trading_bot_v3/lib/ai-analysis.ts

import OpenAI from 'openai'
import fs from 'fs/promises'
import path from 'path'
import { enhancedScreenshotService, ScreenshotConfig } from './enhanced-screenshot'
import { TradingViewCredentials } from './tradingview-automation'

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
})

export interface AnalysisResult {
  summary: string
  marketSentiment: 'BULLISH' | 'BEARISH' | 'NEUTRAL'
  keyLevels: {
    support: number[]
    resistance: number[]
  }
  recommendation: 'BUY' | 'SELL' | 'HOLD'
  confidence: number // 0-100
  reasoning: string
  // Enhanced trading analysis (optional)
  entry?: {
    price: number
    buffer?: string
    rationale: string
  }
  stopLoss?: {
    price: number
    rationale: string
  }
  takeProfits?: {
    tp1?: { price: number; description: string }
    tp2?: { price: number; description: string }
  }
  riskToReward?: string
  confirmationTrigger?: string
  indicatorAnalysis?: {
    rsi?: string
    vwap?: string
    obv?: string
  }
}

export class AIAnalysisService {
  async analyzeScreenshot(filename: string): Promise<AnalysisResult | null> {
    try {
      const screenshotsDir = path.join(process.cwd(), 'screenshots')
      const imagePath = path.join(screenshotsDir, filename)
      // Read image file
      const imageBuffer = await fs.readFile(imagePath)
      const base64Image = imageBuffer.toString('base64')
      
      const prompt = `You are a technical chart analysis expert. Please analyze this TradingView chart image and provide objective technical analysis data.

**Important**: This is for educational and research purposes only. Please analyze the technical indicators, price levels, and chart patterns visible in the image.

Examine the chart and identify:
- Current price action and trend direction
- Key support and resistance levels visible on the chart
- Technical indicator readings (RSI, moving averages, volume if visible)
- Chart patterns or formations
- Market structure elements

Provide your analysis in this exact JSON format (replace values with your analysis):

{
  "summary": "Objective description of what you observe in the chart",
  "marketSentiment": "BULLISH|BEARISH|NEUTRAL",
  "keyLevels": { 
    "support": [list of visible support price levels as numbers], 
    "resistance": [list of visible resistance price levels as numbers] 
  },
  "recommendation": "BUY|SELL|HOLD",
  "confidence": 75,
  "reasoning": "Technical analysis reasoning based on indicators and price action",
  "entry": {
    "price": 150.50,
    "buffer": "±0.25",
    "rationale": "Technical reasoning for entry level"
  },
  "stopLoss": {
    "price": 148.00,
    "rationale": "Technical reasoning for stop level"
  },
  "takeProfits": {
    "tp1": { "price": 152.00, "description": "First target reasoning" },
    "tp2": { "price": 154.00, "description": "Second target reasoning" }
  },
  "riskToReward": "1:2",
  "confirmationTrigger": "Technical signal to watch for",
  "indicatorAnalysis": {
    "rsi": "RSI level and interpretation",
    "vwap": "VWAP relationship to price", 
    "obv": "Volume analysis if visible"
  }
}

Return only the JSON object with your technical analysis.`

      const response = await openai.chat.completions.create({
        model: "gpt-4o-mini", // Cost-effective vision model
        messages: [
          {
            role: "user",
            content: [
              { type: "text", text: prompt },
              { 
                type: "image_url", 
                image_url: { 
                  url: `data:image/png;base64,${base64Image}`,
                  detail: "low" // Reduce token usage
                } 
              }
            ]
          }
        ],
        max_tokens: 1024,
        temperature: 0.1
      })
      
      const content = response.choices[0]?.message?.content
      if (!content) return null
      
      console.log('AI response content:', content)
      
      // Extract JSON from response
      const match = content.match(/\{[\s\S]*\}/)
      if (!match) {
        console.error('No JSON found in response. Full content:', content)
        return null
      }
      
      const json = match[0]
      console.log('Raw JSON from AI:', json)
      
      let result
      try {
        result = JSON.parse(json)
        console.log('Parsed result:', result)
      } catch (parseError) {
        console.error('Failed to parse JSON:', parseError)
        console.error('Raw JSON that failed:', json)
        return null
      }
      
