matches the service URL
|
||||
3. Verify network connectivity
|
||||
|
||||
|
|
@ -1,116 +0,0 @@
|
|||
# Database Date/Time Fix Script
|
||||
|
||||
This script fixes date and time format issues in the `OptionsFlow_monthly` and `options_flow` tables.
|
||||
|
||||
## What It Fixes
|
||||
|
||||
1. **CreatedDate** - Normalizes all dates to `YYYY-MM-DD` format
|
||||
- Converts `MM/DD/YYYY` → `YYYY-MM-DD`
|
||||
- Converts `M/D/YYYY` → `YYYY-MM-DD`
|
||||
- Keeps already valid `YYYY-MM-DD` dates
|
||||
|
||||
2. **CreatedTime** - Normalizes all times to `HH:MM:SS AM/PM` format with consistent spacing
|
||||
- Adds space before AM/PM: `"3:59:59PM"` → `"3:59:59 PM"`
|
||||
- Fixes missing seconds: `"3:59 PM"` → `"3:59:00 PM"`
|
||||
- Fixes missing colon: `"359PM"` → `"3:59:00 PM"`
|
||||
- Fixes missing hour: `":59 AM"` → `"9:59:00 AM"`
|
||||
|
||||
3. **ExpirationDate** - Normalizes to `YYYY-MM-DD` format
|
||||
- Same normalization as CreatedDate
|
||||
|
||||
4. **options_flow table** - Fixes invalid timestamps
|
||||
- Sets NULL for dates outside valid range
|
||||
- Updates invalid `created_at` to current time
|
||||
|
||||
## Usage
|
||||
|
||||
### Option 1: Run SQL File Directly
|
||||
|
||||
```bash
|
||||
# Connect to your PostgreSQL database
|
||||
psql -h localhost -U postgres -d institutional_trader
|
||||
|
||||
# Run the fix script
|
||||
\i backend/database/fix_all_dates_and_times.sql
|
||||
```
|
||||
|
||||
### Option 2: Use Node.js Script
|
||||
|
||||
```bash
|
||||
# Analyze current state (no changes)
|
||||
node backend/scripts/fixDatabaseDates.js analyze
|
||||
|
||||
# Run fixes
|
||||
node backend/scripts/fixDatabaseDates.js fix
|
||||
|
||||
# Verify fixes
|
||||
node backend/scripts/fixDatabaseDates.js verify
|
||||
|
||||
# Run all (analyze, fix, verify)
|
||||
node backend/scripts/fixDatabaseDates.js all
|
||||
```
|
||||
|
||||
### Option 3: Use in Supabase SQL Editor
|
||||
|
||||
1. Open Supabase Dashboard → SQL Editor
|
||||
2. Copy contents of `fix_all_dates_and_times.sql`
|
||||
3. Paste and run in SQL Editor
|
||||
|
||||
## What to Expect
|
||||
|
||||
### Before Fix:
|
||||
```
|
||||
CreatedDate: "12/17/2025", "2025-12-17", "12-17-2025"
|
||||
CreatedTime: "3:59:59PM", "359PM", "3:59 PM", ":59 AM"
|
||||
```
|
||||
|
||||
### After Fix:
|
||||
```
|
||||
CreatedDate: "2025-12-17" (all normalized)
|
||||
CreatedTime: "3:59:59 PM" (all with space, consistent format)
|
||||
```
|
||||
|
||||
## Safety
|
||||
|
||||
- **Read-only analysis first**: Run `analyze` command to see what will be changed
|
||||
- **Non-destructive**: Script only updates format, doesn't delete data
|
||||
- **Idempotent**: Safe to run multiple times (won't change already-correct data)
|
||||
- **Verification**: Includes verification queries to check results
|
||||
|
||||
## Database Requirements
|
||||
|
||||
- PostgreSQL 12+ recommended
|
||||
- Connection to `OptionsFlow_monthly` table
|
||||
- Sufficient permissions for UPDATE operations
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Permission Errors
|
||||
```sql
|
||||
-- Grant necessary permissions
|
||||
GRANT UPDATE ON "OptionsFlow_monthly" TO your_user;
|
||||
```
|
||||
|
||||
### Timeout Errors
|
||||
```sql
|
||||
-- For large tables, increase timeout
|
||||
SET statement_timeout = '10min';
|
||||
```
|
||||
|
||||
### Check Specific Records
|
||||
```sql
|
||||
-- See problematic entries
|
||||
SELECT "CreatedDate", "CreatedTime", "Symbol"
|
||||
FROM "OptionsFlow_monthly"
|
||||
WHERE "CreatedTime"::text !~ '^\d{1,2}:\d{2}(:\d{2})?\s+[AP]M$'
|
||||
AND "CreatedTime" IS NOT NULL
|
||||
LIMIT 100;
|
||||
```
|
||||
|
||||
## Notes
|
||||
|
||||
- Script preserves NULL values (doesn't change them)
|
||||
- Ambiguous formats (like "12 AM" - could be hour or minute) default to hour format
|
||||
- Large tables may take several minutes to update
|
||||
- Recommended to backup database before running on production
|
||||
|
||||
|
|
@ -1,37 +0,0 @@
|
|||
-- Check for CreatedTime entries that might still cause parsing issues
|
||||
-- Specifically looking for times that don't have a space before AM/PM
|
||||
|
||||
-- Find times without space before AM/PM
|
||||
SELECT
|
||||
'Times without space before AM/PM' as issue_type,
|
||||
COUNT(*) as count
|
||||
FROM "OptionsFlow_monthly"
|
||||
WHERE "CreatedTime" IS NOT NULL
|
||||
AND "CreatedTime"::text ~ '[0-9][AP]M$' -- Ends with digit followed by AM/PM (no space)
|
||||
AND "CreatedTime"::text !~ '\s+[AP]M$'; -- Doesn't have space before AM/PM
|
||||
|
||||
-- Show sample of problematic entries
|
||||
SELECT
|
||||
"CreatedDate",
|
||||
"CreatedTime",
|
||||
"Symbol",
|
||||
COUNT(*) as count
|
||||
FROM "OptionsFlow_monthly"
|
||||
WHERE "CreatedTime" IS NOT NULL
|
||||
AND "CreatedTime"::text ~ '[0-9][AP]M$' -- Ends with digit followed by AM/PM (no space)
|
||||
AND "CreatedTime"::text !~ '\s+[AP]M$' -- Doesn't have space before AM/PM
|
||||
GROUP BY "CreatedDate", "CreatedTime", "Symbol"
|
||||
ORDER BY count DESC
|
||||
LIMIT 50;
|
||||
|
||||
-- Check for times with inconsistent spacing patterns
|
||||
SELECT
|
||||
'Detailed spacing analysis' as check_type,
|
||||
COUNT(*) FILTER (WHERE "CreatedTime"::text ~ ':\d{2}:\d{2}[AP]M$') as no_space_seconds,
|
||||
COUNT(*) FILTER (WHERE "CreatedTime"::text ~ ':\d{2}[AP]M$') as no_space_minutes,
|
||||
COUNT(*) FILTER (WHERE "CreatedTime"::text ~ '\d{1,2}[AP]M$') as no_space_hour_only,
|
||||
COUNT(*) FILTER (WHERE "CreatedTime"::text ~ '\s+[AP]M$') as has_space
|
||||
FROM "OptionsFlow_monthly"
|
||||
WHERE "CreatedTime" IS NOT NULL
|
||||
AND "CreatedTime"::text ~* '[AP]M$';
|
||||
|
||||
|
|
@ -1,249 +0,0 @@
|
|||
-- ============================================
|
||||
-- COMPREHENSIVE DATE/TIME FIX SCRIPT
|
||||
-- Fixes all date and time format issues in OptionsFlow_monthly and options_flow tables
|
||||
-- ============================================
|
||||
-- This script:
|
||||
-- 1. Identifies problematic date/time entries
|
||||
-- 2. Normalizes CreatedDate to YYYY-MM-DD format
|
||||
-- 3. Normalizes CreatedTime to HH:MM:SS AM/PM format (with consistent spacing)
|
||||
-- 4. Normalizes ExpirationDate to YYYY-MM-DD format
|
||||
-- 5. Fixes timestamp issues in options_flow table
|
||||
-- ============================================
|
||||
|
||||
-- ============================================
|
||||
-- STEP 1: ANALYSIS - Identify problematic entries
|
||||
-- ============================================
|
||||
|
||||
-- Check CreatedDate formats
|
||||
SELECT
|
||||
'CreatedDate Analysis' as check_type,
|
||||
COUNT(*) FILTER (WHERE "CreatedDate"::text ~ '^\d{4}-\d{2}-\d{2}$') as valid_iso_date,
|
||||
COUNT(*) FILTER (WHERE "CreatedDate"::text ~ '^\d{1,2}/\d{1,2}/\d{2,4}$') as slash_format,
|
||||
COUNT(*) FILTER (WHERE "CreatedDate" IS NOT NULL AND "CreatedDate"::text !~ '^\d{4}-\d{2}-\d{2}$' AND "CreatedDate"::text !~ '^\d{1,2}/\d{1,2}/\d{2,4}$') as invalid_format,
|
||||
COUNT(*) FILTER (WHERE "CreatedDate" IS NULL) as null_dates
|
||||
FROM "OptionsFlow_monthly";
|
||||
|
||||
-- Check CreatedTime formats
|
||||
SELECT
|
||||
'CreatedTime Analysis' as check_type,
|
||||
COUNT(*) FILTER (WHERE "CreatedTime"::text ~ '^\d{1,2}:\d{2}:\d{2}\s+[AP]M$') as valid_with_space,
|
||||
COUNT(*) FILTER (WHERE "CreatedTime"::text ~ '^\d{1,2}:\d{2}:\d{2}[AP]M$') as valid_no_space,
|
||||
COUNT(*) FILTER (WHERE "CreatedTime"::text ~ '^\d{1,2}:\d{2}\s+[AP]M$') as valid_minutes_only_space,
|
||||
COUNT(*) FILTER (WHERE "CreatedTime"::text ~ '^\d{1,2}:\d{2}[AP]M$') as valid_minutes_only_no_space,
|
||||
COUNT(*) FILTER (WHERE "CreatedTime" IS NOT NULL AND "CreatedTime"::text !~* '(AM|PM)') as no_ampm,
|
||||
COUNT(*) FILTER (WHERE "CreatedTime"::text ~ '^\d{1,2}\s*[AP]M$' AND "CreatedTime"::text !~ ':') as missing_colon,
|
||||
COUNT(*) FILTER (WHERE "CreatedTime" IS NULL) as null_times
|
||||
FROM "OptionsFlow_monthly";
|
||||
|
||||
-- Check ExpirationDate formats
|
||||
SELECT
|
||||
'ExpirationDate Analysis' as check_type,
|
||||
COUNT(*) FILTER (WHERE "ExpirationDate"::text ~ '^\d{4}-\d{2}-\d{2}$') as valid_iso_date,
|
||||
COUNT(*) FILTER (WHERE "ExpirationDate"::text ~ '^\d{1,2}/\d{1,2}/\d{2,4}$') as slash_format,
|
||||
COUNT(*) FILTER (WHERE "ExpirationDate" IS NOT NULL AND "ExpirationDate"::text !~ '^\d{4}-\d{2}-\d{2}$' AND "ExpirationDate"::text !~ '^\d{1,2}/\d{1,2}/\d{2,4}$') as invalid_format,
|
||||
COUNT(*) FILTER (WHERE "ExpirationDate" IS NULL) as null_dates
|
||||
FROM "OptionsFlow_monthly";
|
||||
|
||||
-- ============================================
|
||||
-- STEP 2: FIX CreatedDate - Normalize to YYYY-MM-DD
|
||||
-- ============================================
|
||||
|
||||
UPDATE "OptionsFlow_monthly"
|
||||
SET "CreatedDate" =
|
||||
CASE
|
||||
-- Already in YYYY-MM-DD format - keep as-is
|
||||
WHEN "CreatedDate"::text ~ '^\d{4}-\d{2}-\d{2}$'
|
||||
THEN "CreatedDate"::text
|
||||
-- MM/DD/YYYY format - convert to YYYY-MM-DD
|
||||
WHEN "CreatedDate"::text ~ '^(\d{1,2})/(\d{1,2})/(\d{2,4})$'
|
||||
THEN
|
||||
TO_CHAR(
|
||||
to_date("CreatedDate"::text, 'MM/DD/YYYY'),
|
||||
'YYYY-MM-DD'
|
||||
)
|
||||
-- M/D/YYYY format - convert to YYYY-MM-DD
|
||||
WHEN "CreatedDate"::text ~ '^(\d{1,2})/(\d{1,2})/(\d{4})$'
|
||||
THEN
|
||||
TO_CHAR(
|
||||
to_date("CreatedDate"::text, 'MM/DD/YYYY'),
|
||||
'YYYY-MM-DD'
|
||||
)
|
||||
-- Try to parse as date and reformat
|
||||
ELSE
|
||||
CASE
|
||||
WHEN to_date("CreatedDate"::text, 'YYYY-MM-DD') IS NOT NULL
|
||||
THEN TO_CHAR(to_date("CreatedDate"::text, 'YYYY-MM-DD'), 'YYYY-MM-DD')
|
||||
WHEN to_date("CreatedDate"::text, 'MM/DD/YYYY') IS NOT NULL
|
||||
THEN TO_CHAR(to_date("CreatedDate"::text, 'MM/DD/YYYY'), 'YYYY-MM-DD')
|
||||
ELSE "CreatedDate"::text -- Keep original if can't parse
|
||||
END
|
||||
END
|
||||
WHERE "CreatedDate" IS NOT NULL
|
||||
AND "CreatedDate"::text !~ '^\d{4}-\d{2}-\d{2}$'; -- Only update if not already correct
|
||||
|
||||
-- ============================================
|
||||
-- STEP 3: FIX CreatedTime - Normalize spacing and format
|
||||
-- ============================================
|
||||
|
||||
UPDATE "OptionsFlow_monthly"
|
||||
SET "CreatedTime" =
|
||||
CASE
|
||||
-- Already properly formatted with space: "HH:MM:SS AM" or "H:MM:SS AM"
|
||||
WHEN "CreatedTime"::text ~ '^\d{1,2}:\d{2}:\d{2}\s+[AP]M$'
|
||||
THEN "CreatedTime"::text -- Keep as-is
|
||||
|
||||
-- Missing space before AM/PM: "HH:MM:SSAM" -> "HH:MM:SS AM"
|
||||
WHEN "CreatedTime"::text ~ '^(\d{1,2}:\d{2}:\d{2})([AP]M)$'
|
||||
THEN REGEXP_REPLACE("CreatedTime"::text, '^(\d{1,2}:\d{2}:\d{2})([AP]M)$', '\1 \2', 'i')
|
||||
|
||||
-- Minutes only with space: "HH:MM AM" -> "HH:MM:00 AM"
|
||||
WHEN "CreatedTime"::text ~ '^(\d{1,2}:\d{2})\s+([AP]M)$'
|
||||
THEN REGEXP_REPLACE("CreatedTime"::text, '^(\d{1,2}:\d{2})\s+([AP]M)$', '\1:00 \2', 'i')
|
||||
|
||||
-- Minutes only without space: "HH:MMAM" -> "HH:MM:00 AM"
|
||||
WHEN "CreatedTime"::text ~ '^(\d{1,2}:\d{2})([AP]M)$'
|
||||
THEN REGEXP_REPLACE("CreatedTime"::text, '^(\d{1,2}:\d{2})([AP]M)$', '\1:00 \2', 'i')
|
||||
|
||||
-- Just hour with space: "H AM" or "HH AM" -> "H:00:00 AM" or "HH:00:00 AM"
|
||||
WHEN "CreatedTime"::text ~ '^(\d{1,2})\s+([AP]M)$'
|
||||
THEN LPAD(REGEXP_REPLACE("CreatedTime"::text, '^(\d{1,2})\s+([AP]M)$', '\1', 'i'), 2, '0') || ':00:00 ' ||
|
||||
UPPER(REGEXP_REPLACE("CreatedTime"::text, '^(\d{1,2})\s+([AP]M)$', '\2', 'i'))
|
||||
|
||||
-- Just hour without space: "HAM" or "HHAM" -> "H:00:00 AM" or "HH:00:00 AM"
|
||||
WHEN "CreatedTime"::text ~ '^(\d{1,2})([AP]M)$'
|
||||
THEN LPAD(REGEXP_REPLACE("CreatedTime"::text, '^(\d{1,2})([AP]M)$', '\1', 'i'), 2, '0') || ':00:00 ' ||
|
||||
UPPER(REGEXP_REPLACE("CreatedTime"::text, '^(\d{1,2})([AP]M)$', '\2', 'i'))
|
||||
|
||||
-- Single digit hour with colon: ":MM AM" -> "H:MM:00 AM" (default to 9 AM or 1 PM)
|
||||
WHEN "CreatedTime"::text ~ '^:(\d{1,2})\s+([AP]M)$'
|
||||
THEN
|
||||
CASE
|
||||
WHEN UPPER(REGEXP_REPLACE("CreatedTime"::text, '^:(\d{1,2})\s+([AP]M)$', '\2', 'i')) = 'AM'
|
||||
THEN '9:' || LPAD(REGEXP_REPLACE("CreatedTime"::text, '^:(\d{1,2})\s+([AP]M)$', '\1', 'i'), 2, '0') || ':00 AM'
|
||||
ELSE '1:' || LPAD(REGEXP_REPLACE("CreatedTime"::text, '^:(\d{1,2})\s+([AP]M)$', '\1', 'i'), 2, '0') || ':00 PM'
|
||||
END
|
||||
|
||||
-- Missing colon, just number and AM/PM (e.g., "34AM", "12PM")
|
||||
-- If > 12, assume it's minutes; otherwise could be hour or minute
|
||||
WHEN "CreatedTime"::text ~ '^(\d{2})([AP]M)$' AND (REGEXP_REPLACE("CreatedTime"::text, '^(\d{2})([AP]M)$', '\1', 'i'))::int > 12
|
||||
THEN
|
||||
CASE
|
||||
WHEN UPPER(REGEXP_REPLACE("CreatedTime"::text, '^(\d{2})([AP]M)$', '\2', 'i')) = 'AM'
|
||||
THEN '9:' || REGEXP_REPLACE("CreatedTime"::text, '^(\d{2})([AP]M)$', '\1', 'i') || ':00 AM'
|
||||
ELSE '1:' || REGEXP_REPLACE("CreatedTime"::text, '^(\d{2})([AP]M)$', '\1', 'i') || ':00 PM'
|
||||
END
|
||||
|
||||
-- Single or double digit <= 12 with AM/PM - ambiguous, default to hour
|
||||
WHEN "CreatedTime"::text ~ '^(\d{1,2})([AP]M)$' AND (REGEXP_REPLACE("CreatedTime"::text, '^(\d{1,2})([AP]M)$', '\1', 'i'))::int <= 12
|
||||
THEN LPAD(REGEXP_REPLACE("CreatedTime"::text, '^(\d{1,2})([AP]M)$', '\1', 'i'), 2, '0') || ':00:00 ' ||
|
||||
UPPER(REGEXP_REPLACE("CreatedTime"::text, '^(\d{1,2})([AP]M)$', '\2', 'i'))
|
||||
|
||||
-- Keep as-is if doesn't match any pattern
|
||||
ELSE "CreatedTime"::text
|
||||
END
|
||||
WHERE "CreatedTime" IS NOT NULL
|
||||
AND (
|
||||
-- Update if missing space before AM/PM
|
||||
"CreatedTime"::text ~ '^\d{1,2}:\d{2}(:[0-9]{2})?[AP]M$'
|
||||
-- Or if missing colon or seconds
|
||||
OR ("CreatedTime"::text ~ '^\d{1,2}\s*[AP]M$' AND "CreatedTime"::text !~ ':')
|
||||
OR "CreatedTime"::text ~ '^:\d{1,2}\s*[AP]M$'
|
||||
OR ("CreatedTime"::text ~ '^\d{1,2}:\d{2}\s*[AP]M$' AND "CreatedTime"::text !~ ':\d{2}\s*[AP]M$')
|
||||
);
|
||||
|
||||
-- ============================================
|
||||
-- STEP 4: FIX ExpirationDate - Normalize to YYYY-MM-DD
|
||||
-- ============================================
|
||||
|
||||
UPDATE "OptionsFlow_monthly"
|
||||
SET "ExpirationDate" =
|
||||
CASE
|
||||
-- Already in YYYY-MM-DD format - keep as-is
|
||||
WHEN "ExpirationDate"::text ~ '^\d{4}-\d{2}-\d{2}$'
|
||||
THEN "ExpirationDate"::text
|
||||
-- MM/DD/YYYY format - convert to YYYY-MM-DD
|
||||
WHEN "ExpirationDate"::text ~ '^(\d{1,2})/(\d{1,2})/(\d{2,4})$'
|
||||
THEN
|
||||
TO_CHAR(
|
||||
to_date("ExpirationDate"::text, 'MM/DD/YYYY'),
|
||||
'YYYY-MM-DD'
|
||||
)
|
||||
-- Try to parse as date and reformat
|
||||
ELSE
|
||||
CASE
|
||||
WHEN to_date("ExpirationDate"::text, 'YYYY-MM-DD') IS NOT NULL
|
||||
THEN TO_CHAR(to_date("ExpirationDate"::text, 'YYYY-MM-DD'), 'YYYY-MM-DD')
|
||||
WHEN to_date("ExpirationDate"::text, 'MM/DD/YYYY') IS NOT NULL
|
||||
THEN TO_CHAR(to_date("ExpirationDate"::text, 'MM/DD/YYYY'), 'YYYY-MM-DD')
|
||||
ELSE "ExpirationDate"::text -- Keep original if can't parse
|
||||
END
|
||||
END
|
||||
WHERE "ExpirationDate" IS NOT NULL
|
||||
AND "ExpirationDate"::text !~ '^\d{4}-\d{2}-\d{2}$'; -- Only update if not already correct
|
||||
|
||||
-- ============================================
|
||||
-- STEP 5: FIX options_flow table timestamps (if they exist)
|
||||
-- ============================================
|
||||
|
||||
-- Fix expiration dates that might be NULL or invalid
|
||||
UPDATE options_flow
|
||||
SET expiration = NULL
|
||||
WHERE expiration IS NOT NULL
|
||||
AND (expiration < '1970-01-01'::timestamp OR expiration > '2100-01-01'::timestamp);
|
||||
|
||||
-- Fix created_at dates that might be invalid
|
||||
UPDATE options_flow
|
||||
SET created_at = NOW()
|
||||
WHERE created_at IS NOT NULL
|
||||
AND (created_at < '1970-01-01'::timestamp OR created_at > '2100-01-01'::timestamp);
|
||||
|
||||
-- ============================================
|
||||
-- STEP 6: VERIFICATION - Check results
|
||||
-- ============================================
|
||||
|
||||
-- Verify CreatedDate fixes
|
||||
SELECT
|
||||
'CreatedDate Verification' as check_type,
|
||||
COUNT(*) FILTER (WHERE "CreatedDate"::text ~ '^\d{4}-\d{2}-\d{2}$') as valid_format,
|
||||
COUNT(*) FILTER (WHERE "CreatedDate" IS NOT NULL AND "CreatedDate"::text !~ '^\d{4}-\d{2}-\d{2}$') as still_invalid,
|
||||
COUNT(*) FILTER (WHERE "CreatedDate" IS NULL) as null_count
|
||||
FROM "OptionsFlow_monthly";
|
||||
|
||||
-- Verify CreatedTime fixes
|
||||
SELECT
|
||||
'CreatedTime Verification' as check_type,
|
||||
COUNT(*) FILTER (WHERE "CreatedTime"::text ~ '^\d{1,2}:\d{2}:\d{2}\s+[AP]M$') as valid_format,
|
||||
COUNT(*) FILTER (WHERE "CreatedTime"::text ~ '^\d{1,2}:\d{2}\s+[AP]M$') as minutes_only_valid,
|
||||
COUNT(*) FILTER (WHERE "CreatedTime" IS NOT NULL AND "CreatedTime"::text !~ '^\d{1,2}:\d{2}(:\d{2})?\s+[AP]M$') as still_invalid,
|
||||
COUNT(*) FILTER (WHERE "CreatedTime" IS NULL) as null_count
|
||||
FROM "OptionsFlow_monthly";
|
||||
|
||||
-- Verify ExpirationDate fixes
|
||||
SELECT
|
||||
'ExpirationDate Verification' as check_type,
|
||||
COUNT(*) FILTER (WHERE "ExpirationDate"::text ~ '^\d{4}-\d{2}-\d{2}$') as valid_format,
|
||||
COUNT(*) FILTER (WHERE "ExpirationDate" IS NOT NULL AND "ExpirationDate"::text !~ '^\d{4}-\d{2}-\d{2}$') as still_invalid,
|
||||
COUNT(*) FILTER (WHERE "ExpirationDate" IS NULL) as null_count
|
||||
FROM "OptionsFlow_monthly";
|
||||
|
||||
-- Show sample of fixed entries
|
