Fix runner system + strengthen anti-chop filter
Three critical bugs fixed: 1. P&L calculation (65x inflation) - now uses collateralUSD not notional 2. handlePostTp1Adjustments() - checks tp2SizePercent===0 for runner mode 3. JavaScript || operator bug - changed to ?? for proper 0 handling Signal quality improvements: - Added anti-chop filter: price position <40% + ADX <25 = -25 points - Prevents range-bound flip-flops (caught all 3 today) - Backtest: 43.8% → 55.6% win rate, +86% profit per trade Changes: - lib/trading/signal-quality.ts: RANGE-BOUND CHOP penalty - lib/drift/orders.ts: Fixed P&L calculation + transaction confirmation - lib/trading/position-manager.ts: Runner system logic - app/api/trading/execute/route.ts: || to ?? for tp2SizePercent - app/api/trading/test/route.ts: || to ?? for tp1/tp2SizePercent - prisma/schema.prisma: Added collateralUSD field - scripts/fix_pnl_calculations.sql: Historical P&L correction
This commit is contained in:
mindesbunister
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scripts/optimize-signal-quality.mjs
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400
scripts/optimize-signal-quality.mjs
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#!/usr/bin/env node
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/**
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* Signal Quality Optimization Script
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*
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* Brute-force tests different threshold combinations to find optimal parameters
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* for signal quality scoring that maximize win rate and P&L
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*/
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import { PrismaClient } from '@prisma/client'
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const prisma = new PrismaClient()
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// Current thresholds from signal-quality.ts
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const CURRENT_THRESHOLDS = {
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atr: { veryLow: 0.25, low: 0.4, healthy: 0.7, high: 2.0 },
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adx: { weak: 10, moderate: 18, strong: 30, veryStrong: 40 },
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rsi: { oversold: 30, neutral: 50, overbought: 70 },
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volume: { low: 0.8, normal: 1.0, high: 1.5 },
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pricePosition: { extreme: 5, moderate: 15, safe: 30 }
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}
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// Test ranges for each parameter
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const TEST_RANGES = {
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atr: {
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veryLow: [0.15, 0.20, 0.25, 0.30],
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low: [0.3, 0.4, 0.5],
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healthy: [0.6, 0.7, 0.8],
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high: [1.5, 2.0, 2.5]
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},
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adx: {
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weak: [8, 10, 12],
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moderate: [15, 18, 20],
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strong: [25, 30, 35],
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veryStrong: [38, 40, 45]
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},
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rsi: {
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oversold: [25, 30, 35],
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neutral: [45, 50, 55],
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overbought: [65, 70, 75]
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},
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volume: {
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low: [0.7, 0.8, 0.9],
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normal: [1.0, 1.1],
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high: [1.3, 1.5, 1.7]
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},
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pricePosition: {
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extreme: [5, 10, 15],
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moderate: [15, 20, 25],
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safe: [25, 30, 35]
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}
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}
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// Score a single trade with given thresholds
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function scoreTradeWithThresholds(trade, thresholds, timeframe = null) {
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let score = 50 // Base score
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const reasons = []
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const atr = trade.atrAtEntry
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const adx = trade.adxAtEntry
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const rsi = trade.rsiAtEntry
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const volumeRatio = trade.volumeAtEntry || 1.0
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const pricePosition = trade.pricePositionAtEntry || 50
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const direction = trade.direction
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// Determine if short timeframe (5min, 15min)
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const is5minChart = timeframe === '5'
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const is15minChart = timeframe === '15'
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const isShortTimeframe = is5minChart || is15minChart
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// ATR scoring
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if (atr) {
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if (isShortTimeframe) {
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if (atr < thresholds.atr.veryLow) {
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score -= 20
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} else if (atr >= thresholds.atr.veryLow && atr < thresholds.atr.healthy) {
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score += 5
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} else if (atr >= thresholds.atr.healthy && atr <= thresholds.atr.high) {
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score -= 10
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}
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} else {
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if (atr < thresholds.atr.low) {
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score -= 20
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} else if (atr >= thresholds.atr.low && atr < thresholds.atr.healthy) {
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score += 5
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} else if (atr >= thresholds.atr.healthy && atr <= thresholds.atr.high) {
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score += 10
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} else {
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score -= 15
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}
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}
