learning_engine.py

import os, json, asyncio
from datetime import datetime
from helpers import normalize_weights, calculate_market_volatility, should_update_weights

class LearningEngine:
    def __init__(self, r2_service, data_manager):
        self.r2_service = r2_service
        self.data_manager = data_manager
        self.weights = {}
        self.performance_history = []
        self.strategy_effectiveness = {}
        self.market_patterns = {}
        self.risk_profiles = {}
        self.initialized = False
        self.initialization_lock = asyncio.Lock()
        
    async def initialize(self):
        async with self.initialization_lock:
            if self.initialized: return
            print("Initializing learning system...")
            try:
                await self.load_weights_from_r2()
                await self.load_performance_history()
                self.initialized = True
                print("Learning system ready")
            except Exception as e:
                print(f"Weights loading failed: {e}")
                await self.initialize_default_weights()
                self.initialized = True

    async def initialize_enhanced(self):
        async with self.initialization_lock:
            if self.initialized: return
            print("Enhanced learning system initialization...")
            try:
                await self.load_weights_from_r2()
                await self.load_performance_history()
                await self.fix_weights_structure()
                if not self.performance_history:
                    print("Starting learning from scratch")
                    await self.initialize_default_weights()
                self.initialized = True
            except Exception as e:
                print(f"Enhanced initialization failed: {e}")
                await self.initialize_default_weights()
                self.initialized = True
    
    async def fix_weights_structure(self):
        try:
            key = "learning_engine_weights.json"
            response = self.r2_service.s3_client.get_object(Bucket="trading", Key=key)
            current_data = json.loads(response['Body'].read())
            
            if 'strategy_weights' in current_data and 'last_updated' not in current_data:
                fixed_data = {
                    "weights": current_data,
                    "last_updated": datetime.now().isoformat(),
                    "version": "2.0",
                    "performance_metrics": await self.calculate_performance_metrics()
                }
                data_json = json.dumps(fixed_data, indent=2, ensure_ascii=False).encode('utf-8')
                self.r2_service.s3_client.put_object(
                    Bucket="trading", Key=key, Body=data_json, ContentType="application/json"
                )
                print("Weights structure fixed")
        except Exception as e:
            print(f"Weights structure fix failed: {e}")
    
    async def initialize_default_weights(self):
        self.weights = {
            "strategy_weights": {
                "trend_following": 0.18, "mean_reversion": 0.15, "breakout_momentum": 0.22,
                "volume_spike": 0.12, "whale_tracking": 0.15, "pattern_recognition": 0.10,
                "hybrid_ai": 0.08
            },
            "technical_weights": {
                "rsi": 0.15, "macd": 0.18, "ema_cross": 0.12, "bollinger_bands": 0.10,
                "volume_analysis": 0.15, "support_resistance": 0.12, "market_sentiment": 0.18
            },
            "risk_parameters": {
                "max_position_size": 0.1, "max_daily_loss": 0.02, "stop_loss_base": 0.02,
                "risk_reward_ratio": 2.0, "volatility_adjustment": 1.0
            },
            "market_condition_weights": {
                "bull_market": {"trend_following": 0.25, "breakout_momentum": 0.20, "whale_tracking": 0.15},
                "bear_market": {"mean_reversion": 0.25, "pattern_recognition": 0.20, "hybrid_ai": 0.15},
                "sideways_market": {"mean_reversion": 0.30, "volume_spike": 0.20, "pattern_recognition": 0.15}
            }
        }
    
    async def load_weights_from_r2(self):
        try:
            key = "learning_engine_weights.json"
            response = self.r2_service.s3_client.get_object(Bucket="trading", Key=key)
            weights_data = json.loads(response['Body'].read())
            
            if isinstance(weights_data, dict):
                if 'weights' in weights_data:
                    self.weights = weights_data['weights']
                else:
                    self.weights = weights_data
                print(f"Weights loaded from R2")
            else:
                raise ValueError("Invalid weights structure")
        except Exception as e:
            print(f"Weights loading failed: {e}")
            await self.initialize_default_weights()
            await self.save_weights_to_r2()
    
    async def save_weights_to_r2(self):
        try:
            key = "learning_engine_weights.json"
            weights_data = {
                "weights": self.weights,
                "last_updated": datetime.now().isoformat(),
                "version": "2.0",
                "performance_metrics": await self.calculate_performance_metrics()
            }
            data_json = json.dumps(weights_data, indent=2, ensure_ascii=False).encode('utf-8')
            self.r2_service.s3_client.put_object(
                Bucket="trading", Key=key, Body=data_json, ContentType="application/json"
            )
            print("Weights saved to R2")
        except Exception as e:
            print(f"Weights saving failed: {e}")
    