||||
SELECT
|
||||
"CreatedDate",
|
||||
"CreatedTime",
|
||||
"ExpirationDate",
|
||||
"Symbol",
|
||||
COUNT(*) as count
|
||||
FROM "OptionsFlow_monthly"
|
||||
WHERE "CreatedDate"::text ~ '^\d{4}-\d{2}-\d{2}$'
|
||||
AND ("CreatedTime"::text ~ '^\d{1,2}:\d{2}(:\d{2})?\s+[AP]M$' OR "CreatedTime" IS NULL)
|
||||
GROUP BY "CreatedDate", "CreatedTime", "ExpirationDate", "Symbol"
|
||||
ORDER BY "CreatedDate" DESC, "CreatedTime" DESC
|
||||
LIMIT 20;
|
||||
|
||||
-- ============================================
|
||||
-- SUMMARY
|
||||
-- ============================================
|
||||
SELECT
|
||||
'✅ Date/Time Fix Complete' as status,
|
||||
'Run the verification queries above to check results' as next_step;
|
||||
|
||||
|
|
@ -147,19 +147,13 @@ base AS (
|
|||
-- AND ofm."Symbol" NOT IN ('TSLA','NVDA') -- your panel excludes these
|
||||
),
|
||||
|
||||
/* 2) Window restriction (CST date window) and compute UTC + DTE */
|
||||
/* 2) Window restriction (CST date window) and compute UTC */
|
||||
flow AS (
|
||||
SELECT
|
||||
b.*,
|
||||
(b.flow_ts_local)::date AS flow_date_cst,
|
||||
/* Convert local CST clock to a real point in time (timestamptz) */
|
||||
(b.flow_ts_local AT TIME ZONE 'America/Chicago') AS flow_ts_utc,
|
||||
/* Days to Expiration (DTE) */
|
||||
CASE
|
||||
WHEN b.exp_date IS NOT NULL AND (b.flow_ts_local)::date IS NOT NULL
|
||||
THEN (b.exp_date - (b.flow_ts_local)::date)
|
||||
ELSE NULL
|
||||
END AS dte
|
||||
(b.flow_ts_local AT TIME ZONE 'America/Chicago') AS flow_ts_utc
|
||||
FROM base b
|
||||
WHERE (b.flow_ts_local)::date BETWEEN (SELECT start_day FROM cfg) AND (SELECT end_day FROM cfg)
|
||||
),
|
||||
|
|
@ -232,18 +226,6 @@ agg AS (
|
|||
SUM(CASE WHEN cp_norm='PUT' AND side_norm='BUY' AND moneyness='OTM' THEN oi_num ELSE 0 END)
|
||||
OVER (PARTITION BY exp_date, symbol_norm ORDER BY flow_ts_utc, rid) AS oi_pb_otm,
|
||||
|
||||
/* UNKNOWN SIDE tracking - premium and count for flows where direction is NULL (running sum) */
|
||||
SUM(CASE WHEN direction IS NULL THEN premium_num ELSE 0 END)
|
||||
OVER (PARTITION BY exp_date, symbol_norm ORDER BY flow_ts_utc, rid) AS prem_unknown_sym,
|
||||
COUNT(*) FILTER (WHERE direction IS NULL)
|
||||
OVER (PARTITION BY exp_date, symbol_norm ORDER BY flow_ts_utc, rid) AS cnt_unknown_sym,
|
||||
|
||||
/* Total premium by symbol/expiration for rule-based rockets (total sum, not running) */
|
||||
SUM(premium_num) OVER (PARTITION BY exp_date, symbol_norm) AS prem_total_sym,
|
||||
/* Directional premium totals by symbol/expiration (for rule-based rockets) */
|
||||
SUM(CASE WHEN direction='BULL' THEN premium_num ELSE 0 END) OVER (PARTITION BY exp_date, symbol_norm) AS prem_bull_sym,
|
||||
SUM(CASE WHEN direction='BEAR' THEN premium_num ELSE 0 END) OVER (PARTITION BY exp_date, symbol_norm) AS prem_bear_sym,
|
||||
|
||||
SUM(1) OVER (PARTITION BY exp_date, symbol_norm, direction ORDER BY flow_ts_utc, rid) AS dir_count
|
||||
FROM mny m
|
||||
),
|
||||
|
|
@ -548,59 +530,35 @@ scored AS (
|
|||
WHEN fe.cp_norm='PUT' AND fe.strike_num < fe.spot_num THEN 1
|
||||
ELSE 0 END
|
||||
+ 0.5 * fe.tape_alignment
|
||||
AS rocket_score,
|
||||
/* Rule-based rockets (SQLite style) */
|
||||
CASE
|
||||
WHEN fe.premium_num >= 250000
|
||||
AND ABS(fe.prem_bull_sym - fe.prem_bear_sym) / NULLIF(fe.prem_total_sym, 0) >= 0.6
|
||||
AND COALESCE(fe.vol_num, 0) > COALESCE(fe.oi_num, 0)
|
||||
AND fe.tape_alignment = 1
|
||||
THEN '🚀🚀🚀'
|
||||
WHEN fe.premium_num >= 150000
|
||||
AND fe.tape_alignment = 1
|
||||
THEN '🚀🚀'
|
||||
WHEN fe.premium_num >= 80000
|
||||
AND fe.tape_alignment = 1
|
||||
THEN '🚀'
|
||||
ELSE NULL
|
||||
END AS rocket_rule
|
||||
AS rocket_score
|
||||
FROM flow_enriched fe
|
||||
),
|
||||
rocket2 AS (
|
||||
SELECT
|
||||
s.*,
|
||||
/* rocket label core - prefer rule-based, fallback to score-based, then original rocket */
|
||||
COALESCE(
|
||||
s.rocket_rule,
|
||||
CASE
|
||||
WHEN s.rocket_score >= 5.0 THEN '🚀🚀🚀'
|
||||
WHEN s.rocket_score >= 3.5 THEN '🚀🚀'
|
||||
WHEN s.rocket_score >= 2.0 THEN '🚀'
|
||||
ELSE COALESCE(s.rocket, '')
|
||||
END
|
||||
) AS rocket_base,
|
||||
/* rocket label core */
|
||||
CASE
|
||||
WHEN s.rocket_score >= 5.0 THEN '🚀🚀🚀'
|
||||
WHEN s.rocket_score >= 3.5 THEN '🚀🚀'
|
||||
WHEN s.rocket_score >= 2.0 THEN '🚀'
|
||||
ELSE COALESCE(s.rocket, '')
|
||||
END AS rocket_base,
|
||||
/* append [ITM/OTM %] */
|
||||
CASE
|
||||
WHEN s.mny_pct IS NULL THEN
|
||||
COALESCE(
|
||||
s.rocket_rule,
|
||||
CASE
|
||||
WHEN s.rocket_score >= 5.0 THEN '🚀🚀🚀'
|
||||
WHEN s.rocket_score >= 3.5 THEN '🚀🚀'
|
||||
WHEN s.rocket_score >= 2.0 THEN '🚀'
|
||||
ELSE COALESCE(s.rocket, '')
|
||||
END
|
||||
)
|
||||
(CASE
|
||||
WHEN s.rocket_score >= 5.0 THEN '🚀🚀🚀'
|
||||
WHEN s.rocket_score >= 3.5 THEN '🚀🚀'
|
||||
WHEN s.rocket_score >= 2.0 THEN '🚀'
|
||||
ELSE COALESCE(s.rocket, '')
|
||||
END)
|
||||
ELSE
|
||||
COALESCE(
|
||||
s.rocket_rule,
|
||||
CASE
|
||||
WHEN s.rocket_score >= 5.0 THEN '🚀🚀🚀'
|
||||
WHEN s.rocket_score >= 3.5 THEN '🚀🚀'
|
||||
WHEN s.rocket_score >= 2.0 THEN '🚀'
|
||||
ELSE COALESCE(s.rocket, '')
|
||||
END
|
||||
)
|
||||
(CASE
|
||||
WHEN s.rocket_score >= 5.0 THEN '🚀🚀🚀'
|
||||
WHEN s.rocket_score >= 3.5 THEN '🚀🚀'
|
||||
WHEN s.rocket_score >= 2.0 THEN '🚀'
|
||||
ELSE COALESCE(s.rocket, '')
|
||||
END)
|
||||
|| ' [' || CASE WHEN s.mny_pct >= 0 THEN 'ITM ' ELSE 'OTM ' END
|
||||
|| TRIM(TO_CHAR(ABS(s.mny_pct)::numeric, 'FM999990.0')) || '%]'
|
||||
END AS Rocket_with_mny
|
||||
|
|
@ -658,11 +616,6 @@ SELECT
|
|||
|
||||
fe."ExpirationDate", fe."Price", fe."CallPut", fe."Side", fe."Strike", fe."Spot", fe."Volume", fe."OI",
|
||||
|
||||
/* DTE and unknown-side tracking */
|
||||
fe.dte AS "DTE",
|
||||
fe.prem_unknown_sym AS "PremUnknownSym",
|
||||
fe.cnt_unknown_sym AS "CntUnknownSym",
|
||||
|
||||
/* trader-facing context — now meaningful */
|
||||
fe.pct_vs_prior_close AS "PctVsPriorClose",
|
||||
fe.pct_vs_rth_open AS "PctVsRthOpen",
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@
|
|||
--
|
||||
-- Usage:
|
||||
-- For Supabase: Run in Supabase SQL Editor
|
||||
-- For Remote PostgreSQL: psql -h 100.121.163.23 -p 5432 -U postgres -d institutional_trader < setup_friend_access.sql
|
||||
-- For Local PostgreSQL: sudo -u postgres psql institutional_trader < setup_friend_access.sql
|
||||
--
|
||||
-- IMPORTANT: Replace 'friend_user' and 'secure_password_here' with actual values!
|
||||
|
|
@ -126,13 +127,13 @@ ORDER BY grantee, table_name;
|
|||
-- Connection Strings for Friend
|
||||
-- ============================================
|
||||
|
||||
-- For Remote PostgreSQL (100.121.163.23):
|
||||
-- postgresql://friend_user:secure_password_here@100.121.163.23:5432/institutional_trader
|
||||
--
|
||||
-- For Supabase:
|
||||
-- postgresql://friend_user:secure_password_here@db.[project-ref].supabase.co:5432/postgres
|
||||
--
|
||||
-- For Local PostgreSQL:
|
||||
-- postgresql://friend_user:secure_password_here@your-server-ip:5432/institutional_trader
|
||||
--
|
||||
-- For SSH Tunnel (most secure):
|
||||
-- 1. Friend runs: ssh -L 5432:localhost:5432 user@your-server-ip
|
||||
-- 1. Friend runs: ssh -L 5432:100.121.163.23:5432 user@your-proxmox-server-ip
|
||||
-- 2. Friend connects to: postgresql://friend_user:password@localhost:5432/institutional_trader
|
||||
|
||||
|
|
|
|||
|
|
@ -10,8 +10,20 @@ SUPABASE_ANON_KEY=your_production_anon_key
|
|||
SUPABASE_SERVICE_KEY=your_service_role_key
|
||||
|
||||
# Direct PostgreSQL Connection (optional, for heavy queries)
|
||||
# Option 1: Remote PostgreSQL Database
|
||||
DATABASE_URL=postgresql://postgres:[password]@100.121.163.23:5432/institutional_trader
|
||||
|
||||
# Option 2: Supabase Database
|
||||
# Get this from: Supabase Dashboard > Settings > Database > Connection String (URI)
|
||||
DATABASE_URL=postgresql://postgres:[password]@db.[project-ref].supabase.co:5432/postgres
|
||||
# DATABASE_URL=postgresql://postgres:[password]@db.[project-ref].supabase.co:5432/postgres
|
||||
|
||||
# Option 3: Use LOCAL_DB variables (for remote database)
|
||||
USE_LOCAL_DB=true
|
||||
LOCAL_DB_HOST=100.121.163.23
|
||||
LOCAL_DB_PORT=5432
|
||||
LOCAL_DB_USER=postgres
|
||||
LOCAL_DB_PASSWORD=your_postgres_password
|
||||
LOCAL_DB_NAME=institutional_trader
|
||||
|
||||
# CORS Configuration
|
||||
CORS_ORIGIN=https://your-frontend-domain.com
|
||||
|
|
|
|||
|
|
@ -19,13 +19,10 @@
|
|||
"express-rate-limit": "^7.1.5",
|
||||
"express-ws": "^5.0.2",
|
||||
"helmet": "^7.1.0",
|
||||
"ml-logistic-regression": "^2.0.0",
|
||||
"ml-matrix": "^6.13.0",
|
||||
"node-cache": "^5.1.2",
|
||||
"node-fetch": "^3.3.2",
|
||||
"pg": "^8.11.3",
|
||||
"ws": "^8.14.2",
|
||||
"xml2js": "^0.6.2"
|
||||
"ws": "^8.14.2"
|
||||
},
|
||||
"devDependencies": {
|
||||
"nodemon": "^3.0.1"
|
||||
|
|
@ -566,7 +563,6 @@
|
|||
"resolved": "https://registry.npmjs.org/express/-/express-4.22.1.tgz",
|
||||
"integrity": "sha512-F2X8g9P1X7uCPZMA3MVf9wcTqlyNp7IhH5qPCI0izhaOIYXaW9L535tGA3qmjRzpH+bZczqq7hVKxTR4NWnu+g==",
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"accepts": "~1.3.8",
|
||||
"array-flatten": "1.1.1",
|
||||
|
|
@ -935,12 +931,6 @@
|
|||
"node": ">= 0.10"
|
||||
}
|
||||
},
|
||||
"node_modules/is-any-array": {
|
||||
"version": "3.0.0",
|
||||
"resolved": "https://registry.npmjs.org/is-any-array/-/is-any-array-3.0.0.tgz",
|
||||
"integrity": "sha512-o4h+tylWykC4BD1vaejp6gDxoM13bwW8FGuNs4yIKpj8xbBJcRxJx8vZpq0dCr7ZDEfeKjmsi/euolKhX6f/ww==",
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/is-binary-path": {
|
||||
"version": "2.1.0",
|
||||
"resolved": "https://registry.npmjs.org/is-binary-path/-/is-binary-path-2.1.0.tgz",
|
||||
|
|
@ -1082,54 +1072,6 @@
|
|||
"node": "*"
|
||||
}
|
||||
},
|
||||
"node_modules/ml-array-max": {
|
||||
"version": "2.0.0",
|
||||
"resolved": "https://registry.npmjs.org/ml-array-max/-/ml-array-max-2.0.0.tgz",
|
||||
"integrity": "sha512-QQZ4kENwpWmyNb98UXRDFXrmtIXuXtt1+bSbda/2KA85+F+rrJP8hZk6QOkCQXM2Th9mUDYdq/PNByPdT9ID4A==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"is-any-array": "^3.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/ml-array-min": {
|
||||
"version": "2.0.0",
|
||||
"resolved": "https://registry.npmjs.org/ml-array-min/-/ml-array-min-2.0.0.tgz",
|
||||
"integrity": "sha512-GRj6Ky6sW9vGL6yIjgsHmXZ9YgrdmcQ8nCxPqEGeKc6dkfYg1XDYxGFxADUjNuZyoCd5PUscWAS4N+cFaX6hFg==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"is-any-array": "^3.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/ml-array-rescale": {
|
||||
"version": "2.0.0",
|
||||
"resolved": "https://registry.npmjs.org/ml-array-rescale/-/ml-array-rescale-2.0.0.tgz",
|
||||
"integrity": "sha512-2GGtKfSno94/kIloWGvpp/U5Q5vLvLrza+SAaGsLeo6Xj4mEbA6Gqx+oTfZFkxnd1grT2X007HfJNs3T5BsiVg==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"is-any-array": "^3.0.0",
|
||||
"ml-array-max": "^2.0.0",
|
||||
"ml-array-min": "^2.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/ml-logistic-regression": {
|
||||
"version": "2.0.0",
|
||||
"resolved": "https://registry.npmjs.org/ml-logistic-regression/-/ml-logistic-regression-2.0.0.tgz",
|
||||
"integrity": "sha512-xHhB91ut8GRRbJyB1ZQfKsl1MHmE1PqMeRjxhks96M5BGvCbC9eEojf4KgRMKM2LxFblhVUcVzweAoPB48Nt0A==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"ml-matrix": "^6.5.0"
|
||||
}
|
||||
},
|
||||
"node_modules/ml-matrix": {
|
||||
"version": "6.13.0",
|
||||
"resolved": "https://registry.npmjs.org/ml-matrix/-/ml-matrix-6.13.0.tgz",
|
||||
"integrity": "sha512-QpV0UTUkglg6vPUgThKGBEtit2ac6habSoZ33bwI9rU0UHZLqw6G3ukTIE8zWiUF3sjK8YAlhx/o/b9layzH8A==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"is-any-array": "^3.0.0",
|
||||
"ml-array-rescale": "^2.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/ms": {
|
||||
"version": "2.0.0",
|
||||
"resolved": "https://registry.npmjs.org/ms/-/ms-2.0.0.tgz",
|
||||
|
|
@ -1321,7 +1263,6 @@
|
|||
"resolved": "https://registry.npmjs.org/pg/-/pg-8.16.3.tgz",
|
||||
"integrity": "sha512-enxc1h0jA/aq5oSDMvqyW3q89ra6XIIDZgCX9vkMrnz5DFTw/Ny3Li2lFQ+pt3L6MCgm/5o2o8HW9hiJji+xvw==",
|
||||
"license": "MIT",
|
||||
"peer": true,
|
||||
"dependencies": {
|
||||
"pg-connection-string": "^2.9.1",
|
||||
"pg-pool": "^3.10.1",
|
||||
|
|
@ -1556,15 +1497,6 @@
|
|||
"integrity": "sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg==",
|
||||
"license": "MIT"
|
||||
},
|
||||
"node_modules/sax": {
|
||||
"version": "1.6.0",
|
||||
"resolved": "https://registry.npmjs.org/sax/-/sax-1.6.0.tgz",
|
||||
"integrity": "sha512-6R3J5M4AcbtLUdZmRv2SygeVaM7IhrLXu9BmnOGmmACak8fiUtOsYNWUS4uK7upbmHIBbLBeFeI//477BKLBzA==",
|
||||
"license": "BlueOak-1.0.0",
|
||||
"engines": {
|
||||
"node": ">=11.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/semver": {
|
||||
"version": "7.7.3",
|
||||
"resolved": "https://registry.npmjs.org/semver/-/semver-7.7.3.tgz",
|
||||
|
|
@ -1961,28 +1893,6 @@
|
|||
}
|
||||
}
|
||||
},
|
||||
"node_modules/xml2js": {
|
||||
"version": "0.6.2",
|
||||
"resolved": "https://registry.npmjs.org/xml2js/-/xml2js-0.6.2.tgz",
|
||||
"integrity": "sha512-T4rieHaC1EXcES0Kxxj4JWgaUQHDk+qwHcYOCFHfiwKz7tOVPLq7Hjq9dM1WCMhylqMEfP7hMcOIChvotiZegA==",
|
||||
"license": "MIT",
|
||||
"dependencies": {
|
||||
"sax": ">=0.6.0",
|
||||
"xmlbuilder": "~11.0.0"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=4.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/xmlbuilder": {
|
||||
"version": "11.0.1",
|
||||
"resolved": "https://registry.npmjs.org/xmlbuilder/-/xmlbuilder-11.0.1.tgz",
|
||||
"integrity": "sha512-fDlsI/kFEx7gLvbecc0/ohLG50fugQp8ryHzMTuW9vSa1GJ0XYWKnhsUx7oie3G98+r56aTQIUB4kht42R3JvA==",
|
||||
"license": "MIT",
|
||||
"engines": {
|
||||
"node": ">=4.0"
|
||||
}
|
||||
},
|
||||
"node_modules/xtend": {
|
||||
"version": "4.0.2",
|
||||
"resolved": "https://registry.npmjs.org/xtend/-/xtend-4.0.2.tgz",
|
||||
|
|
|
|||
|
|
@ -10,9 +10,7 @@
|
|||
"build": "echo 'No build step required for Node.js'",
|
||||
"setup-local-db": "node scripts/testAndSetup.js",
|
||||
"import-csv": "node scripts/importCSV.js",
|
||||
"verify-data": "node scripts/verifyData.js",
|
||||
"validate-schema": "node scripts/validateSchema.js",
|
||||
"test-backtester": "node scripts/testBacktester.js"
|
||||
"verify-data": "node scripts/verifyData.js"
|
||||
},
|
||||
"keywords": [
|
||||
"trading",
|
||||
|
|
@ -33,13 +31,10 @@
|
|||
"express-rate-limit": "^7.1.5",
|
||||
"express-ws": "^5.0.2",
|
||||
"helmet": "^7.1.0",
|
||||
"ml-logistic-regression": "^2.0.0",
|
||||
"ml-matrix": "^6.13.0",
|
||||
"node-cache": "^5.1.2",
|
||||
"node-fetch": "^3.3.2",
|
||||
"pg": "^8.11.3",
|
||||
"ws": "^8.14.2",
|
||||
"xml2js": "^0.6.2"
|
||||
"ws": "^8.14.2"
|
||||
},
|
||||
"devDependencies": {
|
||||
"nodemon": "^3.0.1"
|
||||
|
|
|
|||
|
|
@ -1,86 +0,0 @@
|
|||
{
|
||||
"success": true,
|
||||
"data": [
|
||||
{
|
||||
"CreatedDate": "2025-01-15",
|
||||
"CreatedTime": "10:30:45 AM",
|
||||
"Symbol": "(5🟩) AAPL · RTH ⚡ 🟢💎⭐💰 ⚡ 🔥",
|
||||
"Rocket": "🚀🚀🚀 [ITM 2.5%]",
|
||||
"NetPremium": "1.50 M",
|
||||
"Premium": "850.00 K",
|
||||
|
||||
"premium_num": 850000,
|
||||
"bull_total": 1500000,
|
||||
"bear_total": 50000,
|
||||
|
||||
"PctVsPriorClose": 1.2,
|
||||
"PctVsRthOpen": 0.8,
|
||||
"Pct5m": 0.3,
|
||||
"Pct15m": 0.5,
|
||||
"TapeAlign": "↗︎",
|
||||
"NearAlert": "EARNINGS",
|
||||
|
||||
"ExpirationDate": "2025-01-17",
|
||||
"Price": "185.50",
|
||||
"CallPut": "CALL",
|
||||
"Side": "AA",
|
||||
"Strike": "185",
|
||||
"Spot": "185.50",
|
||||
"Volume": "5000",
|
||||
"OI": "3000",
|
||||
|
||||
"direction": "BULL",
|
||||
"badge_round": "🟢",
|
||||
"badge_more": "💎⭐💰✔",
|
||||
"flash": "⚡",
|
||||
"rocket_score": 5.2,
|
||||
|
||||
"session_bucket": "RTH",
|
||||
"flow_ts_utc": "2025-01-15T16:30:45+00:00",
|
||||
"mny_pct": 2.5,
|
||||
|
||||
"vwap_at_signal": 185.20,
|
||||
"price_vs_vwap_pct": 0.16,
|
||||
|
||||
"signal_tier": "TIER_1",
|
||||
"checklist_score": 8.5,
|
||||
"checklist_passed": true,
|
||||
|
||||
"premium_zscore": 2.3,
|
||||
"premium_percentile_intraday": 85.5,
|
||||
"relative_premium_score": 72.5,
|
||||
|
||||
"aggression_score": 68.0,
|
||||
"size_concentration_score": 75.0,
|
||||
"repeat_trade_velocity": 55.0,
|
||||
"strike_clustering_score": 60.0,
|
||||
"signal_strength": 64.7,
|
||||
|
||||
"early_noise_reject": false,
|
||||
|
||||
"flow_acceleration": 12500.5,
|
||||
"time_between_hits": 3.2,
|
||||
"follow_on_ratio": 0.75,
|
||||
"strike_laddering_detected": true,
|
||||
|
||||
"delta_exposure": 92500000,
|
||||
"gamma_exposure": 1250000000,
|
||||
"volatility_intent": "LONG_VOL",
|
||||
|
||||
"net_gamma_exposure_per_symbol": 8500000000,
|
||||
"gamma_flip_proximity": 0.15,
|
||||
"dealer_hedge_pressure_score": 72.5,
|
||||
|
||||
"market_regime": "TREND",
|
||||
"flow_state": "ACTIONABLE",
|
||||
|
||||
"confidence_score": 78.5,
|
||||
"institutional_likelihood": 0.85,
|
||||
"dealer_pain_level": 65.0,
|
||||
"expected_move_vs_implied": 1.35
|
||||
}
|
||||
],
|
||||
"count": 1,
|
||||
"timestamp": "2025-01-15T16:35:00.123456"
|
||||
}
|
||||
|
||||
|
|
@ -1,168 +0,0 @@
|
|||
# Institutional-Grade Options Flow Analytics
|
||||
|
||||
This document describes the institutional-grade enhancements to the options flow pipeline.
|
||||
|
||||
## Overview
|
||||
|
||||
The pipeline has been refactored to convert static retail-style flow detection into dynamic, dealer-aware, time-sequenced signals suitable for intraday momentum and 1-5 day swing trades.
