// src/services/market/analysis/trendAnalyzer.ts
import { EventEmitter } from 'events';
var SignalType;
(function (SignalType) {
    SignalType["PRICE_ACTION"] = "price_action";
    SignalType["VOLUME"] = "volume";
    SignalType["MOMENTUM"] = "momentum";
    SignalType["SENTIMENT"] = "sentiment";
    SignalType["CORRELATION"] = "correlation";
})(SignalType || (SignalType = {}));
export class TrendAnalyzer extends EventEmitter {
    aiService;
    activePatterns;
    CONFIDENCE_THRESHOLD = 0.7;
    ANALYSIS_INTERVAL = 60000; // 1 minute
    patternDefinitions;
    constructor(aiService) {
        super();
        this.aiService = aiService;
        this.activePatterns = new Map();
        this.patternDefinitions = this.initializePatternDefinitions();
        this.startAnalysis();
    }
    initializePatternDefinitions() {
        return {
            doubleBottom: {
                minDuration: 7 * 24 * 60 * 60 * 1000, // 7 days
                requirements: [
                    { type: SignalType.PRICE_ACTION, strength: 0.8 },
                    { type: SignalType.VOLUME, strength: 0.7 }
                ]
            },
            bullflag: {
                minDuration: 24 * 60 * 60 * 1000, // 1 day
                requirements: [
                    { type: SignalType.PRICE_ACTION, strength: 0.7 },
                    { type: SignalType.MOMENTUM, strength: 0.6 }
                ]
            },
            breakout: {
                minDuration: 4 * 60 * 60 * 1000, // 4 hours
                requirements: [
                    { type: SignalType.PRICE_ACTION, strength: 0.9 },
                    { type: SignalType.VOLUME, strength: 0.8 },
                    { type: SignalType.MOMENTUM, strength: 0.7 }
                ]
            }
        };
    }
    startAnalysis() {
        setInterval(() => {
            this.analyzeActivePatterns();
        }, this.ANALYSIS_INTERVAL);
    }
    analyzeActivePatterns() {
        for (const pattern of this.activePatterns.values()) {
            try {
                // Check if pattern is still valid
                if (!this.validatePattern(pattern)) {
                    this.activePatterns.delete(pattern.id);
                    this.emit('patternExpired', pattern);
                    continue;
                }
                // Update pattern confidence and prediction
                this.updatePatternPrediction(pattern);
                // Emit events for significant changes
                if (pattern.confidence > 0.9) {
                    this.emit('highConfidencePattern', pattern);
                }
            }
            catch (error) {
                console.error('Error analyzing pattern:', error);
            }
        }
    }
    async analyzeTrend(prices, additionalData = {}) {
        try {
            const signals = await this.generateSignals(prices, additionalData);
            const patterns = await this.identifyPatterns(signals);
            // Filter and validate patterns
            const validPatterns = patterns.filter(pattern => this.validatePattern(pattern));
            // Update active patterns
            validPatterns.forEach(pattern => {
                this.activePatterns.set(pattern.id, pattern);
            });
            return validPatterns;
        }
        catch (error) {
            console.error('Error analyzing trend:', error);
            throw error;
        }
    }
    async generateSignals(prices, additionalData) {
        const signals = [];
        // Price action signals
        signals.push(...this.generatePriceActionSignals(prices));
        // Volume signals
        signals.push(...this.generateVolumeSignals(prices));
        // Momentum signals
        signals.push(...this.generateMomentumSignals(prices));
        // Sentiment signals if available
        if (additionalData.sentiment) {
            signals.push(...this.generateSentimentSignals(additionalData.sentiment));
        }
        // Correlation signals
        if (additionalData.marketData) {
            signals.push(...this.generateCorrelationSignals(prices, additionalData.marketData));
        }
        return signals;
    }
    async identifyPatterns(signals) {
        // Implementation for identifying patterns from signals
        return [];
    }
    validatePattern(pattern) {
        // Implementation for validating a pattern
        return pattern.confidence >= this.CONFIDENCE_THRESHOLD;
    }
    generatePriceActionSignals(prices) {
        const signals = [];
        if (prices.length < 2)
            return signals;
        // Simple Moving Average signals
        const smaSignal = this.calculateSMASignal(prices);
        if (smaSignal)
            signals.push(smaSignal);
