trading_bot_v4/scripts/optimize-signal-quality.mjs

#!/usr/bin/env node
/**
 * Signal Quality Optimization Script
 * 
 * Brute-force tests different threshold combinations to find optimal parameters
 * for signal quality scoring that maximize win rate and P&L
 */

import { PrismaClient } from '@prisma/client'

const prisma = new PrismaClient()

// Current thresholds from signal-quality.ts
const CURRENT_THRESHOLDS = {
  atr: { veryLow: 0.25, low: 0.4, healthy: 0.7, high: 2.0 },
  adx: { weak: 10, moderate: 18, strong: 30, veryStrong: 40 },
  rsi: { oversold: 30, neutral: 50, overbought: 70 },
  volume: { low: 0.8, normal: 1.0, high: 1.5 },
  pricePosition: { extreme: 5, moderate: 15, safe: 30 }
}

// Test ranges for each parameter
const TEST_RANGES = {
  atr: {
    veryLow: [0.15, 0.20, 0.25, 0.30],
    low: [0.3, 0.4, 0.5],
    healthy: [0.6, 0.7, 0.8],
    high: [1.5, 2.0, 2.5]
  },
  adx: {
    weak: [8, 10, 12],
    moderate: [15, 18, 20],
    strong: [25, 30, 35],
    veryStrong: [38, 40, 45]
  },
  rsi: {
    oversold: [25, 30, 35],
    neutral: [45, 50, 55],
    overbought: [65, 70, 75]
  },
  volume: {
    low: [0.7, 0.8, 0.9],
    normal: [1.0, 1.1],
    high: [1.3, 1.5, 1.7]
  },
  pricePosition: {
    extreme: [5, 10, 15],
    moderate: [15, 20, 25],
    safe: [25, 30, 35]
  }
}

// Score a single trade with given thresholds
function scoreTradeWithThresholds(trade, thresholds, timeframe = null) {
  let score = 50 // Base score
  const reasons = []

  const atr = trade.atrAtEntry
  const adx = trade.adxAtEntry
  const rsi = trade.rsiAtEntry
  const volumeRatio = trade.volumeAtEntry || 1.0
  const pricePosition = trade.pricePositionAtEntry || 50
  const direction = trade.direction

  // Determine if short timeframe (5min, 15min)
  const is5minChart = timeframe === '5'
  const is15minChart = timeframe === '15'
  const isShortTimeframe = is5minChart || is15minChart

  // ATR scoring
  if (atr) {
    if (isShortTimeframe) {
      if (atr < thresholds.atr.veryLow) {
        score -= 20
      } else if (atr >= thresholds.atr.veryLow && atr < thresholds.atr.healthy) {
        score += 5
      } else if (atr >= thresholds.atr.healthy && atr <= thresholds.atr.high) {
        score -= 10
      }
    } else {
      if (atr < thresholds.atr.low) {
        score -= 20
      } else if (atr >= thresholds.atr.low && atr < thresholds.atr.healthy) {
        score += 5
      } else if (atr >= thresholds.atr.healthy && atr <= thresholds.atr.high) {
        score += 10
      } else {
        score -= 15
      }
    }
  }

  // ADX scoring
  if (adx) {
    if (isShortTimeframe) {
      if (adx < thresholds.adx.weak) {
        score -= 15
      } else if (adx >= thresholds.adx.weak && adx < thresholds.adx.moderate) {
        score += 5
      } else if (adx >= thresholds.adx.moderate && adx <= thresholds.adx.strong) {
        score += 15
      } else {
        score -= 5
      }
    } else {
      if (adx < thresholds.adx.moderate) {
        score -= 15
      } else if (adx >= thresholds.adx.moderate && adx <= thresholds.adx.strong) {
        score += 15
      } else if (adx > thresholds.adx.veryStrong) {
        score -= 10
      }
    }
  }

  // RSI scoring
  if (rsi) {
    if (direction === 'long') {
      if (rsi < thresholds.rsi.oversold) {
        score += 10
      } else if (rsi >= thresholds.rsi.oversold && rsi < thresholds.rsi.neutral) {
        score += 5
      } else if (rsi > thresholds.rsi.overbought) {
        score -= 10
      }
    } else {
      if (rsi > thresholds.rsi.overbought) {
        score += 10
      } else if (rsi > thresholds.rsi.neutral && rsi <= thresholds.rsi.overbought) {
        score += 5
      } else if (rsi < thresholds.rsi.oversold) {
        score -= 10
      }
    }
  }

  // Volume scoring
  if (volumeRatio) {
    if (volumeRatio < thresholds.volume.low) {
      score -= 10
    } else if (volumeRatio >= thresholds.volume.normal && volumeRatio < thresholds.volume.high) {
      score += 5
    } else if (volumeRatio >= thresholds.volume.high) {
      score += 15
    }
  }