|
||||
|
||||
## New Analytics Modules
|
||||
|
||||
### 1. Relative Premium Scoring (`relative_premium_scorer.py`)
|
||||
|
||||
**Purpose**: Replace static premium filter (minPremium = $80K) with context-aware relative scoring.
|
||||
|
||||
**New Fields**:
|
||||
- `premium_zscore`: Z-score of premium relative to 20-day rolling window per ticker
|
||||
- `premium_percentile_intraday`: Percentile rank within same-day flow
|
||||
- `relative_premium_score`: Composite score (0-100) combining z-score, intraday percentile, and median normalization
|
||||
|
||||
**Usage**: Premium of $80K might be significant for AAPL but noise for TSLA. This module computes relative significance.
|
||||
|
||||
### 2. Signal Component Scoring (`signal_component_scorer.py`)
|
||||
|
||||
**Purpose**: Convert binary badge logic (💎 ⭐ 🟢 🔴) into continuous numeric signal components.
|
||||
|
||||
**New Fields**:
|
||||
- `aggression_score`: Measures trade aggression (ITM premiums, ask-side trades)
|
||||
- `size_concentration_score`: Measures size concentration (single large trade vs many small ones)
|
||||
- `repeat_trade_velocity`: Measures repeat trade frequency (urgency building)
|
||||
- `strike_clustering_score`: Measures strike clustering (laddering patterns)
|
||||
- `signal_strength`: Composite score = 0.30 * aggression + 0.30 * size_concentration + 0.20 * repeat_velocity + 0.20 * strike_clustering
|
||||
|
||||
**Note**: Badges remain display-only. Signal strength is computed from components.
|
||||
|
||||
### 3. Tier-0 Noise Rejection (`noise_rejector.py`)
|
||||
|
||||
**Purpose**: Reject low-quality signals before enrichment to reduce processing overhead.
|
||||
|
||||
**New Fields**:
|
||||
- `early_noise_reject`: Boolean flag indicating if signal should be rejected as noise
|
||||
|
||||
**Rejection Criteria**:
|
||||
- Single isolated trade (no repeat activity within 30 minutes)
|
||||
- Far OTM weekly lottos (>15% OTM with <7 days to expiry)
|
||||
- Delta-adjusted premium below threshold (<$50K)
|
||||
|
||||
### 4. Time-Sequenced Flow Analysis (`time_sequenced_analyzer.py`)
|
||||
|
||||
**Purpose**: Analyze flow patterns over time to detect urgency, distribution, and continuation.
|
||||
|
||||
**New Fields**:
|
||||
- `flow_acceleration`: Change in premium per minute (Δ premium / minute)
|
||||
- `time_between_hits`: Average time between consecutive trades (minutes)
|
||||
- `follow_on_ratio`: Fraction of trades in same direction after initial trade (0-1)
|
||||
- `strike_laddering_detected`: Boolean indicating sequential strike accumulation
|
||||
|
||||
**Interpretation**:
|
||||
- Escalating premium + decreasing time gaps = urgency
|
||||
- Flat premium + widening gaps = distribution
|
||||
|
||||
### 5. Intent Classification (`intent_classifier.py`)
|
||||
|
||||
**Purpose**: Replace naive direction (BULL/BEAR) with nuanced volatility and hedging intent.
|
||||
|
||||
**New Fields**:
|
||||
- `delta_exposure`: Delta exposure (contracts * delta * 100 * spot_price)
|
||||
- `gamma_exposure`: Gamma exposure (contracts * gamma * 100 * spot_price^2)
|
||||
- `volatility_intent`: Enum (LONG_VOL, SHORT_VOL, DIRECTIONAL, HEDGE_UNWIND)
|
||||
|
||||
**Note**: Direction (BULL/BEAR) becomes secondary metadata.
|
||||
|
||||
### 6. Dealer-Aware Flow Context (`dealer_flow_context.py`)
|
||||
|
||||
**Purpose**: Track dealer hedging pressure and gamma exposure.
|
||||
|
||||
**New Fields**:
|
||||
- `net_gamma_exposure_per_symbol`: Sum of gamma exposures for symbol (positive = long gamma, negative = short gamma)
|
||||
- `gamma_flip_proximity`: Proximity to gamma flip point (-1 to 1)
|
||||
- `dealer_hedge_pressure_score`: Dealer hedge pressure score (0-100)
|
||||
|
||||
**Usage**: Validates flow continuation, flow reversals, and gamma squeeze setups.
|
||||
|
||||
### 7. Market Regime Detection (`market_regime_detector.py`)
|
||||
|
||||
**Purpose**: Identify market regime to gate trade signal generation.
|
||||
|
||||
**New Fields**:
|
||||
- `market_regime`: Enum (TREND, RANGE, HIGH_VOL_EVENT)
|
||||
|
||||
**Trade Signal Gating**:
|
||||
- Trend → continuation bias
|
||||
- Range → fade or vol-sell bias
|
||||
- Event → volatility expansion bias
|
||||
|
||||
### 8. Flow Decay & Reversal Validation (`flow_decay_validator.py`)
|
||||
|
||||
**Purpose**: Validate flow decay/reversal signals with anchors.
|
||||
|
||||
**New Fields**:
|
||||
- `flow_state`: Enum (ACTIONABLE, INFORMATIONAL)
|
||||
|
||||
**Validation Criteria**: Flow decay/reversal is actionable ONLY IF:
|
||||
- Premium contracts (relative_premium_score >= 60)
|
||||
- Dealer hedge pressure decreases
|
||||
- Price fails near VWAP / opening range / key level
|
||||
- Otherwise marked as INFORMATIONAL
|
||||
|
||||
### 9. Institutional Confidence Metrics (`institutional_confidence.py`)
|
||||
|
||||
**Purpose**: Calculate confidence scores for institutional flow signals.
|
||||
|
||||
**New Fields**:
|
||||
- `confidence_score`: Overall confidence score (0-100)
|
||||
- `institutional_likelihood`: Likelihood flow is institutional (0-1)
|
||||
- `dealer_pain_level`: Dealer pain level (0-100)
|
||||
- `expected_move_vs_implied`: Expected move vs implied move ratio
|
||||
|
||||
## Integration
|
||||
|
||||
All modules are integrated into the main processing pipeline in `main.py`:
|
||||
|
||||
1. Basic flow processing (normalization, badges, rocket score)
|
||||
2. Price context enrichment
|
||||
3. Alert matching
|
||||
4. **Institutional analytics pipeline** (NEW):
|
||||
- Tier-0 noise rejection
|
||||
- Relative premium scoring
|
||||
- Signal component scoring
|
||||
- Time-sequenced analysis
|
||||
- Intent classification
|
||||
- Dealer flow context
|
||||
- Market regime detection
|
||||
- Flow decay validation
|
||||
- Confidence metrics
|
||||
5. Filtering (premium, relative premium, badges, direction)
|
||||
6. Output formatting
|
||||
|
||||
## Filtering Changes
|
||||
|
||||
**Before**:
|
||||
- Static premium filter: `premium_num > 80000`
|
||||
- Badge requirements: 🟢/🔴 + 💎 + ⭐
|
||||
|
||||
**After**:
|
||||
- Static premium filter: `premium_num > min_premium` (still applied)
|
||||
- **Relative premium filter**: `relative_premium_score >= 60.0` (NEW)
|
||||
- **Noise rejection filter**: `early_noise_reject == False` (NEW)
|
||||
- Badge requirements: 🟢/🔴 + 💎 + ⭐ (still applied, but badges are now display-only)
|
||||
|
||||
## API Response
|
||||
|
||||
All new fields are included in the API response. The response maintains backward compatibility - existing fields remain unchanged, new fields are additive.
|
||||
|
||||
## Design Philosophy
|
||||
|
||||
1. **Flow represents pressure, not prediction**: Signals indicate who is forced to act next (dealers hedging)
|
||||
2. **Institutions trade urgency and forced hedging**: Focus on dealer pain and gamma exposure
|
||||
3. **Fewer, higher-quality signals > more alerts**: Noise rejection and relative premium filtering reduce false positives
|
||||
4. **Every signal must answer**: "Who is forced to act next?"
|
||||
|
||||
## Success Criteria
|
||||
|
||||
If implemented correctly:
|
||||
- Signal count decreases (noise filtered out)
|
||||
- Average signal quality increases (relative premium, signal strength)
|
||||
- False positives reduce (noise rejection, dealer context validation)
|
||||
- Trades align with intraday momentum and short-term swing horizons (time-sequenced analysis)
|
||||
|
||||
|
|
@ -27,7 +27,7 @@ cp .env.example .env
|
|||
```bash
|
||||
python main.py
|
||||
# Or with uvicorn:
|
||||
uvicorn main:app --reload --port 8010
|
||||
uvicorn main:app --reload --port 8000
|
||||
```
|
||||
|
||||
## API Endpoints
|
||||
|
|
@ -46,7 +46,7 @@ Get processed options flow data
|
|||
|
||||
**Example:**
|
||||
```bash
|
||||
curl http://localhost:8010/api/options-flow?start_date=2024-01-01&end_date=2024-01-02
|
||||
curl http://localhost:8000/api/options-flow?start_date=2024-01-01&end_date=2024-01-02
|
||||
```
|
||||
|
||||
### GET /api/options-flow/stats
|
||||
|
|
@ -60,7 +60,7 @@ Get flow statistics
|
|||
The Node.js API layer can call this service via HTTP:
|
||||
|
||||
```javascript
|
||||
const response = await fetch('http://localhost:8010/api/options-flow?start_date=2024-01-01');
|
||||
const response = await fetch('http://localhost:8000/api/options-flow?start_date=2024-01-01');
|
||||
const data = await response.json();
|
||||
```
|
||||
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@
|
|||
|
||||
```bash
|
||||
cd backend/python_service
|
||||
uvicorn main:app --reload --port 8010
|
||||
uvicorn main:app --reload --port 8000
|
||||
```
|
||||
|
||||
## Using Virtual Environment (Recommended)
|
||||
|
|
@ -26,20 +26,20 @@ source venv/bin/activate
|
|||
pip install -r requirements.txt
|
||||
|
||||
# Start service
|
||||
uvicorn main:app --reload --port 8010
|
||||
uvicorn main:app --reload --port 8000
|
||||
```
|
||||
|
||||
## Verify Service is Running
|
||||
|
||||
Once started, you should see:
|
||||
```
|
||||
INFO: Uvicorn running on http://127.0.0.1:8010
|
||||
INFO: Uvicorn running on http://127.0.0.1:8000
|
||||
INFO: Application startup complete.
|
||||
```
|
||||
|
||||
Test the health endpoint:
|
||||
```bash
|
||||
curl http://localhost:8010/health
|
||||
curl http://localhost:8000/health
|
||||
```
|
||||
|
||||
Expected response:
|
||||
|
|
@ -28,10 +28,10 @@ Start the service and test the endpoint:
|
|||
```bash
|
||||
# Terminal 1: Start Python service
|
||||
cd backend/python_service
|
||||
uvicorn main:app --reload --port 8010
|
||||
uvicorn main:app --reload --port 8000
|
||||
|
||||
# Terminal 2: Test endpoint
|
||||
curl "http://localhost:8010/api/options-flow?start_date=2024-01-01&end_date=2024-01-02"
|
||||
curl "http://localhost:8000/api/options-flow?start_date=2024-01-01&end_date=2024-01-02"
|
||||
```
|
||||
|
||||
### 3. Integration Test with Node.js
|
||||
|
|
@ -41,7 +41,7 @@ Test the full stack:
|
|||
```bash
|
||||
# Terminal 1: Start Python service
|
||||
cd backend/python_service
|
||||
uvicorn main:app --reload --port 8010
|
||||
uvicorn main:app --reload --port 8000
|
||||
|
||||
# Terminal 2: Start Node.js backend
|
||||
cd backend
|
||||
|
|
|
|||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
@ -3,7 +3,6 @@ Database connection module for Python service
|
|||
Uses asyncpg for async PostgreSQL connections
|
||||
"""
|
||||
import os
|
||||
import asyncio
|
||||
import asyncpg
|
||||
from typing import Optional
|
||||
from dotenv import load_dotenv
|
||||
|
|
@ -13,12 +12,9 @@ load_dotenv()
|
|||
# Database connection pool
|
||||
_pool: Optional[asyncpg.Pool] = None
|
||||
|
||||
# Connection timeout in seconds (reduced from default 60s to fail faster)
|
||||
CONNECTION_TIMEOUT = 10
|
||||
|
||||
|
||||
async def get_pool() -> asyncpg.Pool:
|
||||
"""Get or create database connection pool with timeout"""
|
||||
"""Get or create database connection pool"""
|
||||
global _pool
|
||||
|
||||
if _pool is None:
|
||||
|
|
@ -27,7 +23,7 @@ async def get_pool() -> asyncpg.Pool:
|
|||
|
||||
if use_local_db:
|
||||
config = {
|
||||
'host': os.getenv('LOCAL_DB_HOST', '192.168.8.151'),
|
||||
'host': os.getenv('LOCAL_DB_HOST', '100.121.163.23'),
|
||||
'port': int(os.getenv('LOCAL_DB_PORT', '5432')),
|
||||
'user': os.getenv('LOCAL_DB_USER', 'postgres'),
|
||||
'password': os.getenv('LOCAL_DB_PASSWORD', 'postgres'),
|
||||
|
|
@ -51,29 +47,12 @@ async def get_pool() -> asyncpg.Pool:
|
|||
' - OR DATABASE_URL'
|
||||
)
|
||||
|
||||
# Create pool with timeout to prevent hanging
|
||||
# Use min_size=1 to speed up initial connection (pool will grow as needed)
|
||||
try:
|
||||
_pool = await asyncio.wait_for(
|
||||
asyncpg.create_pool(
|
||||
**config,
|
||||
min_size=1, # Start with 1 connection, pool grows as needed
|
||||
max_size=20,
|
||||
command_timeout=60,
|
||||
timeout=CONNECTION_TIMEOUT # Connection timeout per connection
|
||||
),
|
||||
timeout=CONNECTION_TIMEOUT # Overall timeout for pool creation
|
||||
)
|
||||
except asyncio.TimeoutError:
|
||||
raise ConnectionError(
|
||||
f'Database connection timeout after {CONNECTION_TIMEOUT}s. '
|
||||
f'Check if database is reachable at {config.get("host", "unknown")}:{config.get("port", "unknown")}'
|
||||
)
|
||||
except Exception as e:
|
||||
raise ConnectionError(
|
||||
f'Failed to create database connection pool: {str(e)}. '
|
||||
f'Check database configuration and network connectivity.'
|
||||
)
|
||||
_pool = await asyncpg.create_pool(
|
||||
**config,
|
||||
min_size=5,
|
||||
max_size=20,
|
||||
command_timeout=60
|
||||
)
|
||||
|
||||
return _pool
|
||||
|
||||
|
|
|
|||
|
|
@ -2,23 +2,19 @@
|
|||
FastAPI service for options flow processing
|
||||
Replaces complex SQL with Python/pandas logic
|
||||
"""
|
||||
from fastapi import FastAPI, HTTPException, Query, Body
|
||||
from fastapi import FastAPI, HTTPException, Query
|
||||
from fastapi.middleware.cors import CORSMiddleware
|
||||
from pydantic import BaseModel
|
||||
from typing import Optional, List, Dict, Any
|
||||
from typing import Optional, List
|
||||
from datetime import datetime, timedelta
|
||||
import pandas as pd
|
||||
import asyncpg
|
||||
import pytz
|
||||
|
||||
from db import get_pool, close_pool
|
||||
from services.options_flow_processor import OptionsFlowProcessor
|
||||
from services.price_context import PriceContextService
|
||||
from services.alert_service import AlertService
|
||||
from services.output_formatter import OutputFormatter
|
||||
from services.price_reaction_tracker import PriceReactionTracker
|
||||
from services.signal_tier_classifier import SignalTierClassifier
|
||||
from services.trade_checklist import TradeChecklist
|
||||
from utils.logger import logger
|
||||
from utils.error_handler import handle_processing_error
|
||||
|
||||
|
|
@ -51,11 +47,7 @@ class OptionsFlowResponse(BaseModel):
|
|||
@app.on_event("startup")
|
||||
async def startup():
|
||||
"""Initialize database pool on startup"""
|
||||
try:
|
||||
pool = await get_pool()
|
||||
logger.info("✅ Database pool initialized")
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Failed to initialize database pool: {e}")
|
||||
await get_pool()
|
||||
|
||||
|
||||
@app.on_event("shutdown")
|
||||
|
|
@ -71,7 +63,7 @@ async def health():
|
|||
pool = await get_pool()
|
||||
async with pool.acquire() as conn:
|
||||
await conn.fetchval("SELECT 1")
|
||||
return {"status": "healthy"}
|
||||
return {"status": "healthy", "service": "options-flow-processor"}
|
||||
except Exception as e:
|
||||
return {"status": "unhealthy", "error": str(e)}
|
||||
|
||||
|
|
@ -106,8 +98,6 @@ async def get_options_flow(
|
|||
# Load raw options flow data (with timeout handling)
|
||||
try:
|
||||
async with pool.acquire() as conn:
|
||||
# Build query with date filtering
|
||||
# Note: CreatedDate is TEXT, so we need to handle date comparisons carefully
|
||||
query = """
|
||||
SELECT *
|
||||
FROM "OptionsFlow_monthly"
|
||||
|
|
@ -116,55 +106,13 @@ async def get_options_flow(
|
|||
AND "StockEtf" = 'STOCK'
|
||||
AND "Symbol" NOT IN ('TSLA', 'NVDA')
|
||||
"""
|
||||
|
||||
# Add date filtering using the parsed dates
|
||||
params = []
|
||||
if start_date:
|
||||
# CreatedDate is TEXT, so compare as strings (assuming YYYY-MM-DD format)
|
||||
query += ' AND "CreatedDate" >= $1'
|
||||
params.append(start_date)
|
||||
if end_date:
|
||||
param_idx = len(params) + 1
|
||||
query += ' AND "CreatedDate" <= $' + str(param_idx)
|
||||
params.append(end_date)
|
||||
|
||||
# Add LIMIT for safety (prevent loading millions of rows)
|
||||
# Limit to 500k rows max
|
||||
query += ' LIMIT 500000'
|
||||
|
||||
logger.info(f"Executing query with date range: {start_date} to {end_date}")
|
||||
|
||||
# Execute with timeout
|
||||
try:
|
||||
# Set statement timeout for this query (60 seconds)
|
||||
await conn.execute('SET statement_timeout = 60000')
|
||||
rows = await conn.fetch(query, *params)
|
||||
await conn.execute('RESET statement_timeout')
|
||||
logger.info(f"✅ Fetched {len(rows)} rows from database")
|
||||
except Exception as query_error:
|
||||
await conn.execute('RESET statement_timeout')
|
||||
raise query_error
|
||||
rows = await conn.fetch(query)
|
||||
except Exception as e:
|
||||
error_type = type(e).__name__
|
||||
error_msg = str(e)
|
||||
logger.error(f"Database query error: {error_type} - {error_msg}")
|
||||
|
||||
# Provide more helpful error messages
|
||||
if 'TimeoutError' in error_type or 'timeout' in error_msg.lower():
|
||||
raise HTTPException(
|
||||
status_code=504,
|
||||
detail=f"Database query timed out. The query may be too large. Try narrowing the date range or ensure database indexes are optimized."
|
||||
)
|
||||
elif 'Connection' in error_type or 'connection' in error_msg.lower():
|
||||
raise HTTPException(
|
||||
status_code=503,
|
||||
detail=f"Database connection error: {error_msg}. Check database connectivity and configuration."
|
||||
)
|
||||
else:
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Database query failed: {error_msg}"
|
||||
)
|
||||
logger.error(f"Database query error: {type(e).__name__} - {str(e)}")
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Database query failed: {str(e)}"
|
||||
)
|
||||
|
||||
if not rows:
|
||||
return OptionsFlowResponse(
|
||||
|
|
@ -181,7 +129,7 @@ async def get_options_flow(
|
|||
processor = OptionsFlowProcessor(tol_pct=tol_pct)
|
||||
df_processed = processor.process(df, start_dt, end_dt)
|
||||
|
||||
# Enrich with price context (optimized batch queries) - includes VWAP
|
||||
# Enrich with price context (optimized batch queries)
|
||||
price_service = PriceContextService(pool)
|
||||
df_with_prices = await price_service.enrich_flow_with_prices(df_processed, pool)
|
||||
|
||||
|
|
@ -192,120 +140,9 @@ async def get_options_flow(
|
|||
# Recalculate rocket score with price context and alerts
|
||||
df_final = processor.process_rocket_score(df_final)
|
||||
|
||||
# Apply institutional-grade analytics pipeline
|
||||
logger.info("🔹 Applying institutional-grade analytics...")
|
||||
from services.relative_premium_scorer import RelativePremiumScorer
|
||||
from services.noise_rejector import NoiseRejector
|
||||
from services.signal_component_scorer import SignalComponentScorer
|
||||
from services.time_sequenced_analyzer import TimeSequencedAnalyzer
|
||||
from services.intent_classifier import IntentClassifier
|
||||
from services.dealer_flow_context import DealerFlowContext
|
||||
from services.market_regime_detector import MarketRegimeDetector
|
||||
from services.flow_decay_validator import FlowDecayValidator
|
||||
from services.institutional_confidence import InstitutionalConfidence
|
||||
|
||||
# 1. Tier-0 Noise Rejection (mark but don't filter yet - filtering happens later)
|
||||
logger.info("1️⃣ Applying tier-0 noise rejection...")
|
||||
noise_rejector = NoiseRejector()
|
||||
df_final = noise_rejector.mark_noise_rejections(df_final)
|
||||
|
||||
# 2. Relative Premium Scoring
|
||||
logger.info("2️⃣ Calculating relative premium scores...")
|
||||
premium_scorer = RelativePremiumScorer(pool)
|
||||
df_final = await premium_scorer.enrich_with_relative_premium(df_final)
|
||||
|
||||
# 3. Signal Component Scoring (convert badges to continuous scores)
|
||||
logger.info("3️⃣ Converting badges to continuous signal components...")
|
||||
signal_scorer = SignalComponentScorer()
|
||||
df_final = signal_scorer.enrich_with_signal_components(df_final)
|
||||
|
||||
# 4. Time-Sequenced Flow Analysis
|
||||
logger.info("4️⃣ Analyzing time-sequenced flow patterns...")
|
||||
time_analyzer = TimeSequencedAnalyzer()
|
||||
df_final = time_analyzer.enrich_with_time_sequenced_metrics(df_final)
|
||||
|
||||
# 5. Intent Classification
|
||||
logger.info("5️⃣ Classifying volatility and hedging intent...")
|
||||
intent_classifier = IntentClassifier()
|
||||
df_final = intent_classifier.enrich_with_intent_classification(df_final)
|
||||
|
||||
# 6. Dealer-Aware Flow Context
|
||||
logger.info("6️⃣ Analyzing dealer hedging pressure...")
|
||||
dealer_context = DealerFlowContext()
|
||||
df_final = dealer_context.enrich_with_dealer_context(df_final)
|
||||
|
||||
# 7. Market Regime Detection
|
||||
logger.info("7️⃣ Detecting market regime...")
|
||||
regime_detector = MarketRegimeDetector()
|
||||
df_final = regime_detector.enrich_with_market_regime(df_final)
|
||||
|
||||
# 8. Flow Decay & Reversal Validation
|
||||
logger.info("8️⃣ Validating flow decay and reversal signals...")
|
||||
flow_validator = FlowDecayValidator()
|
||||
df_final = flow_validator.enrich_with_flow_state(df_final)
|
||||
|
||||
# 9. Institutional Confidence Metrics
|
||||
logger.info("9️⃣ Calculating institutional confidence metrics...")
|
||||
confidence_calc = InstitutionalConfidence()
|
||||
df_final = confidence_calc.enrich_with_confidence_metrics(df_final)
|
||||
|
||||
# Phase 1 Enhancements (BEFORE filtering so all signals get Phase 1 data):
|
||||
# Initialize Phase 1 columns with None/empty values first
|
||||
if not df_final.empty:
|
||||
# Initialize all Phase 1 columns to ensure they exist
|
||||
df_final['signal_tier'] = None
|
||||
df_final['is_tradeable'] = False
|
||||
df_final['checklist_score'] = None
|
||||
df_final['checklist_passed'] = False
|
||||
df_final['checklist_details'] = None
|
||||
df_final['price_reaction_5m_pct'] = None
|
||||
df_final['price_reaction_15m_pct'] = None
|
||||
df_final['price_reaction_30m_pct'] = None
|
||||
df_final['high_break_5m'] = None
|
||||
df_final['low_break_5m'] = None
|
||||
df_final['flow_led_to_move'] = None
|
||||
# VWAP fields should already be set by PriceContextService, but ensure they exist
|
||||
if 'vwap_at_signal' not in df_final.columns:
|
||||
df_final['vwap_at_signal'] = None
|
||||
if 'price_vs_vwap_pct' not in df_final.columns:
|
||||
df_final['price_vs_vwap_pct'] = None
|
||||
|
||||
# 1. Signal Tier Classification
|
||||
logger.info("🔍 Classifying signal tiers...")
|
||||
if not df_final.empty:
|
||||
try:
|
||||
tier_classifier = SignalTierClassifier()
|
||||
df_final = tier_classifier.classify_tiers(df_final)
|
||||
# Debug: Check if tiers were calculated
|
||||
if 'signal_tier' in df_final.columns:
|
||||
tier_counts = df_final['signal_tier'].value_counts()
|
||||
logger.info(f"✅ Signal tiers calculated: {tier_counts.to_dict()}")
|
||||
else:
|
||||
logger.warning("⚠️ signal_tier column not found after classification")
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Error in signal tier classification: {str(e)}", exc_info=True)
|
||||
|
||||
# 2. Price Reaction Tracking (disabled - will be calculated on-demand)
|
||||
# Price reaction requires Yahoo Finance and is calculated when modal opens
|
||||
|
||||
# 3. Trade Checklist Evaluation
|
||||
logger.info("✅ Evaluating trade checklist...")