      // Sanitize the result to ensure no nested objects cause React issues
      const sanitizedResult = {
        summary: typeof result.summary === 'string' ? result.summary : String(result.summary || ''),
        marketSentiment: result.marketSentiment || 'NEUTRAL',
        keyLevels: {
          support: Array.isArray(result.keyLevels?.support) ? result.keyLevels.support : [],
          resistance: Array.isArray(result.keyLevels?.resistance) ? result.keyLevels.resistance : []
        },
        recommendation: result.recommendation || 'HOLD',
        confidence: typeof result.confidence === 'number' ? result.confidence : 0,
        reasoning: typeof result.reasoning === 'string' ? result.reasoning : String(result.reasoning || 'Basic technical analysis'),
        ...(result.entry && { entry: result.entry }),
        ...(result.stopLoss && { stopLoss: result.stopLoss }),
        ...(result.takeProfits && { takeProfits: result.takeProfits }),
        ...(result.riskToReward && { riskToReward: String(result.riskToReward) }),
        ...(result.confirmationTrigger && { confirmationTrigger: String(result.confirmationTrigger) }),
        ...(result.indicatorAnalysis && { indicatorAnalysis: result.indicatorAnalysis })
      }
      
      // Optionally: validate result structure here
      return sanitizedResult as AnalysisResult
    } catch (e) {
      console.error('AI analysis error:', e)
      return null
    }
  }

  async analyzeMultipleScreenshots(filenames: string[]): Promise<AnalysisResult | null> {
    try {
      const screenshotsDir = path.join(process.cwd(), 'screenshots')
      
      // Read all image files and convert to base64
      const images = await Promise.all(
        filenames.map(async (filename) => {
          const imagePath = path.join(screenshotsDir, filename)
          const imageBuffer = await fs.readFile(imagePath)
          const base64Image = imageBuffer.toString('base64')
          return {
            type: "image_url" as const,
            image_url: {
              url: `data:image/png;base64,${base64Image}`,
              detail: "high" as const
            }
          }
        })
      )

      const layoutInfo = filenames.map(f => {
        if (f.includes('_ai_')) return 'AI Layout'
        if (f.includes('_diy_') || f.includes('_Diy module_')) return 'DIY Module Layout'
        return 'Unknown Layout'
      }).join(' and ')

      const prompt = `You are a professional technical chart analysis expert. I'm providing you with ${filenames.length} TradingView chart screenshots from different layouts: ${layoutInfo}.

**IMPORTANT**: Please analyze ALL screenshots together to provide comprehensive trading advice. Compare the different layouts and use ALL available information to make the most informed recommendation.

**Analysis Requirements:**
1. Examine ALL charts for:
   - Current price action and trend direction across all timeframes/layouts
   - Key support and resistance levels visible in any chart
   - Technical indicator readings (RSI, moving averages, volume, etc.)
   - Chart patterns or formations
   - Market structure elements
   - Any divergences or confirmations between the layouts

2. **Cross-Layout Analysis**: 
   - Compare insights from different chart layouts
   - Look for confirmations or contradictions between views
   - Identify which layout provides the clearest signals
   - Use multiple perspectives to increase confidence

3. **Comprehensive Trading Setup**:
   - Provide entry, stop loss, and take profit levels
   - Include risk-to-reward analysis
   - Suggest confirmation triggers
   - Rate your confidence based on multi-layout consensus

**Response Format** (return only valid JSON):

{
  "summary": "Comprehensive analysis of all charts including cross-layout insights and consensus",
  "marketSentiment": "BULLISH|BEARISH|NEUTRAL",
  "keyLevels": { 
    "support": [array of support levels from all charts], 
    "resistance": [array of resistance levels from all charts] 
  },
  "recommendation": "BUY|SELL|HOLD",
  "confidence": 85,
  "reasoning": "Multi-layout technical analysis reasoning explaining how different charts confirm or contradict each other",
  "entry": {
    "price": 150.50,
    "buffer": "±0.25",
    "rationale": "Entry reasoning based on multiple chart analysis"
  },
  "stopLoss": {
    "price": 148.00,
    "rationale": "Stop loss reasoning considering all layouts"
  },
  "takeProfits": {
    "tp1": { "price": 152.00, "description": "First target based on multi-chart analysis" },
    "tp2": { "price": 154.00, "description": "Second target with cross-layout confirmation" }
  },
  "riskToReward": "1:2.5",
  "confirmationTrigger": "Multi-layout signal confirmation to watch for",
  "indicatorAnalysis": {
    "rsi": "RSI analysis across layouts",
    "vwap": "VWAP analysis from multiple views", 
    "obv": "Volume analysis synthesis",
    "crossLayoutConsensus": "How well different layouts agree on the signals"
  },
  "layoutComparison": {
    "aiLayout": "Insights specific to AI layout",
    "diyLayout": "Insights specific to DIY module layout",
    "consensus": "Areas where both layouts agree",
    "divergences": "Areas where layouts show different signals"
  }
}

