feat: Add comprehensive optimization analytics dashboard
- Created /api/optimization/analyze endpoint with 7 SQL analyses - Replaced old TP/SL page with comprehensive dashboard - Analyses: Quality Score Distribution, Direction Performance, Blocked Signals, Runner Performance, ATR vs MFE, Indicator Versions, Data Collection Status - Real-time refresh capability - Actionable recommendations based on data thresholds - Roadmap links at bottom - Addresses user request for automated SQL analysis dashboard
This commit is contained in:
mindesbunister
419
app/api/optimization/analyze/route.ts
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419
app/api/optimization/analyze/route.ts
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import { NextResponse } from 'next/server'
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import { getPrismaClient } from '../../../../lib/database/trades'
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export const dynamic = 'force-dynamic'
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export async function GET() {
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const prisma = getPrismaClient()
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const analyses = []
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try {
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// ============================================================================
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// 1. QUALITY SCORE DISTRIBUTION
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// ============================================================================
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try {
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const qualityDistribution = await prisma.$queryRaw<any[]>`
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SELECT
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CASE
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WHEN "signalQualityScore" >= 95 THEN '95-100 (Excellent)'
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WHEN "signalQualityScore" >= 90 THEN '90-94 (Very Good)'
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WHEN "signalQualityScore" >= 85 THEN '85-89 (Good)'
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WHEN "signalQualityScore" >= 80 THEN '80-84 (Fair)'
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WHEN "signalQualityScore" >= 70 THEN '70-79 (Marginal)'
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WHEN "signalQualityScore" >= 60 THEN '60-69 (Weak)'
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ELSE '<60 (Very Weak)'
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END as tier,
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COUNT(*) as trades,
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ROUND(AVG("signalQualityScore")::numeric, 1) as avg_score,
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SUM(CASE WHEN "realizedPnL" > 0 THEN 1 ELSE 0 END) as wins,
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SUM(CASE WHEN "realizedPnL" <= 0 THEN 1 ELSE 0 END) as losses,
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ROUND(100.0 * SUM(CASE WHEN "realizedPnL" > 0 THEN 1 ELSE 0 END) / COUNT(*)::numeric, 1) as win_rate,
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ROUND(SUM("realizedPnL")::numeric, 2) as total_pnl,
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ROUND(AVG("realizedPnL")::numeric, 2) as avg_pnl
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FROM "Trade"
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WHERE "exitReason" IS NOT NULL
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AND "signalQualityScore" IS NOT NULL
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GROUP BY tier
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ORDER BY MIN("signalQualityScore") DESC
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`
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const formattedData = qualityDistribution.map(row => ({
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tier: row.tier,
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trades: Number(row.trades),
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avg_score: Number(row.avg_score),
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win_rate: Number(row.win_rate),
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total_pnl: Number(row.total_pnl),
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avg_pnl: Number(row.avg_pnl)
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}))
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// Find best performing tier
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const bestTier = formattedData.reduce((best, current) =>
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current.win_rate > best.win_rate ? current : best
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)
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analyses.push({
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name: 'Quality Score Distribution',
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description: 'Win rate and P&L across quality score tiers',
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status: 'success',
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data: formattedData,
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recommendation: bestTier.win_rate >= 70
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? `Quality ${bestTier.tier.split(' ')[0]} shows ${bestTier.win_rate}% WR. Consider raising threshold to this tier.`
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: 'Continue collecting data for reliable quality threshold optimization.'