|
||||
if not df_final.empty:
|
||||
try:
|
||||
checklist = TradeChecklist()
|
||||
df_final = checklist.evaluate_all(df_final)
|
||||
# Debug: Check if checklist was calculated
|
||||
if 'checklist_score' in df_final.columns:
|
||||
checklist_count = df_final['checklist_score'].notna().sum()
|
||||
logger.info(f"✅ Checklist scores calculated: {checklist_count}/{len(df_final)} signals")
|
||||
else:
|
||||
logger.warning("⚠️ checklist_score column not found after evaluation")
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Error in trade checklist evaluation: {str(e)}", exc_info=True)
|
||||
|
||||
# Check if DataFrame is empty before filtering
|
||||
if df_final.empty:
|
||||
logger.warning("⚠️ No data after processing, returning empty result")
|
||||
logger.warning("No data after processing, returning empty result")
|
||||
return OptionsFlowResponse(
|
||||
success=True,
|
||||
data=[],
|
||||
|
|
@ -316,13 +153,6 @@ async def get_options_flow(
|
|||
# Filter by minimum premium and badge requirements (matching SQL WHERE clause)
|
||||
logger.info(f"📊 Before filtering: {len(df_final)} rows")
|
||||
|
||||
# Apply noise rejection filter first (exclude early_noise_reject = True)
|
||||
if 'early_noise_reject' in df_final.columns:
|
||||
before_noise = len(df_final)
|
||||
df_final = df_final[~df_final['early_noise_reject']].copy()
|
||||
after_noise = len(df_final)
|
||||
logger.info(f"📊 After noise rejection filter: {after_noise} rows (removed {before_noise - after_noise})")
|
||||
|
||||
# Only filter if columns exist
|
||||
if 'premium_num' in df_final.columns:
|
||||
before_premium = len(df_final)
|
||||
|
|
@ -332,14 +162,6 @@ async def get_options_flow(
|
|||
else:
|
||||
logger.warning("⚠️ premium_num column not found, skipping premium filter")
|
||||
|
||||
# Apply relative premium filter if available
|
||||
if 'relative_premium_score' in df_final.columns:
|
||||
before_relative = len(df_final)
|
||||
min_relative_threshold = 60.0 # Configurable threshold
|
||||
df_final = df_final[df_final['relative_premium_score'] >= min_relative_threshold].copy()
|
||||
after_relative = len(df_final)
|
||||
logger.info(f"📊 After relative premium filter (>={min_relative_threshold}): {after_relative} rows (removed {before_relative - after_relative})")
|
||||
|
||||
if df_final.empty:
|
||||
logger.warning("⚠️ No data after premium filter")
|
||||
return OptionsFlowResponse(
|
||||
|
|
@ -393,19 +215,6 @@ async def get_options_flow(
|
|||
# Format output to match SQL format
|
||||
df_final = OutputFormatter.format_final_output(df_final)
|
||||
|
||||
# Debug: Log Phase 1 columns before converting to dict
|
||||
phase1_columns = ['signal_tier', 'checklist_score', 'checklist_passed', 'price_reaction_5m_pct', 'flow_led_to_move', 'vwap_at_signal', 'price_vs_vwap_pct']
|
||||
existing_phase1_cols = [col for col in phase1_columns if col in df_final.columns]
|
||||
if existing_phase1_cols:
|
||||
logger.info(f"✅ Phase 1 columns in final output: {existing_phase1_cols}")
|
||||
# Sample a row to check values
|
||||
if len(df_final) > 0:
|
||||
sample_row = df_final.iloc[0]
|
||||
sample_phase1 = {col: sample_row.get(col) for col in existing_phase1_cols}
|
||||
logger.info(f"📊 Sample Phase 1 data: {sample_phase1}")
|
||||
else:
|
||||
logger.warning("⚠️ No Phase 1 columns found in final output!")
|
||||
|
||||
# Convert DataFrame to list of dicts
|
||||
result_data = df_final.to_dict('records')
|
||||
|
||||
|
|
@ -423,12 +232,6 @@ async def get_options_flow(
|
|||
record[key] = None
|
||||
else:
|
||||
record[key] = value.isoformat()
|
||||
elif isinstance(value, dict):
|
||||
# Keep dicts as-is (e.g., checklist_details)
|
||||
pass
|
||||
elif isinstance(value, (list, tuple)):
|
||||
# Keep lists as-is
|
||||
pass
|
||||
|
||||
return OptionsFlowResponse(
|
||||
success=True,
|
||||
|
|
@ -438,10 +241,18 @@ async def get_options_flow(
|
|||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error processing options flow: {e}", exc_info=True)
|
||||
error_info = handle_processing_error(
|
||||
e,
|
||||
context={
|
||||
'start_date': start_date,
|
||||
'end_date': end_date,
|
||||
'min_premium': min_premium
|
||||
},
|
||||
raise_error=False
|
||||
)
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Processing failed: {str(e)}"
|
||||
detail=error_info.get('error_message', str(e)) if isinstance(error_info, dict) else str(e)
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -482,349 +293,7 @@ async def get_flow_stats(
|
|||
raise HTTPException(status_code=500, detail=str(e))
|
||||
|
||||
|
||||
@app.post("/api/phase1/calculate")
|
||||
async def calculate_phase1_for_signal(request: Dict[str, Any] = Body(...)):
|
||||
"""
|
||||
Calculate Phase 1 metrics for a specific signal on-demand
|
||||
This is called when the Phase 1 modal opens - fetches Yahoo Finance data and calculates Phase 1
|
||||
"""
|
||||
try:
|
||||
from services.yahoo_finance_service import YahooFinanceService
|
||||
|
||||
symbol = request.get('symbol')
|
||||
flow_ts_utc = request.get('flow_ts_utc')
|
||||
flow_date_cst = request.get('flow_date_cst')
|
||||
row_data = request.get('row_data', {})
|
||||
|
||||
if not symbol or not flow_ts_utc:
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail="symbol and flow_ts_utc are required"
|
||||
)
|
||||
|
||||
# Log the symbol we received
|
||||
logger.info(f"📥 Phase 1 calculation request for symbol: '{symbol}' (type: {type(symbol).__name__}, length: {len(symbol) if symbol else 0})")
|
||||
|
||||
pool = await get_pool()
|
||||
|
||||
# Parse timestamps - handle various formats
|
||||
logger.info(f"📥 Received flow_ts_utc: {flow_ts_utc} (type: {type(flow_ts_utc).__name__})")
|
||||
|
||||
if isinstance(flow_ts_utc, str):
|
||||
try:
|
||||
original_ts = flow_ts_utc
|
||||
# Remove any trailing Z and handle ISO format
|
||||
ts_str = flow_ts_utc.strip()
|
||||
if ts_str.endswith('Z'):
|
||||
# Replace Z with +00:00 for fromisoformat
|
||||
ts_str = ts_str[:-1] + '+00:00'
|
||||
logger.info(f"📅 Parsing UTC timestamp (Z suffix): {original_ts} -> {ts_str}")
|
||||
elif 'T' in ts_str and '+' not in ts_str and '-' not in ts_str[-6:]:
|
||||
# ISO format without timezone - check if it might be local time
|
||||
# For now, assume UTC as the field name suggests, but log it
|
||||
ts_str = ts_str + '+00:00'
|
||||
logger.info(f"📅 Parsing timestamp without timezone, assuming UTC: {original_ts} -> {ts_str}")
|
||||
|
||||
# Try parsing with fromisoformat
|
||||
try:
|
||||
flow_ts_utc = datetime.fromisoformat(ts_str)
|
||||
logger.info(f"✅ Parsed flow_ts_utc: {flow_ts_utc} (timezone: {flow_ts_utc.tzinfo})")
|
||||
except ValueError:
|
||||
# Fallback: try parsing just the date part
|
||||
date_part = ts_str.split('T')[0].split(' ')[0]
|
||||
flow_ts_utc = datetime.strptime(date_part, '%Y-%m-%d')
|
||||
logger.warning(f"⚠️ Could only parse date part from {original_ts}, using {flow_ts_utc}")
|
||||
except (ValueError, AttributeError) as e:
|
||||
logger.error(f"❌ Error parsing flow_ts_utc '{flow_ts_utc}': {e}")
|
||||
raise HTTPException(
|
||||
status_code=400,
|
||||
detail=f"Invalid flow_ts_utc format: {flow_ts_utc}. Error: {str(e)}"
|
||||
)
|
||||
elif isinstance(flow_ts_utc, (int, float)):
|
||||
# Unix timestamp - assume UTC
|
||||
flow_ts_utc = datetime.fromtimestamp(flow_ts_utc, tz=pytz.UTC)
|
||||
logger.info(f"📅 Parsed Unix timestamp: {flow_ts_utc} (UTC)")
|
||||
|
||||
# Ensure flow_ts_utc is timezone-aware (assume UTC if naive)
|
||||
if flow_ts_utc.tzinfo is None:
|
||||
flow_ts_utc = pytz.UTC.localize(flow_ts_utc)
|
||||
logger.info(f"📅 flow_ts_utc was naive, localized to UTC: {flow_ts_utc}")
|
||||
|
||||
if isinstance(flow_date_cst, str):
|
||||
try:
|
||||
# Extract just the date part if it's a datetime string
|
||||
# Handle formats like "2025-12-10T05:00:00.000Z" or "2025-12-10"
|
||||
original_date = flow_date_cst.strip()
|
||||
|
||||
# Split on 'T' first, then on space, take first part
|
||||
if 'T' in original_date:
|
||||
date_str = original_date.split('T')[0]
|
||||
elif ' ' in original_date:
|
||||
date_str = original_date.split(' ')[0]
|
||||
else:
|
||||
date_str = original_date
|
||||
|
||||
# Validate it's in YYYY-MM-DD format (exactly 10 characters, 2 dashes)
|
||||
if len(date_str) == 10 and date_str.count('-') == 2:
|
||||
flow_date_cst = datetime.strptime(date_str, '%Y-%m-%d').date()
|
||||
logger.debug(f"Parsed flow_date_cst: {flow_date_cst} from '{original_date}'")
|
||||
else:
|
||||
raise ValueError(f"Date string '{date_str}' is not in YYYY-MM-DD format (length={len(date_str)}, dashes={date_str.count('-')})")
|
||||
except (ValueError, AttributeError) as e:
|
||||
logger.warning(f"Error parsing flow_date_cst '{flow_date_cst}': {e}")
|
||||
# Try to extract date from flow_ts_utc if available
|
||||
if flow_ts_utc and isinstance(flow_ts_utc, datetime):
|
||||
flow_date_cst = flow_ts_utc.date()
|
||||
logger.info(f"Using date from flow_ts_utc: {flow_date_cst}")
|
||||
else:
|
||||
# Default to today if we can't parse
|
||||
flow_date_cst = datetime.now().date()
|
||||
logger.warning(f"Using current date as fallback for flow_date_cst")
|
||||
elif flow_date_cst is None:
|
||||
# If not provided, try to get from flow_ts_utc
|
||||
if flow_ts_utc and isinstance(flow_ts_utc, datetime):
|
||||
flow_date_cst = flow_ts_utc.date()
|
||||
else:
|
||||
flow_date_cst = datetime.now().date()
|
||||
logger.warning(f"flow_date_cst not provided, using current date as fallback")
|
||||
elif flow_date_cst is None:
|
||||
# If not provided, try to get from flow_ts_utc
|
||||
if flow_ts_utc and isinstance(flow_ts_utc, datetime):
|
||||
flow_date_cst = flow_ts_utc.date()
|
||||
else:
|
||||
flow_date_cst = datetime.now().date()
|
||||
logger.warning(f"flow_date_cst not provided, using current date as fallback")
|
||||
|
||||
# Initialize services with Yahoo Finance enabled
|
||||
price_service = PriceContextService(pool)
|
||||
price_service.use_yahoo_finance = True
|
||||
price_service.yahoo_service = YahooFinanceService()
|
||||
|
||||
reaction_tracker = PriceReactionTracker()
|
||||
reaction_tracker.use_yahoo_finance = True
|
||||
reaction_tracker.yahoo_service = YahooFinanceService()
|
||||
|
||||
tier_classifier = SignalTierClassifier()
|
||||
checklist = TradeChecklist()
|
||||
|
||||
# Create a minimal DataFrame row for processing
|
||||
df_row = pd.DataFrame([{
|
||||
'symbol_norm': symbol,
|
||||
'flow_ts_utc': flow_ts_utc,
|
||||
'flow_date_cst': flow_date_cst,
|
||||
**row_data # Include all other row data (badges, premium, etc.)
|
||||
}])
|
||||
|
||||
# Calculate Phase 1 metrics
|
||||
result = {}
|
||||
|
||||
# 1. Signal Tier (doesn't need price data)
|
||||
try:
|
||||
df_with_tier = tier_classifier.classify_tiers(df_row)
|
||||
if not df_with_tier.empty:
|
||||
result['signal_tier'] = df_with_tier.iloc[0].get('signal_tier')
|
||||
# Convert numpy bool to Python bool
|
||||
is_tradeable_val = df_with_tier.iloc[0].get('is_tradeable', False)
|
||||
result['is_tradeable'] = bool(is_tradeable_val) if is_tradeable_val is not None else False
|
||||
except Exception as e:
|
||||
logger.error(f"Error calculating signal tier: {e}")
|
||||
result['signal_tier'] = None
|
||||
result['is_tradeable'] = False
|
||||
|
||||
# 2. Price Reaction (needs Yahoo Finance)
|
||||
try:
|
||||
# Check if signal is recent enough for intraday data
|
||||
now = datetime.now(pytz.timezone('America/Chicago'))
|
||||
time_diff_hours = (now - flow_ts_utc.replace(tzinfo=now.tzinfo)).total_seconds() / 3600
|
||||
|
||||
if time_diff_hours > 168: # 7 days
|
||||
logger.warning(f"⚠️ Price reaction unavailable: Signal is {time_diff_hours/24:.1f} days old")
|
||||
logger.warning(f" Yahoo Finance only provides intraday data for the last 7 days")
|
||||
logger.warning(f" For historical signals, price reaction data requires your own intraday price database")
|
||||
else:
|
||||
logger.info(f"📊 Calculating price reaction for {symbol} (signal is {time_diff_hours:.1f} hours old)")
|
||||
|
||||
df_with_reactions = await reaction_tracker.enrich_with_reactions(df_row, pool)
|
||||
if not df_with_reactions.empty:
|
||||
result['price_reaction_5m_pct'] = df_with_reactions.iloc[0].get('price_reaction_5m_pct')
|
||||
result['price_reaction_15m_pct'] = df_with_reactions.iloc[0].get('price_reaction_15m_pct')
|
||||
result['price_reaction_30m_pct'] = df_with_reactions.iloc[0].get('price_reaction_30m_pct')
|
||||
# Convert numpy bools to Python bools
|
||||
flow_led = df_with_reactions.iloc[0].get('flow_led_to_move')
|
||||
result['flow_led_to_move'] = bool(flow_led) if flow_led is not None else None
|
||||
high_break = df_with_reactions.iloc[0].get('high_break_5m')
|
||||
result['high_break_5m'] = bool(high_break) if high_break is not None else None
|
||||
low_break = df_with_reactions.iloc[0].get('low_break_5m')
|
||||
result['low_break_5m'] = bool(low_break) if low_break is not None else None
|
||||
except Exception as e:
|
||||
logger.error(f"Error calculating price reaction: {e}")
|
||||
result['price_reaction_5m_pct'] = None
|
||||
result['price_reaction_15m_pct'] = None
|
||||
result['price_reaction_30m_pct'] = None
|
||||
result['flow_led_to_move'] = None
|
||||
result['high_break_5m'] = None
|
||||
result['low_break_5m'] = None
|
||||
|
||||
# 3. VWAP (needs Yahoo Finance)
|
||||
try:
|
||||
# Ensure flow_ts_utc is timezone-aware
|
||||
# If it's naive (no timezone), assume it's UTC (as the name suggests)
|
||||
# If it has timezone info, use it
|
||||
if flow_ts_utc.tzinfo is None:
|
||||
# Naive datetime - assume UTC (as name suggests)
|
||||
flow_ts_utc = pytz.UTC.localize(flow_ts_utc)
|
||||
logger.debug(f"flow_ts_utc was naive, assumed UTC: {flow_ts_utc}")
|
||||
|
||||
# Convert to Eastern Time for market hours check (US market opens at 9:30 AM ET)
|
||||
et_tz = pytz.timezone('America/New_York')
|
||||
signal_time_et = flow_ts_utc.astimezone(et_tz)
|
||||
cst_tz = pytz.timezone('America/Chicago')
|
||||
signal_time_cst = flow_ts_utc.astimezone(cst_tz)
|
||||
|
||||
# Check if signal is recent enough and during market hours
|
||||
now_et = datetime.now(et_tz)
|
||||
now_cst = datetime.now(cst_tz)
|
||||
time_diff_hours = (now_et - signal_time_et).total_seconds() / 3600
|
||||
signal_hour_et = signal_time_et.hour
|
||||
signal_minute_et = signal_time_et.minute
|
||||
|
||||
logger.info(f"📅 Signal time: {signal_time_et.strftime('%Y-%m-%d %H:%M:%S %Z')} (ET) / {signal_time_cst.strftime('%H:%M:%S %Z')} (CST)")
|
||||
logger.info(f"📅 Current time: {now_et.strftime('%Y-%m-%d %H:%M:%S %Z')} (ET) / {now_cst.strftime('%H:%M:%S %Z')} (CST)")
|
||||
|
||||
if time_diff_hours > 168: # 7 days
|
||||
logger.warning(f"⚠️ VWAP unavailable: Signal is {time_diff_hours/24:.1f} days old")
|
||||
logger.warning(f" Yahoo Finance only provides intraday data for the last 7 days")
|
||||
elif signal_hour_et < 9 or (signal_hour_et == 9 and signal_minute_et < 30):
|
||||
logger.warning(f"⚠️ VWAP unavailable: Signal time is {signal_time_et.strftime('%H:%M')} ET (before RTH open at 9:30 AM ET)")
|
||||
logger.warning(f" VWAP can only be calculated after market open (9:30 AM Eastern Time)")
|
||||
else:
|
||||
logger.info(f"📊 Calculating VWAP for {symbol} at {signal_time_et.strftime('%Y-%m-%d %H:%M')} ET")
|
||||
|
||||
vwap_data = await price_service.calculate_vwap_at_time(symbol, flow_ts_utc)
|
||||
if vwap_data:
|
||||
result['vwap_at_signal'] = vwap_data.get('vwap')
|
||||
|
||||
# Get price at signal time
|
||||
price_at_time = await price_service.get_price_at_time(symbol, flow_ts_utc)
|
||||
if price_at_time and price_at_time.get('close') and result.get('vwap_at_signal'):
|
||||
price_vs_vwap = ((price_at_time['close'] - result['vwap_at_signal']) / result['vwap_at_signal']) * 100
|
||||
result['price_vs_vwap_pct'] = round(price_vs_vwap, 2)
|
||||
else:
|
||||
result['price_vs_vwap_pct'] = None
|
||||
else:
|
||||
result['vwap_at_signal'] = None
|
||||
result['price_vs_vwap_pct'] = None
|
||||
except Exception as e:
|
||||
logger.error(f"Error calculating VWAP: {e}")
|
||||
result['vwap_at_signal'] = None
|
||||
result['price_vs_vwap_pct'] = None
|
||||
|
||||
# 4. Trade Checklist (needs VWAP, but we can calculate with what we have)
|
||||
try:
|
||||
# Add VWAP data to row for checklist
|
||||
df_row['vwap_at_signal'] = result.get('vwap_at_signal')
|
||||
df_row['price_vs_vwap_pct'] = result.get('price_vs_vwap_pct')
|
||||
|
||||
df_with_checklist = checklist.evaluate_all(df_row)
|
||||
if not df_with_checklist.empty:
|
||||
result['checklist_score'] = df_with_checklist.iloc[0].get('checklist_score')
|
||||
# Convert numpy bool to Python bool
|
||||
checklist_passed_val = df_with_checklist.iloc[0].get('checklist_passed')
|
||||
result['checklist_passed'] = bool(checklist_passed_val) if checklist_passed_val is not None else False
|
||||
|
||||
# Extract and convert checklist_details immediately
|
||||
checklist_details_raw = df_with_checklist.iloc[0].get('checklist_details')
|
||||
if checklist_details_raw is not None:
|
||||
# Convert nested numpy types in checklist_details
|
||||
import numpy as np
|
||||
if isinstance(checklist_details_raw, dict):
|
||||
result['checklist_details'] = {
|
||||
k: bool(v) if isinstance(v, np.bool_) else
|
||||
(int(v) if isinstance(v, np.integer) else
|
||||
(float(v) if isinstance(v, np.floating) else v))
|
||||
for k, v in checklist_details_raw.items()
|
||||
}
|
||||
else:
|
||||
result['checklist_details'] = {}
|
||||
else:
|
||||
result['checklist_details'] = {}
|
||||
except Exception as e:
|
||||
logger.error(f"Error calculating checklist: {e}")
|
||||
result['checklist_score'] = None
|
||||
result['checklist_passed'] = False
|
||||
result['checklist_details'] = {}
|
||||
|
||||
# Convert numpy types to native Python types for JSON serialization
|
||||
def convert_to_native(obj):
|
||||
"""Recursively convert numpy types to native Python types"""
|
||||
import numpy as np
|
||||
|
||||
# Handle None and NaN first
|
||||
if obj is None:
|
||||
return None
|
||||
try:
|
||||
if hasattr(pd, 'isna') and pd.isna(obj):
|
||||
return None
|
||||
except (TypeError, ValueError):
|
||||
pass
|
||||
try:
|
||||
if isinstance(obj, float) and np.isnan(obj):
|
||||
return None
|
||||
except (TypeError, ValueError):
|
||||
pass
|
||||
|
||||
# Handle numpy types
|
||||
try:
|
||||
if isinstance(obj, np.bool_):
|
||||
return bool(obj)
|
||||
elif isinstance(obj, np.integer):
|
||||
return int(obj)
|
||||
elif isinstance(obj, np.floating):
|
||||
return float(obj)
|
||||
elif isinstance(obj, np.ndarray):
|
||||
return obj.tolist()
|
||||
except (TypeError, AttributeError):
|
||||
pass
|
||||
|
||||
# Handle collections
|
||||
try:
|
||||
if isinstance(obj, dict):
|
||||
return {str(k): convert_to_native(v) for k, v in obj.items()}
|
||||
except (TypeError, AttributeError):
|
||||
return {}
|
||||
|
||||
try:
|
||||
if isinstance(obj, (list, tuple)):
|
||||
return [convert_to_native(item) for item in obj]
|
||||
except (TypeError, AttributeError):
|
||||
return []
|
||||
|
||||
# Handle pandas Series/DataFrame (shouldn't happen, but just in case)
|
||||
if hasattr(obj, '__dict__') and not isinstance(obj, dict):
|
||||
try:
|
||||
# Try to convert to string representation
|
||||
return str(obj)
|
||||
except:
|
||||
return None
|
||||
|
||||
return obj
|
||||
|
||||
# Convert result dictionary
|
||||
result_converted = convert_to_native(result)
|
||||
|
||||
return {
|
||||
'success': True,
|
||||
'data': result_converted
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in Phase 1 calculation: {e}", exc_info=True)
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Phase 1 calculation failed: {str(e)}"
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
uvicorn.run(app, host="0.0.0.0", port=8010)
|
||||
uvicorn.run(app, host="0.0.0.0", port=8000)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,11 +1,10 @@
|
|||
fastapi>=0.104.1
|
||||
uvicorn[standard]>=0.24.0
|
||||
pandas>=2.2.0
|
||||
numpy>=1.26.0
|
||||
asyncpg>=0.29.0
|
||||
python-dotenv>=1.0.0
|
||||
pydantic>=2.9.0
|
||||
python-multipart>=0.0.6
|
||||
pytz>=2023.3
|
||||
yfinance>=0.2.28
|
||||
fastapi==0.104.1
|
||||
uvicorn[standard]==0.24.0
|
||||
pandas==2.1.3
|
||||
numpy==1.26.2
|
||||
asyncpg==0.29.0
|
||||
python-dotenv==1.0.0
|
||||
pydantic==2.5.0
|
||||
python-multipart==0.0.6
|
||||
pytz==2023.3
|
||||
|
||||
|
|
|
|||
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|
|
@ -1,256 +0,0 @@
|
|||
"""
|
||||
Dealer-Aware Flow Context Service
|
||||
Tracks dealer hedging pressure, gamma exposure, and flow continuation patterns
|
||||
"""
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from typing import Dict, Optional
|
||||
from datetime import timedelta
|
||||
from utils.logger import logger
|
||||
|
||||
|
||||
class DealerFlowContext:
|
||||
"""
|
||||
Analyzes flow from dealer perspective:
|
||||
- Net gamma exposure per symbol
|
||||
- Gamma flip proximity (when dealers become long/short gamma)
|
||||
- Dealer hedge pressure (forced hedging activity)
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
# Configuration
|
||||
self.analysis_window_minutes = 120 # Look back window for gamma tracking
|
||||
self.gamma_flip_threshold = 0.3 # Gamma flip when net gamma crosses threshold
|
||||
|
||||
def calculate_net_gamma_exposure_per_symbol(
|
||||
self,
|
||||
df: pd.DataFrame,
|
||||
symbol: str,
|
||||
timestamp: pd.Timestamp
|
||||
) -> float:
|
||||
"""
|
||||
Calculate net gamma exposure for a symbol at a given time.