Analyze all provided screenshots comprehensively and return only the JSON response.`

      const messages = [
        {
          role: "user" as const,
          content: [
            { type: "text" as const, text: prompt },
            ...images
          ]
        }
      ]

      console.log(`🤖 Sending ${filenames.length} screenshots to OpenAI for multi-layout analysis...`)
      
      const response = await openai.chat.completions.create({
        model: "gpt-4o-mini", // Cost-effective model with vision capabilities
        messages,
        max_tokens: 2000,
        temperature: 0.1
      })

      const content = response.choices[0]?.message?.content
      if (!content) {
        throw new Error('No response from OpenAI')
      }

      console.log('🔍 Raw OpenAI response:', content.substring(0, 200) + '...')

      // Parse JSON response
      const jsonMatch = content.match(/\{[\s\S]*\}/)
      if (!jsonMatch) {
        throw new Error('No JSON found in response')
      }

      const analysis = JSON.parse(jsonMatch[0])
      console.log('✅ Multi-layout analysis parsed successfully')
      
      return analysis as AnalysisResult

    } catch (error: any) {
      console.error('❌ Multi-screenshot AI analysis failed:', error.message)
      console.error('Full error:', error)
      return null
    }
  }

  async captureAndAnalyze(
    symbol: string, 
    timeframe: string, 
    credentials: TradingViewCredentials
  ): Promise<AnalysisResult | null> {
    try {
      console.log(`Starting automated capture and analysis for ${symbol} ${timeframe}`)
      
      // Capture screenshot using automation
      const screenshot = await enhancedScreenshotService.captureQuick(symbol, timeframe, credentials)
      
      if (!screenshot) {
        throw new Error('Failed to capture screenshot')
      }

      console.log(`Screenshot captured: ${screenshot}`)
      
      // Analyze the captured screenshot
      const analysis = await this.analyzeScreenshot(screenshot)
      
      if (!analysis) {
        throw new Error('Failed to analyze screenshot')
      }

      console.log(`Analysis completed for ${symbol} ${timeframe}`)
      return analysis

    } catch (error) {
      console.error('Automated capture and analysis failed:', error)
      return null
    }
  }

  async captureAndAnalyzeMultiple(
    symbols: string[], 
    timeframes: string[], 
    credentials: TradingViewCredentials
  ): Promise<Array<{ symbol: string; timeframe: string; analysis: AnalysisResult | null }>> {
    const results: Array<{ symbol: string; timeframe: string; analysis: AnalysisResult | null }> = []

    for (const symbol of symbols) {
      for (const timeframe of timeframes) {
        try {
          console.log(`Processing ${symbol} ${timeframe}...`)
          const analysis = await this.captureAndAnalyze(symbol, timeframe, credentials)
          
          results.push({
            symbol,
            timeframe,
            analysis
          })

          // Small delay between captures to avoid overwhelming the system
          await new Promise(resolve => setTimeout(resolve, 2000))

        } catch (error) {
          console.error(`Failed to process ${symbol} ${timeframe}:`, error)
          results.push({
            symbol,
            timeframe,
            analysis: null
          })
        }
      }
    }

    return results
  }

  async captureAndAnalyzeWithConfig(config: ScreenshotConfig): Promise<{
    screenshots: string[]
    analysis: AnalysisResult | null
  }> {
    try {
      console.log(`Starting automated capture with config for ${config.symbol} ${config.timeframe}`)
      
      // Capture screenshots using enhanced service
      const screenshots = await enhancedScreenshotService.captureWithLogin(config)
      
      if (screenshots.length === 0) {
        throw new Error('No screenshots captured')
      }

      console.log(`${screenshots.length} screenshot(s) captured`)
      
      let analysis: AnalysisResult | null = null

      if (screenshots.length === 1) {
        // Single screenshot analysis
        analysis = await this.analyzeScreenshot(screenshots[0])
      } else {
        // Multiple screenshots analysis
        analysis = await this.analyzeMultipleScreenshots(screenshots)
      }

      if (!analysis) {
        throw new Error('Failed to analyze screenshots')
      }

      console.log(`Analysis completed for ${config.symbol} ${config.timeframe}`)
      
      return {
        screenshots,
        analysis
      }

    } catch (error) {
      console.error('Automated capture and analysis with config failed:', error)
      return {
        screenshots: [],
        analysis: null
      }
    }
  }
}

export const aiAnalysisService = new AIAnalysisService()