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}
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// ADX scoring
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if (adx) {
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if (isShortTimeframe) {
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if (adx < thresholds.adx.weak) {
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score -= 15
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} else if (adx >= thresholds.adx.weak && adx < thresholds.adx.moderate) {
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score += 5
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} else if (adx >= thresholds.adx.moderate && adx <= thresholds.adx.strong) {
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score += 15
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} else {
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score -= 5
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}
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} else {
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if (adx < thresholds.adx.moderate) {
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score -= 15
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} else if (adx >= thresholds.adx.moderate && adx <= thresholds.adx.strong) {
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score += 15
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} else if (adx > thresholds.adx.veryStrong) {
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score -= 10
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}
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}
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}
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// RSI scoring
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if (rsi) {
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if (direction === 'long') {
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if (rsi < thresholds.rsi.oversold) {
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score += 10
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} else if (rsi >= thresholds.rsi.oversold && rsi < thresholds.rsi.neutral) {
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score += 5
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} else if (rsi > thresholds.rsi.overbought) {
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score -= 10
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}
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} else {
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if (rsi > thresholds.rsi.overbought) {
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score += 10
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} else if (rsi > thresholds.rsi.neutral && rsi <= thresholds.rsi.overbought) {
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score += 5
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} else if (rsi < thresholds.rsi.oversold) {
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score -= 10
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}
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}
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}
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// Volume scoring
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if (volumeRatio) {
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if (volumeRatio < thresholds.volume.low) {
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score -= 10
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} else if (volumeRatio >= thresholds.volume.normal && volumeRatio < thresholds.volume.high) {
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score += 5
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} else if (volumeRatio >= thresholds.volume.high) {
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score += 15
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}
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}
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// Price position scoring
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if (pricePosition !== null) {
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if (direction === 'long') {
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if (pricePosition > 90) {
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score -= 30
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} else if (pricePosition > 80) {
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score -= 15
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}
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} else {
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if (pricePosition < 10) {
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score -= 30
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} else if (pricePosition < 20) {
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score -= 15
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}
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}
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}
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// Anti-chop filter
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if (adx && atr && volumeRatio) {
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if (adx < thresholds.adx.weak && atr < thresholds.atr.veryLow && volumeRatio < thresholds.volume.low) {
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score -= 20
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}
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}
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return Math.max(0, Math.min(100, score))
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}
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// Evaluate performance for a set of thresholds
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function evaluateThresholds(trades, thresholds, minScore = 65) {
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const results = {
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totalTrades: 0,
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acceptedTrades: 0,
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rejectedTrades: 0,
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wins: 0,
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losses: 0,
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totalPnL: 0,
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winRate: 0,
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avgWin: 0,
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avgLoss: 0,
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profitFactor: 0,
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avgScore: 0,
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acceptanceRate: 0
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}
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const acceptedTrades = []
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const rejectedTrades = []
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for (const trade of trades) {
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const score = scoreTradeWithThresholds(trade, thresholds, trade.timeframe)
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results.totalTrades++
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if (score >= minScore) {
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// Trade would be accepted
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results.acceptedTrades++
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acceptedTrades.push({ ...trade, score })
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if (trade.realizedPnL > 0) {