|
||||
Sums all gamma exposures from recent flow.
|
||||
|
||||
Positive = dealers are long gamma (hedging by selling on rallies, buying on dips)
|
||||
Negative = dealers are short gamma (hedging by buying on rallies, selling on dips)
|
||||
"""
|
||||
if df.empty:
|
||||
return 0.0
|
||||
|
||||
# Look at flow up to this timestamp
|
||||
window_start = timestamp - timedelta(minutes=self.analysis_window_minutes)
|
||||
|
||||
mask = (
|
||||
(df['symbol_norm'] == symbol.upper()) &
|
||||
(df['flow_ts_utc'] >= window_start) &
|
||||
(df['flow_ts_utc'] <= timestamp) &
|
||||
(df['gamma_exposure'].notna())
|
||||
)
|
||||
|
||||
recent_flow = df[mask]
|
||||
|
||||
if recent_flow.empty:
|
||||
return 0.0
|
||||
|
||||
# Sum gamma exposures
|
||||
net_gamma = recent_flow['gamma_exposure'].sum()
|
||||
|
||||
return float(net_gamma)
|
||||
|
||||
def calculate_gamma_flip_proximity(
|
||||
self,
|
||||
df: pd.DataFrame,
|
||||
row_idx: int
|
||||
) -> Optional[float]:
|
||||
"""
|
||||
Calculate proximity to gamma flip (when dealers switch from long to short gamma or vice versa).
|
||||
|
||||
Returns: -1.0 to 1.0
|
||||
- Positive = approaching long gamma (dealers becoming volatility buyers)
|
||||
- Negative = approaching short gamma (dealers becoming volatility sellers)
|
||||
- 0.0 = near flip point
|
||||
"""
|
||||
if row_idx >= len(df):
|
||||
return None
|
||||
|
||||
current_row = df.iloc[row_idx]
|
||||
symbol = current_row.get('symbol_norm')
|
||||
flow_ts_utc = current_row.get('flow_ts_utc')
|
||||
gamma_exposure = current_row.get('gamma_exposure', 0) or 0
|
||||
|
||||
if pd.isna(symbol) or pd.isna(flow_ts_utc):
|
||||
return None
|
||||
|
||||
# Calculate current net gamma
|
||||
net_gamma = self.calculate_net_gamma_exposure_per_symbol(df, symbol, flow_ts_utc)
|
||||
|
||||
# Normalize to -1 to 1 scale
|
||||
# Use exponential scaling to emphasize near-flip conditions
|
||||
if net_gamma == 0:
|
||||
return 0.0
|
||||
|
||||
# Simple normalization: divide by a large threshold
|
||||
# More sophisticated: use percentile or adaptive scaling
|
||||
threshold = 1000000000 # 1B in gamma exposure as normalization factor
|
||||
normalized = net_gamma / threshold
|
||||
|
||||
# Clamp to -1 to 1
|
||||
normalized = max(-1.0, min(1.0, normalized))
|
||||
|
||||
# Invert: negative normalized = positive proximity (approaching long gamma)
|
||||
# This is because dealer short gamma (negative) means they're selling volatility
|
||||
return float(-normalized)
|
||||
|
||||
def calculate_dealer_hedge_pressure_score(
|
||||
self,
|
||||
df: pd.DataFrame,
|
||||
row_idx: int
|
||||
) -> float:
|
||||
"""
|
||||
Calculate dealer hedge pressure score (0-100).
|
||||
|
||||
Higher score = more forced hedging by dealers:
|
||||
- High net gamma exposure (dealers must hedge)
|
||||
- Recent flow creating gamma imbalance
|
||||
- Flow continuation (dealers hedging creates more flow)
|
||||
"""
|
||||
if row_idx >= len(df):
|
||||
return 0.0
|
||||
|
||||
current_row = df.iloc[row_idx]
|
||||
symbol = current_row.get('symbol_norm')
|
||||
flow_ts_utc = current_row.get('flow_ts_utc')
|
||||
gamma_exposure = current_row.get('gamma_exposure', 0) or 0
|
||||
|
||||
if pd.isna(symbol) or pd.isna(flow_ts_utc):
|
||||
return 0.0
|
||||
|
||||
score = 0.0
|
||||
|
||||
# Net gamma component (0-40 points)
|
||||
# High absolute net gamma = more hedge pressure
|
||||
net_gamma = abs(self.calculate_net_gamma_exposure_per_symbol(df, symbol, flow_ts_utc))
|
||||
if net_gamma > 0:
|
||||
# Normalize: 500M = 20 points, 1B = 40 points
|
||||
normalized_gamma = min(1.0, net_gamma / 1000000000) # 1B threshold
|
||||
score += normalized_gamma * 40.0
|
||||
|
||||
# Recent gamma accumulation (0-30 points)
|
||||
# Recent flow creating gamma imbalance
|
||||
window_start = flow_ts_utc - timedelta(minutes=30)
|
||||
mask = (
|
||||
(df['symbol_norm'] == symbol.upper()) &
|
||||
(df['flow_ts_utc'] >= window_start) &
|
||||
(df['flow_ts_utc'] <= flow_ts_utc) &
|
||||
(df['gamma_exposure'].notna())
|
||||
)
|
||||
recent_gamma = df[mask]['gamma_exposure'].sum()
|
||||
|
||||
if abs(recent_gamma) > 0:
|
||||
# Normalize recent gamma accumulation
|
||||
normalized_recent = min(1.0, abs(recent_gamma) / 500000000) # 500M threshold
|
||||
score += normalized_recent * 30.0
|
||||
|
||||
# Flow continuation component (0-30 points)
|
||||
# If flow is continuing in same direction, dealers are likely hedging
|
||||
follow_on_ratio = current_row.get('follow_on_ratio')
|
||||
if follow_on_ratio is not None and not pd.isna(follow_on_ratio):
|
||||
# High follow-on ratio = continuation = hedge pressure
|
||||
score += follow_on_ratio * 30.0
|
||||
|
||||
return min(100.0, max(0.0, score))
|
||||
|
||||
def validate_flow_continuation(
|
||||
self,
|
||||
df: pd.DataFrame,
|
||||
row_idx: int
|
||||
) -> bool:
|
||||
"""
|
||||
Validate if flow continuation is likely based on dealer hedge pressure.
|
||||
Returns True if continuation is expected (dealers forced to hedge).
|
||||
"""
|
||||
current_row = df.iloc[row_idx]
|
||||
dealer_pressure = current_row.get('dealer_hedge_pressure_score', 0) or 0
|
||||
net_gamma = current_row.get('net_gamma_exposure_per_symbol', 0) or 0
|
||||
|
||||
# High dealer pressure + significant gamma exposure = continuation likely
|
||||
if dealer_pressure > 50.0 and abs(net_gamma) > 100000000: # 100M threshold
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def validate_flow_reversal(
|
||||
self,
|
||||
df: pd.DataFrame,
|
||||
row_idx: int
|
||||
) -> bool:
|
||||
"""
|
||||
Validate if flow reversal is likely.
|
||||
Reversal happens when:
|
||||
- Dealers finish hedging (gamma exposure neutralizes)
|
||||
- Price fails at key levels (VWAP, opening range)
|
||||
- Flow shows distribution pattern (decreasing premium, widening gaps)
|
||||
"""
|
||||
current_row = df.iloc[row_idx]
|
||||
dealer_pressure = current_row.get('dealer_hedge_pressure_score', 0) or 0
|
||||
follow_on_ratio = current_row.get('follow_on_ratio')
|
||||
flow_acceleration = current_row.get('flow_acceleration')
|
||||
|
||||
# Low dealer pressure + low follow-on ratio = reversal likely
|
||||
if dealer_pressure < 30.0:
|
||||
if follow_on_ratio is not None and follow_on_ratio < 0.3:
|
||||
return True
|
||||
|
||||
# Negative flow acceleration = flow weakening = reversal
|
||||
if flow_acceleration is not None and flow_acceleration < -10000:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def enrich_with_dealer_context(self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
Add dealer-aware flow context metrics to DataFrame.
|
||||
Adds: net_gamma_exposure_per_symbol, gamma_flip_proximity, dealer_hedge_pressure_score
|
||||
"""
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
df = df.copy()
|
||||
|
||||
# Initialize columns
|
||||
df['net_gamma_exposure_per_symbol'] = 0.0
|
||||
df['gamma_flip_proximity'] = None
|
||||
df['dealer_hedge_pressure_score'] = 0.0
|
||||
|
||||
# Sort by timestamp for proper gamma tracking
|
||||
df_sorted = df.sort_values('flow_ts_utc').reset_index(drop=True)
|
||||
|
||||
# Calculate metrics for each row
|
||||
for idx in df_sorted.index:
|
||||
original_idx = df_sorted.index[idx]
|
||||
row = df_sorted.iloc[idx]
|
||||
|
||||
symbol = row.get('symbol_norm')
|
||||
flow_ts_utc = row.get('flow_ts_utc')
|
||||
|
||||
if pd.notna(symbol) and pd.notna(flow_ts_utc):
|
||||
# Net gamma exposure
|
||||
net_gamma = self.calculate_net_gamma_exposure_per_symbol(
|
||||
df_sorted, symbol, flow_ts_utc
|
||||
)
|
||||
df.at[original_idx, 'net_gamma_exposure_per_symbol'] = float(net_gamma)
|
||||
|
||||
# Gamma flip proximity
|
||||
flip_prox = self.calculate_gamma_flip_proximity(df_sorted, idx)
|
||||
if flip_prox is not None:
|
||||
df.at[original_idx, 'gamma_flip_proximity'] = float(flip_prox)
|
||||
|
||||
# Dealer hedge pressure
|
||||
pressure = self.calculate_dealer_hedge_pressure_score(df_sorted, idx)
|
||||
df.at[original_idx, 'dealer_hedge_pressure_score'] = float(pressure)
|
||||
|
||||
logger.info(f"Dealer context enrichment complete. Mean pressure: {df['dealer_hedge_pressure_score'].mean():.2f}")
|
||||
|
||||
return df
|
||||
|
||||
|
|
@ -1,118 +0,0 @@
|
|||
"""
|
||||
Flow Decay & Reversal Validation Service
|
||||
Validates flow decay/reversal signals with anchors (premium, dealer pressure, price levels)
|
||||
"""
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from typing import Optional
|
||||
from enum import Enum
|
||||
from utils.logger import logger
|
||||
|
||||
|
||||
class FlowState(Enum):
|
||||
"""Flow state classification"""
|
||||
ACTIONABLE = "ACTIONABLE" # Flow decay/reversal is actionable (trade signal)
|
||||
INFORMATIONAL = "INFORMATIONAL" # Flow decay/reversal is informational only (no trade signal)
|
||||
|
||||
|
||||
class FlowDecayValidator:
|
||||
"""
|
||||
Validates flow decay and reversal signals.
|
||||
Flow decay/reversal is actionable ONLY IF:
|
||||
- Premium contracts (high relative premium)
|
||||
- Dealer hedge pressure decreases
|
||||
- Price fails near VWAP / opening range / key level
|
||||
Otherwise mark as INFORMATIONAL.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
# Configuration
|
||||
self.min_relative_premium_for_actionable = 60.0 # Minimum relative premium score
|
||||
self.vwap_failure_threshold_pct = 0.5 # Price within 0.5% of VWAP = failure
|
||||
self.dealer_pressure_decrease_threshold = 20.0 # Dealer pressure decrease threshold
|
||||
|
||||
def validate_flow_decay_reversal(
|
||||
self,
|
||||
df: pd.DataFrame,
|
||||
row_idx: int
|
||||
) -> str:
|
||||
"""
|
||||
Validate if flow decay/reversal is actionable.
|
||||
Returns FlowState enum value as string.
|
||||
"""
|
||||
if row_idx >= len(df):
|
||||
return FlowState.INFORMATIONAL.value
|
||||
|
||||
current_row = df.iloc[row_idx]
|
||||
|
||||
# Check 1: Premium contracts (relative premium score)
|
||||
relative_premium_score = current_row.get('relative_premium_score', 0) or 0
|
||||
if relative_premium_score < self.min_relative_premium_for_actionable:
|
||||
return FlowState.INFORMATIONAL.value
|
||||
|
||||
# Check 2: Dealer hedge pressure decreases
|
||||
# Look at recent dealer pressure trend
|
||||
symbol = current_row.get('symbol_norm')
|
||||
flow_ts_utc = current_row.get('flow_ts_utc')
|
||||
|
||||
if pd.notna(symbol) and pd.notna(flow_ts_utc):
|
||||
from datetime import timedelta
|
||||
window_start = flow_ts_utc - timedelta(minutes=30)
|
||||
|
||||
mask = (
|
||||
(df['symbol_norm'] == symbol.upper()) &
|
||||
(df['flow_ts_utc'] >= window_start) &
|
||||
(df['flow_ts_utc'] <= flow_ts_utc) &
|
||||
(df['dealer_hedge_pressure_score'].notna())
|
||||
)
|
||||
|
||||
recent_pressure = df[mask]['dealer_hedge_pressure_score']
|
||||
if len(recent_pressure) >= 2:
|
||||
current_pressure = current_row.get('dealer_hedge_pressure_score', 0) or 0
|
||||
recent_avg = recent_pressure.iloc[:-1].mean()
|
||||
|
||||
pressure_decrease = recent_avg - current_pressure
|
||||
if pressure_decrease < self.dealer_pressure_decrease_threshold:
|
||||
return FlowState.INFORMATIONAL.value
|
||||
|
||||
# Check 3: Price fails near VWAP / opening range / key level
|
||||
price_vs_vwap_pct = current_row.get('price_vs_vwap_pct')
|
||||
pct_vs_rth_open = current_row.get('pct_vs_rth_open')
|
||||
|
||||
price_failure = False
|
||||
|
||||
# VWAP failure
|
||||
if price_vs_vwap_pct is not None and not pd.isna(price_vs_vwap_pct):
|
||||
if abs(price_vs_vwap_pct) <= self.vwap_failure_threshold_pct:
|
||||
price_failure = True
|
||||
|
||||
# Opening range failure
|
||||
if pct_vs_rth_open is not None and not pd.isna(pct_vs_rth_open):
|
||||
if abs(pct_vs_rth_open) <= 0.3: # Within 0.3% of open
|
||||
price_failure = True
|
||||
|
||||
if not price_failure:
|
||||
return FlowState.INFORMATIONAL.value
|
||||
|
||||
# All checks passed: actionable
|
||||
return FlowState.ACTIONABLE.value
|
||||
|
||||
def enrich_with_flow_state(self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
Add flow state validation to DataFrame.
|
||||
Adds: flow_state
|
||||
"""
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
df = df.copy()
|
||||
df['flow_state'] = FlowState.ACTIONABLE.value # Default to actionable
|
||||
|
||||
# Only validate flow decay/reversal cases
|
||||
# For now, mark all as actionable (can be enhanced based on flow_acceleration, follow_on_ratio)
|
||||
# This is a placeholder - in production, you'd identify decay/reversal patterns first
|
||||
|
||||
logger.info(f"Flow state validation complete. Actionable: {(df['flow_state'] == FlowState.ACTIONABLE.value).sum()}")
|
||||
|
||||
return df
|
||||
|
||||
|
|
@ -1,189 +0,0 @@
|
|||
"""
|
||||
Institutional Confidence Metrics Service
|
||||
Calculates confidence scores for institutional flow signals
|
||||
"""
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from typing import Optional
|
||||
from utils.logger import logger
|
||||
|
||||
|
||||
class InstitutionalConfidence:
|
||||
"""
|
||||
Calculates institutional confidence metrics:
|
||||
- confidence_score (0-100)
|
||||
- institutional_likelihood (0-1)
|
||||
- dealer_pain_level (0-100)
|
||||
- expected_move_vs_implied (ratio)
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
# Configuration
|
||||
self.min_premium_for_institutional = 200000 # Minimum premium for institutional classification
|
||||
|
||||
def calculate_confidence_score(self, row: pd.Series) -> float:
|
||||
"""
|
||||
Calculate overall confidence score (0-100) combining multiple factors.
|
||||
Higher = more confidence in signal quality.
|
||||
"""
|
||||
score = 0.0
|
||||
|
||||
# Relative premium component (0-25 points)
|
||||
relative_premium = row.get('relative_premium_score', 0) or 0
|
||||
score += (relative_premium / 100.0) * 25.0
|
||||
|
||||
# Signal strength component (0-25 points)
|
||||
signal_strength = row.get('signal_strength', 0) or 0
|
||||
score += (signal_strength / 100.0) * 25.0
|
||||
|
||||
# Dealer pressure component (0-20 points)
|
||||
dealer_pressure = row.get('dealer_hedge_pressure_score', 0) or 0
|
||||
score += (dealer_pressure / 100.0) * 20.0
|
||||
|
||||
# Flow continuation component (0-15 points)
|
||||
follow_on_ratio = row.get('follow_on_ratio')
|
||||
if follow_on_ratio is not None and not pd.isna(follow_on_ratio):
|
||||
score += follow_on_ratio * 15.0
|
||||
|
||||
# Strike laddering component (0-15 points)
|
||||
strike_laddering = row.get('strike_laddering_detected', False)
|
||||
if strike_laddering:
|
||||
score += 15.0
|
||||
|
||||
return min(100.0, max(0.0, score))
|
||||
|
||||
def calculate_institutional_likelihood(self, row: pd.Series) -> float:
|
||||
"""
|
||||
Calculate likelihood that flow is institutional (0-1).
|
||||
Based on premium size, trade characteristics, and patterns.
|
||||
"""
|
||||
premium_num = row.get('premium_num', 0) or 0
|
||||
relative_premium = row.get('relative_premium_score', 0) or 0
|
||||
size_concentration = row.get('size_concentration_score', 0) or 0
|
||||
|
||||
likelihood = 0.0
|
||||
|
||||
# Premium size component (0-40%)
|
||||
if premium_num >= 1000000: # $1M+
|
||||
likelihood += 0.40
|
||||
elif premium_num >= 500000: # $500K+
|
||||
likelihood += 0.30
|
||||
elif premium_num >= self.min_premium_for_institutional:
|
||||
likelihood += 0.20
|
||||
|
||||
# Relative premium component (0-30%)
|
||||
if relative_premium >= 80:
|
||||
likelihood += 0.30
|
||||
elif relative_premium >= 60:
|
||||
likelihood += 0.20
|
||||
elif relative_premium >= 40:
|
||||
likelihood += 0.10
|
||||
|
||||
# Size concentration component (0-30%)
|
||||
# Institutional trades are often concentrated
|
||||
if size_concentration >= 70:
|
||||
likelihood += 0.30
|
||||
elif size_concentration >= 50:
|
||||
likelihood += 0.20
|
||||
elif size_concentration >= 30:
|
||||
likelihood += 0.10
|
||||
|
||||
return min(1.0, max(0.0, likelihood))
|
||||
|
||||
def calculate_dealer_pain_level(self, row: pd.Series) -> float:
|
||||
"""
|
||||
Calculate dealer pain level (0-100).
|
||||
Higher = dealers in pain (large gamma exposure, forced to hedge).
|
||||
"""
|
||||
dealer_pressure = row.get('dealer_hedge_pressure_score', 0) or 0
|
||||
net_gamma = abs(row.get('net_gamma_exposure_per_symbol', 0) or 0)
|
||||
gamma_flip_prox = row.get('gamma_flip_proximity')
|
||||
|
||||
pain = 0.0
|
||||
|
||||
# Dealer pressure component (0-50 points)
|
||||
pain += (dealer_pressure / 100.0) * 50.0
|
||||
|
||||
# Gamma exposure component (0-30 points)
|
||||
# Large absolute gamma = more pain
|
||||
if net_gamma > 0:
|
||||
normalized_gamma = min(1.0, net_gamma / 1000000000) # 1B threshold
|
||||
pain += normalized_gamma * 30.0
|
||||
|
||||
# Gamma flip proximity component (0-20 points)
|
||||
# Near flip = high pain (dealers forced to adjust)
|
||||
if gamma_flip_prox is not None and not pd.isna(gamma_flip_prox):
|
||||
# Absolute value of proximity (closer to 0 = more pain)
|
||||
pain += (1.0 - abs(gamma_flip_prox)) * 20.0
|
||||
|
||||
return min(100.0, max(0.0, pain))
|
||||
|
||||
def calculate_expected_move_vs_implied(self, row: pd.Series) -> Optional[float]:
|
||||
"""
|
||||
Calculate expected move vs implied move ratio.
|
||||
Estimates expected move from flow characteristics vs implied volatility.
|
||||
|
||||
Returns: ratio (expected_move / implied_move)
|
||||
- >1.0 = flow suggests larger move than implied
|
||||
- <1.0 = flow suggests smaller move than implied
|
||||
- None if cannot calculate
|
||||
"""
|
||||
# Simplified calculation: use premium and delta exposure as proxies
|
||||
premium_num = row.get('premium_num', 0) or 0
|
||||
spot_num = row.get('spot_num', 0) or 0
|
||||
delta_exposure = abs(row.get('delta_exposure', 0) or 0)
|
||||
|
||||
if pd.isna(spot_num) or spot_num == 0:
|
||||
return None
|
||||
|
||||
# Estimate expected move from premium paid
|
||||
# High premium relative to spot = expectation of larger move
|
||||
if premium_num > 0:
|
||||
prem_to_spot_ratio = premium_num / spot_num
|
||||
|
||||
# Estimate implied move (simplified: assume 1% IV = 1% move expectation)
|
||||
# This is a placeholder - in production, use actual IV from options chain
|
||||
implied_move_pct = 2.0 # Default 2% implied move
|
||||
|
||||
# Estimate expected move from premium
|
||||
# Premium of 2% of spot = expectation of ~2% move (rough approximation)
|
||||
expected_move_pct = prem_to_spot_ratio * 100.0
|
||||
|
||||
# Calculate ratio
|
||||
if implied_move_pct > 0:
|
||||
ratio = expected_move_pct / implied_move_pct
|
||||
return float(ratio)
|
||||
|
||||
return None
|
||||
|
||||
def enrich_with_confidence_metrics(self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
Add institutional confidence metrics to DataFrame.
|
||||
Adds: confidence_score, institutional_likelihood, dealer_pain_level, expected_move_vs_implied
|
||||
"""
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
df = df.copy()
|
||||
|
||||
# Initialize columns
|
||||
df['confidence_score'] = 0.0
|
||||
df['institutional_likelihood'] = 0.0
|
||||
df['dealer_pain_level'] = 0.0
|
||||
df['expected_move_vs_implied'] = None
|
||||
|
||||
# Calculate metrics
|
||||
df['confidence_score'] = df.apply(self.calculate_confidence_score, axis=1)
|
||||
df['institutional_likelihood'] = df.apply(self.calculate_institutional_likelihood, axis=1)
|
||||
df['dealer_pain_level'] = df.apply(self.calculate_dealer_pain_level, axis=1)
|
||||
|
||||
# Expected move vs implied (some rows may not have this)
|
||||
for idx in df.index:
|
||||
expected_move = self.calculate_expected_move_vs_implied(df.iloc[idx])
|
||||
if expected_move is not None:
|
||||
df.at[idx, 'expected_move_vs_implied'] = float(expected_move)
|
||||
|
||||
logger.info(f"Confidence metrics complete. Mean confidence: {df['confidence_score'].mean():.2f}")
|
||||
|
||||
return df
|
||||
|
||||
|
|
@ -1,232 +0,0 @@
|
|||
"""
|
||||
Intent Classification Service
|
||||
Replaces naive direction (BULL/BEAR) with nuanced volatility and hedging intent classification
|
||||
"""
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from typing import Optional
|
||||
from enum import Enum
|
||||
from utils.logger import logger
|
||||
from utils.error_handler import safe_divide
|
||||
|
||||
|
||||
class VolatilityIntent(Enum):
|
||||
"""Volatility and hedging intent classification"""
|
||||
LONG_VOL = "LONG_VOL" # Buying volatility (call/put buying, expecting large moves)
|
||||
SHORT_VOL = "SHORT_VOL" # Selling volatility (premium collection, expecting low vol)
|
||||
DIRECTIONAL = "DIRECTIONAL" # Directional positioning (directional bias)
|
||||
HEDGE_UNWIND = "HEDGE_UNWIND" # Hedging or unwinding existing positions
|
||||
|
||||
|
||||
class IntentClassifier:
|
||||
"""
|
||||
Classifies options flow intent beyond simple BULL/BEAR direction.
|
||||
Identifies volatility trading, hedging, and directional positioning.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
# Configuration thresholds
|
||||
self.otm_threshold_pct = 5.0 # Strikes >5% OTM considered OTM
|
||||
self.long_vol_premium_threshold = 100000 # Minimum premium for long vol signal
|
||||
self.short_vol_premium_threshold = 200000 # Minimum premium for short vol signal
|
||||
|
||||
def estimate_delta(self, row: pd.Series) -> float:
|
||||
"""
|
||||
Estimate option delta from moneyness.
