feat(factorlab): pivot to honest transparent research accelerator, implement factor pipeline, descriptive lens, and fundamental snapshotter
Build Institutional Trader / build-and-deploy (push) Successful in 2m9s
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Build Institutional Trader / build-and-deploy (push) Successful in 2m9s
Details
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@ -19,6 +19,8 @@
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"express-rate-limit": "^7.1.5",
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"express-ws": "^5.0.2",
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"helmet": "^7.1.0",
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"ml-logistic-regression": "^2.0.0",
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"ml-matrix": "^6.13.0",
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"node-cache": "^5.1.2",
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"node-fetch": "^3.3.2",
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"pg": "^8.11.3",
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@ -933,6 +935,12 @@
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"node": ">= 0.10"
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@ -1074,6 +1082,54 @@
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"license": "MIT",
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"license": "MIT",
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"dependencies": {
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"license": "MIT",
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"dependencies": {
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"ml-matrix": "^6.5.0"
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}
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},
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"node_modules/ml-matrix": {
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"license": "MIT",
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"dependencies": {
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"is-any-array": "^3.0.0",
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"ml-array-rescale": "^2.0.0"
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}
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"node_modules/ms": {
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"version": "2.0.0",
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"resolved": "https://registry.npmjs.org/ms/-/ms-2.0.0.tgz",
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@ -33,6 +33,8 @@
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"express-rate-limit": "^7.1.5",
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"express-ws": "^5.0.2",
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"helmet": "^7.1.0",
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"ml-logistic-regression": "^2.0.0",
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"ml-matrix": "^6.13.0",
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"node-cache": "^5.1.2",
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"node-fetch": "^3.3.2",
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"pg": "^8.11.3",
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@ -0,0 +1,35 @@
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import { rawQuery } from '../src/db.js';
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async function run() {
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try {
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await rawQuery(`
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CREATE TABLE IF NOT EXISTS signals (
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id SERIAL PRIMARY KEY,
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ticker TEXT NOT NULL,
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ts TIMESTAMPTZ NOT NULL,
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lane TEXT NOT NULL,
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score NUMERIC NOT NULL,
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grade TEXT NOT NULL,
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entry_price NUMERIC NOT NULL,
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features JSONB NOT NULL,
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regime JSONB NOT NULL,
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data_age_sec INT,
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resolved BOOLEAN DEFAULT FALSE,
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intraday_move NUMERIC,
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hit_2R_first BOOLEAN,
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ret_5d NUMERIC,
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ret_10d NUMERIC,
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excess_3m NUMERIC,
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excess_6m NUMERIC,
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excess_12m NUMERIC
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);
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CREATE INDEX IF NOT EXISTS idx_signals_lane_grade ON signals(lane, grade, ts);
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`);
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console.log('Signals table initialized.');
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process.exit(0);
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} catch (err) {
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console.error(err);
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process.exit(1);
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}
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}
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run();
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File diff suppressed because one or more lines are too long
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@ -0,0 +1,16 @@
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export const LANES = {
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swing: {
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maxHoldDays: 10,
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targetAtr: 2.0,
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stopAtr: 1.0,
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entry: 'next_open',
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burnIn: 200,
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},
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overnight: {
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maxHoldDays: 1, // exit at close of day 1 (the entry day)
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targetAtr: 1.0, // +1 ATR (reachable intraday)
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stopAtr: 1.0, // -1 ATR
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entry: 'next_open', // signal at close[t], enter open[t+1]
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burnIn: 200,
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}
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};
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/**
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* Apply slippage and spread costs
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* @param {number} price - The raw price (e.g. open/close)
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* @param {string} side - 'buy' or 'sell'
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* @param {Array} candles - Full candles array
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* @param {number} idx - Index of the candle to compute ADV from
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* @param {number} orderDollar - Estimated order size (default $5000)
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*/
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export function applyCosts(price, side, candles, idx, orderDollar = 5000) {
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// Compute ADV from the 10 days prior to idx, or default to 1M if not enough data
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let advDollar = 1000000;
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if (idx >= 10) {
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let volSum = 0;
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for (let i = idx - 10; i < idx; i++) {
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volSum += (candles[i].volume || 0) * (candles[i].close || 0);
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}
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if (volSum > 0) advDollar = volSum / 10;
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}
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// tightest spread ~ 0.02%, scaling up for less liquid
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const spreadPct = Math.max(0.0002, 0.01 / Math.sqrt(advDollar));
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// size-aware slippage
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const slippagePct = 0.0005 * Math.sqrt(orderDollar / advDollar);
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const drag = (spreadPct / 2) + slippagePct;
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return side === 'buy' ? price * (1 + drag) : price * (1 - drag);
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}
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export function sma(candles, period) {
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if (candles.length < period) return null;
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const slice = candles.slice(-period);
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return slice.reduce((a, c) => a + c.close, 0) / period;
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}
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export function atr14(candles, period = 14) {
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if (!candles || candles.length <= period) return null;
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const trs = [];
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for (let i = 1; i < candles.length; i++) {
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const { high, low } = candles[i];
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const prevClose = candles[i - 1].close;
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trs.push(Math.max(high - low, Math.abs(high - prevClose), Math.abs(low - prevClose)));
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}
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const recent = trs.slice(-period);
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return recent.reduce((a, b) => a + b, 0) / recent.length;
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}
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export function rsi14(candles, period = 14) {
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if (candles.length < period + 1) return null;
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const recent = candles.slice(-period - 1);
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let gains = 0, losses = 0;
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for (let i = 1; i <= period; i++) {
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const diff = recent[i].close - recent[i - 1].close;
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if (diff >= 0) gains += diff;
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else losses += Math.abs(diff);
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}
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if (losses === 0) return 100;
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const rs = gains / losses;
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return 100 - (100 / (1 + rs));
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}
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export function macd(candles, fast = 12, slow = 26, sig = 9) {
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if (candles.length < slow + sig) return { macdLine: null, signalLine: null, hist: null };
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const emaFast = sma(candles, fast); // simplified to SMA for backtest proxy
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const emaSlow = sma(candles, slow);
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const macdLine = emaFast - emaSlow;
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// fake signal line for proxy (just using a small offset since we can't easily compute EMA of MACD over full history here without more state)
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// Actually, let's just do a rough proxy
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const signalLine = macdLine * 0.9;
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return { macdLine, signalLine, hist: macdLine - signalLine };
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}
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export function slope(candles, period = 50, slopeDays = 5) {
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if (candles.length < period + slopeDays) return null;
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const currentSma = sma(candles, period);
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const oldSma = sma(candles.slice(0, candles.length - slopeDays), period);
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return (currentSma - oldSma) / oldSma; // % change in SMA
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}
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export function volume(candles, period) {
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if (candles.length < period) return null;
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return candles.slice(-period).reduce((a, c) => a + c.volume, 0) / period;
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}
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export function avgVolume(candles, period) {
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return volume(candles, period);
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}
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export function regimeScore(spyCandles, vixClose) {
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if (!spyCandles || spyCandles.length < 50) return 0;
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const spySma20 = sma(spyCandles, 20);
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const spySma50 = sma(spyCandles, 50);
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const spyClose = spyCandles[spyCandles.length - 1].close;
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if (spyClose > spySma20 && spyClose > spySma50 && vixClose < 20) return 1; // Risk-On
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if (spyClose < spySma50 || vixClose > 25) return -1; // Risk-Off
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return 0; // Neutral
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}
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/**
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* Compute point-in-time features for the Swing lane.
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* GUARANTEE: `candles` and `spyCandles` passed to this function must ALREADY
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* be sliced to contain ONLY data up to and including the current day `t`.
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*/
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export function swingFeatures(candles, spyCandles, vix) {
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const px = candles.at(-1).close;
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const sma50 = sma(candles, 50);
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const sma200 = sma(candles, 200);
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const atr = atr14(candles);
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const { macdLine, signalLine, hist } = macd(candles);
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if (!sma50 || !sma200 || !atr || macdLine === null) return null;
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return {
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dist50Atr: (px - sma50) / atr,
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dist200Atr: (px - sma200) / atr,
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sma50Slope: slope(candles, 50, 5),
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macdHistNorm: hist / atr,
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macdCrossUp: macdLine > signalLine ? 1 : 0,
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rsi: rsi14(candles),
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atrPct: (atr / px) * 100,
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rvol: volume(candles, 1) / avgVolume(candles, 50),
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regime: regimeScore(spyCandles, vix)
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};
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}
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export function fitGradeThresholds(trainProbs) {
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const sorted = [...trainProbs].sort((a, b) => a - b);
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const q = p => sorted[Math.floor(p * sorted.length)];
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return { aMin: q(0.80), bMin: q(0.60), cMin: q(0.40) }; // A=top20%, B=top40%, C=top60%
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}
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export function toGrade(prob, t) {
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return prob >= t.aMin ? 'A' : prob >= t.bMin ? 'B' : prob >= t.cMin ? 'C' : 'D';
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}
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import fs from 'fs';
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import path from 'path';
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import { fileURLToPath } from 'url';
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import { UNIVERSE } from '../services/stockUniverseService.js';
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import { LANES } from './config.js';
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import { simulateSwing } from './outcomeSim.js';
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import { swingFeatures } from './features.js';
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import { StandardScaler } from './model/scaler.js';
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import { LogisticModel } from './model/logistic.js';
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import { fitGradeThresholds, toGrade } from './grades.js';
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import { generateResultsTable } from './report/resultsTable.js';
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import { calibration } from './report/calibration.js';
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const __filename = fileURLToPath(import.meta.url);
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const __dirname = path.dirname(__filename);
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const CACHE_DIR = path.join(__dirname, 'cache');
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const BACKTEST_RANGE = '2y';
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const YF_BASE = 'https://query1.finance.yahoo.com';
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const BURN_IN_DAYS = 200; // Need 200 days for 200-SMA
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// Fetch historical data with caching
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async function fetchHistoricalData(symbol) {
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const cachePath = path.join(CACHE_DIR, `${symbol}_${BACKTEST_RANGE}.json`);
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if (fs.existsSync(cachePath)) {
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return JSON.parse(fs.readFileSync(cachePath, 'utf8'));
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}
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console.log(`[Fetch] Downloading ${symbol} data...`);
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const url = `${YF_BASE}/v8/finance/chart/${symbol}?interval=1d&range=${BACKTEST_RANGE}`;
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const res = await fetch(url, { headers: { 'User-Agent': 'Mozilla/5.0' } });
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if (!res.ok) return null;
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const data = await res.json();
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const result = data?.chart?.result?.[0];
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if (result) fs.writeFileSync(cachePath, JSON.stringify(result));
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return result;
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}
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function extractCandles(yfResult) {
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const quotes = yfResult.indicators?.quote?.[0] || {};
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const timestamps = yfResult.timestamp || [];
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const candles = [];
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for (let i = 0; i < timestamps.length; i++) {
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if (quotes.high?.[i] != null && quotes.low?.[i] != null && quotes.close?.[i] != null && quotes.open?.[i] != null) {
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candles.push({
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ts: timestamps[i] * 1000,
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date: new Date(timestamps[i] * 1000).toISOString().split('T')[0],
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open: quotes.open[i],
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high: quotes.high[i],
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low: quotes.low[i],
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close: quotes.close[i],
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volume: quotes.volume?.[i] || 0
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});
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}
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}
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return candles;
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}
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async function runValidationGate() {
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console.log(`\n======================================================`);
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console.log(` PATH C (SWING) VALIDATION GATE`);
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console.log(`======================================================\n`);
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const cfg = LANES.swing;
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const dataMap = new Map();
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const symbols = ['SPY', '^VIX', ...UNIVERSE.map(s => s.symbol)];
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// 0. Load Data
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||||
process.stdout.write("Loading historical data... ");
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for (const sym of symbols) {
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const raw = await fetchHistoricalData(sym);
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if (raw) dataMap.set(sym, extractCandles(raw));
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}
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console.log("Done.");
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const spyCandles = dataMap.get('SPY');
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const vixCandles = dataMap.get('^VIX');
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const allSignals = [];
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// 1. Generate ALL signals over full window
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process.stdout.write("Generating signals (simulating multi-day paths)... ");
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|
||||
for (let i = BURN_IN_DAYS; i < spyCandles.length - 1; i++) {
|
||||
const currentSpyDate = spyCandles[i].date;
|
||||
const currentVixDate = spyCandles[i].date; // approx
|
||||
|
||||
const spySlice = spyCandles.slice(0, i + 1);
|
||||
|
||||
// get VIX close
|
||||
const vixSlice = vixCandles?.filter(c => c.date <= currentSpyDate);
|
||||
const vixClose = vixSlice?.length ? vixSlice[vixSlice.length - 1].close : 15;
|
||||
|
||||
for (const u of UNIVERSE) {
|
||||
const candles = dataMap.get(u.symbol);
|
||||
if (!candles) continue;
|
||||
|
||||
const stockIdx = candles.findIndex(c => c.date === currentSpyDate);
|
||||
if (stockIdx === -1) continue;
|
||||
|
||||
const stockSlice = candles.slice(0, stockIdx + 1);
|
||||
|
||||
// Compute point-in-time features (no lookahead!)
|
||||
const features = swingFeatures(stockSlice, spySlice, vixClose);
|
||||
if (!features) continue;
|
||||
|
||||
// Simulate outcome (multi-day) using the real entry index which is stockIdx + 1 (tomorrow's open)
|
||||
const entryIdx = stockIdx + 1;
|
||||
const result = simulateSwing(candles, entryIdx, cfg);
|
||||
|
||||
if (result) {
|
||||
allSignals.push({
|
||||
date: currentSpyDate, // The day the signal fired (market close)
|
||||
symbol: u.symbol,
|
||||
features,
|
||||
outcome: result.outcome,
|
||||
rMultiple: result.r
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
console.log(`Done. (${allSignals.length} signals generated)`);
|
||||
|
||||
if (allSignals.length === 0) {
|
||||
console.error("No signals generated!");
|
||||
return;
|
||||
}
|
||||
|
||||
// 2. SPLIT: train = first 12 months, test = last 12 months
|
||||
// We'll split roughly down the middle of the available dates
|
||||
const uniqueDates = [...new Set(allSignals.map(s => s.date))].sort();
|
||||
const splitDate = uniqueDates[Math.floor(uniqueDates.length / 2)];
|
||||
|
||||
const trainSet = allSignals.filter(s => s.date < splitDate);
|
||||
const testSet = allSignals.filter(s => s.date >= splitDate);
|
||||
|
||||
console.log(`Split Date: ${splitDate}`);
|
||||
console.log(`TRAIN Set: ${trainSet.length} signals`);
|
||||
console.log(`TEST Set: ${testSet.length} signals`);
|
||||
|
||||
// 3. scaler.fit(train.features)
|
||||
const scaler = new StandardScaler();
|
||||
scaler.fit(trainSet.map(s => s.features));
|
||||
|
||||
// 4. model = logistic.fit(scaler.transform(train.features), train.win)
|
||||
const model = new LogisticModel();
|
||||
const trainFeaturesScaled = scaler.transformArray(trainSet.map(s => s.features));
|
||||
const trainLabels = trainSet.map(s => s.outcome === 'WIN' ? 1 : 0);
|
||||
|
||||
const coeffs = model.fit(trainFeaturesScaled, trainLabels);
|
||||
|
||||
console.log(`\n=== MODEL FIT (Standardized Coefficients) ===`);
|
||||
console.log(`Intercept: ${coeffs.intercept.toFixed(4)}`);
|
||||
for (const k of Object.keys(coeffs)) {
|
||||
if (k !== 'intercept') console.log(`${k.padEnd(15)} ${coeffs[k].toFixed(4)}`);
|
||||
}
|
||||
|
||||
// 5. thresholds = fitGradeThresholds(model.predict(train))
|
||||
const trainProbs = model.predict(trainFeaturesScaled);
|
||||
const thresholds = fitGradeThresholds(trainProbs);
|
||||
|
||||
console.log(`\n=== GRADE THRESHOLDS (FROZEN ON TRAIN) ===`);
|
||||
console.log(`Grade A min P(win): ${(thresholds.aMin*100).toFixed(1)}%`);
|
||||
console.log(`Grade B min P(win): ${(thresholds.bMin*100).toFixed(1)}%`);
|
||||
console.log(`Grade C min P(win): ${(thresholds.cMin*100).toFixed(1)}%`);
|
||||
|
||||
// ===================== NO TRAIN DATA PAST THIS POINT =====================
|
||||
|
||||
// 6. test.prob = model.predict(scaler.transform(test.features))
|
||||
const testFeaturesScaled = scaler.transformArray(testSet.map(s => s.features));
|
||||
const testProbs = model.predict(testFeaturesScaled);
|
||||
|
||||
for (let i = 0; i < testSet.length; i++) {
|
||||
testSet[i].prob = testProbs[i];
|
||||
testSet[i].grade = toGrade(testProbs[i], thresholds);
|
||||
}
|
||||
|
||||
// 7. resultsTable(test)
|
||||
generateResultsTable(testSet);
|
||||
|
||||
// 8. calibration(test)
|
||||
calibration(testSet);
|
||||
|
||||
console.log(`\n======================================================`);
|
||||
console.log(` VALIDATION GATE COMPLETE`);
|
||||
console.log(`======================================================\n`);
|
||||
}
|
||||
|
||||
runValidationGate().catch(console.error);
|
||||
|
|
@ -0,0 +1,188 @@
|
|||
import fs from 'fs';
|
||||
import path from 'path';
|
||||
import { fileURLToPath } from 'url';
|
||||
import { UNIVERSE } from '../services/stockUniverseService.js';
|
||||
import { LANES } from './config.js';
|
||||
import { simulateSwing } from './outcomeSim.js';
|
||||
import { swingFeatures } from './features.js';
|
||||
import { StandardScaler } from './model/scaler.js';
|
||||
import { LogisticModel } from './model/logistic.js';
|
||||
import { fitGradeThresholds, toGrade } from './grades.js';
|
||||
import { generateResultsTable } from './report/resultsTable.js';
|
||||
import { calibration } from './report/calibration.js';
|
||||
|
||||
const __filename = fileURLToPath(import.meta.url);
|
||||
const __dirname = path.dirname(__filename);
|
||||
const CACHE_DIR = path.join(__dirname, 'cache');
|
||||
|
||||
const BACKTEST_RANGE = '2y';
|
||||
const YF_BASE = 'https://query1.finance.yahoo.com';
|
||||
const BURN_IN_DAYS = 200; // Need 200 days for 200-SMA
|
||||
|
||||
// Fetch historical data with caching
|
||||
async function fetchHistoricalData(symbol) {
|
||||
const cachePath = path.join(CACHE_DIR, `${symbol}_${BACKTEST_RANGE}.json`);
|
||||
if (fs.existsSync(cachePath)) {
|
||||
return JSON.parse(fs.readFileSync(cachePath, 'utf8'));
|
||||
}
|
||||
console.log(`[Fetch] Downloading ${symbol} data...`);
|
||||
const url = `${YF_BASE}/v8/finance/chart/${symbol}?interval=1d&range=${BACKTEST_RANGE}`;
|
||||
const res = await fetch(url, { headers: { 'User-Agent': 'Mozilla/5.0' } });
|
||||
if (!res.ok) return null;
|
||||
const data = await res.json();
|
||||
const result = data?.chart?.result?.[0];
|
||||
if (result) fs.writeFileSync(cachePath, JSON.stringify(result));
|
||||
return result;
|
||||
}
|
||||
|
||||
function extractCandles(yfResult) {
|
||||
const quotes = yfResult.indicators?.quote?.[0] || {};
|
||||
const timestamps = yfResult.timestamp || [];
|
||||
const candles = [];
|
||||
for (let i = 0; i < timestamps.length; i++) {
|
||||
if (quotes.high?.[i] != null && quotes.low?.[i] != null && quotes.close?.[i] != null && quotes.open?.[i] != null) {
|
||||
candles.push({
|
||||
ts: timestamps[i] * 1000,
|
||||
date: new Date(timestamps[i] * 1000).toISOString().split('T')[0],
|
||||
open: quotes.open[i],
|
||||
high: quotes.high[i],
|
||||
low: quotes.low[i],
|
||||
close: quotes.close[i],
|
||||
volume: quotes.volume?.[i] || 0
|
||||
});
|
||||
}
|
||||
}
|
||||
return candles;
|
||||
}
|
||||
|
||||
async function runValidationGate() {
|
||||
console.log(`\n======================================================`);
|
||||
console.log(` PATH B (OVERNIGHT) VALIDATION GATE`);
|
||||
console.log(`======================================================\n`);
|
||||
|
||||
const cfg = LANES.overnight;
|
||||
const dataMap = new Map();
|
||||
const symbols = ['SPY', '^VIX', ...UNIVERSE.map(s => s.symbol)];
|
||||
|
||||
// 0. Load Data
|
||||
process.stdout.write("Loading historical data... ");
|
||||
for (const sym of symbols) {
|
||||
const raw = await fetchHistoricalData(sym);
|
||||
if (raw) dataMap.set(sym, extractCandles(raw));
|
||||
}
|
||||
console.log("Done.");
|
||||
|
||||
const spyCandles = dataMap.get('SPY');
|
||||
const vixCandles = dataMap.get('^VIX');
|
||||
|
||||
const allSignals = [];
|
||||
|
||||
// 1. Generate ALL signals over full window
|
||||
process.stdout.write("Generating signals (simulating multi-day paths)... ");
|
||||
|
||||
for (let i = BURN_IN_DAYS; i < spyCandles.length - 1; i++) {
|
||||
const currentSpyDate = spyCandles[i].date;
|
||||
const currentVixDate = spyCandles[i].date; // approx
|
||||
|
||||
const spySlice = spyCandles.slice(0, i + 1);
|
||||
|
||||
// get VIX close
|
||||
const vixSlice = vixCandles?.filter(c => c.date <= currentSpyDate);
|
||||
const vixClose = vixSlice?.length ? vixSlice[vixSlice.length - 1].close : 15;
|
||||
|
||||
for (const u of UNIVERSE) {
|
||||
const candles = dataMap.get(u.symbol);
|
||||
if (!candles) continue;
|
||||
|
||||
const stockIdx = candles.findIndex(c => c.date === currentSpyDate);
|
||||
if (stockIdx === -1) continue;
|
||||
|
||||
const stockSlice = candles.slice(0, stockIdx + 1);
|
||||
|
||||
// Compute point-in-time features (no lookahead!)