  // Price position scoring
  if (pricePosition !== null) {
    if (direction === 'long') {
      if (pricePosition > 90) {
        score -= 30
      } else if (pricePosition > 80) {
        score -= 15
      }
    } else {
      if (pricePosition < 10) {
        score -= 30
      } else if (pricePosition < 20) {
        score -= 15
      }
    }
  }

  // Anti-chop filter
  if (adx && atr && volumeRatio) {
    if (adx < thresholds.adx.weak && atr < thresholds.atr.veryLow && volumeRatio < thresholds.volume.low) {
      score -= 20
    }
  }

  return Math.max(0, Math.min(100, score))
}

// Evaluate performance for a set of thresholds
function evaluateThresholds(trades, thresholds, minScore = 65) {
  const results = {
    totalTrades: 0,
    acceptedTrades: 0,
    rejectedTrades: 0,
    wins: 0,
    losses: 0,
    totalPnL: 0,
    winRate: 0,
    avgWin: 0,
    avgLoss: 0,
    profitFactor: 0,
    avgScore: 0,
    acceptanceRate: 0
  }

  const acceptedTrades = []
  const rejectedTrades = []

  for (const trade of trades) {
    const score = scoreTradeWithThresholds(trade, thresholds, trade.timeframe)
    results.totalTrades++

    if (score >= minScore) {
      // Trade would be accepted
      results.acceptedTrades++
      acceptedTrades.push({ ...trade, score })
      
      if (trade.realizedPnL > 0) {
        results.wins++
        results.totalPnL += trade.realizedPnL
      } else {
        results.losses++
        results.totalPnL += trade.realizedPnL
      }
    } else {
      // Trade would be rejected
      results.rejectedTrades++
      rejectedTrades.push({ ...trade, score })
    }
  }

  // Calculate metrics
  if (results.acceptedTrades > 0) {
    results.winRate = (results.wins / results.acceptedTrades) * 100
    results.acceptanceRate = (results.acceptedTrades / results.totalTrades) * 100
    
    const winningTrades = acceptedTrades.filter(t => t.realizedPnL > 0)
    const losingTrades = acceptedTrades.filter(t => t.realizedPnL <= 0)
    
    if (winningTrades.length > 0) {
      results.avgWin = winningTrades.reduce((sum, t) => sum + t.realizedPnL, 0) / winningTrades.length
    }
    
    if (losingTrades.length > 0) {
      results.avgLoss = losingTrades.reduce((sum, t) => sum + t.realizedPnL, 0) / losingTrades.length
    }
    
    if (results.avgLoss !== 0) {
      results.profitFactor = Math.abs(results.avgWin / results.avgLoss)
    }
    
    results.avgScore = acceptedTrades.reduce((sum, t) => sum + t.score, 0) / acceptedTrades.length
  }

  // Calculate what we would have saved/lost by rejecting trades
  results.rejectedPnL = rejectedTrades.reduce((sum, t) => sum + (t.realizedPnL || 0), 0)

  return results
}

// Main optimization function
async function optimizeSignalQuality() {
  console.log('🔬 Signal Quality Optimization Starting...\n')

  // Fetch all closed trades with metrics
  const trades = await prisma.trade.findMany({
    where: {
      exitReason: { not: null },
      realizedPnL: { not: null },
      atrAtEntry: { not: null },
      adxAtEntry: { not: null },
      rsiAtEntry: { not: null }
    },
    select: {
      id: true,
      direction: true,
      realizedPnL: true,
      atrAtEntry: true,
      adxAtEntry: true,
      rsiAtEntry: true,
      volumeAtEntry: true,
      pricePositionAtEntry: true,
      timeframe: true,
      signalQualityScore: true
    }
  })

  console.log(`📊 Analyzing ${trades.length} trades with complete metrics\n`)

  if (trades.length < 20) {
    console.log('⚠️  Warning: Less than 20 trades available. Results may not be statistically significant.\n')
  }

  // Baseline: Current thresholds
  console.log('📈 BASELINE (Current Thresholds):')
  console.log('='.repeat(60))
  const baseline = evaluateThresholds(trades, CURRENT_THRESHOLDS)
  console.log(`Total Trades: ${baseline.totalTrades}`)
  console.log(`Accepted: ${baseline.acceptedTrades} (${baseline.acceptanceRate.toFixed(1)}%)`)
  console.log(`Win Rate: ${baseline.winRate.toFixed(1)}%`)
  console.log(`Total P&L: $${baseline.totalPnL.toFixed(2)}`)
  console.log(`Avg Win: $${baseline.avgWin.toFixed(2)} | Avg Loss: $${baseline.avgLoss.toFixed(2)}`)
  console.log(`Profit Factor: ${baseline.profitFactor.toFixed(2)}`)
  console.log(`Avg Score: ${baseline.avgScore.toFixed(1)}`)
  console.log(`Rejected P&L: $${baseline.rejectedPnL.toFixed(2)} (would have saved/lost)\n`)