    async def load_performance_history(self):
        try:
            key = "learning_performance_history.json"
            response = self.r2_service.s3_client.get_object(Bucket="trading", Key=key)
            history_data = json.loads(response['Body'].read())
            self.performance_history = history_data.get("history", [])
            print(f"Performance history loaded - {len(self.performance_history)} records")
        except Exception as e:
            print(f"Performance history loading failed: {e}")
            self.performance_history = []
    
    async def save_performance_history(self):
        try:
            key = "learning_performance_history.json"
            history_data = {
                "history": self.performance_history[-1000:],
                "last_updated": datetime.now().isoformat()
            }
            data_json = json.dumps(history_data, indent=2, ensure_ascii=False).encode('utf-8')
            self.r2_service.s3_client.put_object(
                Bucket="trading", Key=key, Body=data_json, ContentType="application/json"
            )
        except Exception as e:
            print(f"Performance history saving failed: {e}")
    
    async def analyze_trade_outcome(self, trade_data, outcome):
        if not self.initialized: await self.initialize()
        try:
            strategy = trade_data.get('strategy', 'unknown')
            if strategy == 'unknown':
                decision_data = trade_data.get('decision_data', {})
                strategy = decision_data.get('strategy', 'unknown')
            
            market_context = await self.get_current_market_conditions()
            
            analysis_entry = {
                "timestamp": datetime.now().isoformat(),
                "trade_data": trade_data,
                "outcome": outcome,
                "market_conditions": market_context,
                "strategy_used": strategy,
                "symbol": trade_data.get('symbol', 'unknown'),
                "pnl_usd": trade_data.get('pnl_usd', 0),
                "pnl_percent": trade_data.get('pnl_percent', 0)
            }
            
            self.performance_history.append(analysis_entry)
            await self.update_strategy_effectiveness(analysis_entry)
            await self.update_market_patterns(analysis_entry)
            
            if should_update_weights(len(self.performance_history)):
                await self.adapt_weights_based_on_performance()
                await self.save_weights_to_r2()
                await self.save_performance_history()
            
            print(f"Trade analyzed {trade_data.get('symbol')} - Strategy: {strategy} - Outcome: {outcome}")
        except Exception as e:
            print(f"Trade outcome analysis failed: {e}")
    
    async def update_strategy_effectiveness(self, analysis_entry):
        strategy = analysis_entry['strategy_used']
        outcome = analysis_entry['outcome']
        market_condition = analysis_entry['market_conditions']['current_trend']
        pnl_percent = analysis_entry.get('pnl_percent', 0)
        
        if strategy not in self.strategy_effectiveness:
            self.strategy_effectiveness[strategy] = {
                "total_trades": 0, "successful_trades": 0, "total_profit": 0,
                "total_pnl_percent": 0, "market_conditions": {}
            }
        
        self.strategy_effectiveness[strategy]["total_trades"] += 1
        self.strategy_effectiveness[strategy]["total_pnl_percent"] += pnl_percent
        
        is_success = outcome in ["SUCCESS", "CLOSED_BY_REANALYSIS", "CLOSED_BY_MONITOR"] and pnl_percent > 0
        if is_success: self.strategy_effectiveness[strategy]["successful_trades"] += 1
        
        if market_condition not in self.strategy_effectiveness[strategy]["market_conditions"]:
            self.strategy_effectiveness[strategy]["market_conditions"][market_condition] = {
                "trades": 0, "successes": 0, "total_pnl": 0
            }
        
        self.strategy_effectiveness[strategy]["market_conditions"][market_condition]["trades"] += 1
        self.strategy_effectiveness[strategy]["market_conditions"][market_condition]["total_pnl"] += pnl_percent
        if is_success: self.strategy_effectiveness[strategy]["market_conditions"][market_condition]["successes"] += 1
    
    async def update_market_patterns(self, analysis_entry):
        market_condition = analysis_entry['market_conditions']['current_trend']
        symbol = analysis_entry['symbol']
        outcome = analysis_entry['outcome']
        pnl_percent = analysis_entry.get('pnl_percent', 0)
        
        if market_condition not in self.market_patterns:
            self.market_patterns[market_condition] = {
                "total_trades": 0, "successful_trades": 0, "total_pnl_percent": 0,
                "best_performing_strategies": {}, "best_performing_symbols": {}
            }
        
        self.market_patterns[market_condition]["total_trades"] += 1
        self.market_patterns[market_condition]["total_pnl_percent"] += pnl_percent
        
        is_success = outcome in ["SUCCESS", "CLOSED_BY_REANALYSIS", "CLOSED_BY_MONITOR"] and pnl_percent > 0
        if is_success: self.market_patterns[market_condition]["successful_trades"] += 1
        
        strategy = analysis_entry['strategy_used']
        if strategy not in self.market_patterns[market_condition]["best_performing_strategies"]:
            self.market_patterns[market_condition]["best_performing_strategies"][strategy] = {
                "count": 0, "total_pnl": 0
            }
        