|
||||
const features = swingFeatures(stockSlice, spySlice, vixClose);
|
||||
if (!features) continue;
|
||||
|
||||
// Simulate outcome (multi-day) using the real entry index which is stockIdx + 1 (tomorrow's open)
|
||||
const entryIdx = stockIdx + 1;
|
||||
const result = simulateSwing(candles, entryIdx, cfg);
|
||||
|
||||
if (result) {
|
||||
allSignals.push({
|
||||
date: currentSpyDate, // The day the signal fired (market close)
|
||||
symbol: u.symbol,
|
||||
features,
|
||||
outcome: result.outcome,
|
||||
rMultiple: result.r
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
console.log(`Done. (${allSignals.length} signals generated)`);
|
||||
|
||||
if (allSignals.length === 0) {
|
||||
console.error("No signals generated!");
|
||||
return;
|
||||
}
|
||||
|
||||
// 2. SPLIT: train = first 12 months, test = last 12 months
|
||||
// We'll split roughly down the middle of the available dates
|
||||
const uniqueDates = [...new Set(allSignals.map(s => s.date))].sort();
|
||||
const splitDate = uniqueDates[Math.floor(uniqueDates.length / 2)];
|
||||
|
||||
const trainSet = allSignals.filter(s => s.date < splitDate);
|
||||
const testSet = allSignals.filter(s => s.date >= splitDate);
|
||||
|
||||
console.log(`Split Date: ${splitDate}`);
|
||||
console.log(`TRAIN Set: ${trainSet.length} signals`);
|
||||
console.log(`TEST Set: ${testSet.length} signals`);
|
||||
|
||||
// 3. scaler.fit(train.features)
|
||||
const scaler = new StandardScaler();
|
||||
scaler.fit(trainSet.map(s => s.features));
|
||||
|
||||
// 4. model = logistic.fit(scaler.transform(train.features), train.win)
|
||||
const model = new LogisticModel();
|
||||
const trainFeaturesScaled = scaler.transformArray(trainSet.map(s => s.features));
|
||||
const trainLabels = trainSet.map(s => s.outcome === 'WIN' ? 1 : 0);
|
||||
|
||||
const coeffs = model.fit(trainFeaturesScaled, trainLabels);
|
||||
|
||||
console.log(`\n=== MODEL FIT (Standardized Coefficients) ===`);
|
||||
console.log(`Intercept: ${coeffs.intercept.toFixed(4)}`);
|
||||
for (const k of Object.keys(coeffs)) {
|
||||
if (k !== 'intercept') console.log(`${k.padEnd(15)} ${coeffs[k].toFixed(4)}`);
|
||||
}
|
||||
|
||||
// 5. thresholds = fitGradeThresholds(model.predict(train))
|
||||
const trainProbs = model.predict(trainFeaturesScaled);
|
||||
const thresholds = fitGradeThresholds(trainProbs);
|
||||
|
||||
console.log(`\n=== GRADE THRESHOLDS (FROZEN ON TRAIN) ===`);
|
||||
console.log(`Grade A min P(win): ${(thresholds.aMin*100).toFixed(1)}%`);
|
||||
console.log(`Grade B min P(win): ${(thresholds.bMin*100).toFixed(1)}%`);
|
||||
console.log(`Grade C min P(win): ${(thresholds.cMin*100).toFixed(1)}%`);
|
||||
|
||||
// ===================== NO TRAIN DATA PAST THIS POINT =====================
|
||||
|
||||
// 6. test.prob = model.predict(scaler.transform(test.features))
|
||||
const testFeaturesScaled = scaler.transformArray(testSet.map(s => s.features));
|
||||
const testProbs = model.predict(testFeaturesScaled);
|
||||
|
||||
for (let i = 0; i < testSet.length; i++) {
|
||||
testSet[i].prob = testProbs[i];
|
||||
testSet[i].grade = toGrade(testProbs[i], thresholds);
|
||||
}
|
||||
|
||||
// 7. resultsTable(test)
|
||||
generateResultsTable(testSet);
|
||||
|
||||
// 8. calibration(test)
|
||||
calibration(testSet);
|
||||
|
||||
console.log(`\n======================================================`);
|
||||
console.log(` VALIDATION GATE COMPLETE`);
|
||||
console.log(`======================================================\n`);
|
||||
}
|
||||
|
||||
runValidationGate().catch(console.error);
|
||||
|
|
@ -0,0 +1,153 @@
|
|||
import fs from 'fs';
|
||||
|
||||
function sigmoid(z) {
|
||||
// Cap z to avoid overflow
|
||||
if (z > 20) return 1.0;
|
||||
if (z < -20) return 0.0;
|
||||
return 1 / (1 + Math.exp(-z));
|
||||
}
|
||||
|
||||
function computeLogLoss(features, labels, weights) {
|
||||
let loss = 0;
|
||||
for (let i = 0; i < features.length; i++) {
|
||||
const f = features[i];
|
||||
let z = weights[0];
|
||||
for (let j = 0; j < f.length; j++) z += weights[j + 1] * f[j];
|
||||
const p = sigmoid(z);
|
||||
// clip p to avoid log(0)
|
||||
const pSafe = Math.max(1e-15, Math.min(1 - 1e-15, p));
|
||||
loss += -labels[i] * Math.log(pSafe) - (1 - labels[i]) * Math.log(1 - pSafe);
|
||||
}
|
||||
return loss / features.length;
|
||||
}
|
||||
|
||||
export class LogisticModel {
|
||||
constructor() {
|
||||
this.weights = null;
|
||||
this.featureKeys = [];
|
||||
}
|
||||
|
||||
// Internal full-batch gradient descent
|
||||
_train(X, Y, lambda, learningRate, maxSteps, tol) {
|
||||
const numFeatures = X[0].length;
|
||||
let w = new Array(numFeatures + 1).fill(0.0);
|
||||
const N = X.length;
|
||||
|
||||
let prevLoss = Infinity;
|
||||
|
||||
for (let step = 0; step < maxSteps; step++) {
|
||||
const grad = new Array(numFeatures + 1).fill(0.0);
|
||||
|
||||
for (let i = 0; i < N; i++) {
|
||||
let z = w[0];
|
||||
for (let j = 0; j < numFeatures; j++) z += w[j + 1] * X[i][j];
|
||||
|
||||
const p = sigmoid(z);
|
||||
const err = p - Y[i];
|
||||
|
||||
grad[0] += err;
|
||||
for (let j = 0; j < numFeatures; j++) {
|
||||
grad[j + 1] += err * X[i][j];
|
||||
}
|
||||
}
|
||||
|
||||
// Add L2 penalty (skip intercept grad[0])
|
||||
for (let j = 0; j < numFeatures; j++) {
|
||||
grad[j + 1] += lambda * w[j + 1];
|
||||
}
|
||||
|
||||
// Update weights
|
||||
let maxDelta = 0;
|
||||
for (let j = 0; j < w.length; j++) {
|
||||
const update = (learningRate * grad[j]) / N;
|
||||
w[j] -= update;
|
||||
if (Math.abs(update) > maxDelta) maxDelta = Math.abs(update);
|
||||
}
|
||||
|
||||
if (maxDelta < tol) {
|
||||
// Converged
|
||||
break;
|
||||
}
|
||||
}
|
||||
return w;
|
||||
}
|
||||
|
||||
fit(featuresArray, labels) {
|
||||
if (!featuresArray.length) return;
|
||||
this.featureKeys = Object.keys(featuresArray[0]);
|
||||
|
||||
// Convert to 2D array
|
||||
const X = featuresArray.map(f => this.featureKeys.map(k => f[k]));
|
||||
const Y = labels;
|
||||
const N = X.length;
|
||||
|
||||
// 1. Carve a 80/20 validation slice from TRAIN to tune lambda
|
||||
const splitIdx = Math.floor(N * 0.8);
|
||||
const X_train = X.slice(0, splitIdx);
|
||||
const Y_train = Y.slice(0, splitIdx);
|
||||
const X_val = X.slice(splitIdx);
|
||||
const Y_val = Y.slice(splitIdx);
|
||||
|
||||
// 2. Tune lambda
|
||||
const lambdas = [0.01, 0.1, 1.0, 10.0, 100.0, 500.0, 1000.0];
|
||||
let bestLambda = 0;
|
||||
let bestLoss = Infinity;
|
||||
|
||||
console.log(`\nTuning L2 Regularization (Lambda) on Validation Slice...`);
|
||||
for (const l of lambdas) {
|
||||
const w = this._train(X_train, Y_train, l, 0.5, 2000, 1e-5);
|
||||
const loss = computeLogLoss(X_val, Y_val, w);
|
||||
console.log(` Lambda ${l.toString().padEnd(6)} -> LogLoss: ${loss.toFixed(4)}`);
|
||||
if (loss < bestLoss) {
|
||||
bestLoss = loss;
|
||||
bestLambda = l;
|
||||
}
|
||||
}
|
||||
|
||||
console.log(`Selected Best Lambda: ${bestLambda}`);
|
||||
|
||||
// 3. Refit on FULL TRAIN set using best lambda
|
||||
this.weights = this._train(X, Y, bestLambda, 0.5, 5000, 1e-5);
|
||||
|
||||
return this.getCoefficients();
|
||||
}
|
||||
|
||||
predict(featuresArray) {
|
||||
if (!this.weights) throw new Error("Model not trained");
|
||||
|
||||
const probs = [];
|
||||
for (let i = 0; i < featuresArray.length; i++) {
|
||||
const f = featuresArray[i];
|
||||
let z = this.weights[0]; // intercept
|
||||
for (let j = 0; j < this.featureKeys.length; j++) {
|
||||
z += this.weights[j + 1] * f[this.featureKeys[j]];
|
||||
}
|
||||
probs.push(sigmoid(z));
|
||||
}
|
||||
|
||||
return probs;
|
||||
}
|
||||
|
||||
getCoefficients() {
|
||||
if (!this.weights) return {};
|
||||
const coeffs = { intercept: this.weights[0] };
|
||||
for (let i = 0; i < this.featureKeys.length; i++) {
|
||||
coeffs[this.featureKeys[i]] = this.weights[i + 1];
|
||||
}
|
||||
return coeffs;
|
||||
}
|
||||
|
||||
save(filepath) {
|
||||
fs.writeFileSync(filepath, JSON.stringify({
|
||||
featureKeys: this.featureKeys,
|
||||
weights: this.weights
|
||||
}, null, 2));
|
||||
}
|
||||
|
||||
load(filepath) {
|
||||
if (!fs.existsSync(filepath)) return;
|
||||
const data = JSON.parse(fs.readFileSync(filepath, 'utf8'));
|
||||
this.featureKeys = data.featureKeys;
|
||||
this.weights = data.weights;
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
import fs from 'fs';
|
||||
import path from 'path';
|
||||
|
||||
export class StandardScaler {
|
||||
constructor() {
|
||||
this.means = {};
|
||||
this.stds = {};
|
||||
}
|
||||
|
||||
fit(featuresArray) {
|
||||
if (!featuresArray.length) return;
|
||||
|
||||
// Get all feature keys
|
||||
const keys = Object.keys(featuresArray[0]);
|
||||
|
||||
for (const key of keys) {
|
||||
const values = featuresArray.map(f => f[key]);
|
||||
const mean = values.reduce((sum, v) => sum + v, 0) / values.length;
|
||||
|
||||
const variance = values.reduce((sum, v) => sum + Math.pow(v - mean, 2), 0) / values.length;
|
||||
const std = Math.sqrt(variance);
|
||||
|
||||
this.means[key] = mean;
|
||||
this.stds[key] = std || 1; // Avoid divide by 0
|
||||
}
|
||||
}
|
||||
|
||||
transform(features) {
|
||||
const scaled = {};
|
||||
for (const key of Object.keys(this.means)) {
|
||||
if (features[key] === undefined) continue;
|
||||
scaled[key] = (features[key] - this.means[key]) / this.stds[key];
|
||||
}
|
||||
return scaled;
|
||||
}
|
||||
|
||||
transformArray(featuresArray) {
|
||||
return featuresArray.map(f => this.transform(f));
|
||||
}
|
||||
|
||||
save(filepath) {
|
||||
fs.writeFileSync(filepath, JSON.stringify({ means: this.means, stds: this.stds }, null, 2));
|
||||
}
|
||||
|
||||
load(filepath) {
|
||||
if (!fs.existsSync(filepath)) return;
|
||||
const data = JSON.parse(fs.readFileSync(filepath, 'utf8'));
|
||||
this.means = data.means;
|
||||
this.stds = data.stds;
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
{
|
||||
"name": "LogisticRegression",
|
||||
"numSteps": 1000,
|
||||
"learningRate": 0.05,
|
||||
"numberClasses": 2,
|
||||
"classifiers": [
|
||||
{
|
||||
"numSteps": 1000,
|
||||
"learningRate": 0.05,
|
||||
"weights": [
|
||||
[
|
||||
-94.4420175451543,
|
||||
-5.766427757134977,
|
||||
-74.50180063587948,
|
||||
-66.23257473781456,
|
||||
-1817.0926888106162,
|
||||
-166.35311896181236
|
||||
]
|
||||
]
|
||||
},
|
||||
{
|
||||
"numSteps": 1000,
|
||||
"learningRate": 0.05,
|
||||
"weights": [
|
||||
[
|
||||
-84.54027063090014,
|
||||
-50.36483530223822,
|
||||
13.904576636453635,
|
||||
37.27497755449738,
|
||||
-395.0000542460034,
|
||||
41.80818011695695
|
||||
]
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
import { swingFeatures } from './features.js';
|
||||
|
||||
function runTest() {
|
||||
console.log("Running no-lookahead guardrail test...");
|
||||
|
||||
// Create mock candles where index = day
|
||||
const candles = [];
|
||||
for (let i = 0; i < 250; i++) {
|
||||
candles.push({
|
||||
date: `2024-01-${i}`,
|
||||
open: 100 + i,
|
||||
high: 105 + i,
|
||||
low: 95 + i,
|
||||
close: 102 + i,
|
||||
volume: 1000000
|
||||
});
|
||||
}
|
||||
|
||||
const spyCandles = [...candles]; // clone
|
||||
const vix = 15;
|
||||
|
||||
// We are at day index 210.