|
||||
Returns delta estimate (0-1 for calls, -1-0 for puts, use absolute value).
|
||||
"""
|
||||
spot_num = row.get('spot_num', 0) or 0
|
||||
strike_num = row.get('strike_num', 0) or 0
|
||||
cp_norm = row.get('cp_norm', '')
|
||||
|
||||
if pd.isna(spot_num) or spot_num == 0 or pd.isna(strike_num) or pd.isna(cp_norm):
|
||||
return 0.5 # Default to ATM
|
||||
|
||||
moneyness_ratio = strike_num / spot_num if spot_num > 0 else 1.0
|
||||
|
||||
if cp_norm == 'CALL':
|
||||
if strike_num <= spot_num:
|
||||
# ITM call: delta 0.5 to 1.0
|
||||
delta = 0.5 + (1.0 - moneyness_ratio) * 0.5
|
||||
else:
|
||||
# OTM call: delta 0.5 to 0.0
|
||||
delta = max(0.0, 0.5 * (1.5 - moneyness_ratio) / 0.5)
|
||||
else: # PUT
|
||||
if strike_num >= spot_num:
|
||||
# ITM put: delta -0.5 to -1.0 (return absolute)
|
||||
delta = abs(0.5 + (moneyness_ratio - 1.0) * 0.5)
|
||||
else:
|
||||
# OTM put: delta -0.5 to 0.0 (return absolute)
|
||||
delta = max(0.0, 0.5 * (1.0 - moneyness_ratio) / 0.5)
|
||||
|
||||
return float(delta)
|
||||
|
||||
def estimate_gamma(self, row: pd.Series, delta: float) -> float:
|
||||
"""
|
||||
Estimate option gamma (sensitivity of delta to price changes).
|
||||
Higher near ATM, lower ITM/OTM.
|
||||
Returns gamma estimate (positive value).
|
||||
"""
|
||||
spot_num = row.get('spot_num', 0) or 0
|
||||
strike_num = row.get('strike_num', 0) or 0
|
||||
|
||||
if pd.isna(spot_num) or spot_num == 0 or pd.isna(strike_num):
|
||||
return 0.0
|
||||
|
||||
# Gamma is highest at-the-money
|
||||
moneyness_ratio = strike_num / spot_num if spot_num > 0 else 1.0
|
||||
|
||||
# Simple approximation: gamma peaks at 1.0 (ATM) and decays away
|
||||
# Use normal distribution approximation
|
||||
distance_from_atm = abs(moneyness_ratio - 1.0)
|
||||
|
||||
# Gamma ≈ exp(-distance^2 / (2*sigma^2)) where sigma ≈ 0.1 (10% moneyness)
|
||||
gamma = np.exp(-(distance_from_atm ** 2) / (2 * 0.01))
|
||||
|
||||
return float(gamma)
|
||||
|
||||
def calculate_delta_exposure(self, row: pd.Series) -> float:
|
||||
"""
|
||||
Calculate delta exposure: contracts * delta * 100 * spot_price.
|
||||
Positive = long delta (bullish), Negative = short delta (bearish).
|
||||
"""
|
||||
premium_num = row.get('premium_num', 0) or 0
|
||||
spot_num = row.get('spot_num', 0) or 0
|
||||
vol_num = row.get('vol_num', 0) or 0
|
||||
cp_norm = row.get('cp_norm', '')
|
||||
side_norm = row.get('side_norm', '')
|
||||
|
||||
if pd.isna(spot_num) or spot_num == 0:
|
||||
return 0.0
|
||||
|
||||
# Estimate delta
|
||||
delta = self.estimate_delta(row)
|
||||
|
||||
# Determine sign based on call/put and buy/sell
|
||||
if cp_norm == 'CALL':
|
||||
if side_norm == 'BUY':
|
||||
delta_sign = 1.0 # Long calls = positive delta
|
||||
else: # SELL
|
||||
delta_sign = -1.0 # Short calls = negative delta
|
||||
else: # PUT
|
||||
if side_norm == 'BUY':
|
||||
delta_sign = -1.0 # Long puts = negative delta
|
||||
else: # SELL
|
||||
delta_sign = 1.0 # Short puts = positive delta
|
||||
|
||||
# Delta exposure = contracts * delta * 100 * spot
|
||||
# Use volume as proxy for contracts
|
||||
contracts = vol_num if not pd.isna(vol_num) else 0
|
||||
delta_exposure = contracts * delta * delta_sign * 100 * spot_num
|
||||
|
||||
return float(delta_exposure)
|
||||
|
||||
def calculate_gamma_exposure(self, row: pd.Series) -> float:
|
||||
"""
|
||||
Calculate gamma exposure: contracts * gamma * 100 * spot_price^2.
|
||||
Positive = long gamma (volatility long), Negative = short gamma (volatility short).
|
||||
"""
|
||||
premium_num = row.get('premium_num', 0) or 0
|
||||
spot_num = row.get('spot_num', 0) or 0
|
||||
vol_num = row.get('vol_num', 0) or 0
|
||||
cp_norm = row.get('cp_norm', '')
|
||||
side_norm = row.get('side_norm', '')
|
||||
|
||||
if pd.isna(spot_num) or spot_num == 0:
|
||||
return 0.0
|
||||
|
||||
# Estimate delta and gamma
|
||||
delta = self.estimate_delta(row)
|
||||
gamma = self.estimate_gamma(row, delta)
|
||||
|
||||
# Determine sign: buying options = long gamma, selling = short gamma
|
||||
if side_norm == 'BUY':
|
||||
gamma_sign = 1.0 # Long gamma
|
||||
else: # SELL
|
||||
gamma_sign = -1.0 # Short gamma
|
||||
|
||||
# Gamma exposure = contracts * gamma * 100 * spot^2
|
||||
contracts = vol_num if not pd.isna(vol_num) else 0
|
||||
gamma_exposure = contracts * gamma * gamma_sign * 100 * (spot_num ** 2)
|
||||
|
||||
return float(gamma_exposure)
|
||||
|
||||
def classify_volatility_intent(self, row: pd.Series) -> str:
|
||||
"""
|
||||
Classify volatility intent based on trade characteristics.
|
||||
Returns VolatilityIntent enum value as string.
|
||||
"""
|
||||
premium_num = row.get('premium_num', 0) or 0
|
||||
spot_num = row.get('spot_num', 0) or 0
|
||||
strike_num = row.get('strike_num', 0) or 0
|
||||
cp_norm = row.get('cp_norm', '')
|
||||
side_norm = row.get('side_norm', '')
|
||||
vol_num = row.get('vol_num', 0) or 0
|
||||
oi_num = row.get('oi_num', 0) or 0
|
||||
|
||||
# Calculate moneyness
|
||||
if pd.isna(spot_num) or spot_num == 0 or pd.isna(strike_num):
|
||||
mny_pct = 0.0
|
||||
else:
|
||||
if cp_norm == 'CALL':
|
||||
mny_pct = ((strike_num - spot_num) / spot_num * 100.0) if spot_num > 0 else 0
|
||||
else: # PUT
|
||||
mny_pct = ((spot_num - strike_num) / spot_num * 100.0) if spot_num > 0 else 0
|
||||
|
||||
is_otm = abs(mny_pct) > self.otm_threshold_pct
|
||||
|
||||
# Long volatility: buying OTM options (calls or puts)
|
||||
# High premium, OTM strikes, buying side
|
||||
if (side_norm == 'BUY' and
|
||||
is_otm and
|
||||
premium_num >= self.long_vol_premium_threshold):
|
||||
return VolatilityIntent.LONG_VOL.value
|
||||
|
||||
# Short volatility: selling options, collecting premium
|
||||
# High premium, selling side, vol > OI (opening new short positions)
|
||||
if (side_norm == 'SELL' and
|
||||
premium_num >= self.short_vol_premium_threshold and
|
||||
vol_num > oi_num):
|
||||
return VolatilityIntent.SHORT_VOL.value
|
||||
|
||||
# Directional: ITM options, buying side, strong directional flow
|
||||
if (side_norm == 'BUY' and
|
||||
not is_otm and
|
||||
premium_num >= 50000):
|
||||
return VolatilityIntent.DIRECTIONAL.value
|
||||
|
||||
# Hedge/Unwind: Selling existing positions (vol < OI)
|
||||
# Or buying protective puts/calls
|
||||
if (side_norm == 'SELL' and vol_num < oi_num) or \
|
||||
(side_norm == 'BUY' and is_otm and premium_num < self.long_vol_premium_threshold):
|
||||
return VolatilityIntent.HEDGE_UNWIND.value
|
||||
|
||||
# Default: directional (fallback)
|
||||
return VolatilityIntent.DIRECTIONAL.value
|
||||
|
||||
def enrich_with_intent_classification(self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
Add intent classification metrics to DataFrame.
|
||||
Adds: delta_exposure, gamma_exposure, volatility_intent
|
||||
"""
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
df = df.copy()
|
||||
|
||||
# Initialize columns
|
||||
df['delta_exposure'] = 0.0
|
||||
df['gamma_exposure'] = 0.0
|
||||
df['volatility_intent'] = VolatilityIntent.DIRECTIONAL.value
|
||||
|
||||
# Calculate metrics
|
||||
df['delta_exposure'] = df.apply(self.calculate_delta_exposure, axis=1)
|
||||
df['gamma_exposure'] = df.apply(self.calculate_gamma_exposure, axis=1)
|
||||
df['volatility_intent'] = df.apply(self.classify_volatility_intent, axis=1)
|
||||
|
||||
# Log distribution
|
||||
intent_counts = df['volatility_intent'].value_counts()
|
||||
logger.info(f"Intent classification complete. Distribution: {intent_counts.to_dict()}")
|
||||
|
||||
return df
|
||||
|
||||
|
|
@ -1,104 +0,0 @@
|
|||
"""
|
||||
Market Regime Detection Service
|
||||
Identifies market regime to gate trade signal generation
|
||||
"""
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from typing import Optional
|
||||
from enum import Enum
|
||||
from utils.logger import logger
|
||||
|
||||
|
||||
class MarketRegime(Enum):
|
||||
"""Market regime classification"""
|
||||
TREND = "TREND" # Trending market (continuation bias)
|
||||
RANGE = "RANGE" # Range-bound market (fade or vol-sell bias)
|
||||
HIGH_VOL_EVENT = "HIGH_VOL_EVENT" # High volatility event (volatility expansion bias)
|
||||
|
||||
|
||||
class MarketRegimeDetector:
|
||||
"""
|
||||
Detects market regime to inform trade signal bias:
|
||||
- Trend: continuation trades preferred
|
||||
- Range: mean reversion / vol selling preferred
|
||||
- High Vol Event: volatility expansion trades preferred
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
# Configuration
|
||||
self.trend_threshold_pct = 1.5 # >1.5% move = trending
|
||||
self.range_threshold_pct = 0.5 # <0.5% move = ranging
|
||||
self.high_vol_threshold_pct = 3.0 # >3% move = high vol event
|
||||
self.lookback_minutes = 60 # Lookback window for regime detection
|
||||
|
||||
def detect_regime(
|
||||
self,
|
||||
df: pd.DataFrame,
|
||||
row_idx: int
|
||||
) -> str:
|
||||
"""
|
||||
Detect market regime for a given flow event.
|
||||
Returns MarketRegime enum value as string.
|
||||
"""
|
||||
if row_idx >= len(df):
|
||||
return MarketRegime.RANGE.value
|
||||
|
||||
current_row = df.iloc[row_idx]
|
||||
symbol = current_row.get('symbol_norm')
|
||||
flow_ts_utc = current_row.get('flow_ts_utc')
|
||||
|
||||
if pd.isna(symbol) or pd.isna(flow_ts_utc):
|
||||
return MarketRegime.RANGE.value
|
||||
|
||||
# Get price movement over lookback window
|
||||
pct_vs_rth_open = current_row.get('pct_vs_rth_open')
|
||||
pct_vs_prior_close = current_row.get('pct_vs_prior_close')
|
||||
pct_15m_momo = current_row.get('pct_15m_momo')
|
||||
|
||||
# Use most appropriate price metric
|
||||
price_move_pct = None
|
||||
if pct_vs_rth_open is not None and not pd.isna(pct_vs_rth_open):
|
||||
price_move_pct = abs(pct_vs_rth_open)
|
||||
elif pct_vs_prior_close is not None and not pd.isna(pct_vs_prior_close):
|
||||
price_move_pct = abs(pct_vs_prior_close)
|
||||
elif pct_15m_momo is not None and not pd.isna(pct_15m_momo):
|
||||
price_move_pct = abs(pct_15m_momo)
|
||||
|
||||
if price_move_pct is None:
|
||||
return MarketRegime.RANGE.value # Default to range
|
||||
|
||||
# Classify regime
|
||||
if price_move_pct >= self.high_vol_threshold_pct:
|
||||
return MarketRegime.HIGH_VOL_EVENT.value
|
||||
elif price_move_pct >= self.trend_threshold_pct:
|
||||
return MarketRegime.TREND.value
|
||||
elif price_move_pct <= self.range_threshold_pct:
|
||||
return MarketRegime.RANGE.value
|
||||
else:
|
||||
# Between range and trend threshold - classify based on momentum
|
||||
if pct_15m_momo is not None and not pd.isna(pct_15m_momo):
|
||||
if abs(pct_15m_momo) > 0.75:
|
||||
return MarketRegime.TREND.value
|
||||
return MarketRegime.RANGE.value
|
||||
|
||||
def enrich_with_market_regime(self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
Add market regime classification to DataFrame.
|
||||
Adds: market_regime
|
||||
"""
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
df = df.copy()
|
||||
df['market_regime'] = MarketRegime.RANGE.value
|
||||
|
||||
# Detect regime for each row
|
||||
for idx in df.index:
|
||||
regime = self.detect_regime(df, idx)
|
||||
df.at[idx, 'market_regime'] = regime
|
||||
|
||||
regime_counts = df['market_regime'].value_counts()
|
||||
logger.info(f"Market regime detection complete. Distribution: {regime_counts.to_dict()}")
|
||||
|
||||
return df
|
||||
|
||||
|
|
@ -1,213 +0,0 @@
|
|||
"""
|
||||
Tier-0 Noise Rejection Service
|
||||
Filters out low-quality signals before enrichment to reduce processing overhead
|
||||
"""
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from typing import Optional
|
||||
from datetime import timedelta
|
||||
from utils.logger import logger
|
||||
from utils.error_handler import safe_divide
|
||||
|
||||
|
||||
class NoiseRejector:
|
||||
"""
|
||||
Rejects early-stage noise before enrichment to optimize processing.
|
||||
|
||||
Rejects if:
|
||||
- Single isolated trade (no repeat activity)
|
||||
- Far OTM weekly lottos
|
||||
- Delta-adjusted premium below threshold
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
# Configuration
|
||||
self.repeat_activity_window_minutes = 30 # Minutes to look for repeat activity
|
||||
self.min_delta_adjusted_premium = 50000 # Minimum delta-adjusted premium
|
||||
self.max_otm_percentage = 15.0 # Reject strikes >15% OTM
|
||||
self.min_expiry_days = 1 # Minimum days to expiry (reject same-day/weekly lottos)
|
||||
|
||||
def is_isolated_trade(
|
||||
self,
|
||||
df: pd.DataFrame,
|
||||
row_idx: int
|
||||
) -> bool:
|
||||
"""
|
||||
Check if trade is isolated (no repeat activity within window).
|
||||
Returns True if isolated (should reject).
|
||||
"""
|
||||
if row_idx >= len(df):
|
||||
return True
|
||||
|
||||
current_row = df.iloc[row_idx]
|
||||
symbol = current_row.get('symbol_norm')
|
||||
direction = current_row.get('direction')
|
||||
flow_ts_utc = current_row.get('flow_ts_utc')
|
||||
|
||||
if pd.isna(symbol) or pd.isna(flow_ts_utc):
|
||||
return True
|
||||
|
||||
# Look for trades in same direction within window
|
||||
window_start = flow_ts_utc - timedelta(minutes=self.repeat_activity_window_minutes)
|
||||
|
||||
same_symbol = df['symbol_norm'] == symbol
|
||||
same_direction = df['direction'] == direction
|
||||
in_window = (df['flow_ts_utc'] >= window_start) & (df['flow_ts_utc'] < flow_ts_utc)
|
||||
|
||||
# Exclude current row
|
||||
other_trades = df[same_symbol & same_direction & in_window]
|
||||
|
||||
# If no other trades in window, it's isolated
|
||||
return len(other_trades) == 0
|
||||
|
||||
def is_far_otm_lotto(
|
||||
self,
|
||||
row: pd.Series
|
||||
) -> bool:
|
||||
"""
|
||||
Check if trade is a far OTM weekly lotto (low probability, low signal value).
|
||||
Returns True if should reject.
|
||||
"""
|
||||
spot_num = row.get('spot_num', 0) or 0
|
||||
strike_num = row.get('strike_num', 0) or 0
|
||||
cp_norm = row.get('cp_norm', '')
|
||||
exp_date = row.get('exp_date')
|
||||
flow_date = row.get('flow_date_cst')
|
||||
|
||||
if pd.isna(spot_num) or spot_num == 0 or pd.isna(strike_num) or pd.isna(cp_norm):
|
||||
return False
|
||||
|
||||
# Calculate moneyness percentage
|
||||
if cp_norm == 'CALL':
|
||||
otm_pct = ((strike_num - spot_num) / spot_num * 100.0) if spot_num > 0 else 0
|
||||
else: # PUT
|
||||
otm_pct = ((spot_num - strike_num) / spot_num * 100.0) if spot_num > 0 else 0
|
||||
|
||||
# Reject if >15% OTM
|
||||
if otm_pct > self.max_otm_percentage:
|
||||
return True
|
||||
|
||||
# Check if weekly lotto (expiry within 7 days)
|
||||
if pd.notna(exp_date) and pd.notna(flow_date):
|
||||
try:
|
||||
if isinstance(exp_date, str):
|
||||
from datetime import datetime
|
||||
exp_date = datetime.strptime(exp_date, '%Y-%m-%d').date()
|
||||
if isinstance(flow_date, str):
|
||||
from datetime import datetime
|
||||
flow_date = datetime.strptime(flow_date, '%Y-%m-%d').date()
|
||||
|
||||
days_to_expiry = (exp_date - flow_date).days
|
||||
|
||||
# Reject weekly lottos that are also far OTM
|
||||
if days_to_expiry <= 7 and otm_pct > 10.0:
|
||||
return True
|
||||
except (ValueError, TypeError, AttributeError):
|
||||
pass
|
||||
|
||||
return False
|
||||
|
||||
def calculate_delta_adjusted_premium(
|
||||
self,
|
||||
row: pd.Series
|
||||
) -> float:
|
||||
"""
|
||||
Calculate delta-adjusted premium (premium * |delta|).
|
||||
Approximates intrinsic value component of premium.
|
||||
|
||||
For simplicity, we estimate delta from moneyness:
|
||||
- ATM: delta ≈ 0.5
|
||||
- ITM: delta increases toward 1.0
|
||||
- OTM: delta decreases toward 0.0
|
||||
"""
|
||||
premium_num = row.get('premium_num', 0) or 0
|
||||
spot_num = row.get('spot_num', 0) or 0
|
||||
strike_num = row.get('strike_num', 0) or 0
|
||||
cp_norm = row.get('cp_norm', '')
|
||||
|
||||
if pd.isna(premium_num) or premium_num == 0 or pd.isna(spot_num) or spot_num == 0:
|
||||
return 0.0
|
||||
|
||||
# Estimate delta from moneyness
|
||||
if cp_norm == 'CALL':
|
||||
if strike_num <= spot_num:
|
||||
# ITM call: delta from 0.5 to 1.0
|
||||
moneyness_ratio = strike_num / spot_num if spot_num > 0 else 1.0
|
||||
# Strike at spot = 0.5, strike at 0 = 1.0
|
||||
estimated_delta = 0.5 + (1.0 - moneyness_ratio) * 0.5
|
||||
else:
|
||||
# OTM call: delta from 0.5 to 0.0
|
||||
moneyness_ratio = strike_num / spot_num if spot_num > 0 else 1.0
|
||||
# Strike at spot = 0.5, strike at 1.15x spot = 0.0
|
||||
estimated_delta = max(0.0, 0.5 * (1.5 - moneyness_ratio) / 0.5)
|
||||
else: # PUT
|
||||
if strike_num >= spot_num:
|
||||
# ITM put: delta from -0.5 to -1.0 (use absolute value)
|
||||
moneyness_ratio = strike_num / spot_num if spot_num > 0 else 1.0
|
||||
estimated_delta = 0.5 + (moneyness_ratio - 1.0) * 0.5
|
||||
else:
|
||||
# OTM put: delta from 0.5 to 0.0 (use absolute value)
|
||||
moneyness_ratio = strike_num / spot_num if spot_num > 0 else 1.0
|
||||
estimated_delta = max(0.0, 0.5 * (1.0 - moneyness_ratio) / 0.5)
|
||||
|
||||
# Delta-adjusted premium
|
||||
delta_adj_premium = premium_num * abs(estimated_delta)
|
||||
|
||||
return float(delta_adj_premium)
|
||||
|
||||
def should_reject(
|
||||
self,
|
||||
df: pd.DataFrame,
|
||||
row_idx: int
|
||||
) -> bool:
|
||||
"""
|
||||
Determine if row should be rejected as noise.
|
||||
Returns True if should reject (mark early_noise_reject = True).
|
||||
"""
|
||||
if row_idx >= len(df):
|
||||
return True
|
||||
|
||||
row = df.iloc[row_idx]
|
||||
|
||||
# Reject isolated trades
|
||||
if self.is_isolated_trade(df, row_idx):
|
||||
return True
|
||||
|
||||
# Reject far OTM lottos
|
||||
if self.is_far_otm_lotto(row):
|
||||
return True
|
||||
|
||||
# Reject if delta-adjusted premium below threshold
|
||||
delta_adj_premium = self.calculate_delta_adjusted_premium(row)
|
||||
if delta_adj_premium < self.min_delta_adjusted_premium:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def mark_noise_rejections(self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
Mark rows for early noise rejection.
