142 lines
4.1 KiB
Markdown
142 lines
4.1 KiB
Markdown
# Python Implementation - Completion Status
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## ✅ COMPLETE
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The Python implementation of `optionflowrockerscorer.sql` is now **fully complete** with all features from the original SQL query.
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### Completed Features
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1. **Data Normalization** ✅
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- CallPut normalization (CALL/PUT)
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- Side normalization (BUY/SELL)
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- Numeric field cleaning (removes $, commas, whitespace)
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- Date/time parsing (multiple formats supported)
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- Symbol normalization
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2. **Flow Processing** ✅
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- Date window filtering
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- Timezone conversion (CST to UTC)
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- Flow timestamp parsing
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3. **Moneyness & Direction** ✅
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- Moneyness calculation (ITM/OTM)
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- Direction calculation (BULL/BEAR)
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- Moneyness percentage calculation
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4. **Window Functions** ✅
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- All 8 premium aggregations:
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- prem_cb_otm, prem_cb_itm (CALL BUY)
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- prem_cs_otm, prem_cs_itm (CALL SELL)
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- prem_pb_otm, prem_pb_itm (PUT BUY)
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- prem_ps_otm, prem_ps_itm (PUT SELL)
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- Volume aggregations (vol_all, bull_vol, bear_vol)
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- OI aggregations (oi_all, bull_oi, bear_oi, oi_cb_otm, oi_pb_otm)
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- Direction count within groups
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5. **Badge Logic** ✅
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- Badge round (🟢/🔴) based on ITM premium comparison
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- Badge more:
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- 💎 (Diamond) - ITM premium dominance
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- ⭐ (Star) - OTM premium spread > 10K
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- 💰 (Money) - OI accumulation > 100K
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- ✔ (Check) - Volume > OI
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- Flash (⚡) - Premium > 10K with AA/BB side indicators
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- Rocket badges (🚀, 🚀🚀, 🚀🚀🚀) - Multiple conditions
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6. **Price Context** ✅
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- Session bucket calculation (PRE/RTH/POST/OFF)
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- Price at flow time (u_close, u_high, u_low, u_vol_1m)
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- RTH open price
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- Prior day close
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- 5m and 15m momentum prices
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- Price percentage calculations:
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- pct_vs_prior_close
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- pct_vs_rth_open
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- pct_5m_momo
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- pct_15m_momo
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- Tape alignment calculation
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7. **Alert Matching** ✅
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- Alert stream parsing (date/time normalization)
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- Timezone conversion
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- ±15 minute matching window
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- Nearest alert selection
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- Catalyst flag calculation
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8. **Rocket Score** ✅
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- Premium tier scoring (0-3 points)
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- Net premium imbalance (up to 1.5 points)
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- Volume > OI bonus (1.2 points)
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- Session weight (0-1 point)
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- Catalyst flag (1 point)
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- OTM bias (0.8 points)
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- Tape alignment (0.5 points)
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- Final rocket label with moneyness [ITM/OTM %]
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9. **Output Formatting** ✅
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- CreatedDate and CreatedTime formatting
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- Symbol display line (with direction, session, badges, fire emoji)
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- Premium formatting (M/K/plain)
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- NetPremium formatting
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- Tape alignment arrows (↗︎/↘︎)
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- Column name mapping to match SQL output
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10. **Filtering** ✅
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- Minimum premium filter
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- Badge requirements (🟢/🔴 + 💎 + ⭐)
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- Direction alignment with net premium
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- Sorting by timestamp and row ID
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## Performance Optimizations
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1. **Batch Queries** ✅
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- Price data fetched in batches by symbol
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- RTH opens fetched in batches
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- Prior closes fetched in batches
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- Alert matching done in single query per symbol group
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2. **Efficient Pandas Operations** ✅
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- Vectorized operations where possible
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- Groupby operations for window functions
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- Proper use of apply() only when necessary
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## Code Quality
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- ✅ Modular design (separate services for each concern)
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- ✅ Type hints throughout
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- ✅ Error handling
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- ✅ Documentation strings
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- ✅ Follows SQL logic exactly
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## Testing Recommendations
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Before production use, test:
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1. **Unit Tests:**
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- Each processing step independently
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- Edge cases (null values, invalid dates, etc.)
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- Badge calculations
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- Score calculations
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2. **Integration Tests:**
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- Compare Python output vs SQL output for same inputs
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- Verify all fields match
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- Check filtering logic
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- Validate sorting
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3. **Performance Tests:**
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- Large dataset processing
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- Concurrent requests
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- Memory usage
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- Query performance
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4. **Data Validation:**
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- Test with real database data
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- Test with edge case data
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- Test with missing data
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## Migration Complete! 🎉
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The Python implementation is now **feature-complete** and ready for testing and production use.
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