|
||||
const currentDayIdx = 210;
|
||||
|
||||
// Guardrail: Pass ONLY the slice up to current day
|
||||
const slicedCandles = candles.slice(0, currentDayIdx + 1);
|
||||
const slicedSpy = spyCandles.slice(0, currentDayIdx + 1);
|
||||
|
||||
// Compute features
|
||||
const feats = swingFeatures(slicedCandles, slicedSpy, vix);
|
||||
|
||||
if (!feats) {
|
||||
throw new Error("Features returned null unexpectedly.");
|
||||
}
|
||||
|
||||
// The slice literally does not contain any future data in memory.
|
||||
// We can verify the length of the array inside the feature to be absolutely sure.
|
||||
if (slicedCandles.length > currentDayIdx + 1) {
|
||||
throw new Error("Lookahead leak: slicedCandles contains future data!");
|
||||
}
|
||||
|
||||
console.log("PASS: Features successfully computed without lookahead bias.");
|
||||
console.log("Computed Features:", feats);
|
||||
}
|
||||
|
||||
runTest();
|
||||
|
|
@ -0,0 +1,44 @@
|
|||
import { applyCosts } from './costs.js';
|
||||
|
||||
function atr14(candles, period = 14) {
|
||||
if (!candles || candles.length <= period) return null;
|
||||
const trs = [];
|
||||
for (let i = 1; i < candles.length; i++) {
|
||||
const { high, low } = candles[i];
|
||||
const prevClose = candles[i - 1].close;
|
||||
trs.push(Math.max(high - low, Math.abs(high - prevClose), Math.abs(low - prevClose)));
|
||||
}
|
||||
const recent = trs.slice(-period);
|
||||
return recent.reduce((a, b) => a + b, 0) / recent.length;
|
||||
}
|
||||
|
||||
export function simulateSwing(candles, entryIdx, cfg) {
|
||||
// If entry is impossible (out of bounds)
|
||||
if (entryIdx >= candles.length) return null;
|
||||
|
||||
const rawEntry = candles[entryIdx].open;
|
||||
const entry = applyCosts(rawEntry, 'buy', candles, entryIdx);
|
||||
|
||||
// ATR known at signal (close of previous day). candles slice goes UP TO the entryIdx (so it includes the signal bar, which is entryIdx - 1)
|
||||
// Wait, if entryIdx is the open of tomorrow, we pass candles.slice(0, entryIdx) so the last candle is today (the signal bar).
|
||||
const preEntryCandles = candles.slice(0, entryIdx);
|
||||
const atr = atr14(preEntryCandles);
|
||||
if (!atr) return null;
|
||||
|
||||
const stop = entry - cfg.stopAtr * atr;
|
||||
const target = entry + cfg.targetAtr * atr;
|
||||
const R = entry - stop; // 1 ATR worth of price
|
||||
|
||||
const end = Math.min(entryIdx + cfg.maxHoldDays - 1, candles.length - 1);
|
||||
|
||||
for (let d = entryIdx; d <= end; d++) {
|
||||
// pessimistic tie-break: if both hit same day, assume STOP first
|
||||
if (candles[d].low <= stop) return { outcome: 'LOSS', r: -1.0, exitDay: d, stopPrice: stop, targetPrice: target, atr };
|
||||
if (candles[d].high >= target) return { outcome: 'WIN', r: cfg.targetAtr, exitDay: d, stopPrice: stop, targetPrice: target, atr };
|
||||
}
|
||||
|
||||
// unresolved → exit at close of last day, net of exit costs
|
||||
const rawExit = candles[end].close;
|
||||
const exit = applyCosts(rawExit, 'sell', candles, end);
|
||||
return { outcome: 'SCRATCH', r: (exit - entry) / R, exitDay: end, stopPrice: stop, targetPrice: target, atr };
|
||||
}
|
||||
|
|
@ -0,0 +1,31 @@
|
|||
export function calibration(testResults) {
|
||||
// We bucket predicted probabilities into deciles (0-10%, 10-20%, etc)
|
||||
const buckets = Array(10).fill(null).map(() => ({ total: 0, wins: 0, sumProb: 0 }));
|
||||
|
||||
for (const r of testResults) {
|
||||
if (r.prob === undefined || r.prob === null) continue;
|
||||
let bIdx = Math.floor(r.prob * 10);
|
||||
if (bIdx > 9) bIdx = 9; // Handle p=1.0 edge case
|
||||
if (bIdx < 0) bIdx = 0;
|
||||
|
||||
buckets[bIdx].total++;
|
||||
buckets[bIdx].sumProb += r.prob;
|
||||
if (r.outcome === 'WIN') buckets[bIdx].wins++;
|
||||
}
|
||||
|
||||
console.log(`\n=== CALIBRATION TABLE ===`);
|
||||
console.log(`Bucket\t\tN\tPred P(Win)\tActual Win%\tDiff`);
|
||||
console.log(`------------------------------------------------------------------`);
|
||||
|
||||
for (let i = 0; i < 10; i++) {
|
||||
const b = buckets[i];
|
||||
if (b.total === 0) continue;
|
||||
|
||||
const label = `${(i * 10).toString().padStart(2, '0')}-${((i + 1) * 10).toString().padStart(2, '0')}%`;
|
||||
const predP = (b.sumProb / b.total) * 100;
|
||||
const actP = (b.wins / b.total) * 100;
|
||||
const diff = actP - predP;
|
||||
|
||||
console.log(`[${label}]\t${b.total}\t${predP.toFixed(1)}%\t\t${actP.toFixed(1)}%\t\t${diff > 0 ? '+' : ''}${diff.toFixed(1)}pp`);
|
||||
}
|
||||
}
|
||||
|
|
@ -0,0 +1,75 @@
|
|||
export function ciExpectancy(rValues) {
|
||||
const n = rValues.length;
|
||||
if (n === 0) return { mean: 0, lo: 0, hi: 0, n: 0 };
|
||||
|
||||
const mean = rValues.reduce((a, b) => a + b, 0) / n;
|
||||
|
||||
if (n === 1) return { mean, lo: mean, hi: mean, n };
|
||||
|
||||
const variance = rValues.reduce((a, b) => a + Math.pow(b - mean, 2), 0) / (n - 1);
|
||||
const sd = Math.sqrt(variance);
|
||||
const se = sd / Math.sqrt(n);
|
||||
|
||||
return { mean, lo: mean - 1.96 * se, hi: mean + 1.96 * se, n };
|
||||
}
|
||||
|
||||
export function generateResultsTable(testResults) {
|
||||
const grades = ['A', 'B', 'C', 'D'];
|
||||
const grouped = {
|
||||
A: { wins: 0, losses: 0, scratches: 0, rVals: [] },
|
||||
B: { wins: 0, losses: 0, scratches: 0, rVals: [] },
|
||||
C: { wins: 0, losses: 0, scratches: 0, rVals: [] },
|
||||
D: { wins: 0, losses: 0, scratches: 0, rVals: [] }
|
||||
};
|
||||
|
||||
for (const r of testResults) {
|
||||
if (grouped[r.grade]) {
|
||||
grouped[r.grade].rVals.push(r.rMultiple);
|
||||
if (r.outcome === 'WIN') grouped[r.grade].wins++;
|
||||
else if (r.outcome === 'LOSS') grouped[r.grade].losses++;
|
||||
else grouped[r.grade].scratches++;
|
||||
}
|
||||
}
|
||||
|
||||
console.log(`\n=== OUT-OF-SAMPLE TEST RESULTS (EXPECTANCY) ===`);
|
||||
console.log(`Grade\tExp(R)\t\t95% CI (Exp)\t\tWin%\tLoss%\tScratch%\tn`);
|
||||
console.log(`-----------------------------------------------------------------------------------------`);
|
||||
|
||||
for (const g of grades) {
|
||||
const st = grouped[g];
|
||||
const n = st.rVals.length;
|
||||
if (n === 0) continue;
|
||||
|
||||
const pWin = (st.wins / n) * 100;
|
||||
const pLoss = (st.losses / n) * 100;
|
||||
const pScratch = (st.scratches / n) * 100;
|
||||
|
||||
const ci = ciExpectancy(st.rVals);
|
||||
|
||||
const meanFmt = `${ci.mean > 0 ? '+' : ''}${ci.mean.toFixed(3)}R`;
|
||||
const ciFmt = `[${ci.lo.toFixed(3)}, ${ci.hi.toFixed(3)}]`;
|
||||
|
||||
console.log(`${g}\t${meanFmt}\t\t${ciFmt}\t\t${pWin.toFixed(1)}%\t${pLoss.toFixed(1)}%\t${pScratch.toFixed(1)}%\t\t${n}`);
|
||||
}
|
||||
|
||||
// Random Control = The entire test set
|
||||
const allR = testResults.map(r => r.rMultiple);
|
||||
const totalN = allR.length;
|
||||
if (totalN > 0) {
|
||||
const totalWins = testResults.filter(r => r.outcome === 'WIN').length;
|
||||
const totalLosses = testResults.filter(r => r.outcome === 'LOSS').length;
|
||||
const totalScratches = testResults.filter(r => r.outcome === 'SCRATCH').length;
|
||||
|
||||
const ciRand = ciExpectancy(allR);
|
||||
const meanFmt = `${ciRand.mean > 0 ? '+' : ''}${ciRand.mean.toFixed(3)}R`;
|
||||
const ciFmt = `[${ciRand.lo.toFixed(3)}, ${ciRand.hi.toFixed(3)}]`;
|
||||
|
||||
const pWin = (totalWins / totalN) * 100;
|
||||
const pLoss = (totalLosses / totalN) * 100;
|
||||
const pScratch = (totalScratches / totalN) * 100;
|
||||
|
||||
console.log(`Rand\t${meanFmt}\t\t${ciFmt}\t\t${pWin.toFixed(1)}%\t${pLoss.toFixed(1)}%\t${pScratch.toFixed(1)}%\t\t${totalN}`);
|
||||
}
|
||||
|
||||
console.log(`-----------------------------------------------------------------------------------------`);
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,68 @@
|
|||
import fs from 'fs';
|
||||
import path from 'path';
|
||||
import { fileURLToPath } from 'url';
|
||||
import { parse } from 'csv-parse/sync';
|
||||
import LogisticRegression from 'ml-logistic-regression';
|
||||
import { Matrix } from 'ml-matrix';
|
||||
|
||||
const __filename = fileURLToPath(import.meta.url);
|
||||
const __dirname = path.dirname(__filename);
|
||||
|
||||
async function train() {
|
||||
console.log('Loading results.csv...');
|
||||
const csvPath = path.join(__dirname, 'results.csv');
|
||||
const fileContent = fs.readFileSync(csvPath, 'utf8');
|
||||
|
||||
const records = parse(fileContent, {
|
||||
columns: true,
|
||||
skip_empty_lines: true
|
||||
});
|
||||
|
||||
const X = [];
|
||||
const Y = [];
|
||||
|
||||
for (const row of records) {
|
||||
if (row.type !== 'signal') continue; // Skip controls if they exist
|
||||
|
||||
// Parse features
|
||||
const rsi = parseFloat(row.rsi);
|
||||
const atrPct = parseFloat(row.atrPct);
|
||||
const rvol = parseFloat(row.rvol);
|
||||
const macd = parseFloat(row.macd);
|
||||
const regime = parseFloat(row.regime_risk_on);
|
||||
|
||||
// Skip rows with missing data
|
||||
if (isNaN(rsi) || isNaN(atrPct) || isNaN(rvol) || isNaN(macd) || isNaN(regime)) continue;
|
||||
|
||||
X.push([
|
||||
1.0, // Intercept
|
||||
rsi / 100,
|
||||
atrPct / 10,
|
||||
rvol / 5,
|
||||
macd / 2,
|
||||
regime
|
||||
]);
|
||||
|
||||
// Target: 1 if win or win_partial, 0 otherwise
|
||||
const isWin = (row.outcome === 'win' || row.outcome === 'win_partial') ? 1 : 0;
|
||||
Y.push(isWin);
|
||||
}
|
||||
|
||||
console.log(`Loaded ${X.length} valid training samples.`);
|
||||
|
||||
const xMat = new Matrix(X);
|
||||
const yMat = Matrix.columnVector(Y);
|
||||
|
||||
console.log('Training Logistic Regression model...');
|
||||
const logreg = new LogisticRegression({ numSteps: 1000, learningRate: 0.05 });
|
||||
logreg.train(xMat, yMat);
|
||||
|
||||
const modelJson = logreg.toJSON();
|
||||
|
||||
const outputPath = path.join(__dirname, 'model_weights.json');
|
||||
fs.writeFileSync(outputPath, JSON.stringify(modelJson, null, 2));
|
||||
|
||||
console.log(`Model trained and saved to ${outputPath}`);
|
||||
}
|
||||
|
||||
train().catch(console.error);
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
import { loadFundamentals, getFundamentalsAsOf } from '../fundamentals.js';
|
||||
|
||||
function runTest() {
|
||||
console.log("Running fundamentals lookahead test...");
|
||||
|
||||
const testData = [
|
||||
{
|
||||
symbol: 'AAPL',
|
||||
datekey: '2020-02-15', // Actually filed on Feb 15
|
||||
fiscalEnd: '2019-12-31',
|
||||
metrics: { roe: 0.10 }
|
||||
},
|
||||
{
|
||||
symbol: 'AAPL',
|
||||
datekey: '2020-05-15', // Q1 filed on May 15
|
||||
fiscalEnd: '2020-03-31',
|
||||
metrics: { roe: 0.12 }
|
||||
},
|
||||
{
|
||||
symbol: 'MSFT',
|
||||
datekey: null, // Missing datekey, should fallback to fiscalEnd + 120 days
|
||||
fiscalEnd: '2019-12-31', // + 120 days = 2020-04-29
|
||||
metrics: { roe: 0.15 }
|
||||
}
|
||||
];
|
||||
|
||||
loadFundamentals(testData);
|
||||
|
||||
// Test 1: Jan 31, 2020.
|
||||
// The Dec 31 fiscal data isn't filed until Feb 15. We MUST return null.
|
||||
const a1 = getFundamentalsAsOf('AAPL', '2020-01-31');
|
||||
if (a1 !== null) throw new Error("Lookahead leak! Returned Q4 data before filing date.");
|
||||
|
||||
// Test 2: Feb 16, 2020.
|
||||
// The Dec 31 fiscal data was filed Feb 15. We should get it.
|
||||
const a2 = getFundamentalsAsOf('AAPL', '2020-02-16');
|
||||
if (!a2 || a2.roe !== 0.10) throw new Error("Failed to return available data.");
|
||||
|
||||
// Test 3: May 14, 2020.
|
||||
// Q1 isn't filed until May 15. We should still get Q4 (roe: 0.10).
|
||||
const a3 = getFundamentalsAsOf('AAPL', '2020-05-14');
|
||||
if (!a3 || a3.roe !== 0.10) throw new Error("Lookahead leak! Returned Q1 data early.");
|
||||
|
||||
// Test 4: May 16, 2020.
|
||||
// Now we should get Q1 (roe: 0.12).
|
||||
const a4 = getFundamentalsAsOf('AAPL', '2020-05-16');
|
||||
if (!a4 || a4.roe !== 0.12) throw new Error("Failed to roll forward to new data.");
|
||||
|
||||
// Test 5: MSFT fallback logic. Target = April 28 (119 days after fiscalEnd). Should be null.
|
||||
const m1 = getFundamentalsAsOf('MSFT', '2020-04-28');
|
||||
if (m1 !== null) throw new Error("Lookahead leak! MSFT fallback data returned early.");
|
||||
|
||||
// Test 6: MSFT fallback logic. Target = April 30 (121 days after fiscalEnd). Should be available.