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})
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} catch (error) {
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analyses.push({
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name: 'Quality Score Distribution',
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description: 'Win rate and P&L across quality score tiers',
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status: 'error',
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data: { error: (error as Error).message }
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})
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}
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// ============================================================================
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// 2. DIRECTION PERFORMANCE (Long vs Short)
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// ============================================================================
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try {
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const directionPerformance = await prisma.$queryRaw<any[]>`
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SELECT
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direction,
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COUNT(*) as trades,
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SUM(CASE WHEN "realizedPnL" > 0 THEN 1 ELSE 0 END) as wins,
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ROUND(100.0 * SUM(CASE WHEN "realizedPnL" > 0 THEN 1 ELSE 0 END) / COUNT(*)::numeric, 1) as win_rate,
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ROUND(SUM("realizedPnL")::numeric, 2) as total_pnl,
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ROUND(AVG("realizedPnL")::numeric, 2) as avg_pnl,
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ROUND(AVG("signalQualityScore")::numeric, 1) as avg_quality
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FROM "Trade"
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WHERE "exitReason" IS NOT NULL
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AND direction IS NOT NULL
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GROUP BY direction
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ORDER BY win_rate DESC
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`
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const formattedData = directionPerformance.map(row => ({
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direction: String(row.direction).toUpperCase(),
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trades: Number(row.trades),
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wins: Number(row.wins),
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win_rate: Number(row.win_rate),
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total_pnl: Number(row.total_pnl),
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avg_pnl: Number(row.avg_pnl),
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avg_quality: Number(row.avg_quality)
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}))
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const longData = formattedData.find(d => d.direction === 'LONG')
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const shortData = formattedData.find(d => d.direction === 'SHORT')
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let recommendation = ''
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if (longData && shortData) {
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const wrDiff = Math.abs(longData.win_rate - shortData.win_rate)
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if (wrDiff > 15) {
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const better = longData.win_rate > shortData.win_rate ? 'LONG' : 'SHORT'
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const worse = better === 'LONG' ? 'SHORT' : 'LONG'
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recommendation = `${better} signals perform ${wrDiff.toFixed(1)}% better. Consider raising ${worse} quality threshold or reducing ${worse} position size.`
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} else {
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recommendation = 'Direction performance is balanced. No threshold adjustment needed.'
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}
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}
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analyses.push({
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name: 'Direction Performance',
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description: 'Compare LONG vs SHORT trade outcomes',
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status: 'success',
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data: formattedData,
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recommendation
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})
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} catch (error) {
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analyses.push({
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name: 'Direction Performance',
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description: 'Compare LONG vs SHORT trade outcomes',
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status: 'error',
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data: { error: (error as Error).message }
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})
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}
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// ============================================================================
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// 3. BLOCKED SIGNALS ANALYSIS
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// ============================================================================
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try {
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const blockedSignals = await prisma.$queryRaw<any[]>`
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SELECT
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"blockReason",
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COUNT(*) as count,
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ROUND(AVG("signalQualityScore")::numeric, 1) as avg_score,
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ROUND(AVG(adx)::numeric, 1) as avg_adx,
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ROUND(AVG(atr)::numeric, 3) as avg_atr
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FROM "BlockedSignal"
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WHERE "blockReason" IS NOT NULL
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GROUP BY "blockReason"
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ORDER BY count DESC
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`
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const formattedData = blockedSignals.map(row => ({
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reason: String(row.blockReason),
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count: Number(row.count),
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avg_score: Number(row.avg_score),
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avg_adx: Number(row.avg_adx),
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avg_atr: Number(row.avg_atr)
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}))
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const qualityBlocked = formattedData.find(d => d.reason === 'QUALITY_SCORE_TOO_LOW')
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let recommendation = ''
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if (qualityBlocked && qualityBlocked.count >= 20) {
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recommendation = `${qualityBlocked.count} signals blocked by quality threshold. Ready for Phase 2 analysis - check if these would have been profitable.`
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} else {
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const needed = qualityBlocked ? 20 - qualityBlocked.count : 20
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recommendation = `Need ${needed} more blocked signals for reliable analysis. Keep collecting data.`
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}
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analyses.push({
