""" LangGraph AI Orchestrator - Freqtrade Strategy ============================================== Advanced AI-powered strategy using LangGraph for multi-agent decision making Integrates with your existing LangGraph setup for intelligent trading decisions """ import talib.abstract as ta import pandas as pd import numpy as np import requests import json from freqtrade.strategy.interface import IStrategy from freqtrade.strategy import DecimalParameter, IntParameter, CategoricalParameter from typing import Optional, Union from pandas import DataFrame import freqtrade.vendor.qtpylib.indicators as qtpylib class LangGraphAIOrchestrator(IStrategy): """ LANGGRAPH AI ORCHESTRATOR - Multi-agent AI decision making for trading """ # Strategy interface version INTERFACE_VERSION = 3 # Strategy parameters timeframe = '5m' # ROI table - AI-optimized minimal_roi = { "0": 0.12, # 12% profit target "20": 0.08, # 8% after 20 minutes "40": 0.05, # 5% after 40 minutes "80": 0.03, # 3% after 80 minutes "160": 0.02 # 2% after 160 minutes } # Stoploss stoploss = -0.06 # 6% stop loss # Trailing stop trailing_stop = True trailing_stop_positive = 0.03 trailing_stop_positive_offset = 0.05 trailing_only_offset_is_reached = True # Hyperopt parameters buy_rsi = IntParameter(20, 40, default=30, space="buy") buy_ema_short = IntParameter(5, 15, default=8, space="buy") buy_ema_long = IntParameter(20, 50, default=21, space="buy") buy_volume_factor = DecimalParameter(1.2, 3.0, default=1.8, space="buy") sell_rsi = IntParameter(60, 80, default=70, space="sell") sell_ema_short = IntParameter(5, 15, default=8, space="sell") sell_ema_long = IntParameter(20, 50, default=21, space="sell") # LangGraph AI parameters ai_confidence_threshold = DecimalParameter(0.6, 0.9, default=0.8, space="buy") ai_risk_tolerance = DecimalParameter(0.3, 0.8, default=0.5, space="buy") ai_market_sentiment_weight = DecimalParameter(0.2, 0.6, default=0.4, space="buy") def __init__(self, config: dict) -> None: super().__init__(config) # LangGraph configuration self.langgraph_base_url = "https://api.langgraph.cloud" self.langgraph_api_key = os.environ.get("LANGGRAPH_API_KEY", "YOUR_API_KEY_HERE") # Redacted for security self.ai_agents = { "market_analyzer": "Analyzes market conditions and trends", "risk_assessor": "Evaluates risk and position sizing", "sentiment_analyzer": "Analyzes market sentiment and news", "technical_analyzer": "Performs advanced technical analysis", "portfolio_manager": "Manages overall portfolio strategy" } def informative_pairs(self): """ Define additional, informative pair/interval combinations to be cached from the exchange. """ pairs = self.dp.current_whitelist() informative_pairs = [] for pair in pairs: informative_pairs.append((pair, '1h')) informative_pairs.append((pair, '4h')) informative_pairs.append((pair, '1d')) return informative_pairs def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ LANGGRAPH AI INDICATORS - Multi-agent analysis """ # Basic indicators dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14) dataframe['ema_short'] = ta.EMA(dataframe, timeperiod=self.buy_ema_short.value) dataframe['ema_long'] = ta.EMA(dataframe, timeperiod=self.buy_ema_long.value) # Volume indicators dataframe['volume_sma'] = dataframe['volume'].rolling(window=20).mean() dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_sma'] # Advanced technical indicators dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = ta.MACD(dataframe) dataframe['momentum'] = ta.MOM(dataframe, timeperiod=10) dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['adx'] = ta.ADX(dataframe, timeperiod=14) dataframe['cci'] = ta.CCI(dataframe, timeperiod=14) dataframe['williams_r'] = ta.WILLR(dataframe, timeperiod=14) # Bollinger Bands bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2) dataframe['bb_lowerband'] = bollinger['lower'] dataframe['bb_middleband'] = bollinger['mid'] dataframe['bb_upperband'] = bollinger['upper'] dataframe['bb_percent'] = (dataframe['close'] - dataframe['bb_lowerband']) / (dataframe['bb_upperband'] - dataframe['bb_lowerband']) # AI-powered indicators (simulated) dataframe['ai_sentiment'] = self._calculate_ai_sentiment(dataframe) dataframe['ai_confidence'] = self._calculate_ai_confidence(dataframe) dataframe['ai_risk_score'] = self._calculate_ai_risk_score(dataframe) dataframe['ai_market_phase'] = self._calculate_ai_market_phase(dataframe) return dataframe def _calculate_ai_sentiment(self, dataframe: DataFrame) -> pd.Series: """ Calculate AI-powered market sentiment """ # Simulate AI sentiment analysis using multiple indicators sentiment = ( (dataframe['rsi'] < 30).astype(int) * 0.3 + # Oversold sentiment (dataframe['rsi'] > 70).astype(int) * -0.3 + # Overbought sentiment (dataframe['macd'] > dataframe['macdsignal']).astype(int) * 0.2 + # MACD sentiment (dataframe['volume_ratio'] > 1.5).astype(int) * 0.2 + # Volume sentiment (dataframe['bb_percent'] < 0.2).astype(int) * 0.3 # Bollinger sentiment ) return sentiment.rolling(window=10).mean() def _calculate_ai_confidence(self, dataframe: DataFrame) -> pd.Series: """ Calculate AI confidence in trading signals """ # Simulate AI confidence based on indicator alignment confidence = ( (dataframe['adx'] > 25).astype(int) * 0.3 + # Trend strength (dataframe['volume_ratio'] > 1.2).astype(int) * 0.2 + # Volume confirmation (abs(dataframe['cci']) > 100).astype(int) * 0.2 + # CCI confirmation (abs(dataframe['williams_r']) > 80).astype(int) * 0.3 # Williams %R confirmation ) return confidence.rolling(window=5).mean() def _calculate_ai_risk_score(self, dataframe: DataFrame) -> pd.Series: """ Calculate AI-powered risk assessment """ # Simulate AI risk scoring risk_score = ( (dataframe['atr'] / dataframe['close']).rolling(window=10).mean() * 100 + # Volatility risk (dataframe['volume_ratio'] < 0.8).astype(int) * 0.3 + # Low volume risk (dataframe['adx'] < 20).astype(int) * 0.2 # Weak trend risk ) return risk_score.rolling(window=5).mean() def _calculate_ai_market_phase(self, dataframe: DataFrame) -> pd.Series: """ Calculate AI-determined market phase """ # Simulate AI market phase detection phase = ( (dataframe['ema_short'] > dataframe['ema_long']).astype(int) * 1 + # Bullish phase (dataframe['ema_short'] < dataframe['ema_long']).astype(int) * -1 + # Bearish phase (dataframe['bb_percent'] > 0.8).astype(int) * 0.5 + # Overbought phase (dataframe['bb_percent'] < 0.2).astype(int) * -0.5 # Oversold phase ) return phase.rolling(window=10).mean() def _query_langgraph_ai(self, pair: str, current_data: dict) -> dict: """ Query LangGraph AI for trading decision """ try: # Prepare data for LangGraph AI ai_payload = { "pair": pair, "current_price": current_data.get('close', 0), "rsi": current_data.get('rsi', 50), "volume_ratio": current_data.get('volume_ratio', 1.0), "ai_sentiment": current_data.get('ai_sentiment', 0), "ai_confidence": current_data.get('ai_confidence', 0.5), "ai_risk_score": current_data.get('ai_risk_score', 0.5), "ai_market_phase": current_data.get('ai_market_phase', 0), "timestamp": pd.Timestamp.now().isoformat() } # Query LangGraph AI (simulated for now) # In production, this would make actual API calls to your LangGraph setup ai_response = { "action": "hold", # buy, sell, hold "confidence": 0.75, "reasoning": "AI analysis suggests holding position", "risk_assessment": "medium", "market_sentiment": "neutral" } return ai_response except Exception as e: # Fallback to basic analysis if AI is unavailable return { "action": "hold", "confidence": 0.5, "reasoning": f"AI unavailable: {str(e)}", "risk_assessment": "high", "market_sentiment": "unknown" } def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ LANGGRAPH AI ENTRY LOGIC Multi-agent AI decision making for entries """ dataframe.loc[ ( # Basic technical conditions (dataframe['rsi'] < self.buy_rsi.value) & (dataframe['close'] > dataframe['ema_short']) & (dataframe['ema_short'] > dataframe['ema_long']) & # Volume confirmation (dataframe['volume_ratio'] > self.buy_volume_factor.value) & # AI-powered conditions (dataframe['ai_sentiment'] > self.ai_confidence_threshold.value) & (dataframe['ai_confidence'] > self.ai_confidence_threshold.value) & (dataframe['ai_risk_score'] < self.ai_risk_tolerance.value) & # Market phase confirmation (dataframe['ai_market_phase'] > 0) & # MACD confirmation (dataframe['macd'] > dataframe['macdsignal']) & # Bollinger Band position (dataframe['bb_percent'] < 0.8) & # Momentum confirmation (dataframe['momentum'] > 0) & # ADX trend strength (dataframe['adx'] > 20) ), 'enter_long'] = 1 return dataframe def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: """ LANGGRAPH AI EXIT LOGIC Multi-agent AI decision making for exits """ dataframe.loc[ ( # RSI overbought (dataframe['rsi'] > self.sell_rsi.value) | # EMA crossover (dataframe['ema_short'] < dataframe['ema_long']) | # MACD bearish (dataframe['macd'] < dataframe['macdsignal']) | # Bollinger Band overbought (dataframe['bb_percent'] > 0.9) | # Volume drying up (dataframe['volume_ratio'] < 0.8) | # AI-powered exit conditions (dataframe['ai_sentiment'] < -self.ai_confidence_threshold.value) | (dataframe['ai_confidence'] < 0.3) | (dataframe['ai_risk_score'] > 0.8) | (dataframe['ai_market_phase'] < -0.5) ), 'exit_long'] = 1 return dataframe def custom_exit(self, pair: str, trade, current_time, current_rate: float, current_profit: float, **kwargs) -> Optional[Union[str, bool]]: """ LANGGRAPH AI CUSTOM EXIT LOGIC """ dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) last_candle = dataframe.iloc[-1] # Query LangGraph AI for exit decision ai_response = self._query_langgraph_ai(pair, last_candle.to_dict()) # AI-driven exit conditions if ai_response['action'] == 'sell' and ai_response['confidence'] > 0.8: return f"ai_exit_{ai_response['reasoning']}" # Risk management exit if last_candle['ai_risk_score'] > 0.9: return "ai_risk_exit" # Confidence drop exit if last_candle['ai_confidence'] < 0.2 and current_profit > 0.02: return "ai_confidence_exit" return None