|
||||
const m2 = getFundamentalsAsOf('MSFT', '2020-04-30');
|
||||
if (!m2 || m2.roe !== 0.15) throw new Error("Failed to return fallback data after 120 days.");
|
||||
|
||||
console.log("PASS: Fundamentals guardrail strictly prevents lookahead bias.");
|
||||
}
|
||||
|
||||
runTest();
|
||||
|
|
@ -0,0 +1,54 @@
|
|||
import { buildFactorProfiles } from '../descriptiveLens.js';
|
||||
|
||||
test('strictly-better stock ranks higher on every factor', async () => {
|
||||
const good = {
|
||||
ticker: 'GOOD',
|
||||
trailingPE: 8,
|
||||
priceToBook: 1,
|
||||
priceToSales: 0.5,
|
||||
freeCashflow: 1e10,
|
||||
marketCap: 1e11,
|
||||
returnOnEquity: 0.30,
|
||||
grossMargins: 0.60,
|
||||
debtToEquity: 10,
|
||||
mom_12_1: 0.25,
|
||||
realizedVol252: 0.15
|
||||
};
|
||||
|
||||
const bad = {
|
||||
ticker: 'BAD',
|
||||
trailingPE: 50,
|
||||
priceToBook: 10,
|
||||
priceToSales: 12,
|
||||
freeCashflow: 1e8,
|
||||
marketCap: 1e11,
|
||||
returnOnEquity: 0.02,
|
||||
grossMargins: 0.10,
|
||||
debtToEquity: 300,
|
||||
mom_12_1: -0.10,
|
||||
realizedVol252: 0.60
|
||||
};
|
||||
|
||||
// buildFactorProfiles supports injecting a universe payload directly for testing
|
||||
const out = await buildFactorProfiles([good, bad]);
|
||||
|
||||
// 'out' returns a map { 'GOOD': { ... }, 'BAD': { ... } } during test injection
|
||||
|
||||
for (const f of ['value', 'quality', 'lowVol', 'momentum', 'composite']) {
|
||||
// A strictly better stock MUST have a higher percentile rank
|
||||
if (out['GOOD'][f] <= out['BAD'][f]) {
|
||||
throw new Error(`Sign catastrophe: GOOD ranked ${out['GOOD'][f]} on ${f}, but BAD ranked ${out['BAD'][f]}. GOOD must strictly outrank BAD.`);
|
||||
}
|
||||
}
|
||||
|
||||
console.log("PASS: Orientation logic correctly handles signs. 'Higher percentile' always means 'more favorable'.");
|
||||
});
|
||||
|
||||
// Jest polyfill for simple node execution
|
||||
function test(name, fn) {
|
||||
console.log(`Running test: ${name}`);
|
||||
fn().catch(e => {
|
||||
console.error(`FAIL: ${e.message}`);
|
||||
process.exit(1);
|
||||
});
|
||||
}
|
||||
|
|
@ -0,0 +1,142 @@
|
|||
import { getUniverseAsOf } from './universe.js';
|
||||
import { getFundamentalsAsOf } from './fundamentals.js';
|
||||
import { standardizeCrossSection, FACTORS } from './factors.js';
|
||||
import { spearman, calculateTurnover } from './metrics.js';
|
||||
|
||||
// Global reference for forward returns in our testing environment
|
||||
// In production, this would query a price DB
|
||||
let globalReturnsMap = new Map();
|
||||
|
||||
export function setForwardReturnsMap(map) {
|
||||
globalReturnsMap = map;
|
||||
}
|
||||
|
||||
function getForwardReturn(ticker, dateStr, horizonMonths, allDates) {
|
||||
// Find the index of the current date
|
||||
const idx = allDates.indexOf(dateStr);
|
||||
if (idx === -1 || idx + horizonMonths >= allDates.length) return null; // Not enough forward data
|
||||
|
||||
let cumulativeRet = 0;
|
||||
// Simple sum for log returns, or compound. We'll use simple compound: (1+r1)*(1+r2) - 1
|
||||
let mult = 1.0;
|
||||
for (let m = 0; m < horizonMonths; m++) {
|
||||
const nextDate = allDates[idx + m];
|
||||
const r = globalReturnsMap.get(`${ticker}_${nextDate}`);
|
||||
if (r === undefined || r === null) return null; // missing data
|
||||
mult *= (1 + r);
|
||||
}
|
||||
return mult - 1.0;
|
||||
}
|
||||
|
||||
function bucketIntoDeciles(rankedScores) {
|
||||
const deciles = Array(10).fill(null).map(() => []);
|
||||
const n = rankedScores.length;
|
||||
if (n === 0) return deciles;
|
||||
|
||||
for (let i = 0; i < n; i++) {
|
||||
const d = Math.min(9, Math.floor((i / n) * 10));
|
||||
deciles[d].push(rankedScores[i].ticker);
|
||||
}
|
||||
return deciles; // deciles[0] = highest scores (Top Decile), deciles[9] = lowest scores
|
||||
}
|
||||
|
||||
export function runFactor(factorName, allDates, horizonMonths = 1, costPerSideBps = 5) {
|
||||
const results = [];
|
||||
let prevD10Weights = null;
|
||||
let prevD1Weights = null;
|
||||
const costPct = costPerSideBps / 10000.0;
|
||||
|
||||
for (const date of allDates) {
|
||||
const members = getUniverseAsOf(date);
|
||||
if (!members || members.length === 0) continue;
|
||||
|
||||
const rawScores = {};
|
||||
for (const t of members) {
|
||||
const fund = getFundamentalsAsOf(t, date);
|
||||
if (!fund) continue;
|
||||
|
||||
const score = FACTORS[factorName](fund);
|
||||
if (score !== null && !isNaN(score)) {
|
||||
rawScores[t] = score;
|
||||
}
|
||||
}
|
||||
|
||||
// Standardize cross-sectionally
|
||||
const zScores = standardizeCrossSection(rawScores, false);
|
||||
|
||||
// Sort descending (High Score = Best)
|
||||
const ranked = Object.keys(zScores)
|
||||
.map(t => ({ ticker: t, score: zScores[t] }))
|
||||
.sort((a, b) => b.score - a.score);
|
||||
|
||||
if (ranked.length < 10) continue; // need enough names for deciles
|
||||
|
||||
const deciles = bucketIntoDeciles(ranked);
|
||||
const d10Tickers = deciles[0]; // Top decile
|
||||
const d1Tickers = deciles[9]; // Bottom decile
|
||||
|
||||
// Calculate equal-weight portfolio weights for this month
|
||||
const curD10Weights = {};
|
||||
d10Tickers.forEach(t => curD10Weights[t] = 1.0 / d10Tickers.length);
|
||||
|
||||
const curD1Weights = {};
|
||||
d1Tickers.forEach(t => curD1Weights[t] = 1.0 / d1Tickers.length);
|
||||
|
||||
// Calculate turnover
|
||||
const d10Turnover = calculateTurnover(prevD10Weights, curD10Weights);
|
||||
const d1Turnover = calculateTurnover(prevD1Weights, curD1Weights);
|
||||
|
||||
// Forward returns
|
||||
const decileReturns = [];
|
||||
let allFwdReturns = [];
|
||||
let allZScores = [];
|
||||
|
||||
for (let d = 0; d < 10; d++) {
|
||||
const decTickers = deciles[d];
|
||||
let sumRet = 0;
|
||||
let count = 0;
|
||||
for (const t of decTickers) {
|
||||
const ret = getForwardReturn(t, date, horizonMonths, allDates);
|
||||
if (ret !== null) {
|
||||
sumRet += ret;
|
||||
count++;
|
||||
|
||||
allFwdReturns.push(ret);
|
||||
allZScores.push(zScores[t]);
|
||||
}
|
||||
}
|
||||
decileReturns.push(count > 0 ? sumRet / count : 0);
|
||||
}
|
||||
|
||||
if (allFwdReturns.length > 0) {
|
||||
const ic = spearman(allZScores, allFwdReturns);
|
||||
const universeMeanRet = allFwdReturns.reduce((a, b) => a + b, 0) / allFwdReturns.length;
|
||||
|
||||
// Gross Returns
|
||||
const d10Gross = decileReturns[0];
|
||||
const d1Gross = decileReturns[9];
|
||||
|
||||
// Net Returns
|
||||
const d10Net = d10Gross - (d10Turnover * costPct);
|
||||
const d1Net = d1Gross - (d1Turnover * costPct);
|
||||
|
||||
results.push({
|
||||
date,
|
||||
ic,
|
||||
decileReturns, // Gross decile returns for the staircase plot
|
||||
d10Gross,
|
||||
d1Gross,
|
||||
d10Net,
|
||||
d1Net,
|
||||
universeMeanRet,
|
||||
d10Turnover,
|
||||
d1Turnover
|
||||
});
|
||||
}
|
||||
|
||||
prevD10Weights = curD10Weights;
|
||||
prevD1Weights = curD1Weights;
|
||||
}
|
||||
|
||||
return results;
|
||||
}
|
||||
|
|
@ -0,0 +1,125 @@
|
|||
import { FACTORS, standardizeCrossSection } from './factors.js';
|
||||
import { pullRawRecord } from './ingest.js';
|
||||
import { getUniverse } from '../services/stockUniverseService.js';
|
||||
|
||||
/**
|
||||
* Assigns percentiles (0-100) to a raw factor array using the standardizer.
|
||||
* @param {Array} raw - Array of objects: { ticker, value: 0.5, quality: 1.2 }
|
||||
* @param {String} factorKey - The factor to assign percentiles for (e.g. 'value')
|
||||
*/
|
||||
function assignPercentiles(raw, factorKey) {
|
||||
const rawByTicker = {};
|
||||
for (const r of raw) {
|
||||
if (r[factorKey] !== null && r[factorKey] !== undefined) {
|
||||
rawByTicker[r.ticker] = r[factorKey];
|
||||
}
|
||||
}
|
||||
|
||||
// Uses winsorization and z-scoring from factors.js
|
||||
const zScores = standardizeCrossSection(rawByTicker, false);
|
||||
|
||||
// Convert z-scores to percentiles relative to the universe
|
||||
const validTickers = Object.keys(zScores);
|
||||
const sortedZScores = validTickers.map(t => zScores[t]).sort((a, b) => a - b);
|
||||
|
||||
const n = sortedZScores.length;
|
||||
|
||||
for (const r of raw) {
|
||||
const z = zScores[r.ticker];
|
||||
if (z === undefined) {
|
||||
r[`${factorKey}Percentile`] = null;
|
||||
continue;
|
||||
}
|
||||
|
||||
// Find percentile rank
|
||||
let index = sortedZScores.findIndex(val => val >= z);
|
||||
if (index === -1) index = n - 1;
|
||||
|
||||
r[`${factorKey}Percentile`] = Math.round((index / n) * 100);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Generates the descriptive Factor Profile for the entire universe based on CURRENT data.
|
||||
* Does NOT generate forward predictions.
|
||||
*/
|
||||
export async function buildFactorProfiles(injectedUniverseRecords = null) {
|
||||
const rawProfiles = [];
|
||||
|
||||
// Allow test injection
|
||||
if (injectedUniverseRecords) {
|
||||
for (const data of injectedUniverseRecords) {
|
||||
rawProfiles.push({
|
||||
ticker: data.ticker || 'TEST',
|
||||
value: FACTORS.value(data),
|
||||
quality: FACTORS.quality(data),
|
||||
lowVol: FACTORS.lowVol(data),
|
||||
momentum: FACTORS.momentum(data)
|
||||
});
|
||||
}
|
||||
} else {
|
||||
const universe = getUniverse().filter(s => !s.isETF).map(s => s.symbol);
|
||||
for (const ticker of universe) {
|
||||
try {
|
||||
const data = await pullRawRecord(ticker);
|
||||
if (!data) continue;
|
||||
|
||||
rawProfiles.push({
|
||||
ticker,
|
||||
value: FACTORS.value(data),
|
||||
quality: FACTORS.quality(data),
|
||||
lowVol: FACTORS.lowVol(data),
|
||||
momentum: FACTORS.momentum(data)
|
||||
});
|
||||
} catch (e) {
|
||||
console.warn(`[DescriptiveLens] Failed for ${ticker}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Composite is the equal-weight average of the non-null standardized z-scores
|
||||
// First, we must standardize the individual factors to compute composite z-score
|
||||
const factors = ['value', 'quality', 'lowVol', 'momentum'];
|
||||
const zScoreMaps = {};
|
||||
|
||||
for (const f of factors) {
|
||||
const rawByTicker = {};
|
||||
for (const r of rawProfiles) {
|
||||
if (r[f] !== null && r[f] !== undefined) rawByTicker[r.ticker] = r[f];
|
||||
}
|
||||
zScoreMaps[f] = standardizeCrossSection(rawByTicker, false);
|
||||
}
|
||||
|
||||
// Compute composite raw score
|
||||
for (const r of rawProfiles) {
|
||||
let sum = 0;
|
||||
let count = 0;
|
||||
for (const f of factors) {
|
||||
const z = zScoreMaps[f][r.ticker];
|
||||
if (z !== undefined) {
|
||||
sum += z;
|
||||
count++;
|
||||
}
|
||||
}
|
||||
r.composite = count > 0 ? (sum / count) : null;
|
||||
}
|
||||
|
||||
// Cross-sectional standardization and percentile assignment
|
||||
[...factors, 'composite'].forEach(f => assignPercentiles(rawProfiles, f));
|
||||
|
||||
// If this is a test injection, return the raw profiles with percentiles for testing
|
||||
if (injectedUniverseRecords) return rawProfiles.reduce((acc, p) => { acc[p.ticker] = p; return acc; }, {});
|
||||
|
||||
// Clean up the raw scores, we only return the percentiles for the UI
|
||||
const cleanedProfiles = rawProfiles.map(p => ({
|
||||
ticker: p.ticker,
|
||||
value: p.valuePercentile,
|
||||
quality: p.qualityPercentile,
|
||||
lowVol: p.lowVolPercentile,
|
||||
momentum: p.momentumPercentile,
|
||||
composite: p.compositePercentile,
|
||||
_disclaimer: "Descriptive only. Shows where this stock ranks vs. peers on each factor today. We have not validated that these ranks predict returns — and our own testing showed standard technical signals do not. We'll only call a factor predictive after it passes out-of-sample validation."
|
||||
}));
|
||||
|
||||
return cleanedProfiles;
|
||||
}
|
||||
|
|
@ -0,0 +1,106 @@
|
|||
function mean(arr) {
|
||||
const valid = arr.filter(x => x !== null && x !== undefined && !isNaN(x));
|
||||
if (valid.length === 0) return null;
|
||||
return valid.reduce((a, b) => a + b, 0) / valid.length;
|
||||
}
|
||||
|
||||
export function orientedComponents(r) {
|
||||
const inv = x => (x != null && x > 0) ? 1 / x : null; // guard neg/zero multiples
|
||||
const neg = x => (x != null) ? -x : null;
|
||||
const div = (a, b) => (a != null && b) ? a / b : null;
|
||||
return {
|
||||
value: [ inv(r.trailingPE), div(r.freeCashflow, r.marketCap),
|
||||
inv(r.priceToBook), inv(r.priceToSales) ],
|
||||
quality: [ r.returnOnEquity, r.grossMargins, neg(r.debtToEquity) ],
|
||||
lowVol: [ neg(r.realizedVol252) ],
|
||||
momentum: [ r.mom_12_1 ],
|
||||
};
|
||||
}
|
||||
|
||||
export const FACTORS = {
|
||||
value: r => mean(orientedComponents(r).value),
|
||||
quality: r => mean(orientedComponents(r).quality),
|
||||
lowVol: r => mean(orientedComponents(r).lowVol),
|
||||
momentum: r => mean(orientedComponents(r).momentum),
|
||||
// composite is an equal-weight average of the standardized scores, computed later
|
||||
};
|
||||
|
||||
function winsorize(arr, trimPercent) {
|
||||
if (arr.length < 5) return arr; // Don't winsorize tiny arrays
|
||||
const sorted = [...arr].sort((a, b) => a - b);
|
||||
const lowerIdx = Math.floor(arr.length * trimPercent);
|
||||
let upperIdx = Math.floor(arr.length * (1 - trimPercent)) - 1;
|
||||
if (upperIdx < lowerIdx) upperIdx = arr.length - 1;
|
||||
|
||||
const lowerBound = sorted[lowerIdx];
|
||||
const upperBound = sorted[upperIdx];
|
||||
|
||||
return arr.map(v => {
|
||||
if (v < lowerBound) return lowerBound;
|
||||
if (v > upperBound) return upperBound;
|
||||
return v;
|
||||
});
|
||||
}
|
||||
|
||||
function avg(arr) {
|
||||
if (!arr.length) return 0;
|
||||
return arr.reduce((a, b) => a + b, 0) / arr.length;
|
||||
}
|
||||
|
||||
function std(arr, meanVal) {
|
||||
if (arr.length <= 1) return 1;
|
||||
const variance = arr.reduce((sum, v) => sum + Math.pow(v - meanVal, 2), 0) / (arr.length - 1);
|
||||
return Math.sqrt(variance) || 1;
|
||||
}
|
||||
|
||||
export function standardizeCrossSection(rawByTicker, neutralizeSector = false, sectorsByTicker = {}) {
|
||||
// Extract non-null values
|
||||
const tickers = Object.keys(rawByTicker);
|
||||
let validTickers = tickers.filter(t => rawByTicker[t] != null && !isNaN(rawByTicker[t]));
|
||||
|
||||
if (validTickers.length === 0) return {};
|
||||
|
||||
const result = {};
|
||||
|
||||
if (neutralizeSector) {
|
||||
// Group by sector
|
||||
const sectorGroups = {};
|
||||
for (const t of validTickers) {
|
||||
const sec = sectorsByTicker[t] || 'UNKNOWN';
|
||||
if (!sectorGroups[sec]) sectorGroups[sec] = [];
|
||||
sectorGroups[sec].push(t);
|
||||
}
|
||||
|
||||
for (const sec in sectorGroups) {
|
||||
const secTickers = sectorGroups[sec];
|
||||
const secVals = secTickers.map(t => rawByTicker[t]);
|
||||
|
||||
const w = winsorize(secVals, 0.02);
|
||||
const m = avg(w);
|
||||
const s = std(w, m);
|
||||
|
||||
for (let i = 0; i < secTickers.length; i++) {
|
||||
const t = secTickers[i];
|
||||
let val = rawByTicker[t];
|
||||
if (val < w[0]) val = w[0]; // approx winsorize clip (since w is same order)
|
||||
// Actually, winsorize maps index-to-index.
|
||||
const clipped = w[i];
|
||||
result[t] = (clipped - m) / s;
|
||||
}
|
||||
}
|
||||
} else {
|
||||
// Global standard
|
||||
const vals = validTickers.map(t => rawByTicker[t]);
|
||||
const w = winsorize(vals, 0.02);
|
||||
const m = avg(w);
|
||||
const s = std(w, m);
|
||||
|
||||
for (let i = 0; i < validTickers.length; i++) {
|
||||
const t = validTickers[i];
|
||||
const clipped = w[i];
|
||||
result[t] = (clipped - m) / s;
|
||||
}
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
// In a real setup, this data would be loaded from Sharadar/Compustat.
|
||||
// For testing, we allow an injected dataset.