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name: 'Blocked Signals Analysis',
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description: 'Signals rejected by quality filters',
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status: 'success',
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data: formattedData,
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recommendation,
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action: qualityBlocked && qualityBlocked.count >= 20
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? 'Run price tracking analysis to determine if blocked signals would have been profitable'
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: undefined
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})
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} catch (error) {
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analyses.push({
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name: 'Blocked Signals Analysis',
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description: 'Signals rejected by quality filters',
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status: 'error',
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data: { error: (error as Error).message }
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})
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}
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// ============================================================================
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// 4. RUNNER PERFORMANCE ANALYSIS
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// ============================================================================
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try {
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const runnerPerformance = await prisma.$queryRaw<any[]>`
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SELECT
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COUNT(*) as total_trades,
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SUM(CASE WHEN "tp1Hit" = true THEN 1 ELSE 0 END) as tp1_hits,
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SUM(CASE WHEN "tp2Hit" = true THEN 1 ELSE 0 END) as tp2_hits,
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ROUND(100.0 * SUM(CASE WHEN "tp1Hit" = true THEN 1 ELSE 0 END) / COUNT(*)::numeric, 1) as tp1_rate,
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ROUND(100.0 * SUM(CASE WHEN "tp2Hit" = true THEN 1 ELSE 0 END) / COUNT(*)::numeric, 1) as tp2_rate,
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ROUND(AVG("maxFavorableExcursion")::numeric, 2) as avg_mfe,
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ROUND(AVG("maxAdverseExcursion")::numeric, 2) as avg_mae
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FROM "Trade"
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WHERE "exitReason" IS NOT NULL
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AND "createdAt" >= NOW() - INTERVAL '30 days'
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`
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const data = runnerPerformance[0]
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const formattedData = {
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total_trades: Number(data.total_trades),
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tp1_hits: Number(data.tp1_hits),
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tp2_hits: Number(data.tp2_hits),
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tp1_rate: Number(data.tp1_rate),
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tp2_rate: Number(data.tp2_rate),
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avg_mfe: Number(data.avg_mfe),
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avg_mae: Number(data.avg_mae)
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}
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let recommendation = ''
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if (formattedData.avg_mfe > formattedData.tp1_rate * 1.5) {
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recommendation = `Avg MFE (${formattedData.avg_mfe.toFixed(2)}%) significantly exceeds TP1 rate. Consider widening TP1 or increasing runner size.`
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} else if (formattedData.tp2_rate > 50) {
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recommendation = `TP2 hit rate is ${formattedData.tp2_rate}%. Trailing stop working well - keep current settings.`
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} else {
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recommendation = 'Runner performance is within expected range. Continue monitoring.'
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}
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analyses.push({
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name: 'Runner Performance',
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description: 'TP1/TP2 hit rates and max excursion analysis',
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status: 'success',
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data: formattedData,
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recommendation
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})
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} catch (error) {
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analyses.push({
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name: 'Runner Performance',
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description: 'TP1/TP2 hit rates and max excursion analysis',
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status: 'error',
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data: { error: (error as Error).message }
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})
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}
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// ============================================================================
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// 5. ATR CORRELATION WITH MFE
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// ============================================================================
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try {
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const atrCorrelation = await prisma.$queryRaw<any[]>`
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SELECT
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CASE
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WHEN atr < 0.3 THEN '<0.3 (Low Vol)'
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WHEN atr < 0.5 THEN '0.3-0.5 (Med Vol)'
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WHEN atr < 0.7 THEN '0.5-0.7 (High Vol)'
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ELSE '0.7+ (Very High Vol)'
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END as atr_range,
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COUNT(*) as trades,
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ROUND(AVG("maxFavorableExcursion")::numeric, 2) as avg_mfe,
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ROUND(AVG("realizedPnL")::numeric, 2) as avg_pnl,
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ROUND(100.0 * SUM(CASE WHEN "realizedPnL" > 0 THEN 1 ELSE 0 END) / COUNT(*)::numeric, 1) as win_rate
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FROM "Trade"
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WHERE "exitReason" IS NOT NULL
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AND atr IS NOT NULL
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GROUP BY atr_range
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ORDER BY MIN(atr)
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`
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const formattedData = atrCorrelation.map(row => ({
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atr_range: String(row.atr_range),
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trades: Number(row.trades),
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avg_mfe: Number(row.avg_mfe),
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avg_pnl: Number(row.avg_pnl),
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win_rate: Number(row.win_rate)
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}))
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let recommendation = 'ATR-based targets already implemented. Monitor correlation over time.'