  // Test different minimum score thresholds
  console.log('🎯 Testing Different Minimum Score Thresholds:')
  console.log('='.repeat(60))
  const scoreThresholds = [50, 55, 60, 65, 70, 75, 80]
  
  let bestScoreThreshold = { minScore: 65, result: baseline }
  
  for (const minScore of scoreThresholds) {
    const result = evaluateThresholds(trades, CURRENT_THRESHOLDS, minScore)
    
    console.log(`\nMin Score: ${minScore}`)
    console.log(`  Accepted: ${result.acceptedTrades}/${result.totalTrades} (${result.acceptanceRate.toFixed(1)}%)`)
    console.log(`  Win Rate: ${result.winRate.toFixed(1)}%`)
    console.log(`  Total P&L: $${result.totalPnL.toFixed(2)}`)
    console.log(`  Profit Factor: ${result.profitFactor.toFixed(2)}`)
    
    // Best = highest P&L with decent acceptance rate (>30%)
    if (result.acceptanceRate > 30 && result.totalPnL > bestScoreThreshold.result.totalPnL) {
      bestScoreThreshold = { minScore, result }
    }
  }

  console.log('\n\n🏆 BEST MINIMUM SCORE THRESHOLD:')
  console.log('='.repeat(60))
  console.log(`Min Score: ${bestScoreThreshold.minScore}`)
  console.log(`Win Rate: ${bestScoreThreshold.result.winRate.toFixed(1)}%`)
  console.log(`Total P&L: $${bestScoreThreshold.result.totalPnL.toFixed(2)}`)
  console.log(`Acceptance Rate: ${bestScoreThreshold.result.acceptanceRate.toFixed(1)}%`)
  console.log(`Profit Factor: ${bestScoreThreshold.result.profitFactor.toFixed(2)}\n`)

  // Now test key threshold variations
  console.log('\n🔧 Testing Key Threshold Variations:')
  console.log('='.repeat(60))
  
  const variations = []
  
  // Test ADX thresholds (most impactful)
  for (const moderate of TEST_RANGES.adx.moderate) {
    const testThresholds = {
      ...CURRENT_THRESHOLDS,
      adx: { ...CURRENT_THRESHOLDS.adx, moderate }
    }
    const result = evaluateThresholds(trades, testThresholds, bestScoreThreshold.minScore)
    variations.push({
      name: `ADX Moderate: ${moderate}`,
      thresholds: testThresholds,
      result
    })
  }

  // Test ATR thresholds
  for (const low of TEST_RANGES.atr.low) {
    const testThresholds = {
      ...CURRENT_THRESHOLDS,
      atr: { ...CURRENT_THRESHOLDS.atr, low }
    }
    const result = evaluateThresholds(trades, testThresholds, bestScoreThreshold.minScore)
    variations.push({
      name: `ATR Low: ${low}`,
      thresholds: testThresholds,
      result
    })
  }

  // Test price position thresholds
  for (const extreme of TEST_RANGES.pricePosition.extreme) {
    const testThresholds = {
      ...CURRENT_THRESHOLDS,
      pricePosition: { ...CURRENT_THRESHOLDS.pricePosition, extreme }
    }
    const result = evaluateThresholds(trades, testThresholds, bestScoreThreshold.minScore)
    variations.push({
      name: `Price Extreme: ${extreme}`,
      thresholds: testThresholds,
      result
    })
  }

  // Sort by P&L
  variations.sort((a, b) => b.result.totalPnL - a.result.totalPnL)

  console.log('\nTop 10 Variations by P&L:')
  console.log('-'.repeat(60))
  variations.slice(0, 10).forEach((v, i) => {
    console.log(`${i + 1}. ${v.name}`)
    console.log(`   Win Rate: ${v.result.winRate.toFixed(1)}% | P&L: $${v.result.totalPnL.toFixed(2)} | Accepted: ${v.result.acceptedTrades}/${v.result.totalTrades}`)
  })

  console.log('\n\n📋 FINAL RECOMMENDATIONS:')
  console.log('='.repeat(60))
  
  const best = variations[0]
  console.log(`\nBest Configuration Found:`)
  console.log(`- ${best.name}`)
  console.log(`- Min Score Threshold: ${bestScoreThreshold.minScore}`)
  console.log(`\nPerformance Improvement:`)
  console.log(`- Current P&L: $${baseline.totalPnL.toFixed(2)}`)
  console.log(`- Optimized P&L: $${best.result.totalPnL.toFixed(2)}`)
  console.log(`- Improvement: $${(best.result.totalPnL - baseline.totalPnL).toFixed(2)} (${(((best.result.totalPnL - baseline.totalPnL) / Math.abs(baseline.totalPnL)) * 100).toFixed(1)}%)`)
  console.log(`- Current Win Rate: ${baseline.winRate.toFixed(1)}%`)
  console.log(`- Optimized Win Rate: ${best.result.winRate.toFixed(1)}%`)
  console.log(`- Acceptance Rate: ${best.result.acceptanceRate.toFixed(1)}% (${best.result.acceptedTrades}/${best.result.totalTrades} trades)`)

  await prisma.$disconnect()
}

// Run optimization
optimizeSignalQuality().catch(console.error)