        self.market_patterns[market_condition]["best_performing_strategies"][strategy]["count"] += 1
        self.market_patterns[market_condition]["best_performing_strategies"][strategy]["total_pnl"] += pnl_percent
        
        if symbol not in self.market_patterns[market_condition]["best_performing_symbols"]:
            self.market_patterns[market_condition]["best_performing_symbols"][symbol] = {
                "count": 0, "total_pnl": 0
            }
        
        self.market_patterns[market_condition]["best_performing_symbols"][symbol]["count"] += 1
        self.market_patterns[market_condition]["best_performing_symbols"][symbol]["total_pnl"] += pnl_percent
    
    async def adapt_weights_based_on_performance(self):
        print("Updating weights based on performance...")
        try:
            if not self.strategy_effectiveness:
                print("Insufficient performance data, using gradual adjustment")
                await self.gradual_weights_adjustment()
                return

            total_performance = 0
            strategy_performance = {}
            
            for strategy, data in self.strategy_effectiveness.items():
                if data["total_trades"] > 0:
                    success_rate = data["successful_trades"] / data["total_trades"]
                    avg_pnl = data["total_pnl_percent"] / data["total_trades"]
                    composite_performance = (success_rate * 0.7) + (min(avg_pnl, 10) / 10 * 0.3)
                    strategy_performance[strategy] = composite_performance
                    total_performance += composite_performance
            
            if total_performance > 0 and strategy_performance:
                for strategy, performance in strategy_performance.items():
                    current_weight = self.weights["strategy_weights"].get(strategy, 0.1)
                    new_weight = current_weight * 0.7 + (performance * 0.3)
                    self.weights["strategy_weights"][strategy] = new_weight
                
                normalize_weights(self.weights["strategy_weights"])
                print("Weights updated based on real performance")
            else:
                await self.gradual_weights_adjustment()
        except Exception as e:
            print(f"Weights update failed: {e}")
            await self.gradual_weights_adjustment()

    async def gradual_weights_adjustment(self):
        print("Gradual weights adjustment...")
        if self.market_patterns:
            for market_condition, data in self.market_patterns.items():
                if data.get("total_trades", 0) > 0:
                    best_strategy = max(data["best_performing_strategies"].items(), 
                                      key=lambda x: x[1]["total_pnl"])[0] if data["best_performing_strategies"] else None
                    if best_strategy:
                        current_weight = self.weights["strategy_weights"].get(best_strategy, 0.1)
                        self.weights["strategy_weights"][best_strategy] = min(current_weight * 1.1, 0.3)
        
        normalize_weights(self.weights["strategy_weights"])
        print("Gradual weights adjustment completed")

    async def get_current_market_conditions(self):
        try:
            if not self.data_manager: raise ValueError("DataManager unavailable")
            market_context = await self.data_manager.get_market_context_async()
            if not market_context: raise ValueError("Market context fetch failed")
            return {
                "current_trend": market_context.get('market_trend', 'sideways_market'),
                "volatility": calculate_market_volatility(market_context),
                "market_sentiment": market_context.get('btc_sentiment', 'NEUTRAL'),
                "whale_activity": market_context.get('general_whale_activity', {}).get('sentiment', 'NEUTRAL'),
                "fear_greed_index": market_context.get('fear_and_greed_index', 50)
            }
        except Exception as e:
            print(f"Market conditions fetch failed: {e}")
            return {
                "current_trend": "sideways_market", "volatility": "medium",
                "market_sentiment": "neutral", "whale_activity": "low", "fear_greed_index": 50
            }
    
    async def calculate_performance_metrics(self):
        if not self.performance_history: return {"status": "No performance data yet"}
        recent_trades = self.performance_history[-50:]
        total_trades = len(recent_trades)
        successful_trades = sum(1 for trade in recent_trades 
                              if trade['outcome'] in ["SUCCESS", "CLOSED_BY_REANALYSIS", "CLOSED_BY_MONITOR"] and trade.get('pnl_percent', 0) > 0)
        success_rate = successful_trades / total_trades if total_trades > 0 else 0
        total_pnl = sum(trade.get('pnl_percent', 0) for trade in recent_trades)
        avg_pnl = total_pnl / total_trades if total_trades > 0 else 0
        
        strategy_performance = {}
        for strategy, data in self.strategy_effectiveness.items():
            if data["total_trades"] > 0:
                strategy_success_rate = data["successful_trades"] / data["total_trades"]
                strategy_avg_pnl = data["total_pnl_percent"] / data["total_trades"]
                strategy_performance[strategy] = {
                    "success_rate": strategy_success_rate, "avg_pnl_percent": strategy_avg_pnl,
                    "total_trades": data["total_trades"], "successful_trades": data["successful_trades"]
                }
        
