trading_bot_v4/app/api/analytics/reentry-check/route.ts

import { NextRequest, NextResponse } from 'next/server'
import { getMarketDataCache } from '@/lib/trading/market-data-cache'
import { getPrismaClient } from '@/lib/database/trades'
import { scoreSignalQuality } from '@/lib/trading/signal-quality'

/**
 * Re-Entry Analytics Endpoint
 * 
 * Validates manual trades using:
 * 1. Fresh TradingView market data (if available)
 * 2. Recent trade performance (last 3 trades for symbol + direction)
 * 3. Signal quality scoring with performance modifiers
 * 
 * Called by Telegram bot before executing manual "long sol" / "short eth" commands
 */

interface ReentryAnalytics {
  should_enter: boolean
  score: number
  reason: string
  data_source: 'tradingview_real' | 'fallback_historical' | 'no_data'
  data_age_seconds?: number
  metrics: {
    atr: number
    adx: number
    rsi: number
    volumeRatio: number
    pricePosition: number
    timeframe: string
    recentTradeStats: {
      last3Trades: number
      winRate: number
      avgPnL: number
    }
  }
}

export async function POST(request: NextRequest) {
  try {
    const body = await request.json()
    const { symbol, direction } = body

    if (!symbol || !direction) {
      return NextResponse.json(
        { error: 'Missing symbol or direction' },
        { status: 400 }
      )
    }

    if (!['long', 'short'].includes(direction)) {
      return NextResponse.json(
        { error: 'Direction must be "long" or "short"' },
        { status: 400 }
      )
    }

    console.log(`🔍 Analyzing re-entry for ${direction.toUpperCase()} ${symbol}`)

    // 1. Try to get REAL market data from TradingView cache
    const marketCache = getMarketDataCache()
    const cachedData = marketCache.get(symbol)

    let metrics: any
    let dataSource: 'tradingview_real' | 'fallback_historical' | 'no_data'
    let dataAgeSeconds: number | undefined

    if (cachedData) {
      // Use REAL TradingView data (less than 5min old)
      dataAgeSeconds = Math.round((Date.now() - cachedData.timestamp) / 1000)
      dataSource = 'tradingview_real'
      
      console.log(`✅ Using real TradingView data (${dataAgeSeconds}s old)`)
      metrics = {
        atr: cachedData.atr,
        adx: cachedData.adx,
        rsi: cachedData.rsi,
        volumeRatio: cachedData.volumeRatio,
        pricePosition: cachedData.pricePosition,
        timeframe: cachedData.timeframe
      }
    } else {
      // Fallback to most recent trade metrics
      console.log(`⚠️ No fresh TradingView data, using historical metrics from last trade`)
      const prisma = getPrismaClient()
      const lastTrade = await prisma.trade.findFirst({
        where: { symbol },
        orderBy: { createdAt: 'desc' }
      }) as any // Trade type has optional metric fields

      if (lastTrade && lastTrade.atr && lastTrade.adx && lastTrade.rsi) {
        dataSource = 'fallback_historical'
        const tradeAge = Math.round((Date.now() - lastTrade.createdAt.getTime()) / 1000)
        console.log(`📊 Using metrics from last trade (${tradeAge}s ago)`)
        metrics = {
          atr: lastTrade.atr,
          adx: lastTrade.adx,
          rsi: lastTrade.rsi,
          volumeRatio: lastTrade.volumeRatio || 1.2,
          pricePosition: lastTrade.pricePosition || 50,
          timeframe: '5'
        }
      } else {
        // No data available at all
        console.log(`❌ No market data available for ${symbol}`)
        dataSource = 'no_data'
        metrics = {
          atr: 1.0,
          adx: 20,
          rsi: direction === 'long' ? 45 : 55,
          volumeRatio: 1.2,
          pricePosition: 50,
          timeframe: '5'
        }
      }
    }