|
||||
Adds: early_noise_reject (boolean)
|
||||
"""
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
df = df.copy()
|
||||
|
||||
# Initialize column
|
||||
df['early_noise_reject'] = False
|
||||
|
||||
# Sort by timestamp for proper isolation detection
|
||||
df_sorted = df.sort_values('flow_ts_utc').reset_index(drop=True)
|
||||
|
||||
# Mark rejections
|
||||
for idx in df_sorted.index:
|
||||
if self.should_reject(df_sorted, idx):
|
||||
original_idx = df_sorted.index[idx]
|
||||
df.at[original_idx, 'early_noise_reject'] = True
|
||||
|
||||
rejection_count = df['early_noise_reject'].sum()
|
||||
logger.info(f"Noise rejection: {rejection_count}/{len(df)} rows marked for rejection ({rejection_count/len(df)*100:.1f}%)")
|
||||
|
||||
return df
|
||||
|
||||
|
|
@ -84,10 +84,20 @@ class OptionsFlowProcessor:
|
|||
|
||||
def parse_timestamp(self, date_str, time_str) -> Optional[datetime]:
|
||||
"""Parse timestamp from date and time strings"""
|
||||
if pd.isna(date_str) or pd.isna(time_str):
|
||||
if pd.isna(date_str):
|
||||
return None
|
||||
|
||||
date_str = str(date_str).strip()
|
||||
|
||||
# If time_str is None/NaN, parse just the date and use midnight as default
|
||||
if pd.isna(time_str) or time_str is None:
|
||||
# Try to parse just the date
|
||||
date_obj = self.parse_date(date_str)
|
||||
if date_obj:
|
||||
# Use midnight (00:00:00) as default time
|
||||
return datetime.combine(date_obj.date(), datetime.min.time())
|
||||
return None
|
||||
|
||||
time_str = str(time_str).strip()
|
||||
|
||||
# Try various formats
|
||||
|
|
|
|||
|
|
@ -139,21 +139,7 @@ class OutputFormatter:
|
|||
'Rocket_with_mny': 'Rocket',
|
||||
}
|
||||
|
||||
# Only rename columns that exist (don't drop Phase 1 columns)
|
||||
existing_mapping = {k: v for k, v in column_mapping.items() if k in df.columns}
|
||||
df = df.rename(columns=existing_mapping)
|
||||
|
||||
# Ensure Phase 1 columns are preserved (don't drop them)
|
||||
# Phase 1 columns should remain as-is (signal_tier, checklist_score, etc.)
|
||||
|
||||
# Ensure all new institutional analytics columns are preserved:
|
||||
# - premium_zscore, premium_percentile_intraday, relative_premium_score
|
||||
# - aggression_score, size_concentration_score, repeat_trade_velocity, strike_clustering_score, signal_strength
|
||||
# - early_noise_reject, flow_acceleration, time_between_hits, follow_on_ratio, strike_laddering_detected
|
||||
# - delta_exposure, gamma_exposure, volatility_intent
|
||||
# - net_gamma_exposure_per_symbol, gamma_flip_proximity, dealer_hedge_pressure_score
|
||||
# - market_regime, flow_state
|
||||
# - confidence_score, institutional_likelihood, dealer_pain_level, expected_move_vs_implied
|
||||
df = df.rename(columns=column_mapping)
|
||||
|
||||
return df
|
||||
|
||||
|
|
|
|||
|
|
@ -1,15 +1,12 @@
|
|||
"""
|
||||
Price Context Service
|
||||
Handles price data enrichment for options flow
|
||||
Uses Yahoo Finance for real-time data instead of database
|
||||
"""
|
||||
import pandas as pd
|
||||
import asyncpg
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Dict, Optional
|
||||
import pytz
|
||||
from services.yahoo_finance_service import YahooFinanceService
|
||||
from utils.logger import logger
|
||||
|
||||
|
||||
class PriceContextService:
|
||||
|
|
@ -18,8 +15,6 @@ class PriceContextService:
|
|||
def __init__(self, pool: asyncpg.Pool):
|
||||
self.pool = pool
|
||||
self.ct_tz = pytz.timezone('America/Chicago')
|
||||
self.use_yahoo_finance = False # Temporarily disabled - focus on Phase 1 button
|
||||
self.yahoo_service = YahooFinanceService() if self.use_yahoo_finance else None
|
||||
|
||||
def get_session_bucket(self, flow_ts_local: datetime) -> str:
|
||||
"""Determine session bucket from flow timestamp"""
|
||||
|
|
@ -42,108 +37,65 @@ class PriceContextService:
|
|||
self,
|
||||
symbol: str,
|
||||
timestamp: datetime,
|
||||
pool: asyncpg.Pool = None # Not used anymore, kept for compatibility
|
||||
pool: asyncpg.Pool
|
||||
) -> Optional[Dict]:
|
||||
"""Get price data at or before a specific timestamp using Yahoo Finance"""
|
||||
if not self.use_yahoo_finance or not self.yahoo_service:
|
||||
return None # Yahoo Finance disabled
|
||||
try:
|
||||
price = self.yahoo_service.get_price_at_time(symbol, timestamp)
|
||||
if price:
|
||||
return {
|
||||
'close': price,
|
||||
'high': price, # Approximate
|
||||
'low': price, # Approximate
|
||||
'volume': None,
|
||||
'ts': timestamp
|
||||
}
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.debug(f"Error getting price at time from Yahoo Finance: {e}")
|
||||
"""Get price data at or before a specific timestamp"""
|
||||
async with pool.acquire() as conn:
|
||||
row = await conn.fetchrow("""
|
||||
SELECT close, high, low, volume, ts
|
||||
FROM prices_intraday_1m
|
||||
WHERE UPPER(symbol) = $1
|
||||
AND ts <= $2
|
||||
ORDER BY ts DESC
|
||||
LIMIT 1
|
||||
""", symbol.upper(), timestamp)
|
||||
|
||||
if row:
|
||||
return dict(row)
|
||||
return None
|
||||
|
||||
async def get_rth_open(
|
||||
self,
|
||||
symbol: str,
|
||||
flow_date_cst: datetime.date,
|
||||
pool: asyncpg.Pool = None # Not used anymore, kept for compatibility
|
||||
pool: asyncpg.Pool
|
||||
) -> Optional[Dict]:
|
||||
"""Get first RTH bar for a given date using Yahoo Finance"""
|
||||
if not self.use_yahoo_finance or not self.yahoo_service:
|
||||
return None # Yahoo Finance disabled
|
||||
try:
|
||||
rth_open_price = self.yahoo_service.get_rth_open(symbol, flow_date_cst)
|
||||
if rth_open_price:
|
||||
rth_time = self.ct_tz.localize(
|
||||
datetime.combine(flow_date_cst, datetime.min.time().replace(hour=9, minute=30))
|
||||
)
|
||||
return {
|
||||
'open': rth_open_price,
|
||||
'ts': rth_time
|
||||
}
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.debug(f"Error getting RTH open from Yahoo Finance: {e}")
|
||||
"""Get first RTH bar for a given date"""
|
||||
async with pool.acquire() as conn:
|
||||
row = await conn.fetchrow("""
|
||||
SELECT open, ts
|
||||
FROM prices_intraday_1m
|
||||
WHERE UPPER(symbol) = $1
|
||||
AND (timezone('America/Chicago', ts))::date = $2
|
||||
AND (timezone('America/Chicago', ts))::time >= time '09:30:00'
|
||||
ORDER BY ts ASC
|
||||
LIMIT 1
|
||||
""", symbol.upper(), flow_date_cst)
|
||||
|
||||
if row:
|
||||
return dict(row)
|
||||
return None
|
||||
|
||||
async def get_prior_close(
|
||||
self,
|
||||
symbol: str,
|
||||
flow_date_cst: datetime.date,
|
||||
pool: asyncpg.Pool = None # Not used anymore, kept for compatibility
|
||||
pool: asyncpg.Pool
|
||||
) -> Optional[float]:
|
||||
"""Get prior day's close using Yahoo Finance"""
|
||||
if not self.use_yahoo_finance or not self.yahoo_service:
|
||||
return None # Yahoo Finance disabled
|
||||
try:
|
||||
return self.yahoo_service.get_prior_close(symbol, flow_date_cst)
|
||||
except Exception as e:
|
||||
logger.debug(f"Error getting prior close from Yahoo Finance: {e}")
|
||||
return None
|
||||
|
||||
async def calculate_vwap_at_time(
|
||||
self,
|
||||
symbol: str,
|
||||
timestamp: datetime,
|
||||
pool: asyncpg.Pool = None # Not used anymore, kept for compatibility
|
||||
) -> Optional[Dict]:
|
||||
"""
|
||||
Calculate VWAP (Volume Weighted Average Price) up to the given timestamp
|
||||
for the trading day using Yahoo Finance. VWAP = SUM(price * volume) / SUM(volume)
|
||||
"""
|
||||
try:
|
||||
# Convert timestamp to CST if needed
|
||||
if timestamp.tzinfo is None:
|
||||
timestamp = self.ct_tz.localize(timestamp)
|
||||
else:
|
||||
timestamp = timestamp.astimezone(self.ct_tz)
|
||||
"""Get prior day's close"""
|
||||
async with pool.acquire() as conn:
|
||||
prior_date = flow_date_cst - timedelta(days=1)
|
||||
row = await conn.fetchrow("""
|
||||
SELECT close
|
||||
FROM prices_daily
|
||||
WHERE UPPER(symbol) = $1
|
||||
AND "Date" = $2
|
||||
ORDER BY "Date" DESC
|
||||
LIMIT 1
|
||||
""", symbol.upper(), prior_date)
|
||||
|
||||
# Convert to Eastern Time for market hours check (US market opens at 9:30 AM ET)
|
||||
et_tz = pytz.timezone('America/New_York')
|
||||
timestamp_et = timestamp.astimezone(et_tz) if timestamp.tzinfo else et_tz.localize(timestamp)
|
||||
trade_time = timestamp_et.time()
|
||||
|
||||
# Only calculate VWAP if we're at or after RTH open (9:30 AM ET)
|
||||
if trade_time < pd.Timestamp('09:30:00').time():
|
||||
# Before RTH, return None (no VWAP yet)
|
||||
logger.debug(f"Before RTH open (9:30 AM ET): {timestamp_et.strftime('%H:%M:%S %Z')}")
|
||||
return None
|
||||
|
||||
# Calculate VWAP from RTH open (9:30 AM) to the given timestamp
|
||||
if not self.use_yahoo_finance or not self.yahoo_service:
|
||||
return None # Yahoo Finance disabled
|
||||
vwap = await self.yahoo_service.calculate_vwap(symbol, timestamp)
|
||||
|
||||
if vwap:
|
||||
return {
|
||||
'vwap': vwap,
|
||||
'total_volume': None, # Yahoo Finance doesn't provide this easily
|
||||
'bar_count': None
|
||||
}
|
||||
|
||||
return None
|
||||
except Exception as e:
|
||||
logger.debug(f"Error calculating VWAP from Yahoo Finance: {e}")
|
||||
if row:
|
||||
return row['close']
|
||||
return None
|
||||
|
||||
async def enrich_flow_with_prices(
|
||||
|
|
@ -160,89 +112,111 @@ class PriceContextService:
|
|||
if df.empty:
|
||||
return df
|
||||
|
||||
# Batch fetch all price data using Yahoo Finance
|
||||
# Get unique symbols and dates
|
||||
unique_symbols = df['symbol_norm'].unique().tolist()
|
||||
unique_dates = df['flow_date_cst'].unique().tolist()
|
||||
|
||||
# Batch fetch prices at flow times using Yahoo Finance
|
||||
# Group by symbol to reduce API calls
|
||||
price_data_dict = {}
|
||||
for symbol in unique_symbols:
|
||||
symbol_flows = df[df['symbol_norm'] == symbol]
|
||||
timestamps = symbol_flows['flow_ts_utc'].dropna().unique().tolist()
|
||||
# Batch fetch all price data
|
||||
async with pool.acquire() as conn:
|
||||
# Get unique symbols and dates
|
||||
unique_symbols = df['symbol_norm'].unique().tolist()
|
||||
unique_dates = df['flow_date_cst'].unique().tolist()
|
||||
|
||||
if not timestamps:
|
||||
continue
|
||||
|
||||
# For each timestamp, get the latest price <= that timestamp
|
||||
# Only fetch unique timestamps per symbol
|
||||
for ts in timestamps:
|
||||
try:
|
||||
price_data = await self.get_price_at_time(symbol, ts)
|
||||
if price_data:
|
||||
price_data_dict[(symbol.upper(), ts)] = price_data
|
||||
# Batch fetch prices at flow times (for each symbol, get prices <= each timestamp)
|
||||
price_data_dict = {}
|
||||
for symbol in unique_symbols:
|
||||
symbol_flows = df[df['symbol_norm'] == symbol]
|
||||
timestamps = symbol_flows['flow_ts_utc'].dropna().unique().tolist()
|
||||
|
||||
if not timestamps:
|
||||
continue
|
||||
|
||||
# For each timestamp, get the latest price <= that timestamp
|
||||
for ts in timestamps:
|
||||
row = await conn.fetchrow("""
|
||||
SELECT close, high, low, volume, ts
|
||||
FROM prices_intraday_1m
|
||||
WHERE UPPER(symbol) = $1
|
||||
AND ts <= $2
|
||||
ORDER BY ts DESC
|
||||
LIMIT 1
|
||||
""", symbol.upper(), ts)
|
||||
|
||||
# Also get 5m and 15m ago prices (only if needed)
|
||||
# Skip if we already have this timestamp
|
||||
ts_5m = ts - timedelta(minutes=5)
|
||||
if (symbol.upper(), ts_5m) not in price_data_dict and self.use_yahoo_finance and self.yahoo_service:
|
||||
try:
|
||||
price_5m = self.yahoo_service.get_price_at_time(symbol, ts_5m)
|
||||
if price_5m:
|
||||
price_data_dict[(symbol.upper(), ts_5m)] = {'close': price_5m}
|
||||
except Exception as e:
|
||||
pass
|
||||
if row:
|
||||
price_data_dict[(symbol.upper(), ts)] = dict(row)
|
||||
|
||||
ts_15m = ts - timedelta(minutes=15)
|
||||
if (symbol.upper(), ts_15m) not in price_data_dict and self.use_yahoo_finance and self.yahoo_service:
|
||||
try:
|
||||
price_15m = self.yahoo_service.get_price_at_time(symbol, ts_15m)
|
||||
if price_15m:
|
||||
price_data_dict[(symbol.upper(), ts_15m)] = {'close': price_15m}
|
||||
except Exception as e:
|
||||
pass
|
||||
except Exception as e:
|
||||
logger.debug(f"Error fetching price for {symbol} at {ts}: {e}")
|
||||
# Add small delay on error to avoid rate limiting
|
||||
import asyncio
|
||||
await asyncio.sleep(0.2)
|
||||
|
||||
# Batch fetch RTH opens using Yahoo Finance
|
||||
rth_open_dict = {}
|
||||
for symbol in unique_symbols:
|
||||
for date in unique_dates:
|
||||
try:
|
||||
rth_data = await self.get_rth_open(symbol, date)
|
||||
if rth_data:
|
||||
rth_open_dict[(symbol.upper(), date)] = rth_data
|
||||
except Exception as e:
|
||||
logger.debug(f"Error fetching RTH open for {symbol} on {date}: {e}")
|
||||
|
||||
# Batch fetch prior closes using Yahoo Finance
|
||||
prior_close_dict = {}
|
||||
for symbol in unique_symbols:
|
||||
for date in unique_dates:
|
||||
try:
|
||||
prior_close = await self.get_prior_close(symbol, date)
|
||||
if prior_close:
|
||||
prior_close_dict[(symbol.upper(), date)] = prior_close
|
||||
except Exception as e:
|
||||
logger.debug(f"Error fetching prior close for {symbol} before {date}: {e}")
|
||||
|
||||
# Batch fetch VWAP at signal times using Yahoo Finance
|
||||
vwap_dict = {}
|
||||
for symbol in unique_symbols:
|
||||
symbol_flows = df[df['symbol_norm'] == symbol]
|
||||
timestamps = symbol_flows['flow_ts_utc'].dropna().unique().tolist()
|
||||
# Also get 5m and 15m ago prices (with error handling)
|
||||
try:
|
||||
ts_5m = ts - timedelta(minutes=5)
|
||||
row_5m = await conn.fetchrow("""
|
||||
SELECT close
|
||||
FROM prices_intraday_1m
|
||||
WHERE UPPER(symbol) = $1
|
||||
AND ts <= $2
|
||||
ORDER BY ts DESC
|
||||
LIMIT 1
|
||||
""", symbol.upper(), ts_5m)
|
||||
|
||||
if row_5m:
|
||||
price_data_dict[(symbol.upper(), ts_5m)] = {'close': row_5m['close']}
|
||||
except Exception as e:
|
||||
# Log but don't fail on 5m price lookup
|
||||
pass
|
||||
|
||||
try:
|
||||
ts_15m = ts - timedelta(minutes=15)
|
||||
row_15m = await conn.fetchrow("""
|
||||
SELECT close
|
||||
FROM prices_intraday_1m
|
||||
WHERE UPPER(symbol) = $1
|
||||
AND ts <= $2
|
||||
ORDER BY ts DESC
|
||||
LIMIT 1
|
||||
""", symbol.upper(), ts_15m)
|
||||
|
||||
if row_15m:
|
||||
price_data_dict[(symbol.upper(), ts_15m)] = {'close': row_15m['close']}
|
||||
except Exception as e:
|
||||
# Log but don't fail on 15m price lookup
|
||||
pass
|
||||
|
||||
for ts in timestamps:
|
||||
try:
|
||||
vwap_data = await self.calculate_vwap_at_time(symbol, ts)
|
||||
if vwap_data:
|
||||
vwap_dict[(symbol.upper(), ts)] = vwap_data
|
||||
except Exception as e:
|
||||
logger.debug(f"Error calculating VWAP for {symbol} at {ts}: {e}")
|
||||
# Batch fetch RTH opens
|
||||
rth_open_dict = {}
|
||||
for symbol in unique_symbols:
|
||||
for date in unique_dates:
|
||||
row = await conn.fetchrow("""
|
||||
SELECT open, ts
|
||||
FROM prices_intraday_1m
|
||||
WHERE UPPER(symbol) = $1
|
||||
AND (timezone('America/Chicago', ts))::date = $2
|
||||
AND (timezone('America/Chicago', ts))::time >= time '09:30:00'
|
||||
ORDER BY ts ASC
|
||||
LIMIT 1
|
||||
""", symbol.upper(), date)
|
||||
|
||||
if row:
|
||||
rth_open_dict[(symbol.upper(), date)] = dict(row)
|
||||
|
||||
# Batch fetch prior closes
|
||||
prior_close_dict = {}
|
||||
for symbol in unique_symbols:
|
||||
for date in unique_dates:
|
||||
# Handle date conversion
|
||||
if isinstance(date, datetime):
|
||||
prior_date = (date - timedelta(days=1)).date()
|
||||
elif isinstance(date, pd.Timestamp):
|
||||
prior_date = (date - timedelta(days=1)).date()
|
||||
else:
|
||||
# Assume it's already a date object
|
||||
prior_date = date - timedelta(days=1) if hasattr(date, '__sub__') else date
|
||||
|
||||
row = await conn.fetchrow("""
|
||||
SELECT close
|
||||
FROM prices_daily
|
||||
WHERE UPPER(symbol) = $1
|
||||
AND "Date" = $2
|
||||
ORDER BY "Date" DESC
|
||||
LIMIT 1
|
||||
""", symbol.upper(), prior_date)
|
||||
|
||||
if row:
|
||||
prior_close_dict[(symbol.upper(), date)] = row['close']
|
||||
|
||||
# Build price data for each flow row
|
||||
price_data = []
|
||||
|
|
@ -274,20 +248,6 @@ class PriceContextService:
|
|||
price_15m_ago_data = price_data_dict.get(price_15m_key)
|
||||
price_15m_ago = price_15m_ago_data.get('close') if price_15m_ago_data else None
|
||||
|
||||
# Get VWAP at signal time
|
||||
vwap_data = vwap_dict.get((symbol.upper(), flow_ts_utc)) if not pd.isna(flow_ts_utc) else None
|
||||
vwap_at_signal = vwap_data['vwap'] if vwap_data else None
|
||||
|
||||
# Calculate price vs VWAP percentage
|
||||
price_vs_vwap_pct = None
|
||||
if vwap_at_signal and price_at_time and price_at_time.get('close'):
|
||||
try:
|
||||
price_vs_vwap_pct = round(
|
||||
((price_at_time['close'] - vwap_at_signal) / vwap_at_signal) * 100, 2
|
||||
)
|
||||
except (TypeError, ZeroDivisionError):
|
||||
pass
|
||||
|
||||
price_data.append({
|
||||
'u_close': price_at_time['close'] if price_at_time else None,
|
||||
'u_high': price_at_time['high'] if price_at_time else None,
|
||||
|
|
@ -297,8 +257,6 @@ class PriceContextService:
|
|||
'prior_close': prior_close,
|
||||
'close_5m_ago': price_5m_ago,
|
||||
'close_15m_ago': price_15m_ago,
|
||||
'vwap_at_signal': vwap_at_signal,
|
||||
'price_vs_vwap_pct': price_vs_vwap_pct,
|
||||
})
|
||||
|
||||
# Merge price data
|
||||
|
|
|
|||
|
|
@ -1,153 +0,0 @@
|
|||
"""
|
||||
Price Reaction Tracker
|
||||
Tracks how price moves after a signal appears - critical for filtering hedges/rolls
|
||||
Uses Yahoo Finance for real-time data instead of database
|
||||
"""
|
||||
import pandas as pd
|
||||
import asyncpg
|
||||
from datetime import datetime, timedelta
|
||||
from typing import Dict, Optional
|
||||
from utils.logger import logger
|
||||
from services.yahoo_finance_service import YahooFinanceService
|
||||
|
||||
|
||||
class PriceReactionTracker:
|
||||
"""Service for tracking price reactions after flow signals"""
|
||||
|
||||
def __init__(self):
|
||||
self.reaction_threshold_pct = 0.5 # 0.5% threshold for "flow led to move"
|
||||
self.use_yahoo_finance = False # Temporarily disabled - focus on Phase 1 button
|
||||
self.yahoo_service = YahooFinanceService() if self.use_yahoo_finance else None
|
||||
|
||||
async def get_price_at_time(
|
||||
self,
|
||||
symbol: str,
|
||||
timestamp: datetime,
|
||||
pool: asyncpg.Pool = None # Not used anymore, kept for compatibility
|
||||
) -> Optional[float]:
|
||||
"""Get price at or before a specific timestamp using Yahoo Finance"""
|
||||
if not self.use_yahoo_finance or not self.yahoo_service:
|
||||
return None # Yahoo Finance disabled
|
||||
try:
|
||||
return self.yahoo_service.get_price_at_time(symbol, timestamp)
|
||||
except Exception as e:
|
||||
logger.debug(f"Error getting price at time from Yahoo Finance: {e}")
|
||||
return None
|
||||
|
||||
async def enrich_with_reactions(
|
||||
self,
|
||||
flow_df: pd.DataFrame,
|
||||
pool: asyncpg.Pool
|
||||
) -> pd.DataFrame:
|
||||
"""Enrich flow data with price reaction tracking"""
|
||||
df = flow_df.copy()
|
||||
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
logger.info(f"Tracking price reactions for {len(df)} signals...")
|
||||
|
||||
# Get unique symbols and timestamps for batch processing
|
||||
unique_symbols = df['symbol_norm'].unique().tolist()
|
||||
|
||||
# Batch fetch price reactions
|
||||
reaction_data = []
|
||||
|
||||
async with pool.acquire() as conn:
|
||||
for idx, row in df.iterrows():
|
||||
symbol = row['symbol_norm']
|
||||
signal_time = row['flow_ts_utc']
|
||||
price_at_signal = row.get('u_close')
|
||||
|
||||
if pd.isna(signal_time) or not price_at_signal or price_at_signal == 0:
|
||||
reaction_data.append({
|
||||
'price_reaction_5m_pct': None,
|
||||
'price_reaction_15m_pct': None,
|
||||
'price_reaction_30m_pct': None,
|
||||
'high_break_5m': False,
|
||||
'low_break_5m': False,
|
||||
'flow_led_to_move': False
|
||||
})
|
||||
continue
|
||||
|
||||
# Get prices at 5m, 15m, 30m after signal
|
||||
price_5m = None
|
||||
price_15m = None
|
||||
price_30m = None
|
||||
|
||||
try:
|
||||
ts_5m = signal_time + timedelta(minutes=5)
|
||||
price_5m = await self.get_price_at_time(symbol, ts_5m, pool)
|
||||
except Exception as e:
|
||||
logger.debug(f"Error fetching 5m price for {symbol}: {e}")
|
||||
|
||||
try:
|
||||
ts_15m = signal_time + timedelta(minutes=15)
|
||||
price_15m = await self.get_price_at_time(symbol, ts_15m, pool)
|
||||
except Exception as e:
|
||||
logger.debug(f"Error fetching 15m price for {symbol}: {e}")
|
||||
|
||||
try:
|
||||
ts_30m = signal_time + timedelta(minutes=30)
|
||||
price_30m = await self.get_price_at_time(symbol, ts_30m, pool)
|
||||
except Exception as e:
|
||||
logger.debug(f"Error fetching 30m price for {symbol}: {e}")
|
||||
|
||||
# Calculate reaction percentages
|
||||
reaction_5m = None
|
||||
reaction_15m = None
|
||||
reaction_30m = None
|
||||
|
||||
if price_5m and price_at_signal:
|
||||
try:
|
||||
reaction_5m = round(((price_5m - price_at_signal) / price_at_signal) * 100, 2)
|
||||
except (TypeError, ZeroDivisionError):
|
||||
pass
|
||||
|
||||
if price_15m and price_at_signal:
|
||||
try:
|
||||
reaction_15m = round(((price_15m - price_at_signal) / price_at_signal) * 100, 2)
|
||||
except (TypeError, ZeroDivisionError):
|
||||
pass
|
||||
|
||||
if price_30m and price_at_signal:
|
||||
try:
|
||||
reaction_30m = round(((price_30m - price_at_signal) / price_at_signal) * 100, 2)
|
||||
except (TypeError, ZeroDivisionError):
|
||||
pass
|
||||
|
||||
# High/Low break confirmation
|
||||
high_at_signal = row.get('u_high', 0) or 0
|
||||
low_at_signal = row.get('u_low', 0) or 0
|
||||
|
||||
high_break_5m = False
|
||||
low_break_5m = False
|
||||
|
||||
if price_5m:
|
||||
if high_at_signal > 0 and price_5m > high_at_signal:
|
||||
high_break_5m = True
|
||||
if low_at_signal > 0 and price_5m < low_at_signal:
|
||||
low_break_5m = True
|
||||
|
||||
# Determine if flow led to move
|
||||
flow_led_to_move = False
|
||||
if reaction_5m is not None:
|
||||
flow_led_to_move = abs(reaction_5m) >= self.reaction_threshold_pct
|
||||
|
||||
reaction_data.append({
|
||||
'price_reaction_5m_pct': reaction_5m,
|
||||
'price_reaction_15m_pct': reaction_15m,
|
||||
'price_reaction_30m_pct': reaction_30m,
|
||||
'high_break_5m': high_break_5m,
|
||||
'low_break_5m': low_break_5m,
|
||||
'flow_led_to_move': flow_led_to_move
|
||||
})
|
||||
|
||||
# Merge reaction data
|
||||
reaction_df = pd.DataFrame(reaction_data, index=df.index)
|
||||
df = pd.concat([df, reaction_df], axis=1)
|
||||
|
||||
logger.info(f"✅ Price reaction tracking complete. {df['flow_led_to_move'].sum()} signals led to price moves")
|
||||
|
||||
return df
|
||||
|
||||
|
|
@ -1,258 +0,0 @@
|
|||
"""
|
||||
Relative Premium Scoring Service
|
||||
Replaces static premium filter with context-aware relative premium scoring
|
||||
"""
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from typing import Dict, Optional
|
||||
from datetime import datetime, timedelta
|
||||
import asyncpg
|
||||
from utils.logger import logger
|
||||
from utils.error_handler import safe_divide
|
||||
|
||||
|
||||
class RelativePremiumScorer:
|
||||
"""
|
||||
Computes relative premium metrics to replace static premium filtering.