|
||||
|
||||
let globalFundamentalsBySymbol = new Map();
|
||||
|
||||
export function loadFundamentals(data) {
|
||||
globalFundamentalsBySymbol.clear();
|
||||
// Group by symbol
|
||||
for (const record of data) {
|
||||
if (!globalFundamentalsBySymbol.has(record.symbol)) {
|
||||
globalFundamentalsBySymbol.set(record.symbol, []);
|
||||
}
|
||||
globalFundamentalsBySymbol.get(record.symbol).push(record);
|
||||
}
|
||||
|
||||
// Sort each group by datekey ascending
|
||||
for (const [sym, records] of globalFundamentalsBySymbol.entries()) {
|
||||
records.sort((a, b) => new Date(a.datekey) - new Date(b.datekey));
|
||||
}
|
||||
}
|
||||
|
||||
export function getFundamentalsAsOf(symbol, dateStr) {
|
||||
const targetDate = new Date(dateStr);
|
||||
const records = globalFundamentalsBySymbol.get(symbol);
|
||||
if (!records) return null;
|
||||
|
||||
// Find the most recent fundamental report where filing date <= targetDate
|
||||
let latest = null;
|
||||
for (const record of records) {
|
||||
|
||||
// GUARDRAIL: We absolutely require a filing date (datekey).
|
||||
// If datekey <= targetDate, it's safe.
|
||||
// If datekey is missing, fallback to fiscalEnd + 4 months (120 days).
|
||||
let isAvailable = false;
|
||||
if (record.datekey) {
|
||||
if (new Date(record.datekey) <= targetDate) isAvailable = true;
|
||||
} else if (record.fiscalEnd) {
|
||||
const safeReleaseDate = new Date(record.fiscalEnd);
|
||||
safeReleaseDate.setDate(safeReleaseDate.getDate() + 120); // Add 4 months roughly
|
||||
if (safeReleaseDate <= targetDate) isAvailable = true;
|
||||
} else {
|
||||
throw new Error(`Record for ${symbol} lacks both datekey and fiscalEnd. Unsafe.`);
|
||||
}
|
||||
|
||||
if (isAvailable) {
|
||||
latest = record;
|
||||
} else {
|
||||
if (record.datekey && new Date(record.datekey) > targetDate) break;
|
||||
}
|
||||
}
|
||||
|
||||
return latest ? latest.metrics : null;
|
||||
}
|
||||
|
|
@ -0,0 +1,98 @@
|
|||
import { generateSyntheticData } from './synthetic.js';
|
||||
import { loadUniverse } from './universe.js';
|
||||
import { loadFundamentals } from './fundamentals.js';
|
||||
import { setForwardReturnsMap, runFactor } from './crossSection.js';
|
||||
import { mean, neweyWestStdErr } from './metrics.js';
|
||||
|
||||
function printReport(title, results, horizonMonths) {
|
||||
if (results.length === 0) {
|
||||
console.log(`\n=== ${title} ===`);
|
||||
console.log("No valid results.");
|
||||
return;
|
||||
}
|
||||
|
||||
const ics = results.map(r => r.ic);
|
||||
const meanIc = mean(ics);
|
||||
// We use Newey-West standard error for the t-stat if overlapping.
|
||||
// For 1-month horizon, lags = 0 (simple std err).
|
||||
// For H-month horizon, lags = H - 1.
|
||||
const lags = Math.max(0, horizonMonths - 1);
|
||||
const seIc = neweyWestStdErr(ics, lags);
|
||||
const tStatIc = meanIc / seIc;
|
||||
|
||||
// Average Decile Returns (Gross)
|
||||
const deciles = Array(10).fill(0);
|
||||
for (const r of results) {
|
||||
for (let d = 0; d < 10; d++) {
|
||||
deciles[d] += r.decileReturns[d];
|
||||
}
|
||||
}
|
||||
for (let d = 0; d < 10; d++) deciles[d] /= results.length;
|
||||
|
||||
// Long/Short Spread (Net)
|
||||
const lsSpreads = results.map(r => r.d10Net - r.d1Net);
|
||||
const meanLsSpread = mean(lsSpreads);
|
||||
const seLsSpread = neweyWestStdErr(lsSpreads, lags);
|
||||
const tStatLs = meanLsSpread / seLsSpread;
|
||||
const sharpeLs = (meanLsSpread / (seLsSpread * Math.sqrt(results.length))) * Math.sqrt(12 / horizonMonths); // Approx Annualized Gross Sharpe
|
||||
|
||||
// Long-Only Top-Decile Excess (Net)
|
||||
const loExcess = results.map(r => r.d10Net - r.universeMeanRet);
|
||||
const meanLoExcess = mean(loExcess);
|
||||
|
||||
// Consistency
|
||||
const numPositiveLs = lsSpreads.filter(s => s > 0).length;
|
||||
const consistencyPct = (numPositiveLs / lsSpreads.length) * 100;
|
||||
|
||||
// Turnover (Avg over periods)
|
||||
const avgTurnoverD10 = mean(results.map(r => r.d10Turnover)) * 100;
|
||||
|
||||
console.log(`\n=== ${title} ===`);
|
||||
console.log(`Periods (N): ${results.length} months`);
|
||||
console.log(`Horizon: ${horizonMonths} month(s)`);
|
||||
console.log(`Mean IC: ${meanIc.toFixed(4)}`);
|
||||
console.log(`IC t-stat: ${tStatIc.toFixed(2)} (Target: >= 3.0)`);
|
||||
console.log(`L/S Spread (Net): ${(meanLsSpread * 100).toFixed(2)}% per period (t-stat: ${tStatLs.toFixed(2)})`);
|
||||
console.log(`Long-Only Excess: ${(meanLoExcess * 100).toFixed(2)}% per period`);
|
||||
console.log(`Consistency: ${consistencyPct.toFixed(1)}% positive periods`);
|
||||
console.log(`D10 Avg Turnover: ${avgTurnoverD10.toFixed(1)}% per rebalance`);
|
||||
|
||||
console.log(`\nDecile Monotonicity (Gross):`);
|
||||
const maxRet = Math.max(...deciles);
|
||||
const minRet = Math.min(...deciles);
|
||||
for (let d = 0; d < 10; d++) {
|
||||
// simple text bar chart
|
||||
const pct = deciles[d] * 100;
|
||||
const barLen = Math.max(1, Math.floor((deciles[d] - minRet) / (maxRet - minRet + 0.0001) * 20));
|
||||
console.log(` D${d+1}: ${pct.toFixed(2).padStart(5)}% | ${'#'.repeat(barLen)}`);
|
||||
}
|
||||
}
|
||||
|
||||
function runSyntheticTests() {
|
||||
console.log("==================================================");
|
||||
console.log(" SYNTHETIC KNOWN-ANSWER TESTS");
|
||||
console.log("==================================================");
|
||||
|
||||
// Generate 25 years (300 months) of data for 500 stocks
|
||||
// 1. NULL TEST (IC = 0)
|
||||
const nullData = generateSyntheticData({ numMonths: 300, numStocks: 500, plantedIC: 0.0 });
|
||||
loadUniverse(nullData.universe);
|
||||
loadFundamentals(nullData.fundamentals);
|
||||
setForwardReturnsMap(nullData.forwardReturnsMap);
|
||||
|
||||
const allDatesNull = nullData.universe.map(u => u.date);
|
||||
const resultsNull = runFactor('value', allDatesNull, 1, 5); // 5 bps costs
|
||||
printReport("TEST 1: NULL FACTOR (Target: IC ≈ 0, t-stat < 3)", resultsNull, 1);
|
||||
|
||||
// 2. PLANTED SIGNAL TEST (IC = 0.05)
|
||||
const plantedData = generateSyntheticData({ numMonths: 300, numStocks: 500, plantedIC: 0.05 });
|
||||
loadUniverse(plantedData.universe);
|
||||
loadFundamentals(plantedData.fundamentals);
|
||||
setForwardReturnsMap(plantedData.forwardReturnsMap);
|
||||
|
||||
const allDatesPlanted = plantedData.universe.map(u => u.date);
|
||||
const resultsPlanted = runFactor('value', allDatesPlanted, 1, 5); // 5 bps costs
|
||||
printReport("TEST 2: PLANTED SIGNAL (Target: IC ≈ 0.05, t-stat >= 3, Monotonic)", resultsPlanted, 1);
|
||||
}
|
||||
|
||||
runSyntheticTests();
|
||||
|
|
@ -0,0 +1,75 @@
|
|||
import { fetchQuoteSummary } from '../services/fundamentalsService.js';
|
||||
|
||||
const DEFAULT_HEADERS = {
|
||||
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
|
||||
'Accept': 'application/json',
|
||||
};
|
||||
|
||||
async function fetchHistory(symbol) {
|
||||
const url = `https://query1.finance.yahoo.com/v8/finance/chart/${symbol}?range=2y&interval=1d`;
|
||||
try {
|
||||
const res = await fetch(url, { headers: DEFAULT_HEADERS });
|
||||
if (!res.ok) return null;
|
||||
const data = await res.json();
|
||||
const result = data?.chart?.result?.[0];
|
||||
if (!result) return null;
|
||||
return result.indicators?.quote?.[0]?.close || [];
|
||||
} catch (e) {
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
function momentum12_1(closes) {
|
||||
if (!closes || closes.length < 252) return null;
|
||||
const n = closes.length;
|
||||
// 12-1 month: close[-21] / close[-252] - 1
|
||||
const current = closes[n - 21];
|
||||
const past = closes[n - 252];
|
||||
if (!current || !past) return null;
|
||||
return (current / past) - 1;
|
||||
}
|
||||
|
||||
function realizedVol252(closes) {
|
||||
if (!closes || closes.length < 252) return null;
|
||||
const n = closes.length;
|
||||
const returns = [];
|
||||
for (let i = n - 251; i < n; i++) {
|
||||
const prev = closes[i - 1];
|
||||
const cur = closes[i];
|
||||
if (prev && cur) returns.push((cur - prev) / prev);
|
||||
}
|
||||
if (returns.length < 200) return null;
|
||||
|
||||
const mean = returns.reduce((a, b) => a + b, 0) / returns.length;
|
||||
const variance = returns.reduce((a, b) => a + Math.pow(b - mean, 2), 0) / (returns.length - 1);
|
||||
// Annualize daily vol
|
||||
return Math.sqrt(variance) * Math.sqrt(252);
|
||||
}
|
||||
|
||||
export async function pullRawRecord(ticker, asOf = new Date().toISOString().split('T')[0]) {
|
||||
const info = await fetchQuoteSummary(ticker);
|
||||
if (!info) return null;
|
||||
|
||||
const closes = await fetchHistory(ticker);
|
||||
|
||||
return {
|
||||
snapshot_date: asOf,
|
||||
ticker,
|
||||
price: info.price,
|
||||
marketCap: info.market_cap,
|
||||
|
||||
// raw inputs
|
||||
trailingPE: info.pe_ttm,
|
||||
priceToBook: info.price_to_book,
|
||||
priceToSales: info.price_to_sales,
|
||||
freeCashflow: info.free_cash_flow,
|
||||
returnOnEquity: info.return_on_equity,
|
||||
grossMargins: info.gross_margins,
|
||||
debtToEquity: info.debt_to_equity,
|
||||
|
||||
mom_12_1: momentum12_1(closes),
|
||||
realizedVol252: realizedVol252(closes),
|
||||
|
||||
fundamentals_period_end: info.fiscal_end
|
||||
};
|
||||
}
|
||||
|
|
@ -0,0 +1,84 @@
|
|||
export function rank(arr) {
|
||||
const sorted = arr.map((val, ind) => ({ val, ind })).sort((a, b) => a.val - b.val);
|
||||
const ranks = new Array(arr.length);
|
||||
for (let i = 0; i < sorted.length; i++) {
|
||||
ranks[sorted[i].ind] = i + 1;
|
||||
}
|
||||
// Handle ties (average rank) if needed, but simple rank is fine for large N
|
||||
return ranks;
|
||||
}
|
||||
|
||||
export function spearman(x, y) {
|
||||
if (x.length !== y.length || x.length === 0) return null;
|
||||
const rankX = rank(x);
|
||||
const rankY = rank(y);
|
||||
const n = x.length;
|
||||
let dSqSum = 0;
|
||||
for (let i = 0; i < n; i++) {
|
||||
dSqSum += Math.pow(rankX[i] - rankY[i], 2);
|
||||
}
|
||||
return 1 - ((6 * dSqSum) / (n * (Math.pow(n, 2) - 1)));
|
||||
}
|
||||
|
||||
// Simple mean
|
||||
export function mean(arr) {
|
||||
if (!arr || arr.length === 0) return 0;
|
||||
return arr.reduce((a, b) => a + b, 0) / arr.length;
|
||||
}
|
||||
|
||||
// Simple standard deviation
|
||||
export function std(arr, m) {
|
||||
if (!arr || arr.length <= 1) return 1;
|
||||
const variance = arr.reduce((sum, v) => sum + Math.pow(v - m, 2), 0) / (arr.length - 1);
|
||||
return Math.sqrt(variance) || 1;
|
||||
}
|
||||
|
||||
/**
|
||||
* Calculate Newey-West standard error for a time-series of means
|
||||
* Lags = 0 is equivalent to standard error.
|
||||
* For N-month overlapping returns, lag = N - 1.
|
||||
*/
|
||||
export function neweyWestStdErr(ts, lags) {
|
||||
const n = ts.length;
|
||||
if (n <= 1) return 1;
|
||||
const m = mean(ts);
|
||||
|
||||
// Variance
|
||||
let s0 = 0;
|
||||
for (let i = 0; i < n; i++) {
|
||||
s0 += Math.pow(ts[i] - m, 2);
|
||||
}
|
||||
s0 = s0 / n;
|
||||
|
||||
// Covariances
|
||||
let sLags = 0;
|
||||
for (let l = 1; l <= lags; l++) {
|
||||
let cov = 0;
|
||||
for (let i = l; i < n; i++) {
|
||||
cov += (ts[i] - m) * (ts[i - l] - m);
|
||||
}
|
||||
cov = cov / n;
|
||||
const weight = 1 - (l / (lags + 1));
|
||||
sLags += 2 * weight * cov;
|
||||
}
|
||||
|
||||
const S = s0 + sLags;
|
||||
return Math.sqrt(S / n) || (std(ts, m) / Math.sqrt(n)); // fallback
|
||||
}
|
||||
|
||||
/**
|
||||
* Compute turnover given two sets of weights.
|
||||
* w1, w2 are objects: { 'AAPL': 0.05, 'MSFT': 0.02, ... }
|
||||
* turnover = 0.5 * sum(|w2_i - w1_i|)
|
||||
*/
|
||||
export function calculateTurnover(wOld, wNew) {
|
||||
if (!wOld) return 1.0; // 100% turnover on first month
|
||||
let turnover = 0;
|
||||
const allTickers = new Set([...Object.keys(wOld), ...Object.keys(wNew)]);
|
||||
for (const t of allTickers) {
|
||||
const oldWt = wOld[t] || 0;
|
||||
const newWt = wNew[t] || 0;
|
||||
turnover += Math.abs(newWt - oldWt);
|
||||
}
|
||||
return 0.5 * turnover;
|
||||
}
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
import fs from 'fs';
|
||||
import path from 'path';
|
||||
import { pullRawRecord } from './ingest.js';
|
||||
import { getUniverse } from '../services/stockUniverseService.js';
|
||||
import { fileURLToPath } from 'url';
|
||||
|
||||
const __filename = fileURLToPath(import.meta.url);
|
||||
const __dirname = path.dirname(__filename);
|
||||
const SNAPSHOT_FILE = path.join(__dirname, '../../../data/snapshots/fundamentals_snapshot.jsonl');
|
||||
|
||||
/**
|
||||
* Monthly cron script to snapshot current point-in-time fundamentals.
|
||||
* Append-only. Immutable.
|
||||
*/
|
||||
export async function runSnapshot() {
|
||||
const snapshot_date = new Date().toISOString().split('T')[0];
|
||||
console.log(`Starting PIT Snapshot for ${snapshot_date}`);
|
||||
|
||||
// Ensure directory exists
|
||||
const dir = path.dirname(SNAPSHOT_FILE);
|
||||
if (!fs.existsSync(dir)) {
|
||||
fs.mkdirSync(dir, { recursive: true });
|
||||
}
|
||||
|
||||
const universe = getUniverse();
|
||||
let successCount = 0;
|
||||
|
||||
for (const stock of universe) {
|
||||
if (stock.isETF) continue; // Skip ETFs for fundamental factors
|
||||
const symbol = stock.symbol;
|
||||
|
||||
try {
|
||||
const data = await pullRawRecord(symbol, snapshot_date);
|
||||
if (!data) {
|
||||
console.warn(`[SNAPSHOT] No data for ${symbol}`);
|
||||
continue;
|
||||
}
|
||||
|
||||
// Add index membership flag to the raw record
|
||||
data.in_universe = true;
|
||||
|
||||
// Append-only rule: We never overwrite.
|
||||
fs.appendFileSync(SNAPSHOT_FILE, JSON.stringify(data) + '\n');
|
||||
successCount++;
|
||||
} catch (e) {
|
||||
console.error(`[SNAPSHOT] Failed for ${symbol}: ${e.message}`);
|
||||
}
|
||||
}
|
||||
|
||||
console.log(`Snapshot complete. Safely appended ${successCount} records to ${SNAPSHOT_FILE}.`);
|
||||
}
|
||||
|
||||
// Allow running directly
|
||||
if (process.argv[1] === __filename) {
|
||||
runSnapshot().catch(console.error);
|
||||
}
|
||||
|
|
@ -0,0 +1,88 @@
|
|||
/**
|
||||
* synthetic.js
|
||||
* Generates synthetic datasets for Known-Answer tests.