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if (formattedData.length >= 3) {
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const highestMFE = formattedData.reduce((best, current) =>
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current.avg_mfe > best.avg_mfe ? current : best
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)
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recommendation = `${highestMFE.atr_range} shows highest avg MFE (${highestMFE.avg_mfe}%). ATR-based targets adapting correctly.`
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}
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analyses.push({
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name: 'ATR vs MFE Correlation',
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description: 'How volatility affects profit potential',
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status: 'success',
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data: formattedData,
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recommendation
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})
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} catch (error) {
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analyses.push({
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name: 'ATR vs MFE Correlation',
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description: 'How volatility affects profit potential',
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status: 'error',
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data: { error: (error as Error).message }
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})
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}
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// ============================================================================
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// 6. INDICATOR VERSION COMPARISON
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// ============================================================================
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try {
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const indicatorComparison = await prisma.$queryRaw<any[]>`
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SELECT
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"indicatorVersion",
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COUNT(*) as trades,
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SUM(CASE WHEN "realizedPnL" > 0 THEN 1 ELSE 0 END) as wins,
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ROUND(100.0 * SUM(CASE WHEN "realizedPnL" > 0 THEN 1 ELSE 0 END) / COUNT(*)::numeric, 1) as win_rate,
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ROUND(SUM("realizedPnL")::numeric, 2) as total_pnl,
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ROUND(AVG("realizedPnL")::numeric, 2) as avg_pnl,
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ROUND(AVG("signalQualityScore")::numeric, 1) as avg_quality
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FROM "Trade"
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WHERE "exitReason" IS NOT NULL
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AND "indicatorVersion" IS NOT NULL
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GROUP BY "indicatorVersion"
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ORDER BY "indicatorVersion" DESC
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`
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const formattedData = indicatorComparison.map(row => ({
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version: String(row.indicatorVersion),
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trades: Number(row.trades),
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wins: Number(row.wins),
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win_rate: Number(row.win_rate),
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total_pnl: Number(row.total_pnl),
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avg_pnl: Number(row.avg_pnl),
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avg_quality: Number(row.avg_quality)
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}))
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const v8Data = formattedData.find(d => d.version === 'v8')
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let recommendation = ''
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if (v8Data && v8Data.trades >= 20) {
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recommendation = `v8 has ${v8Data.trades} trades with ${v8Data.win_rate}% WR. Sufficient data for statistical confidence.`
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} else if (v8Data) {
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recommendation = `v8 has ${v8Data.trades} trades. Need ${20 - v8Data.trades} more for statistical validation.`
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} else {
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recommendation = 'No v8 indicator data yet. Ensure TradingView alerts include IND:v8 field.'
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}
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analyses.push({
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name: 'Indicator Version Comparison',
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description: 'Performance across TradingView strategy versions',
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status: 'success',
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data: formattedData,
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recommendation
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})
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} catch (error) {
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analyses.push({
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name: 'Indicator Version Comparison',
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description: 'Performance across TradingView strategy versions',
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status: 'error',
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data: { error: (error as Error).message }
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})
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}
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// ============================================================================
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// 7. DATA COLLECTION STATUS
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// ============================================================================
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try {
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const dataStatus = await prisma.$queryRaw<any[]>`
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SELECT
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COUNT(*) FILTER (WHERE "exitReason" IS NOT NULL) as completed_trades,
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COUNT(*) FILTER (WHERE "signalQualityScore" IS NOT NULL) as with_quality,
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COUNT(*) FILTER (WHERE atr IS NOT NULL) as with_atr,
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COUNT(*) FILTER (WHERE "maxFavorableExcursion" IS NOT NULL) as with_mfe,
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COUNT(*) FILTER (WHERE "indicatorVersion" = 'v8') as v8_trades,
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(SELECT COUNT(*) FROM "BlockedSignal" WHERE "blockReason" = 'QUALITY_SCORE_TOO_LOW') as blocked_quality
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FROM "Trade"
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`
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const data = dataStatus[0]
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const formattedData = {
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completed_trades: Number(data.completed_trades),
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with_quality: Number(data.with_quality),
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with_atr: Number(data.with_atr),
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with_mfe: Number(data.with_mfe),
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v8_trades: Number(data.v8_trades),
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blocked_quality: Number(data.blocked_quality)
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}
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const blockedNeeded = Math.max(0, 20 - formattedData.blocked_quality)
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const v8Needed = Math.max(0, 50 - formattedData.v8_trades)
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let action = ''
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if (blockedNeeded > 0) {
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action = `Need ${blockedNeeded} more blocked signals for Phase 2 quality threshold analysis.`
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}
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if (v8Needed > 0) {
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if (action) action += ' '
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action += `Need ${v8Needed} more v8 indicator trades for statistical validation.`
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}
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analyses.push({
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name: 'Data Collection Status',
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description: 'Progress toward analysis milestones',
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status: 'success',
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data: formattedData,
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recommendation: action || 'Data collection milestones reached! Ready for optimization decisions.',
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action: action || undefined
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})
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} catch (error) {
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analyses.push({
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name: 'Data Collection Status',
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description: 'Progress toward analysis milestones',
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status: 'error',
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data: { error: (error as Error).message }
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})
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}
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return NextResponse.json({
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success: true,
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analyses,
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timestamp: new Date().toISOString()
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})
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} catch (error) {
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console.error('❌ Optimization analysis failed:', error)
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return NextResponse.json({
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success: false,
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error: 'Failed to run optimization analyses',
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message: (error as Error).message
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}, { status: 500 })
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}
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}
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