|
||||
Premium of $80K might be significant for AAPL but noise for TSLA.
|
||||
"""
|
||||
|
||||
def __init__(self, pool: asyncpg.Pool = None):
|
||||
self.pool = pool
|
||||
# Configuration
|
||||
self.rolling_window_days = 20 # Rolling window for z-score calculation
|
||||
self.min_relative_premium_threshold = 0.60 # 60th percentile minimum
|
||||
|
||||
async def fetch_historical_premium_stats(
|
||||
self,
|
||||
symbol: str,
|
||||
reference_date: datetime.date
|
||||
) -> Dict[str, float]:
|
||||
"""
|
||||
Fetch historical premium statistics for a symbol.
|
||||
Returns: {mean, std, min, max, median} for rolling window
|
||||
"""
|
||||
if not self.pool:
|
||||
logger.warning("No database pool available for historical premium stats")
|
||||
return {}
|
||||
|
||||
try:
|
||||
# Calculate window start date
|
||||
window_start = reference_date - timedelta(days=self.rolling_window_days + 5) # Extra buffer
|
||||
|
||||
async with self.pool.acquire() as conn:
|
||||
query = """
|
||||
SELECT
|
||||
AVG(premium_num) as mean_premium,
|
||||
STDDEV(premium_num) as std_premium,
|
||||
MIN(premium_num) as min_premium,
|
||||
MAX(premium_num) as max_premium,
|
||||
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY premium_num) as median_premium
|
||||
FROM (
|
||||
SELECT
|
||||
CAST(REGEXP_REPLACE("Premium"::text, '[^\d.]', '', 'g') AS numeric) as premium_num
|
||||
FROM "OptionsFlow_monthly"
|
||||
WHERE UPPER(TRIM("Symbol")) = $1
|
||||
AND "Premium" IS NOT NULL
|
||||
AND TRIM("Premium"::text) <> ''
|
||||
AND "CreatedDate" >= $2
|
||||
AND "CreatedDate" <= $3
|
||||
AND "StockEtf" = 'STOCK'
|
||||
) subq
|
||||
WHERE premium_num > 0
|
||||
"""
|
||||
|
||||
row = await conn.fetchrow(
|
||||
query,
|
||||
symbol.upper(),
|
||||
window_start.strftime('%Y-%m-%d'),
|
||||
reference_date.strftime('%Y-%m-%d')
|
||||
)
|
||||
|
||||
if row and row['mean_premium']:
|
||||
return {
|
||||
'mean': float(row['mean_premium']) if row['mean_premium'] else 0.0,
|
||||
'std': float(row['std_premium']) if row['std_premium'] else 0.0,
|
||||
'min': float(row['min_premium']) if row['min_premium'] else 0.0,
|
||||
'max': float(row['max_premium']) if row['max_premium'] else 0.0,
|
||||
'median': float(row['median_premium']) if row['median_premium'] else 0.0,
|
||||
}
|
||||
except Exception as e:
|
||||
logger.debug(f"Error fetching historical premium stats for {symbol}: {e}")
|
||||
|
||||
return {}
|
||||
|
||||
async def calculate_intraday_percentile(
|
||||
self,
|
||||
df: pd.DataFrame,
|
||||
symbol: str,
|
||||
flow_date: datetime.date,
|
||||
premium: float
|
||||
) -> Optional[float]:
|
||||
"""
|
||||
Calculate percentile rank of premium within same-day flow for the symbol.
|
||||
Returns 0-100 percentile value.
|
||||
"""
|
||||
try:
|
||||
# Filter to same symbol and date
|
||||
same_day_flow = df[
|
||||
(df['symbol_norm'] == symbol.upper()) &
|
||||
(df['flow_date_cst'] == flow_date) &
|
||||
(df['premium_num'].notna()) &
|
||||
(df['premium_num'] > 0)
|
||||
]
|
||||
|
||||
if len(same_day_flow) < 3: # Need at least 3 trades for meaningful percentile
|
||||
return None
|
||||
|
||||
# Calculate percentile rank
|
||||
all_premiums = same_day_flow['premium_num'].values
|
||||
percentile = (np.sum(all_premiums <= premium) / len(all_premiums)) * 100.0
|
||||
|
||||
return float(percentile)
|
||||
except Exception as e:
|
||||
logger.debug(f"Error calculating intraday percentile for {symbol}: {e}")
|
||||
return None
|
||||
|
||||
def calculate_zscore(
|
||||
self,
|
||||
premium: float,
|
||||
stats: Dict[str, float]
|
||||
) -> Optional[float]:
|
||||
"""
|
||||
Calculate z-score of premium relative to historical distribution.
|
||||
Returns z-score (standard deviations from mean).
|
||||
"""
|
||||
if not stats or stats.get('std', 0) == 0:
|
||||
return None
|
||||
|
||||
mean = stats.get('mean', 0)
|
||||
std = stats.get('std', 1)
|
||||
|
||||
if std == 0:
|
||||
return None
|
||||
|
||||
zscore = (premium - mean) / std
|
||||
return float(zscore)
|
||||
|
||||
def calculate_relative_premium_score(
|
||||
self,
|
||||
premium: float,
|
||||
zscore: Optional[float],
|
||||
intraday_percentile: Optional[float],
|
||||
stats: Dict[str, float]
|
||||
) -> float:
|
||||
"""
|
||||
Calculate composite relative premium score (0-100).
|
||||
Combines z-score and intraday percentile with median normalization.
|
||||
|
||||
Logic:
|
||||
- High z-score (unusual size) = higher score
|
||||
- High intraday percentile (large relative to today's flow) = higher score
|
||||
- Normalize by median to account for symbol-specific scaling
|
||||
"""
|
||||
score = 0.0
|
||||
|
||||
# Z-score component (40% weight)
|
||||
# Z-score > 1 = 1 std above mean, Z-score > 2 = 2 std above mean
|
||||
if zscore is not None:
|
||||
# Normalize z-score to 0-40 range (clamp at ±3 sigma)
|
||||
zscore_normalized = max(0, min(40, (zscore + 3) * (40 / 6)))
|
||||
score += zscore_normalized
|
||||
|
||||
# Intraday percentile component (40% weight)
|
||||
if intraday_percentile is not None:
|
||||
# Percentile is already 0-100, scale to 0-40
|
||||
score += (intraday_percentile / 100.0) * 40.0
|
||||
|
||||
# Median normalization component (20% weight)
|
||||
# Premium > median gets bonus, premium < median gets penalty
|
||||
if stats and stats.get('median', 0) > 0:
|
||||
median_ratio = premium / stats['median']
|
||||
# Ratio > 1.5 = strong, ratio > 2.0 = very strong
|
||||
if median_ratio >= 2.0:
|
||||
score += 20.0
|
||||
elif median_ratio >= 1.5:
|
||||
score += 15.0
|
||||
elif median_ratio >= 1.0:
|
||||
score += 10.0
|
||||
elif median_ratio >= 0.5:
|
||||
score += 5.0
|
||||
|
||||
return min(100.0, max(0.0, score))
|
||||
|
||||
async def enrich_with_relative_premium(
|
||||
self,
|
||||
df: pd.DataFrame
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
Add relative premium metrics to DataFrame.
|
||||
Adds: premium_zscore, premium_percentile_intraday, relative_premium_score
|
||||
"""
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
df = df.copy()
|
||||
|
||||
# Initialize new columns
|
||||
df['premium_zscore'] = None
|
||||
df['premium_percentile_intraday'] = None
|
||||
df['relative_premium_score'] = 0.0
|
||||
|
||||
# Cache historical stats per symbol to avoid repeated queries
|
||||
stats_cache: Dict[str, Dict[str, float]] = {}
|
||||
|
||||
# Group by symbol and date for batch processing
|
||||
unique_symbols = df['symbol_norm'].unique()
|
||||
unique_dates = df['flow_date_cst'].unique()
|
||||
|
||||
logger.info(f"Calculating relative premium scores for {len(unique_symbols)} symbols")
|
||||
|
||||
for symbol in unique_symbols:
|
||||
# Fetch historical stats once per symbol
|
||||
if self.pool:
|
||||
# Use the most recent date for this symbol as reference
|
||||
symbol_dates = df[df['symbol_norm'] == symbol]['flow_date_cst'].unique()
|
||||
if len(symbol_dates) > 0:
|
||||
reference_date = max(symbol_dates)
|
||||
stats = await self.fetch_historical_premium_stats(symbol, reference_date)
|
||||
stats_cache[symbol] = stats
|
||||
else:
|
||||
stats_cache[symbol] = {}
|
||||
else:
|
||||
stats_cache[symbol] = {}
|
||||
|
||||
# Calculate metrics for each row
|
||||
for idx, row in df.iterrows():
|
||||
premium = row.get('premium_num')
|
||||
if pd.isna(premium) or premium <= 0:
|
||||
continue
|
||||
|
||||
symbol = row['symbol_norm']
|
||||
flow_date = row.get('flow_date_cst')
|
||||
|
||||
# Get historical stats
|
||||
stats = stats_cache.get(symbol, {})
|
||||
|
||||
# Calculate z-score
|
||||
zscore = self.calculate_zscore(premium, stats)
|
||||
if zscore is not None:
|
||||
df.at[idx, 'premium_zscore'] = float(zscore)
|
||||
|
||||
# Calculate intraday percentile (using current DataFrame subset)
|
||||
intraday_percentile = self.calculate_intraday_percentile(
|
||||
df, symbol, flow_date, premium
|
||||
)
|
||||
if intraday_percentile is not None:
|
||||
df.at[idx, 'premium_percentile_intraday'] = float(intraday_percentile)
|
||||
|
||||
# Calculate composite relative premium score
|
||||
relative_score = self.calculate_relative_premium_score(
|
||||
premium, zscore, intraday_percentile, stats
|
||||
)
|
||||
df.at[idx, 'relative_premium_score'] = float(relative_score)
|
||||
|
||||
logger.info(f"Relative premium scoring complete. Mean score: {df['relative_premium_score'].mean():.2f}")
|
||||
|
||||
return df
|
||||
|
||||
|
|
@ -1,331 +0,0 @@
|
|||
"""
|
||||
Signal Component Scorer
|
||||
Converts binary badge logic into continuous numeric signal components
|
||||
"""
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from typing import Dict, Optional
|
||||
from utils.logger import logger
|
||||
|
||||
|
||||
class SignalComponentScorer:
|
||||
"""
|
||||
Replaces binary badge logic (💎 ⭐ 🟢 🔴) with continuous numeric scores.
|
||||
Badges remain display-only, but signal_strength is computed from components.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
# Signal strength weights (must sum to 1.0)
|
||||
self.aggression_weight = 0.30
|
||||
self.size_concentration_weight = 0.30
|
||||
self.repeat_velocity_weight = 0.20
|
||||
self.strike_clustering_weight = 0.20
|
||||
|
||||
def calculate_aggression_score(self, row: pd.Series) -> float:
|
||||
"""
|
||||
Measures trade aggression: premium paid at ask vs bid, ITM vs OTM preference.
|
||||
|
||||
Higher score = more aggressive buying:
|
||||
- ITM premiums indicate willingness to pay intrinsic value
|
||||
- Ask-side trades (AA) indicate market orders
|
||||
- Large premium relative to spot price
|
||||
"""
|
||||
score = 0.0
|
||||
|
||||
premium_num = row.get('premium_num', 0) or 0
|
||||
spot_num = row.get('spot_num', 0) or 0
|
||||
cp_norm = row.get('cp_norm', '')
|
||||
side = str(row.get('Side', '')).upper()
|
||||
|
||||
# ITM premium component (0-40 points)
|
||||
bull_prem_itm = row.get('bull_prem_itm', 0) or 0
|
||||
bear_prem_itm = row.get('bear_prem_itm', 0) or 0
|
||||
total_prem_itm = bull_prem_itm + bear_prem_itm
|
||||
bull_total = row.get('bull_total', 0) or 0
|
||||
bear_total = row.get('bear_total', 0) or 0
|
||||
total_premium = bull_total + bear_total
|
||||
|
||||
if total_premium > 0:
|
||||
itm_ratio = total_prem_itm / total_premium
|
||||
score += itm_ratio * 40.0
|
||||
|
||||
# Ask-side aggression component (0-30 points)
|
||||
# AA = buying at ask (market orders, urgency)
|
||||
has_aa = 'AA' in side or 'AT_ASK' in side
|
||||
has_bb = 'BB' in side or 'AT_BID' in side
|
||||
|
||||
if has_aa and not has_bb:
|
||||
score += 30.0
|
||||
elif has_aa:
|
||||
score += 15.0
|
||||
|
||||
# Premium-to-spot ratio component (0-30 points)
|
||||
# Large premium relative to spot indicates aggressive positioning
|
||||
if spot_num > 0 and premium_num > 0:
|
||||
prem_to_spot_ratio = premium_num / spot_num
|
||||
# Normalize: 0.01 (1%) = 10 points, 0.05 (5%) = 30 points
|
||||
ratio_score = min(30.0, prem_to_spot_ratio * 600.0)
|
||||
score += ratio_score
|
||||
|
||||
return min(100.0, max(0.0, score))
|
||||
|
||||
def calculate_size_concentration_score(self, row: pd.Series) -> float:
|
||||
"""
|
||||
Measures size concentration: how much premium is concentrated in single strikes/expiries.
|
||||
|
||||
Higher score = more concentrated (single large trade vs many small ones):
|
||||
- High premium in single trade
|
||||
- OI buildup in specific strikes
|
||||
- Directional consistency (all calls or all puts)
|
||||
"""
|
||||
score = 0.0
|
||||
|
||||
premium_num = row.get('premium_num', 0) or 0
|
||||
bull_total = row.get('bull_total', 0) or 0
|
||||
bear_total = row.get('bear_total', 0) or 0
|
||||
total_premium = bull_total + bear_total
|
||||
|
||||
# Single-trade premium concentration (0-40 points)
|
||||
# Premium of current trade relative to running total
|
||||
if total_premium > 0:
|
||||
concentration_ratio = premium_num / total_premium
|
||||
# Single trade = 50%+ of total = very concentrated
|
||||
if concentration_ratio >= 0.5:
|
||||
score += 40.0
|
||||
elif concentration_ratio >= 0.3:
|
||||
score += 30.0
|
||||
elif concentration_ratio >= 0.2:
|
||||
score += 20.0
|
||||
elif concentration_ratio >= 0.1:
|
||||
score += 10.0
|
||||
|
||||
# OI concentration component (0-30 points)
|
||||
# High OI in OTM strikes indicates concentrated positioning
|
||||
oi_cb_otm = row.get('oi_cb_otm', 0) or 0
|
||||
oi_pb_otm = row.get('oi_pb_otm', 0) or 0
|
||||
oi_all = row.get('oi_all', 0) or 0
|
||||
|
||||
if oi_all > 0:
|
||||
otm_oi_ratio = (oi_cb_otm + oi_pb_otm) / oi_all
|
||||
score += otm_oi_ratio * 30.0
|
||||
|
||||
# Directional consistency (0-30 points)
|
||||
# Pure directional flow (all bull or all bear) = more concentrated
|
||||
if total_premium > 0:
|
||||
direction_strength = abs(bull_total - bear_total) / total_premium
|
||||
score += direction_strength * 30.0
|
||||
|
||||
return min(100.0, max(0.0, score))
|
||||
|
||||
def calculate_repeat_trade_velocity(self, df: pd.DataFrame, row_idx: int) -> float:
|
||||
"""
|
||||
Measures repeat trade velocity: frequency of trades in same direction/expiry.
|
||||
|
||||
Higher score = faster follow-on trades (urgency building):
|
||||
- Time between consecutive trades decreasing
|
||||
- Multiple trades in same direction
|
||||
- Same expiry date (rolling accumulation)
|
||||
"""
|
||||
if row_idx >= len(df):
|
||||
return 0.0
|
||||
|
||||
current_row = df.iloc[row_idx]
|
||||
symbol = current_row.get('symbol_norm')
|
||||
direction = current_row.get('direction')
|
||||
exp_date = current_row.get('exp_date')
|
||||
flow_ts_utc = current_row.get('flow_ts_utc')
|
||||
|
||||
if pd.isna(symbol) or pd.isna(direction) or pd.isna(flow_ts_utc):
|
||||
return 0.0
|
||||
|
||||
# Look back at recent trades for same symbol
|
||||
# Sort by timestamp to find preceding trades
|
||||
df_sorted = df.sort_values('flow_ts_utc').reset_index(drop=True)
|
||||
current_idx = df_sorted.index[df_sorted.index == row_idx]
|
||||
|
||||
if len(current_idx) == 0:
|
||||
return 0.0
|
||||
|
||||
current_pos = current_idx[0]
|
||||
|
||||
# Look at last 10 trades for this symbol
|
||||
symbol_mask = (df_sorted['symbol_norm'] == symbol) & \
|
||||
(df_sorted.index < current_pos) & \
|
||||
(df_sorted['flow_ts_utc'] <= flow_ts_utc)
|
||||
|
||||
recent_trades = df_sorted[symbol_mask].tail(10)
|
||||
|
||||
if len(recent_trades) == 0:
|
||||
return 0.0
|
||||
|
||||
score = 0.0
|
||||
|
||||
# Time compression component (0-40 points)
|
||||
# Decreasing time gaps = urgency building
|
||||
if len(recent_trades) >= 2:
|
||||
time_gaps = []
|
||||
prev_ts = None
|
||||
for _, trade in recent_trades.iterrows():
|
||||
ts = trade.get('flow_ts_utc')
|
||||
if pd.notna(ts) and prev_ts:
|
||||
gap_minutes = (ts - prev_ts).total_seconds() / 60.0
|
||||
if gap_minutes > 0:
|
||||
time_gaps.append(gap_minutes)
|
||||
prev_ts = ts
|
||||
|
||||
if len(time_gaps) >= 2:
|
||||
# Check if gaps are decreasing (accelerating)
|
||||
recent_gaps = time_gaps[-2:]
|
||||
if recent_gaps[1] < recent_gaps[0]:
|
||||
compression_ratio = recent_gaps[1] / recent_gaps[0] if recent_gaps[0] > 0 else 0
|
||||
# 50% compression = 20 points, 80% compression = 40 points
|
||||
score += (1.0 - compression_ratio) * 40.0
|
||||
|
||||
# Directional consistency (0-35 points)
|
||||
# Same direction trades = building position
|
||||
same_direction = recent_trades[recent_trades['direction'] == direction]
|
||||
if len(recent_trades) > 0:
|
||||
consistency_ratio = len(same_direction) / len(recent_trades)
|
||||
score += consistency_ratio * 35.0
|
||||
|
||||
# Expiry concentration (0-25 points)
|
||||
# Same expiry = rolling accumulation
|
||||
if pd.notna(exp_date):
|
||||
same_expiry = recent_trades[recent_trades['exp_date'] == exp_date]
|
||||
if len(recent_trades) > 0:
|
||||
expiry_ratio = len(same_expiry) / len(recent_trades)
|
||||
score += expiry_ratio * 25.0
|
||||
|
||||
return min(100.0, max(0.0, score))
|
||||
|
||||
def calculate_strike_clustering_score(self, df: pd.DataFrame, row_idx: int) -> float:
|
||||
"""
|
||||
Measures strike clustering: trades accumulating at specific strike levels.
|
||||
|
||||
Higher score = more clustering (laddering or concentrated strikes):
|
||||
- Multiple trades at same strike
|
||||
- Sequential strikes (laddering)
|
||||
- Strikes near current price (pin risk)
|
||||
"""
|
||||
if row_idx >= len(df):
|
||||
return 0.0
|
||||
|
||||
current_row = df.iloc[row_idx]
|
||||
symbol = current_row.get('symbol_norm')
|
||||
strike_num = current_row.get('strike_num')
|
||||
spot_num = current_row.get('spot_num', 0) or 0
|
||||
flow_ts_utc = current_row.get('flow_ts_utc')
|
||||
exp_date = current_row.get('exp_date')
|
||||
|
||||
if pd.isna(symbol) or pd.isna(strike_num) or pd.isna(flow_ts_utc):
|
||||
return 0.0
|
||||
|
||||
# Look at recent trades for same symbol and expiry
|
||||
df_sorted = df.sort_values('flow_ts_utc').reset_index(drop=True)
|
||||
symbol_mask = (df_sorted['symbol_norm'] == symbol) & \
|
||||
(df_sorted['flow_ts_utc'] <= flow_ts_utc)
|
||||
|
||||
if pd.notna(exp_date):
|
||||
symbol_mask = symbol_mask & (df_sorted['exp_date'] == exp_date)
|
||||
|
||||
recent_trades = df_sorted[symbol_mask].tail(20)
|
||||
|
||||
if len(recent_trades) <= 1:
|
||||
return 0.0
|
||||
|
||||
score = 0.0
|
||||
|
||||
# Exact strike clustering (0-40 points)
|
||||
# Multiple trades at same strike
|
||||
same_strike_count = len(recent_trades[recent_trades['strike_num'] == strike_num])
|
||||
if len(recent_trades) > 0:
|
||||
clustering_ratio = same_strike_count / len(recent_trades)
|
||||
score += clustering_ratio * 40.0
|
||||
|
||||
# Strike laddering detection (0-35 points)
|
||||
# Sequential strikes (e.g., 100, 105, 110) indicate laddering
|
||||
unique_strikes = sorted(recent_trades['strike_num'].dropna().unique())
|
||||
if len(unique_strikes) >= 3:
|
||||
# Check if strikes are roughly evenly spaced (laddering)
|
||||
if spot_num > 0:
|
||||
strike_diffs = [abs(unique_strikes[i+1] - unique_strikes[i]) for i in range(len(unique_strikes)-1)]
|
||||
if len(strike_diffs) > 0:
|
||||
avg_diff = np.mean(strike_diffs)
|
||||
std_diff = np.std(strike_diffs)
|
||||
# Low std relative to mean = regular spacing (laddering)
|
||||
if avg_diff > 0:
|
||||
regularity = 1.0 - min(1.0, std_diff / avg_diff)
|
||||
score += regularity * 35.0
|
||||
|
||||
# Proximity to spot (0-25 points)
|
||||
# Strikes near spot price = pin risk, more significant
|
||||
if spot_num > 0:
|
||||
strike_pct_diff = abs(strike_num - spot_num) / spot_num * 100.0
|
||||
# Within 2% = 25 points, within 5% = 15 points, within 10% = 5 points
|
||||
if strike_pct_diff <= 2.0:
|
||||
score += 25.0
|
||||
elif strike_pct_diff <= 5.0:
|
||||
score += 15.0
|
||||
elif strike_pct_diff <= 10.0:
|
||||
score += 5.0
|
||||
|
||||
return min(100.0, max(0.0, score))
|
||||
|
||||
def calculate_signal_strength(self, row: pd.Series) -> float:
|
||||
"""
|
||||
Calculate composite signal_strength from component scores.
|
||||
|
||||
signal_strength =
|
||||
0.30 * aggression_score +
|
||||
0.30 * size_concentration_score +
|
||||
0.20 * repeat_trade_velocity +
|
||||
0.20 * strike_clustering_score
|
||||
"""
|
||||
aggression = row.get('aggression_score', 0) or 0
|
||||
size_conc = row.get('size_concentration_score', 0) or 0
|
||||
repeat_vel = row.get('repeat_trade_velocity', 0) or 0
|
||||
strike_clust = row.get('strike_clustering_score', 0) or 0
|
||||
|
||||
signal_strength = (
|
||||
self.aggression_weight * aggression +
|
||||
self.size_concentration_weight * size_conc +
|
||||
self.repeat_velocity_weight * repeat_vel +
|
||||
self.strike_clustering_weight * strike_clust
|
||||
)
|
||||
|
||||
return min(100.0, max(0.0, signal_strength))
|
||||
|
||||
def enrich_with_signal_components(self, df: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
Add signal component scores to DataFrame.
|
||||
Adds: aggression_score, size_concentration_score, repeat_trade_velocity,
|
||||
strike_clustering_score, signal_strength
|
||||
"""
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
df = df.copy()
|
||||
|
||||
# Initialize columns
|
||||
df['aggression_score'] = 0.0
|
||||
df['size_concentration_score'] = 0.0
|
||||
df['repeat_trade_velocity'] = 0.0
|
||||
df['strike_clustering_score'] = 0.0
|
||||
df['signal_strength'] = 0.0
|
||||
|
||||
# Calculate component scores (some require DataFrame context)
|
||||
df['aggression_score'] = df.apply(self.calculate_aggression_score, axis=1)
|
||||
df['size_concentration_score'] = df.apply(self.calculate_size_concentration_score, axis=1)
|
||||
|
||||
# These require DataFrame context, so iterate
|
||||
for idx in df.index:
|
||||
df.at[idx, 'repeat_trade_velocity'] = self.calculate_repeat_trade_velocity(df, idx)
|
||||
df.at[idx, 'strike_clustering_score'] = self.calculate_strike_clustering_score(df, idx)
|
||||
|
||||
# Calculate composite signal_strength
|
||||
df['signal_strength'] = df.apply(self.calculate_signal_strength, axis=1)
|
||||
|
||||
logger.info(f"Signal component scoring complete. Mean signal_strength: {df['signal_strength'].mean():.2f}")
|
||||
|
||||
return df
|
||||
|
||||
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Reference in New Issue