# source: https://raw.githubusercontent.com/Steffan005/UNITY_PRIOR_ART_LIBRARY/e2bc2aeac260e13f8970b5671c83c335bafa700b/03_EVOLUTION_OF_SELF/LangGraphAIOrchestrator.py
"""
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 Github_Steffan005_UNITY_PRIOR_ART_LIBRARY__LangGraphAIOrchestrator__20260119_012014(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