        // Support/Resistance signals
        const supportResistanceSignal = this.identifySupportResistance(prices);
        if (supportResistanceSignal)
            signals.push(supportResistanceSignal);
        // Price pattern signals
        const patternSignal = this.identifyPricePatterns(prices);
        if (patternSignal)
            signals.push(patternSignal);
        return signals;
    }
    generateMomentumSignals(prices) {
        // Implementation for generating momentum signals
        return [];
    }
    generateSentimentSignals(sentimentData) {
        // Implementation for generating sentiment signals
        return [];
    }
    generateCorrelationSignals(prices, marketData) {
        // Implementation for generating correlation signals
        return [];
    }
    calculateSMASignal(prices) {
        if (prices.length < 50)
            return null;
        // Calculate 20 and 50 period SMAs
        const sma20 = this.calculateSMA(prices.slice(-20));
        const sma50 = this.calculateSMA(prices.slice(-50));
        if (!sma20 || !sma50)
            return null;
        // Generate signal based on SMA crossover
        const crossoverStrength = this.calculateCrossoverStrength(sma20, sma50, prices[prices.length - 1].price);
        if (Math.abs(crossoverStrength) < 0.1)
            return null;
        return {
            type: SignalType.PRICE_ACTION,
            strength: Math.abs(crossoverStrength),
            timeframe: 20 * 5 * 60 * 1000, // 20 periods * 5 minutes
            metadata: {
                sma20,
                sma50,
                crossover: crossoverStrength > 0 ? 'bullish' : 'bearish'
            }
        };
    }
    calculateSMA(prices) {
        if (prices.length === 0)
            return null;
        return prices.reduce((sum, price) => sum + price.price, 0) / prices.length;
    }
    calculateCrossoverStrength(sma20, sma50, currentPrice) {
        const difference = (sma20 - sma50) / sma50;
        const pricePosition = (currentPrice - Math.min(sma20, sma50)) / Math.abs(sma20 - sma50);
        return difference * pricePosition;
    }
    identifySupportResistance(prices) {
        if (prices.length < 100)
            return null;
        const levels = this.findSupportResistanceLevels(prices);
        if (levels.length === 0)
            return null;
        const currentPrice = prices[prices.length - 1].price;
        const nearestLevel = this.findNearestLevel(currentPrice, levels);
        const proximity = this.calculateLevelProximity(currentPrice, nearestLevel);
        if (proximity < 0.1)
            return null;
        return {
            type: SignalType.PRICE_ACTION,
            strength: proximity,
            timeframe: 100 * 5 * 60 * 1000, // 100 periods * 5 minutes
            metadata: {
                level: nearestLevel,
                type: currentPrice > nearestLevel ? 'support' : 'resistance',
                levels
            }
        };
    }
    findSupportResistanceLevels(prices) {
        const levels = [];
        const pricePoints = prices.map(p => p.price);
        // Find local maxima and minima
        for (let i = 1; i < pricePoints.length - 1; i++) {
            if (this.isLocalExtremum(pricePoints, i)) {
                levels.push(pricePoints[i]);
            }
        }
        // Cluster nearby levels
        return this.clusterLevels(levels, 0.02); // 2% threshold
    }
    isLocalExtremum(prices, index) {
        const isMax = prices[index] > prices[index - 1] && prices[index] > prices[index + 1];
        const isMin = prices[index] < prices[index - 1] && prices[index] < prices[index + 1];
        return isMax || isMin;
    }
    clusterLevels(levels, threshold) {
        const clusters = [];
        levels.forEach(level => {
            let added = false;
            for (const cluster of clusters) {
                if (Math.abs(cluster[0] - level) / cluster[0] <= threshold) {
                    cluster.push(level);
                    added = true;
                    break;
                }
            }
            if (!added) {
                clusters.push([level]);
            }
        });
        return clusters.map(cluster => cluster.reduce((sum, val) => sum + val, 0) / cluster.length);
    }
    findNearestLevel(price, levels) {
        return levels.reduce((nearest, level) => Math.abs(level - price) < Math.abs(nearest - price) ? level : nearest);
    }
    calculateLevelProximity(price, level) {
        return 1 - Math.min(Math.abs(price - level) / price, 1);
    }
    identifyPricePatterns(prices) {
        // Check for various patterns
        const patterns = [
            this.checkDoubleBottom(prices),
            this.checkHeadAndShoulders(prices),