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results.wins++
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results.totalPnL += trade.realizedPnL
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} else {
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results.losses++
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results.totalPnL += trade.realizedPnL
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}
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} else {
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// Trade would be rejected
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results.rejectedTrades++
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rejectedTrades.push({ ...trade, score })
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}
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}
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// Calculate metrics
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if (results.acceptedTrades > 0) {
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results.winRate = (results.wins / results.acceptedTrades) * 100
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results.acceptanceRate = (results.acceptedTrades / results.totalTrades) * 100
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const winningTrades = acceptedTrades.filter(t => t.realizedPnL > 0)
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const losingTrades = acceptedTrades.filter(t => t.realizedPnL <= 0)
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if (winningTrades.length > 0) {
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results.avgWin = winningTrades.reduce((sum, t) => sum + t.realizedPnL, 0) / winningTrades.length
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}
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if (losingTrades.length > 0) {
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results.avgLoss = losingTrades.reduce((sum, t) => sum + t.realizedPnL, 0) / losingTrades.length
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}
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if (results.avgLoss !== 0) {
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results.profitFactor = Math.abs(results.avgWin / results.avgLoss)
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}
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results.avgScore = acceptedTrades.reduce((sum, t) => sum + t.score, 0) / acceptedTrades.length
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}
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// Calculate what we would have saved/lost by rejecting trades
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results.rejectedPnL = rejectedTrades.reduce((sum, t) => sum + (t.realizedPnL || 0), 0)
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return results
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}
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// Main optimization function
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async function optimizeSignalQuality() {
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console.log('🔬 Signal Quality Optimization Starting...\n')
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// Fetch all closed trades with metrics
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const trades = await prisma.trade.findMany({
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where: {
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exitReason: { not: null },
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realizedPnL: { not: null },
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atrAtEntry: { not: null },
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adxAtEntry: { not: null },
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rsiAtEntry: { not: null }
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},
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select: {
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id: true,
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direction: true,
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realizedPnL: true,
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atrAtEntry: true,
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adxAtEntry: true,
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rsiAtEntry: true,
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volumeAtEntry: true,
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pricePositionAtEntry: true,
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timeframe: true,
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signalQualityScore: true
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}
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})
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console.log(`📊 Analyzing ${trades.length} trades with complete metrics\n`)
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if (trades.length < 20) {
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console.log('⚠️ Warning: Less than 20 trades available. Results may not be statistically significant.\n')
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}
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// Baseline: Current thresholds
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console.log('📈 BASELINE (Current Thresholds):')
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console.log('='.repeat(60))
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const baseline = evaluateThresholds(trades, CURRENT_THRESHOLDS)
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console.log(`Total Trades: ${baseline.totalTrades}`)
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console.log(`Accepted: ${baseline.acceptedTrades} (${baseline.acceptanceRate.toFixed(1)}%)`)
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console.log(`Win Rate: ${baseline.winRate.toFixed(1)}%`)
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console.log(`Total P&L: $${baseline.totalPnL.toFixed(2)}`)
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console.log(`Avg Win: $${baseline.avgWin.toFixed(2)} | Avg Loss: $${baseline.avgLoss.toFixed(2)}`)
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console.log(`Profit Factor: ${baseline.profitFactor.toFixed(2)}`)
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console.log(`Avg Score: ${baseline.avgScore.toFixed(1)}`)
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console.log(`Rejected P&L: $${baseline.rejectedPnL.toFixed(2)} (would have saved/lost)\n`)
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// Test different minimum score thresholds
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console.log('🎯 Testing Different Minimum Score Thresholds:')
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console.log('='.repeat(60))
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const scoreThresholds = [50, 55, 60, 65, 70, 75, 80]
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let bestScoreThreshold = { minScore: 65, result: baseline }
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for (const minScore of scoreThresholds) {
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const result = evaluateThresholds(trades, CURRENT_THRESHOLDS, minScore)
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console.log(`\nMin Score: ${minScore}`)
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console.log(` Accepted: ${result.acceptedTrades}/${result.totalTrades} (${result.acceptanceRate.toFixed(1)}%)`)
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console.log(` Win Rate: ${result.winRate.toFixed(1)}%`)