|
||||
*/
|
||||
|
||||
export function generateSyntheticData({ numMonths, numStocks, plantedIC = 0.0 }) {
|
||||
const universe = [];
|
||||
const fundamentals = [];
|
||||
const forwardReturnsMap = new Map(); // key: `${symbol}_${date}`, value: 1mo forward return
|
||||
|
||||
// Base date
|
||||
const startDate = new Date('2000-01-31');
|
||||
|
||||
for (let m = 0; m < numMonths; m++) {
|
||||
const d = new Date(startDate);
|
||||
d.setMonth(d.getMonth() + m);
|
||||
// Approximate end of month
|
||||
d.setDate(0);
|
||||
const dateStr = d.toISOString().split('T')[0];
|
||||
|
||||
const symbols = [];
|
||||
for (let s = 1; s <= numStocks; s++) {
|
||||
const sym = `SYM${s}`;
|
||||
symbols.push(sym);
|
||||
|
||||
// Generate a base signal (standard normal approx)
|
||||
const u1 = Math.random(), u2 = Math.random();
|
||||
const zScore = Math.sqrt(-2.0 * Math.log(u1)) * Math.cos(2.0 * Math.PI * u2);
|
||||
|
||||
// We will define a "value" factor that is just this zScore.
|
||||
// For the null test, plantedIC = 0.
|
||||
// For planted signal, plantedIC = 0.05.
|
||||
|
||||
// forwardReturn = plantedIC * zScore + noise
|
||||
const noise_u1 = Math.random(), noise_u2 = Math.random();
|
||||
const noise = Math.sqrt(-2.0 * Math.log(noise_u1)) * Math.cos(2.0 * Math.PI * noise_u2);
|
||||
|
||||
// Adjust noise variance so that correlation between zScore and fwdRet is roughly plantedIC
|
||||
// Var(fwd) = IC^2 * Var(Z) + Var(noise)
|
||||
// Since Var(Z)=1, if we want Cor(Z, fwd) = IC:
|
||||
// Cor = Cov(Z, IC*Z + noise) / sqrt(1 * (IC^2 + Var(noise)))
|
||||
// Cor = IC / sqrt(IC^2 + Var(noise))
|
||||
// To get Cor = IC, we need sqrt(IC^2 + Var(noise)) = 1 => Var(noise) = 1 - IC^2
|
||||
const noiseScale = Math.sqrt(Math.max(0, 1 - plantedIC * plantedIC));
|
||||
|
||||
const fwdRet = plantedIC * zScore + noiseScale * noise;
|
||||
|
||||
// Scale fwdRet to realistic monthly return (e.g. mean 0.5%, vol 5%)
|
||||
const monthlyReturn = 0.005 + 0.05 * fwdRet;
|
||||
|
||||
forwardReturnsMap.set(`${sym}_${dateStr}`, monthlyReturn);
|
||||
|
||||
// Record fundamental.
|
||||
// value factor = mean([ earningsYield, fcfYield, bookToPrice, salesToPrice ])
|
||||
// We'll just set earningsYield = zScore, and others to 0 to keep it simple.
|
||||
// So mean is zScore / 4.
|
||||
// To make the actual score == zScore, we'll set all 4 to zScore.
|
||||
fundamentals.push({
|
||||
symbol: sym,
|
||||
datekey: dateStr, // assume filed on the exact rebalance date for testing
|
||||
fiscalEnd: dateStr,
|
||||
metrics: {
|
||||
earningsYield: zScore,
|
||||
fcfYield: zScore,
|
||||
bookToPrice: zScore,
|
||||
salesToPrice: zScore,
|
||||
|
||||
// quality, lowVol, momentum just random noise
|
||||
roe: Math.random(),
|
||||
grossMargin: Math.random(),
|
||||
accruals: Math.random(),
|
||||
debtToEquity: Math.random()
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
universe.push({
|
||||
date: dateStr,
|
||||
symbols: symbols
|
||||
});
|
||||
}
|
||||
|
||||
return {
|
||||
universe,
|
||||
fundamentals,
|
||||
forwardReturnsMap
|
||||
};
|
||||
}
|
||||
|
|
@ -0,0 +1,22 @@
|
|||
// In a real setup, this data would be loaded from Sharadar/Compustat.
|
||||
// For testing, we allow an injected dataset.
|
||||
|
||||
let globalUniverseData = []; // Array of { date, symbols: [ 'AAPL', 'MSFT', ... ] }
|
||||
|
||||
export function loadUniverse(data) {
|
||||
globalUniverseData = [...data].sort((a, b) => new Date(a.date) - new Date(b.date));
|
||||
}
|
||||
|
||||
export function getUniverseAsOf(dateStr) {
|
||||
// Find the most recent universe membership <= dateStr
|
||||
const targetDate = new Date(dateStr);
|
||||
let latest = null;
|
||||
for (const record of globalUniverseData) {
|
||||
if (new Date(record.date) <= targetDate) {
|
||||
latest = record;
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
}
|
||||
return latest ? latest.symbols : [];
|
||||
}
|
||||
|
|
@ -10,8 +10,9 @@ import { UNIVERSE, getSymbolMeta, classifyCapTier } from '../services/stockUnive
|
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import { fetchBasicQuote, fetchQuoteSummary, fetchTechnicals, fetchFullAnalysis } from '../services/fundamentalsService.js';
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import { fetchInstitutionalHolders, fetchInsiderTransactions } from '../services/institutionalHoldersService.js';
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import { fetchNewsForSymbol, fetchMarketNews } from '../services/newsService.js';
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import { scoreSuitability } from '../services/suitabilityService.js';
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import { scoreSuitability } from '../services/scoringEngine.js';
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import { classifyRegime } from '../services/regimeService.js';
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import { logSignals } from '../services/signalLogger.js';
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const router = express.Router();
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const universeCache = new NodeCache({ stdTTL: 60 });
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@ -227,6 +228,11 @@ router.get('/screener', async (req, res) => {
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const CONCURRENCY = 6;
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const scored = [];
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const [spyTech, vixQuote] = await Promise.all([
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fetchTechnicals('SPY'),
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fetchBasicQuote('^VIX')
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]);
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for (let i = 0; i < UNIVERSE.length; i += CONCURRENCY) {
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const batch = UNIVERSE.slice(i, i + CONCURRENCY);
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const results = await Promise.allSettled(
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@ -240,7 +246,6 @@ router.get('/screener', async (req, res) => {
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const t = technicals.status === 'fulfilled' ? technicals.value : null;
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const f = fundamentals.status === 'fulfilled' ? fundamentals.value : null;
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const suitability = scoreSuitability({ quote: q, technicals: t, fundamentals: f });
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const capTier = classifyCapTier(q?.market_cap ?? f?.market_cap ?? null, entry.isETF);
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return {
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@ -253,11 +258,8 @@ router.get('/screener', async (req, res) => {
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change_pct: q?.change_pct ?? null,
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volume: q?.volume ?? null,
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market_cap: q?.market_cap ?? f?.market_cap ?? null,
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beta: f?.beta ?? null,
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rsi_14: t?.rsi_14 ?? null,
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above_sma200: t?.above_sma200 ?? null,
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recommendation: f?.recommendation ?? null,
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suitability,
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above_sma50: t?.above_sma50 ?? null,
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q, t, f
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};
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})
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);
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@ -265,9 +267,56 @@ router.get('/screener', async (req, res) => {
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if (i + CONCURRENCY < UNIVERSE.length) await new Promise(r => setTimeout(r, 100));
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}
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// Compute regime
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const totalWith50 = scored.filter(s => s.above_sma50 !== null).length;
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const above50 = scored.filter(s => s.above_sma50 === true).length;
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const breadthPctAbove50 = totalWith50 > 0 ? (above50 / totalWith50) * 100 : 50;
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const regime = classifyRegime(spyTech, vixQuote?.price ?? 20, breadthPctAbove50);
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// Compute scores and log signals
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const signalsToLog = [];
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const timestamp = new Date().toISOString();
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scored.forEach(s => {
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// Data freshness (using quote timestamp or assumed fresh if pulled recently)
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// Usually would parse last trade time, but for phase 0 we approximate based on cache age or current time.
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const dataAgeSec = s.q ? 15 : 120; // 15s if we got quote, 120s if missing
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const suitability = scoreSuitability({
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quote: s.q,
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technicals: s.t,
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fundamentals: s.f,
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regime,
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dataAgeSec
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});
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s.suitability = suitability;
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// Clean up raw payloads for frontend transport size
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delete s.q; delete s.t; delete s.f; delete s.above_sma50;
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// Build signals for logging (only log strong candidates to save DB space, or all. Let's log all.)
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for (const lane of ['daytrade', 'shortTerm', 'longTerm']) {
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signalsToLog.push({
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ticker: s.symbol,
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ts: timestamp,
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lane,
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score: suitability[lane].score,
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grade: suitability[lane].grade,
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entry_price: s.price ?? 0,
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features: suitability[lane].features,
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regime: regime.raw,
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data_age_sec: suitability[lane].dataAgeSec
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});
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}
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});
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// Fire and forget logging
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logSignals(signalsToLog);
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// Sort each lane by score desc, take top 15 for each
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const sortByScore = (key) => [...scored].sort((a, b) => b.suitability[key].score - a.suitability[key].score).slice(0, 15);
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const data = {
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regime,
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daytrade: sortByScore('daytrade'),
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shortTerm: sortByScore('shortTerm'),
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longTerm: sortByScore('longTerm'),
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@ -300,6 +349,8 @@ router.get('/search/:symbol', async (req, res) => {
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quote: analysis.quote,
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technicals: analysis.technicals,
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fundamentals: analysis.fundamentals,
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regime: { trend: 'neutral', vol: 'normal', breadth: 'mixed', raw: {} },
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dataAgeSec: 15
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});
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const capTier = classifyCapTier(
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@ -0,0 +1,76 @@
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/**
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* Feature Service
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* Extracts raw mathematical features from raw data to feed the scoring engine.
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*/
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/**
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* Calculate ATR as a percentage of the last close price.
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* ATR% = the real intraday "is this tradeable?" metric.
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*
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* @param {Array} candles - Array of candle objects {high, low, close}, most recent last.
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* @param {number} period - Period for ATR calculation (default: 14)
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* @returns {number|null} ATR as a percentage (e.g. 5.8 means 5.8% average range)
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*/
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export function atrPercent(candles, period = 14) {
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if (!candles || candles.length <= period) return null;
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const trs = [];
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for (let i = 1; i < candles.length; i++) {
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const { high, low } = candles[i];
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const prevClose = candles[i - 1].close;
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// True Range
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trs.push(Math.max(
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high - low,
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Math.abs(high - prevClose),
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Math.abs(low - prevClose)
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));
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}
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// Simple moving average of TRs for the last `period` days
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const recent = trs.slice(-period);
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const atr = recent.reduce((a, b) => a + b, 0) / recent.length;
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const lastClose = candles[candles.length - 1].close;
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if (!lastClose) return null;
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return (atr / lastClose) * 100;
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}
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/**
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* Extract Estimate Revisions (Up vs Down) direction.
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* If quoteSummary contains earningsTrend (from Yahoo Finance).
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*
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* @param {Object} rawData - The raw data payload from Yahoo Finance
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* @returns {number} Score representing net revisions: positive = more up than down
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*/
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export function estimateRevisionsNet(rawData) {
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// If we don't have the raw earningsTrend array, return 0 (neutral)
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const trends = rawData?.quoteSummary?.result?.[0]?.earningsTrend?.trend;
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if (!trends || !Array.isArray(trends)) return 0;
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// We want to look at the current quarter (0q) or current year (0y)
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// Usually the first two elements are current qtr and next qtr
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let netUp = 0;
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for (const t of trends.slice(0, 2)) {
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const up = t?.earningsEstimate?.upLast30days?.raw || 0;
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const down = t?.earningsEstimate?.downLast30days?.raw || 0;
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netUp += (up - down);
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}
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return netUp;
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}
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/**
|
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* Calculates Relative Volume
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* @param {number} volume - Today's volume
|
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* @param {number} avgVolume10d - 10-day average volume
|
||||
* @param {number} avgVolume3m - 3-month average volume
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*/
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export function calculateRvol(volume, avgVolume10d, avgVolume3m) {
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if (!volume) return null;
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if (avgVolume10d) return volume / avgVolume10d;
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if (avgVolume3m) return volume / avgVolume3m;
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return null;
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}
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@ -186,18 +186,34 @@ export async function fetchTechnicals(symbol) {
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const closes = result.indicators?.quote?.[0]?.close?.filter(v => v != null) || [];
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if (closes.length < 26) return null;
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// Extract candles for ATR
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const quote = result.indicators?.quote?.[0] || {};
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const candles = [];
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for (let i = 0; i < closes.length; i++) {
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if (quote.high?.[i] != null && quote.low?.[i] != null && quote.close?.[i] != null) {
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candles.push({
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high: quote.high[i],
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low: quote.low[i],
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close: quote.close[i]
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});
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}
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}
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const rsi = calculateRSI(closes, 14);
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const macd = calculateMACD(closes, 12, 26, 9);
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const sma20 = closes.length >= 20 ? avg(closes.slice(-20)) : null;
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const sma50 = closes.length >= 50 ? avg(closes.slice(-50)) : null;
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const sma200 = closes.length >= 200 ? avg(closes.slice(-200)) : null;
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const currentPrice = closes[closes.length - 1];
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const result_data = {
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candles, // Pass candles to featureService
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rsi_14: rsi !== null ? Math.round(rsi * 100) / 100 : null,
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macd_line: macd?.line ?? null,
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macd_signal: macd?.signal ?? null,
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macd_histogram: macd?.histogram ?? null,
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sma_20: sma20 ? Math.round(sma20 * 100) / 100 : null,
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sma_50: sma50 ? Math.round(sma50 * 100) / 100 : null,
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sma_200: sma200 ? Math.round(sma200 * 100) / 100 : null,
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above_sma50: sma50 ? currentPrice > sma50 : null,
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@ -0,0 +1,47 @@
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/**
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* Regime Service
|
||||
* Classifies the broad market state (fast, mechanical).
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*/
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/**
|
||||
* Classify Market Regime based on SPY, VIX, and Breadth
|
||||
* @param {Object} spyTechnicals - Technical data for SPY (needs close, sma_20, sma_50)
|
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* @param {number} vixClose - Latest close price of ^VIX
|
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* @param {number} breadthPctAbove50 - Percentage of universe stocks above their 50 SMA
|
||||
* @returns {Object} Regime classification
|
||||
*/
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||||
export function classifyRegime(spyTechnicals, vixClose, breadthPctAbove50) {
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||||
if (!spyTechnicals || vixClose == null || breadthPctAbove50 == null) {
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return { trend: 'neutral', vol: 'normal_vol', breadth: 'mixed', raw: {} };
|
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}
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// If we have candles, get the latest close. Otherwise fallback to current price if passed differently.
|
||||
const close = spyTechnicals.candles ? spyTechnicals.candles[spyTechnicals.candles.length - 1].close : 0;
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const sma20 = spyTechnicals.sma_20;
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const sma50 = spyTechnicals.sma_50;
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// Trend logic: mechanical
|
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const trend = (close > sma50 && sma20 > sma50) ? 'risk_on'
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: (close < sma50 && sma20 < sma50) ? 'risk_off'
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: 'neutral';
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||||
|
||||
// Volatility logic (VIX)
|
||||
const vol = vixClose > 25 ? 'high_vol' : vixClose < 15 ? 'low_vol' : 'normal_vol';
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|
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// Breadth logic
|
||||
const breadth = breadthPctAbove50 > 60 ? 'broad'
|
||||
: breadthPctAbove50 < 40 ? 'narrow' : 'mixed';
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||||
return {
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||||
trend,
|
||||
vol,
|
||||
breadth,
|
||||
raw: {
|
||||
spy_close: close,
|
||||
spy_sma20: sma20,
|
||||
spy_sma50: sma50,
|
||||
vix: vixClose,
|
||||
breadthPctAbove50
|
||||
}
|
||||
};
|
||||
}
|
||||
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@ -1,35 +1,193 @@
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export function calculateRocketScore(row) {
|
||||
let score = 0;
|
||||
import { atrPercent, estimateRevisionsNet, calculateRvol } from './featureService.js';
|
||||
|
||||
// Premium tier (0-3 points)
|
||||
if (row.premium_num >= 2000000) score += 3.0;
|
||||
else if (row.premium_num >= 800000) score += 2.0;
|
||||
else if (row.premium_num >= 200000) score += 1.0;
|
||||
/**
|
||||
* Scoring Engine
|
||||
* Generates testable signals.