    // 2. Get recent trade performance for this symbol + direction
    const prisma = getPrismaClient()
    const recentTrades = await prisma.trade.findMany({
      where: {
        symbol,
        direction,
        exitTime: { not: null },
        createdAt: {
          gte: new Date(Date.now() - 24 * 60 * 60 * 1000) // Last 24h
        }
      },
      orderBy: { createdAt: 'desc' },
      take: 3
    })

    const last3Count = recentTrades.length
    const winningTrades = recentTrades.filter((t: any) => (t.realizedPnL || 0) > 0)
    const winRate = last3Count > 0 ? (winningTrades.length / last3Count) * 100 : 0
    const avgPnL = last3Count > 0 
      ? recentTrades.reduce((sum: number, t: any) => sum + (t.realizedPnL || 0), 0) / last3Count 
      : 0

    console.log(`📊 Recent performance: ${last3Count} trades, ${winRate.toFixed(0)}% WR, ${avgPnL.toFixed(2)}% avg P&L`)

    // 3. Score the re-entry with real/fallback metrics
    const qualityResult = await scoreSignalQuality({
      atr: metrics.atr,
      adx: metrics.adx,
      rsi: metrics.rsi,
      volumeRatio: metrics.volumeRatio,
      pricePosition: metrics.pricePosition,
      direction: direction as 'long' | 'short',
      symbol: symbol,
      skipFrequencyCheck: true, // Re-entry check already considers recent trades
    })

    let finalScore = qualityResult.score

    // 4. Apply recent performance modifiers
    if (last3Count >= 2 && avgPnL < -5) {
      finalScore -= 20
      console.log(`⚠️ Recent trades losing (${avgPnL.toFixed(2)}% avg) - applying -20 penalty`)
    }

    if (last3Count >= 2 && avgPnL > 5 && winRate >= 66) {
      finalScore += 10
      console.log(`✨ Recent trades winning (${winRate.toFixed(0)}% WR) - applying +10 bonus`)
    }

    // 5. Penalize if using stale/no data
    if (dataSource === 'fallback_historical') {
      finalScore -= 5
      console.log(`⚠️ Using historical data - applying -5 penalty`)
    } else if (dataSource === 'no_data') {
      finalScore -= 10
      console.log(`⚠️ No market data available - applying -10 penalty`)
    }

    // 6. Determine if should enter
    const MIN_REENTRY_SCORE = 55
    const should_enter = finalScore >= MIN_REENTRY_SCORE

    let reason = ''
    if (!should_enter) {
      if (dataSource === 'no_data') {
        reason = `No market data available (score: ${finalScore})`
      } else if (dataSource === 'fallback_historical') {
        reason = `Using stale data (score: ${finalScore})`
      } else if (finalScore < MIN_REENTRY_SCORE) {
        reason = `Quality score too low (${finalScore} < ${MIN_REENTRY_SCORE})`
      }
      
      if (last3Count >= 2 && avgPnL < -5) {
        reason += `. Recent ${direction} trades losing (${avgPnL.toFixed(2)}% avg)`
      }
    } else {
      reason = `Quality score acceptable (${finalScore}/${MIN_REENTRY_SCORE})`
      
      if (dataSource === 'tradingview_real') {
        reason += ` [✅ FRESH TradingView data: ${dataAgeSeconds}s old]`
      } else if (dataSource === 'fallback_historical') {
        reason += ` [⚠️ Historical data - consider waiting for fresh signal]`
      } else {
        reason += ` [❌ No data - risky entry]`
      }
      
      if (winRate >= 66 && last3Count >= 2) {
        reason += `. Recent win rate: ${winRate.toFixed(0)}%`
      }
    }

    const response: ReentryAnalytics = {
      should_enter,
      score: finalScore,
      reason,
      data_source: dataSource,
      data_age_seconds: dataAgeSeconds,
      metrics: {
        ...metrics,
        recentTradeStats: {
          last3Trades: last3Count,
          winRate,
          avgPnL
        }
      }
    }

    console.log(`📊 Re-entry analysis complete:`, {
      should_enter,
      score: finalScore,
      data_source: dataSource
    })

    return NextResponse.json(response)

  } catch (error) {
    console.error('❌ Re-entry analysis error:', error)
    return NextResponse.json(
      { error: 'Internal server error' },
      { status: 500 }
    )
  }
}