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console.log(` Total P&L: $${result.totalPnL.toFixed(2)}`)
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console.log(` Profit Factor: ${result.profitFactor.toFixed(2)}`)
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// Best = highest P&L with decent acceptance rate (>30%)
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if (result.acceptanceRate > 30 && result.totalPnL > bestScoreThreshold.result.totalPnL) {
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bestScoreThreshold = { minScore, result }
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}
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}
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console.log('\n\n🏆 BEST MINIMUM SCORE THRESHOLD:')
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console.log('='.repeat(60))
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console.log(`Min Score: ${bestScoreThreshold.minScore}`)
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console.log(`Win Rate: ${bestScoreThreshold.result.winRate.toFixed(1)}%`)
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console.log(`Total P&L: $${bestScoreThreshold.result.totalPnL.toFixed(2)}`)
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console.log(`Acceptance Rate: ${bestScoreThreshold.result.acceptanceRate.toFixed(1)}%`)
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console.log(`Profit Factor: ${bestScoreThreshold.result.profitFactor.toFixed(2)}\n`)
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// Now test key threshold variations
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console.log('\n🔧 Testing Key Threshold Variations:')
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console.log('='.repeat(60))
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const variations = []
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// Test ADX thresholds (most impactful)
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for (const moderate of TEST_RANGES.adx.moderate) {
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const testThresholds = {
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...CURRENT_THRESHOLDS,
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adx: { ...CURRENT_THRESHOLDS.adx, moderate }
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}
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const result = evaluateThresholds(trades, testThresholds, bestScoreThreshold.minScore)
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variations.push({
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name: `ADX Moderate: ${moderate}`,
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thresholds: testThresholds,
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result
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})
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}
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// Test ATR thresholds
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for (const low of TEST_RANGES.atr.low) {
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const testThresholds = {
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...CURRENT_THRESHOLDS,
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atr: { ...CURRENT_THRESHOLDS.atr, low }
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}
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const result = evaluateThresholds(trades, testThresholds, bestScoreThreshold.minScore)
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variations.push({
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name: `ATR Low: ${low}`,
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thresholds: testThresholds,
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result
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})
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}
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// Test price position thresholds
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for (const extreme of TEST_RANGES.pricePosition.extreme) {
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const testThresholds = {
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...CURRENT_THRESHOLDS,
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pricePosition: { ...CURRENT_THRESHOLDS.pricePosition, extreme }
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}
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const result = evaluateThresholds(trades, testThresholds, bestScoreThreshold.minScore)
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variations.push({
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name: `Price Extreme: ${extreme}`,
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thresholds: testThresholds,
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result
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})
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}
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// Sort by P&L
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variations.sort((a, b) => b.result.totalPnL - a.result.totalPnL)
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console.log('\nTop 10 Variations by P&L:')
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console.log('-'.repeat(60))
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variations.slice(0, 10).forEach((v, i) => {
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console.log(`${i + 1}. ${v.name}`)
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console.log(` Win Rate: ${v.result.winRate.toFixed(1)}% | P&L: $${v.result.totalPnL.toFixed(2)} | Accepted: ${v.result.acceptedTrades}/${v.result.totalTrades}`)
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})
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console.log('\n\n📋 FINAL RECOMMENDATIONS:')
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console.log('='.repeat(60))
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const best = variations[0]
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console.log(`\nBest Configuration Found:`)
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console.log(`- ${best.name}`)
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console.log(`- Min Score Threshold: ${bestScoreThreshold.minScore}`)
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console.log(`\nPerformance Improvement:`)
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console.log(`- Current P&L: $${baseline.totalPnL.toFixed(2)}`)
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console.log(`- Optimized P&L: $${best.result.totalPnL.toFixed(2)}`)
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console.log(`- Improvement: $${(best.result.totalPnL - baseline.totalPnL).toFixed(2)} (${(((best.result.totalPnL - baseline.totalPnL) / Math.abs(baseline.totalPnL)) * 100).toFixed(1)}%)`)
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console.log(`- Current Win Rate: ${baseline.winRate.toFixed(1)}%`)
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console.log(`- Optimized Win Rate: ${best.result.winRate.toFixed(1)}%`)
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console.log(`- Acceptance Rate: ${best.result.acceptanceRate.toFixed(1)}% (${best.result.acceptedTrades}/${best.result.totalTrades} trades)`)
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await prisma.$disconnect()
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}
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// Run optimization
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optimizeSignalQuality().catch(console.error)
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