|
||||
*/
|
||||
|
||||
// Net premium imbalance (-1.5 to +1.5 points)
|
||||
const totalPrem = row.bull_total + row.bear_total;
|
||||
if (totalPrem > 0) {
|
||||
score += 1.5 * ((row.bull_total - row.bear_total) / totalPrem);
|
||||
}
|
||||
|
||||
// Vol > OI (1.2 points)
|
||||
if (row.vol_num > row.oi_num) score += 1.2;
|
||||
|
||||
// Session weight (0-1 point)
|
||||
const sessionWeights = { RTH: 1.0, POST: 0.5, PRE: 0.3, OFF: 0 };
|
||||
score += sessionWeights[row.session_bucket] || 0;
|
||||
|
||||
// Catalyst flag (1 point)
|
||||
if (row.near_alert_type) score += 1.0;
|
||||
|
||||
// OTM bias (0.8 points)
|
||||
const isOTM =
|
||||
(row.cp_norm === 'CALL' && row.strike_num > row.spot_num) ||
|
||||
(row.cp_norm === 'PUT' && row.strike_num < row.spot_num);
|
||||
if (isOTM) score += 0.8;
|
||||
|
||||
// Tape alignment (0.5 points)
|
||||
if (row.tape_alignment === 1) score += 0.5;
|
||||
|
||||
return parseFloat(score.toFixed(2));
|
||||
function gradeLabel(score) {
|
||||
if (score >= 80) return { label: 'A', color: 'emerald' };
|
||||
if (score >= 60) return { label: 'B', color: 'blue' };
|
||||
if (score >= 40) return { label: 'C', color: 'yellow' };
|
||||
if (score >= 20) return { label: 'D', color: 'orange' };
|
||||
return { label: 'F', color: 'red' };
|
||||
}
|
||||
|
||||
/**
|
||||
* Score a single stock entry
|
||||
* @returns {Object} { daytrade, shortTerm, longTerm } where each is { score, grade, drivers, dataAgeSec, stale, ... }
|
||||
*/
|
||||
export function scoreSuitability({ quote, technicals, fundamentals, regime, dataAgeSec }) {
|
||||
const q = quote || {};
|
||||
const t = technicals || {};
|
||||
const f = fundamentals || {};
|
||||
const stale = dataAgeSec > 90; // stale if older than 90s
|
||||
|
||||
// Compute raw features
|
||||
const rvol = calculateRvol(q.volume, f.avg_volume_10d, f.avg_volume_3m);
|
||||
const atrPct = t.candles ? atrPercent(t.candles, 14) : null;
|
||||
const revisionsNet = estimateRevisionsNet(f);
|
||||
|
||||
const rsi = t.rsi_14 ?? null;
|
||||
const macdH = t.macd_histogram ?? null;
|
||||
const pctSma50 = t.pct_from_sma50 ?? null;
|
||||
const above200 = t.above_sma200 ?? null;
|
||||
const pctSma200 = t.pct_from_sma200 ?? null;
|
||||
const eg = f.earnings_growth ?? null;
|
||||
const pm = f.profit_margins ?? null;
|
||||
|
||||
// ─── Daytrade Score (Logistic Regression) ───────────────────────────────────
|
||||
// Weights from Phase 2 training (features: rsi/100, atrPct/10, rvol/5, macd/2, regime)
|
||||
const dtParts = [];
|
||||
|
||||
// Safe values for dot product
|
||||
const safeRsi = rsi ?? 50;
|
||||
const safeAtr = atrPct ?? 1.0;
|
||||
const safeRvol = rvol ?? 1.0;
|
||||
const safeMacd = macdH ?? 0.0;
|
||||
const regimeFlag = regime?.trend === 'risk_on' ? 1 : 0;
|
||||
|
||||
// Log-odds calculation (z)
|
||||
const intercept = -84.54;
|
||||
const wRsi = -50.36 * (safeRsi / 100);
|
||||
const wAtr = 13.90 * (safeAtr / 10);
|
||||
const wRvol = 37.27 * (safeRvol / 5);
|
||||
const wMacd = -395.0 * (safeMacd / 2);
|
||||
const wRegime = 41.80 * regimeFlag;
|
||||
|
||||
const z = intercept + wRsi + wAtr + wRvol + wMacd + wRegime;
|
||||
|
||||
// Sigmoid
|
||||
const pWin = 1 / (1 + Math.exp(-z));
|
||||
|
||||
// We use P(win) * 100 as the "score" for sorting, but we'll export it cleanly
|
||||
const dtScore = Math.round(pWin * 100);
|
||||
|
||||
// Expose feature importance in the drivers for the UI
|
||||
dtParts.push({ key: 'rsi', label: 'RSI Impact', value: safeRsi, points: Math.round(wRsi), desc: `Oversold effect (w: ${wRsi.toFixed(1)})` });
|
||||
dtParts.push({ key: 'atrPct', label: 'Volatility Impact', value: safeAtr, points: Math.round(wAtr), desc: `ATR push (w: ${wAtr.toFixed(1)})` });
|
||||
dtParts.push({ key: 'rvol', label: 'Volume Impact', value: safeRvol, points: Math.round(wRvol), desc: `Relative Vol (w: ${wRvol.toFixed(1)})` });
|
||||
dtParts.push({ key: 'macd', label: 'Trend Impact', value: safeMacd, points: Math.round(wMacd), desc: `MACD (w: ${wMacd.toFixed(1)})` });
|
||||
if (regimeFlag) dtParts.push({ key: 'regime', label: 'Regime Impact', value: 1, points: Math.round(wRegime), desc: `Risk-On Boost` });
|
||||
|
||||
|
||||
// ─── Short Term (Swing) Score ─────────────────────────────────────────────
|
||||
const stParts = [];
|
||||
|
||||
let stMacdPts = 0, stMacdLbl = 'MACD neutral';
|
||||
if (macdH != null) {
|
||||
if (macdH > 0.5) { stMacdPts = 30; stMacdLbl = `Strong MACD bullish`; }
|
||||
else if (macdH > 0) { stMacdPts = 22; stMacdLbl = `MACD bullish`; }
|
||||
else if (macdH > -0.2) { stMacdPts = 10; stMacdLbl = `MACD near crossover`; }
|
||||
}
|
||||
stParts.push({ key: 'macd', label: 'MACD setup', value: macdH, points: stMacdPts, desc: stMacdLbl });
|
||||
|
||||
let stRsiPts = 0, stRsiLbl = 'RSI extended';
|
||||
if (rsi != null) {
|
||||
if (rsi >= 40 && rsi <= 60) { stRsiPts = 25; stRsiLbl = `RSI ${rsi.toFixed(0)} (swing zone)`; }
|
||||
else if (rsi >= 30 && rsi < 40) { stRsiPts = 20; stRsiLbl = `RSI ${rsi.toFixed(0)} (reset/dip)`; }
|
||||
else if (rsi > 60 && rsi <= 70) { stRsiPts = 15; stRsiLbl = `RSI ${rsi.toFixed(0)} (momentum)`; }
|
||||
else if (rsi <= 30) { stRsiPts = 10; stRsiLbl = `RSI oversold`; }
|
||||
else { stRsiPts = 2; }
|
||||
}
|
||||
stParts.push({ key: 'rsi', label: 'RSI sweet spot', value: rsi, points: stRsiPts, desc: stRsiLbl });
|
||||
|
||||
let stSmaPts = 0, stSmaLbl = 'Extended from SMA50';
|
||||
if (pctSma50 != null) {
|
||||
const abs = Math.abs(pctSma50);
|
||||
if (abs <= 3) { stSmaPts = 25; stSmaLbl = `Near SMA50 (${pctSma50.toFixed(1)}%) - coiled`; }
|
||||
else if (abs <= 7) { stSmaPts = 18; stSmaLbl = `${pctSma50 > 0 ? 'Above' : 'Below'} SMA50 by ${abs.toFixed(1)}%`; }
|
||||
else if (abs <= 15) { stSmaPts = 8; stSmaLbl = `${abs.toFixed(1)}% from SMA50`; }
|
||||
}
|
||||
stParts.push({ key: 'dist50', label: 'SMA50 proximity', value: pctSma50, points: stSmaPts, desc: stSmaLbl });
|
||||
|
||||
// Revisions (Replacing Analyst Targets)
|
||||
let revPts = 0, revLbl = 'Revisions neutral';
|
||||
if (revisionsNet > 0) {
|
||||
if (revisionsNet >= 5) { revPts = 20; revLbl = `Strong upward revisions`; }
|
||||
else if (revisionsNet >= 2) { revPts = 14; revLbl = `Net positive revisions`; }
|
||||
else { revPts = 8; revLbl = `Slight upward revisions`; }
|
||||
} else if (revisionsNet < 0) {
|
||||
revLbl = `Negative revisions`;
|
||||
}
|
||||
stParts.push({ key: 'revisions', label: 'Estimate revisions', value: revisionsNet, points: revPts, desc: revLbl });
|
||||
|
||||
const stScore = Math.min(100, Math.round(stParts.reduce((s, p) => s + p.points, 0)));
|
||||
|
||||
// ─── Long Term Score ──────────────────────────────────────────────────────
|
||||
const ltParts = [];
|
||||
|
||||
let ltSmaPts = 0, ltSmaLbl = 'Below SMA200';
|
||||
if (above200 != null) {
|
||||
if (above200 && pctSma200 != null && pctSma200 > 10) { ltSmaPts = 30; ltSmaLbl = `${pctSma200.toFixed(0)}% above SMA200`; }
|
||||
else if (above200) { ltSmaPts = 22; ltSmaLbl = `Above SMA200`; }
|
||||
else if (pctSma200 != null && pctSma200 > -5) { ltSmaPts = 10; ltSmaLbl = `Near SMA200 support`; }
|
||||
}
|
||||
ltParts.push({ key: 'dist200', label: 'Macro trend', value: pctSma200, points: ltSmaPts, desc: ltSmaLbl });
|
||||
|
||||
let egPts = 0, egLbl = 'Negative growth';
|
||||
if (eg != null) {
|
||||
if (eg >= 0.30) { egPts = 25; egLbl = `${(eg * 100).toFixed(0)}% earnings growth`; }
|
||||
else if (eg >= 0.15) { egPts = 18; egLbl = `${(eg * 100).toFixed(0)}% earnings growth`; }
|
||||
else if (eg >= 0.05) { egPts = 11; egLbl = `${(eg * 100).toFixed(0)}% growth`; }
|
||||
else if (eg >= 0) { egPts = 5; egLbl = `Flat growth`; }
|
||||
}
|
||||
ltParts.push({ key: 'epsGrowth', label: 'Earnings power', value: eg, points: egPts, desc: egLbl });
|
||||
|
||||
const totalRec = (f.rec_strong_buy || 0) + (f.rec_buy || 0) + (f.rec_hold || 0) + (f.rec_sell || 0) + (f.rec_strong_sell || 0);
|
||||
const bullishRec = (f.rec_strong_buy || 0) + (f.rec_buy || 0);
|
||||
let recPts = 0, recLbl = 'Bearish consensus';
|
||||
if (totalRec > 0) {
|
||||
const bullPct = bullishRec / totalRec;
|
||||
if (bullPct >= 0.75) { recPts = 25; recLbl = `${(bullPct * 100).toFixed(0)}% bullish`; }
|
||||
else if (bullPct >= 0.55) { recPts = 16; recLbl = `${(bullPct * 100).toFixed(0)}% bullish`; }
|
||||
else if (bullPct >= 0.40) { recPts = 8; recLbl = `Mixed consensus`; }
|
||||
}
|
||||
ltParts.push({ key: 'conviction', label: 'Analyst conviction', value: bullishRec/totalRec, points: recPts, desc: recLbl });
|
||||
|
||||
let pmPts = 0, pmLbl = 'Unprofitable';
|
||||
if (pm != null) {
|
||||
if (pm >= 0.25) { pmPts = 20; pmLbl = `${(pm * 100).toFixed(0)}% margin`; }
|
||||
else if (pm >= 0.15) { pmPts = 14; pmLbl = `${(pm * 100).toFixed(0)}% margin`; }
|
||||
else if (pm >= 0.05) { pmPts = 7; pmLbl = `${(pm * 100).toFixed(0)}% margin`; }
|
||||
else if (pm >= 0) { pmPts = 2; pmLbl = `Thin margin`; }
|
||||
}
|
||||
ltParts.push({ key: 'margin', label: 'Profit durability', value: pm, points: pmPts, desc: pmLbl });
|
||||
|
||||
const ltScore = Math.min(100, Math.round(ltParts.reduce((s, p) => s + p.points, 0)));
|
||||
|
||||
// Raw features map for logging
|
||||
const featuresLog = {
|
||||
rvol, atrPct, rsi, macdHist: macdH, dist50: pctSma50, dist200: pctSma200,
|
||||
revisions: revisionsNet, epsGrowth: eg, margin: pm,
|
||||
beta: q.beta
|
||||
};
|
||||
|
||||
return {
|
||||
daytrade: {
|
||||
score: dtScore,
|
||||
grade: gradeLabel(dtScore),
|
||||
probability: pWin,
|
||||
drivers: dtParts.sort((a, b) => b.points - a.points),
|
||||
dataAgeSec,
|
||||
stale,
|
||||
features: featuresLog
|
||||
},
|
||||
shortTerm: {
|
||||
score: stScore,
|
||||
grade: gradeLabel(stScore),
|
||||
drivers: stParts.sort((a, b) => b.points - a.points),
|
||||
dataAgeSec,
|
||||
stale,
|
||||
features: featuresLog
|
||||
},
|
||||
longTerm: {
|
||||
score: ltScore,
|
||||
grade: gradeLabel(ltScore),
|
||||
drivers: ltParts.sort((a, b) => b.points - a.points),
|
||||
dataAgeSec,
|
||||
stale,
|
||||
features: featuresLog
|
||||
},
|
||||
};
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,51 @@
|
|||
import { rawQuery } from '../db.js';
|
||||
|
||||
/**
|
||||
* Signal Logger
|
||||
* Writes generated signals to the database asynchronously.
|
||||
*/
|
||||
|
||||
/**
|
||||
* Log a batch of signals to PostgreSQL
|
||||
*
|
||||
* @param {Array} signals - Array of signal objects from scoringEngine
|
||||
*/
|
||||
export async function logSignals(signals) {
|
||||
if (!signals || signals.length === 0) return;
|
||||
|
||||
try {
|
||||
// Generate a single INSERT with multiple VALUES clauses for batching
|
||||
const values = [];
|
||||
const params = [];
|
||||
let paramIdx = 1;
|
||||
|
||||
for (const s of signals) {
|
||||
values.push(`($${paramIdx++}, $${paramIdx++}, $${paramIdx++}, $${paramIdx++}, $${paramIdx++}, $${paramIdx++}, $${paramIdx++}, $${paramIdx++}, $${paramIdx++})`);
|
||||
params.push(
|
||||
s.ticker,
|
||||
s.ts, // TIMESTAMPTZ
|
||||
s.lane,
|
||||
s.score,
|
||||
s.grade,
|
||||
s.entry_price,
|
||||
JSON.stringify(s.features),
|
||||
JSON.stringify(s.regime),
|
||||
s.data_age_sec
|
||||
);
|
||||
}
|
||||
|
||||
const sql = `
|
||||
INSERT INTO signals (
|
||||
ticker, ts, lane, score, grade, entry_price, features, regime, data_age_sec
|
||||
) VALUES ${values.join(', ')}
|
||||
`;
|
||||
|
||||
// Fire and forget (don't block the caller)
|
||||
rawQuery(sql, params).catch(err => {
|
||||
console.error('[signalLogger] Async insert failed:', err.message);
|
||||
});
|
||||
|
||||
} catch (err) {
|
||||
console.error('[signalLogger] Sync setup failed:', err.message);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,183 +0,0 @@
|
|||
/**
|
||||
* Suitability Service
|
||||
* Scores stocks across three trading horizons using available market data.
|
||||
*
|
||||
* Daytrade: Volatility × Relative Volume × Momentum
|
||||
* Short Term: Technical setup (MACD, RSI zone, SMA proximity)
|
||||
* Long Term: Analyst consensus × Earnings trend × SMA200 uptrend
|
||||
*
|
||||
* All scores are 0–100. Returns top candidates per lane.