            this.checkBullFlag(prices)
        ];
        // Return the strongest pattern found
        const validPatterns = patterns.filter(p => p !== null);
        if (validPatterns.length === 0)
            return null;
        return validPatterns.reduce((strongest, current) => current.strength > strongest.strength ? current : strongest);
    }
    checkDoubleBottom(prices) {
        if (prices.length < 50)
            return null;
        // Implementation for double bottom pattern recognition
        // Should look for two similar lows with a peak in between
        return null;
    }
    checkHeadAndShoulders(prices) {
        if (prices.length < 60)
            return null;
        // Implementation for head and shoulders pattern recognition
        // Should look for three peaks with the middle one being highest
        return null;
    }
    checkBullFlag(prices) {
        if (prices.length < 30)
            return null;
        // Implementation for bull flag pattern recognition
        // Should look for strong uptrend followed by consolidation
        return null;
    }
    generateVolumeSignals(prices) {
        const signals = [];
        if (prices.length < 20)
            return signals;
        // Volume trend analysis
        const volumeTrend = this.analyzeVolumeTrend(prices);
        if (volumeTrend)
            signals.push(volumeTrend);
        // Volume breakout detection
        const volumeBreakout = this.detectVolumeBreakout(prices);
        if (volumeBreakout)
            signals.push(volumeBreakout);
        // Volume/Price divergence
        const volumeDivergence = this.detectVolumeDivergence(prices);
        if (volumeDivergence)
            signals.push(volumeDivergence);
        return signals;
    }
    analyzeVolumeTrend(prices) {
        const recentVolumes = prices.slice(-20).map(p => p.volume);
        const averageVolume = recentVolumes.reduce((sum, vol) => sum + vol, 0) / recentVolumes.length;
        const currentVolume = prices[prices.length - 1].volume;
        if (Math.abs(currentVolume - averageVolume) / averageVolume < 0.2)
            return null;
        return {
            type: SignalType.VOLUME,
            strength: Math.min(Math.abs(currentVolume - averageVolume) / averageVolume, 1),
            timeframe: 20 * 5 * 60 * 1000,
            metadata: {
                averageVolume,
                currentVolume,
                trend: currentVolume > averageVolume ? 'increasing' : 'decreasing'
            }
        };
    }
    detectVolumeBreakout(prices) {
        // Implementation for volume breakout detection
        return null;
    }
    detectVolumeDivergence(prices) {
        // Implementation for volume/price divergence detection
        return null;
    }
    async updatePatternPrediction(pattern) {
        const signals = pattern.signals;
        if (signals.length === 0)
            return;
        // Calculate signal strength trends
        const strengthTrend = this.calculateStrengthTrend(signals);
        // Update prediction based on signal trends
        pattern.prediction = {
            direction: this.determineDirection(strengthTrend),
            magnitude: this.calculateMagnitude(signals),
            timeframe: this.estimateTimeframe(pattern),
            confidence: this.calculateConfidence(signals)
        };
    }
    calculateStrengthTrend(signals) {
        const recentSignals = signals.slice(-5); // Look at last 5 signals
        return recentSignals.reduce((acc, signal) => acc + signal.strength, 0) / recentSignals.length;
    }
    determineDirection(strengthTrend) {
        if (strengthTrend > 0.6)
            return 'up';
        if (strengthTrend < 0.4)
            return 'down';
        return 'sideways';
    }
    calculateMagnitude(signals) {
        return signals.reduce((acc, signal) => {
            const weight = this.getSignalTypeWeight(signal.type);
            return acc + (signal.strength * weight);
        }, 0) / signals.length;
    }
    getSignalTypeWeight(type) {
        const weights = {
            [SignalType.PRICE_ACTION]: 0.3,
            [SignalType.VOLUME]: 0.2,
            [SignalType.MOMENTUM]: 0.2,
            [SignalType.SENTIMENT]: 0.15,
            [SignalType.CORRELATION]: 0.15
        };
        return weights[type];
    }
    estimateTimeframe(pattern) {
        // Estimate based on pattern duration and signal timeframes
        return Math.max(pattern.duration, ...pattern.signals.map(s => s.timeframe));
    }
    calculateConfidence(signals) {
        if (signals.length === 0)
            return 0;
        const totalStrength = signals.reduce((sum, signal) => sum + signal.strength, 0);
        return totalStrength / signals.length;
    }
}