        market_performance = {}
        for condition, data in self.market_patterns.items():
            if data["total_trades"] > 0:
                market_success_rate = data["successful_trades"] / data["total_trades"]
                market_avg_pnl = data["total_pnl_percent"] / data["total_trades"]
                market_performance[condition] = {
                    "success_rate": market_success_rate, "avg_pnl_percent": market_avg_pnl,
                    "total_trades": data["total_trades"]
                }
        
        return {
            "overall_success_rate": success_rate, "overall_avg_pnl_percent": avg_pnl,
            "total_analyzed_trades": len(self.performance_history), "recent_trades_analyzed": total_trades,
            "strategy_performance": strategy_performance, "market_performance": market_performance,
            "last_updated": datetime.now().isoformat()
        }
    
    async def get_optimized_strategy_weights(self, market_condition):
        try:
            if not self.initialized: return await self.get_default_strategy_weights()
            if (not self.weights or "strategy_weights" not in self.weights or not self.weights["strategy_weights"]):
                return await self.get_default_strategy_weights()
            base_weights = self.weights["strategy_weights"].copy()
            if not any(weight > 0 for weight in base_weights.values()):
                return await self.get_default_strategy_weights()
            print(f"Using learned weights: {base_weights}")
            return base_weights
        except Exception as e:
            print(f"Optimized weights calculation failed: {e}")
            return await self.get_default_strategy_weights()
    
    async def get_default_strategy_weights(self):
        return {
            "trend_following": 0.18, "mean_reversion": 0.15, "breakout_momentum": 0.22,
            "volume_spike": 0.12, "whale_tracking": 0.15, "pattern_recognition": 0.10,
            "hybrid_ai": 0.08
        }
    
    async def get_risk_parameters(self, symbol_volatility):
        if not self.weights or "risk_parameters" not in self.weights: await self.initialize_default_weights()
        risk_params = self.weights.get("risk_parameters", {}).copy()
        if symbol_volatility == "HIGH":
            risk_params["stop_loss_base"] *= 1.5
            risk_params["max_position_size"] *= 0.7
            risk_params["risk_reward_ratio"] = 1.5
        elif symbol_volatility == "LOW":
            risk_params["stop_loss_base"] *= 0.7
            risk_params["max_position_size"] *= 1.2
            risk_params["risk_reward_ratio"] = 2.5
        return risk_params
    
    async def suggest_improvements(self):
        improvements = []
        if not self.performance_history:
            improvements.append("Start collecting performance data from first trades")
            return improvements
        
        for strategy, data in self.strategy_effectiveness.items():
            if data["total_trades"] >= 3:
                success_rate = data["successful_trades"] / data["total_trades"]
                avg_pnl = data["total_pnl_percent"] / data["total_trades"]
                if success_rate < 0.3 and avg_pnl < 0:
                    improvements.append(f"Strategy {strategy} poor performance ({success_rate:.1%} success, {avg_pnl:+.1f}% average) - suggest reducing usage")
                elif success_rate > 0.6 and avg_pnl > 2:
                    improvements.append(f"Strategy {strategy} excellent performance ({success_rate:.1%} success, {avg_pnl:+.1f}% average) - suggest increasing usage")
                elif success_rate > 0.7:
                    improvements.append(f"Strategy {strategy} high success ({success_rate:.1%}) - focus on trade quality")
        
        for market_condition, data in self.market_patterns.items():
            if data["total_trades"] >= 5:
                success_rate = data["successful_trades"] / data["total_trades"]
                avg_pnl = data["total_pnl_percent"] / data["total_trades"]
                if success_rate < 0.4:
                    improvements.append(f"Poor performance in {market_condition} market ({success_rate:.1%} success) - needs strategy review")
                
                best_strategy = None
                best_performance = -100
                for strategy, stats in data["best_performing_strategies"].items():
                    if stats["count"] >= 2:
                        strategy_avg_pnl = stats["total_pnl"] / stats["count"]
                        if strategy_avg_pnl > best_performance:
                            best_performance = strategy_avg_pnl
                            best_strategy = strategy
                
                if best_strategy and best_performance > 1:
                    improvements.append(f"Best strategy in {market_condition}: {best_strategy} ({best_performance:+.1f}% average profit)")
        
        if not improvements: improvements.append("No suggested improvements currently - continue data collection")
        return improvements

    async def force_strategy_learning(self):
        print("Forcing strategy update from current data...")
        if not self.performance_history:
            print("No performance data to learn from")
            return
        for entry in self.performance_history:
            await self.update_strategy_effectiveness(entry)
            await self.update_market_patterns(entry)
        await self.adapt_weights_based_on_performance()
        await self.save_weights_to_r2()
        print("Strategy update forced successfully")

print("Enhanced self-learning system loaded - ready for continuous learning and adaptation")