|
||||
*/
|
||||
|
||||
/**
|
||||
* Score a single stock entry (quote + technicals + optional fundamentals)
|
||||
* Returns { daytrade, shortTerm, longTerm, reasons }
|
||||
*/
|
||||
export function scoreSuitability({ quote, technicals, fundamentals }) {
|
||||
const q = quote || {};
|
||||
const t = technicals || {};
|
||||
const f = fundamentals || {};
|
||||
|
||||
// ─── Daytrade Score ───────────────────────────────────────────────────────
|
||||
// Needs: liquidity spike, beta, momentum in a tradeable RSI zone, MACD
|
||||
let dt = 0;
|
||||
|
||||
// Relative volume (current vs 10-day avg): 0–35 pts
|
||||
const relVol = q.volume && f.avg_volume_10d
|
||||
? q.volume / f.avg_volume_10d
|
||||
: q.volume && f.avg_volume_3m
|
||||
? q.volume / f.avg_volume_3m
|
||||
: null;
|
||||
const dtReasons = [];
|
||||
if (relVol != null) {
|
||||
if (relVol >= 3.0) { dt += 35; dtReasons.push(`🔥 ${relVol.toFixed(1)}x rel. volume`); }
|
||||
else if (relVol >= 2.0) { dt += 28; dtReasons.push(`⚡ ${relVol.toFixed(1)}x rel. volume`); }
|
||||
else if (relVol >= 1.5) { dt += 20; dtReasons.push(`📈 ${relVol.toFixed(1)}x rel. volume`); }
|
||||
else if (relVol >= 1.0) { dt += 10; dtReasons.push(`Avg volume`); }
|
||||
else { dt += 3; dtReasons.push(`Low volume`); }
|
||||
}
|
||||
|
||||
// Beta: 0–25 pts
|
||||
const beta = f.beta ?? null;
|
||||
if (beta != null) {
|
||||
if (beta >= 2.0) { dt += 25; dtReasons.push(`Beta ${beta.toFixed(1)} (high vol)`); }
|
||||
else if (beta >= 1.5) { dt += 20; dtReasons.push(`Beta ${beta.toFixed(1)}`); }
|
||||
else if (beta >= 1.2) { dt += 13; }
|
||||
else if (beta >= 0.8) { dt += 5; }
|
||||
else { dt += 0; dtReasons.push(`Low beta`); }
|
||||
}
|
||||
|
||||
// RSI zone (tradeable, not exhausted): 0–20 pts
|
||||
const rsi = t.rsi_14 ?? null;
|
||||
if (rsi != null) {
|
||||
if (rsi >= 40 && rsi <= 65) { dt += 20; dtReasons.push(`RSI ${rsi.toFixed(0)} (momentum zone)`); }
|
||||
else if (rsi >= 30 && rsi < 40) { dt += 14; dtReasons.push(`RSI ${rsi.toFixed(0)} (bouncing)`); }
|
||||
else if (rsi > 65 && rsi <= 75) { dt += 10; dtReasons.push(`RSI ${rsi.toFixed(0)} (extended but running)`); }
|
||||
else if (rsi > 75) { dt += 2; dtReasons.push(`RSI ${rsi.toFixed(0)} (overbought)`); }
|
||||
else { dt += 2; dtReasons.push(`RSI ${rsi.toFixed(0)} (oversold)`); }
|
||||
}
|
||||
|
||||
// MACD histogram positive: 0–20 pts
|
||||
const macdH = t.macd_histogram ?? null;
|
||||
if (macdH != null) {
|
||||
if (macdH > 0) { dt += 20; dtReasons.push(`MACD bullish`); }
|
||||
else if (macdH > -0.1) { dt += 10; }
|
||||
else { dt += 0; dtReasons.push(`MACD bearish`); }
|
||||
}
|
||||
|
||||
// ─── Short Term (Swing) Score ─────────────────────────────────────────────
|
||||
// Needs: MACD setup, RSI not extended, near SMA50, analyst upside
|
||||
let st = 0;
|
||||
const stReasons = [];
|
||||
|
||||
// MACD histogram: 0–30 pts
|
||||
if (macdH != null) {
|
||||
if (macdH > 0.5) { st += 30; stReasons.push(`Strong MACD bullish`); }
|
||||
else if (macdH > 0) { st += 22; stReasons.push(`MACD bullish`); }
|
||||
else if (macdH > -0.2) { st += 10; stReasons.push(`MACD near crossover`); }
|
||||
else { st += 0; }
|
||||
}
|
||||
|
||||
// RSI sweet spot (not extended, not exhausted): 0–25 pts
|
||||
if (rsi != null) {
|
||||
if (rsi >= 40 && rsi <= 60) { st += 25; stReasons.push(`RSI ${rsi.toFixed(0)} (ideal swing zone)`); }
|
||||
else if (rsi >= 30 && rsi < 40) { st += 20; stReasons.push(`RSI ${rsi.toFixed(0)} (reset - buy dip)`); }
|
||||
else if (rsi > 60 && rsi <= 70) { st += 15; stReasons.push(`RSI ${rsi.toFixed(0)} (momentum run)`); }
|
||||
else if (rsi <= 30) { st += 10; stReasons.push(`RSI oversold`); }
|
||||
else { st += 2; }
|
||||
}
|
||||
|
||||
// % from SMA50: -10% to +10% = best setup for swing: 0–25 pts
|
||||
const pctSma50 = t.pct_from_sma50 ?? null;
|
||||
if (pctSma50 != null) {
|
||||
const abs = Math.abs(pctSma50);
|
||||
if (abs <= 3) { st += 25; stReasons.push(`Near SMA50 (${pctSma50.toFixed(1)}%) - coiled`); }
|
||||
else if (abs <= 7) { st += 18; stReasons.push(`${pctSma50 > 0 ? 'Above' : 'Below'} SMA50 by ${abs.toFixed(1)}%`); }
|
||||
else if (abs <= 15) { st += 8; }
|
||||
else { st += 0; stReasons.push(`Extended from SMA50`); }
|
||||
}
|
||||
|
||||
// Analyst upside: 0–20 pts
|
||||
const price = q.price ?? null;
|
||||
const targetMean = f.target_mean ?? null;
|
||||
if (price && targetMean) {
|
||||
const upside = ((targetMean - price) / price) * 100;
|
||||
if (upside >= 20) { st += 20; stReasons.push(`${upside.toFixed(0)}% analyst upside`); }
|
||||
else if (upside >= 10) { st += 14; stReasons.push(`${upside.toFixed(0)}% analyst upside`); }
|
||||
else if (upside >= 5) { st += 8; }
|
||||
else if (upside > 0) { st += 3; }
|
||||
else { st += 0; stReasons.push(`Analyst consensus cautious`); }
|
||||
}
|
||||
|
||||
// ─── Long Term Score ──────────────────────────────────────────────────────
|
||||
// Needs: above SMA200 uptrend, earnings growth, analyst conviction, margins
|
||||
let lt = 0;
|
||||
const ltReasons = [];
|
||||
|
||||
// Above SMA200 (price in long-term uptrend): 0–30 pts
|
||||
const above200 = t.above_sma200 ?? null;
|
||||
const pctSma200 = t.pct_from_sma200 ?? null;
|
||||
if (above200 != null) {
|
||||
if (above200 && pctSma200 != null && pctSma200 > 10) { lt += 30; ltReasons.push(`${pctSma200.toFixed(0)}% above SMA200`); }
|
||||
else if (above200) { lt += 22; ltReasons.push(`Above SMA200`); }
|
||||
else if (pctSma200 != null && pctSma200 > -5) { lt += 10; ltReasons.push(`Near SMA200 support`); }
|
||||
else { lt += 0; ltReasons.push(`Below SMA200`); }
|
||||
}
|
||||
|
||||
// Earnings growth: 0–25 pts
|
||||
const eg = f.earnings_growth ?? null;
|
||||
if (eg != null) {
|
||||
if (eg >= 0.30) { lt += 25; ltReasons.push(`${(eg * 100).toFixed(0)}% earnings growth`); }
|
||||
else if (eg >= 0.15) { lt += 18; ltReasons.push(`${(eg * 100).toFixed(0)}% earnings growth`); }
|
||||
else if (eg >= 0.05) { lt += 11; }
|
||||
else if (eg >= 0) { lt += 5; }
|
||||
else { lt += 0; ltReasons.push(`Negative earnings growth`); }
|
||||
}
|
||||
|
||||
// Analyst conviction (strong buy/buy count as % of total ratings): 0–25 pts
|
||||
const totalRec = (f.rec_strong_buy || 0) + (f.rec_buy || 0) + (f.rec_hold || 0) + (f.rec_sell || 0) + (f.rec_strong_sell || 0);
|
||||
const bullishRec = (f.rec_strong_buy || 0) + (f.rec_buy || 0);
|
||||
if (totalRec > 0) {
|
||||
const bullPct = bullishRec / totalRec;
|
||||
if (bullPct >= 0.75) { lt += 25; ltReasons.push(`${(bullPct * 100).toFixed(0)}% analysts bullish (${totalRec} coverage)`); }
|
||||
else if (bullPct >= 0.55) { lt += 16; ltReasons.push(`${(bullPct * 100).toFixed(0)}% analysts bullish`); }
|
||||
else if (bullPct >= 0.40) { lt += 8; }
|
||||
else { lt += 0; ltReasons.push(`Bearish analyst consensus`); }
|
||||
}
|
||||
|
||||
// Profit margins (durable business): 0–20 pts
|
||||
const pm = f.profit_margins ?? null;
|
||||
if (pm != null) {
|
||||
if (pm >= 0.25) { lt += 20; ltReasons.push(`${(pm * 100).toFixed(0)}% profit margin`); }
|
||||
else if (pm >= 0.15) { lt += 14; ltReasons.push(`${(pm * 100).toFixed(0)}% profit margin`); }
|
||||
else if (pm >= 0.05) { lt += 7; }
|
||||
else if (pm >= 0) { lt += 2; }
|
||||
else { lt += 0; ltReasons.push(`Unprofitable`); }
|
||||
}
|
||||
|
||||
return {
|
||||
daytrade: {
|
||||
score: Math.min(100, Math.round(dt)),
|
||||
reasons: dtReasons.slice(0, 3),
|
||||
grade: gradeLabel(dt),
|
||||
},
|
||||
shortTerm: {
|
||||
score: Math.min(100, Math.round(st)),
|
||||
reasons: stReasons.slice(0, 3),
|
||||
grade: gradeLabel(st),
|
||||
},
|
||||
longTerm: {
|
||||
score: Math.min(100, Math.round(lt)),
|
||||
reasons: ltReasons.slice(0, 3),
|
||||
grade: gradeLabel(lt),
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
function gradeLabel(score) {
|
||||
if (score >= 80) return { label: 'Strong', color: 'emerald' };
|
||||
if (score >= 60) return { label: 'Good', color: 'blue' };
|
||||
if (score >= 40) return { label: 'Moderate', color: 'yellow' };
|
||||
if (score >= 20) return { label: 'Weak', color: 'orange' };
|
||||
return { label: 'Poor', color: 'red' };
|
||||
}
|
||||
|
|
@ -90,7 +90,17 @@ function StockCard({ item, lane, onSelect }) {
|
|||
{item.name}
|
||||
</div>
|
||||
</div>
|
||||
<ScoreRing score={s.score} color={s.grade.color} />
|
||||
{s.probability != null ? (
|
||||
<div className="flex flex-col items-end text-right ml-2 bg-slate-950/40 p-2 rounded-lg border border-slate-800">
|
||||
<div className="flex items-baseline gap-1.5">
|
||||
<span className={`text-xl font-bold ${s.grade.color}`}>{s.grade.label}</span>
|
||||
<span className="text-white text-lg font-bold">· {(s.probability * 100).toFixed(1)}%</span>
|
||||
</div>
|
||||
<span className="text-[10px] text-slate-400 font-medium">to hit +2R before -1R</span>
|
||||
</div>
|
||||
) : (
|
||||
<ScoreRing score={s.score} color={s.grade.color} />
|
||||
)}
|
||||
</div>
|
||||
|
||||
{/* Price + change */}
|
||||
|
|
@ -104,12 +114,15 @@ function StockCard({ item, lane, onSelect }) {
|
|||
</div>
|
||||
|
||||
{/* Signals / reasons */}
|
||||
{s.reasons.length > 0 && (
|
||||
{s.drivers && s.drivers.length > 0 && (
|
||||
<div className="space-y-1">
|
||||
{s.reasons.map((r, i) => (
|
||||
<div key={i} className="text-[11px] text-slate-400 flex items-center gap-1.5">
|
||||
<span className="text-slate-600">•</span>
|
||||
{r}
|
||||
{s.drivers.slice(0,3).map((d, i) => (
|
||||
<div key={i} className="text-[11px] text-slate-400 flex items-center justify-between">
|
||||
<div className="flex items-center gap-1.5">
|
||||
<span className="text-slate-600">•</span>
|
||||
<span title={d.label}>{d.desc}</span>
|
||||
</div>
|
||||
<span className={`font-mono text-[10px] ${d.points >= 15 ? 'text-emerald-500/70' : 'text-slate-500'}`}>+{d.points}</span>
|
||||
</div>
|
||||
))}
|
||||
</div>
|
||||
|
|
@ -117,9 +130,17 @@ function StockCard({ item, lane, onSelect }) {
|
|||
|
||||
{/* Footer: sector + grade badge */}
|
||||
<div className="flex items-center justify-between mt-3 pt-2.5 border-t border-slate-800/60">
|
||||
<span className="text-[10px] text-slate-600 bg-slate-800/60 px-1.5 py-0.5 rounded">
|
||||
{item.sector}
|
||||
</span>
|
||||
<div className="flex items-center gap-2">
|
||||
<span className="text-[10px] text-slate-600 bg-slate-800/60 px-1.5 py-0.5 rounded">
|
||||
{item.sector}
|
||||
</span>
|
||||
{s.dataAgeSec != null && (
|
||||
<div
|
||||
className={`w-2 h-2 rounded-full ${s.stale ? 'bg-red-500' : s.dataAgeSec < 30 ? 'bg-emerald-500' : 'bg-yellow-500'}`}
|
||||
title={`Data age: ${s.dataAgeSec}s${s.stale ? ' (Stale)' : ''}`}
|
||||
/>
|
||||
)}
|
||||
</div>
|
||||
<span className={`text-[10px] font-semibold px-1.5 py-0.5 rounded ${lc.badge}`}>
|
||||
{s.grade.label}
|
||||
</span>
|
||||
|
|
@ -129,14 +150,21 @@ function StockCard({ item, lane, onSelect }) {
|
|||
}
|
||||
|
||||
// ─── Swimlane column ──────────────────────────────────────────────────────────
|
||||
function Swimlane({ title, icon, description, items = [], lane, onSelect, loading, accentClass }) {
|
||||
function Swimlane({ title, icon, description, badge, items = [], lane, onSelect, loading, accentClass }) {
|
||||
return (
|
||||
<div className={`flex flex-col bg-slate-950/40 border ${accentClass} rounded-2xl overflow-hidden`}>
|
||||
{/* Lane header */}
|
||||
<div className={`px-4 py-3.5 border-b ${accentClass} bg-gradient-to-r from-slate-900 to-slate-950/50`}>
|
||||
<div className="flex items-center gap-2 mb-1">
|
||||
<span className="text-xl">{icon}</span>
|
||||
<h3 className="font-bold text-white text-base">{title}</h3>
|
||||
<h3 className="font-bold text-white text-base flex items-center gap-2">
|
||||
{title}
|
||||
{badge && (
|
||||
<span className="text-[9px] uppercase tracking-wider font-bold bg-slate-800 text-slate-400 px-1.5 py-0.5 rounded border border-slate-700">
|
||||
{badge}
|
||||
</span>
|
||||
)}
|
||||
</h3>
|
||||
{!loading && items.length > 0 && (
|
||||
<span className="ml-auto text-xs text-slate-500">{items.length} stocks</span>
|
||||
)}
|
||||
|
|
@ -237,6 +265,7 @@ function SearchBar({ onResult, onClear }) {
|
|||
export default function MarketScreenerPanel({ onSelectSymbol }) {
|
||||
const [screenerData, setScreenerData] = useState(null);
|
||||
const [leaderboard, setLeaderboard] = useState(null);
|
||||
const [regime, setRegime] = useState(null);
|
||||
const [loading, setLoading] = useState(true);
|
||||
const [lastUpdated, setLastUpdated] = useState(null);
|
||||
const [capFilter, setCapFilter] = useState('all');
|
||||
|
|
@ -249,6 +278,7 @@ export default function MarketScreenerPanel({ onSelectSymbol }) {
|
|||
]);
|
||||
if (screenerRes.status === 'fulfilled' && screenerRes.value.success) {
|
||||
setScreenerData(screenerRes.value.data);
|
||||
setRegime(screenerRes.value.data.regime);
|
||||
setLastUpdated(new Date());
|
||||
}
|
||||
if (lbRes.status === 'fulfilled' && lbRes.value.success) {
|
||||
|
|
@ -287,6 +317,31 @@ export default function MarketScreenerPanel({ onSelectSymbol }) {
|
|||
|
||||
return (
|
||||
<div className="space-y-5">
|
||||
{/* Regime Banner */}
|
||||
{regime && (
|
||||
<div className="bg-slate-900/80 border border-slate-700/50 rounded-xl p-3 flex items-center justify-between shadow-sm">
|
||||
<div className="flex items-center gap-3">
|
||||
<span className="text-lg">🏛</span>
|
||||
<div>
|
||||
<div className="text-xs text-slate-400 uppercase tracking-wider font-semibold">Market Regime</div>
|
||||
<div className="text-sm text-white font-medium flex items-center gap-2 mt-0.5">
|
||||
<span className={regime.trend === 'risk_on' ? 'text-emerald-400' : regime.trend === 'risk_off' ? 'text-red-400' : 'text-yellow-400'}>
|
||||
{regime.trend === 'risk_on' ? 'Risk-On (Uptrend)' : regime.trend === 'risk_off' ? 'Risk-Off (Downtrend)' : 'Neutral Trend'}
|
||||
</span>
|
||||
<span className="text-slate-600">•</span>
|
||||
<span>{regime.vol === 'low_vol' ? 'Low Vol' : regime.vol === 'high_vol' ? 'High Vol' : 'Normal Vol'}</span>
|
||||
<span className="text-slate-600">•</span>
|
||||
<span>{regime.breadth === 'broad' ? 'Broad Breadth' : regime.breadth === 'narrow' ? 'Narrow Breadth' : 'Mixed Breadth'}</span>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div className="text-right">
|
||||
<div className="text-[10px] text-slate-500">SPY Close</div>
|
||||
<div className="text-xs font-mono text-slate-300">${regime.raw?.spy_close?.toFixed(2)}</div>
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* Leaderboard strip */}
|
||||
{leaderboard && (
|
||||
<div className="grid grid-cols-3 gap-4">
|
||||
|
|
@ -355,24 +410,26 @@ export default function MarketScreenerPanel({ onSelectSymbol }) {
|
|||
onSelect={onSelectSymbol}
|
||||
/>
|
||||
<Swimlane
|
||||
title="Short Term"
|
||||
icon="🔄"
|
||||
lane="shortTerm"
|
||||
accentClass="border-blue-500/25"
|
||||
description="MACD setup · Near SMA50 · Analyst upside catalyst"
|
||||
title="Short Term (Swing)"
|
||||
icon="📅"
|
||||
description="RSI dips, SMA50 bounce, Earnings Revisions"
|
||||
badge="Not yet validated"
|
||||
items={filterByCap(screenerData?.shortTerm)}
|
||||
loading={loading}
|
||||
lane="shortTerm"
|
||||
onSelect={onSelectSymbol}
|
||||
loading={loading}
|
||||
accentClass="border-blue-500/20 shadow-[0_0_15px_rgba(59,130,246,0.05)]"
|
||||
/>
|
||||
<Swimlane
|
||||
title="Long Term"
|
||||
icon="🌱"
|
||||
lane="longTerm"
|
||||
accentClass="border-emerald-500/25"
|
||||
description="Above SMA200 · Earnings growth · Analyst conviction"
|
||||
icon="💎"
|
||||
description="SMA200 uptrend, Earnings power, Margins"
|
||||
badge="Not yet validated"
|
||||
items={filterByCap(screenerData?.longTerm)}
|
||||
loading={loading}
|
||||
lane="longTerm"
|
||||
onSelect={onSelectSymbol}
|
||||
loading={loading}
|
||||
accentClass="border-emerald-500/20 shadow-[0_0_15px_rgba(16,185,129,0.05)]"
|
||||
/>
|
||||
</div>
|
||||
|
||||
|
|
|
|||
12
k8s/app.yaml
12
k8s/app.yaml
|
|
@ -23,19 +23,17 @@ spec:
|
|||
- name: NODE_ENV
|
||||
value: "production"
|
||||
livenessProbe:
|
||||
httpGet:
|
||||
path: /health
|
||||
tcpSocket:
|
||||
port: 3010
|
||||
initialDelaySeconds: 30
|
||||
initialDelaySeconds: 15
|
||||
periodSeconds: 20
|
||||
failureThreshold: 5
|
||||
readinessProbe:
|
||||
httpGet:
|
||||
path: /health
|
||||
tcpSocket:
|
||||
port: 3010
|
||||
initialDelaySeconds: 15
|
||||
initialDelaySeconds: 10
|
||||
periodSeconds: 10
|
||||
failureThreshold: 6
|
||||
failureThreshold: 3
|
||||
---
|
||||
apiVersion: v1
|
||||
kind: Service
|
||||
|
|
|
|||
Loading…
Reference in New Issue