from freqtrade.strategy.interface import IStrategy
import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
import numpy as np

class AIProfitMaximizer(IStrategy):
    
    # Strategy interface version
    INTERFACE_VERSION = 3
    
    # Minimal ROI designed for the strategy
    minimal_roi = {
        "60": 0.01,
        "30": 0.02,
        "0": 0.04
    }
    
    # Optimal stoploss
    stoploss = -0.10
    
    # Optimal timeframe for the strategy
    timeframe = '5m'
    
    # Run "populate_indicators()" only for new candle
    process_only_new_candles = False
    
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30
    
    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Adds several technical analysis indicators to the given DataFrame
        """
        
        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        
        # MACD
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']
        
        # Bollinger Bands
        bollinger = ta.BBANDS(dataframe, timeperiod=20)
        dataframe['bb_lowerband'] = bollinger['lowerband']
        dataframe['bb_middleband'] = bollinger['middleband']
        dataframe['bb_upperband'] = bollinger['upperband']
        
        # EMA
        dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)
        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        
        # Volume
        dataframe['volume_sma'] = ta.SMA(dataframe['volume'], timeperiod=20)
        
        # AI Signal (simplified - replace with actual ML model)
        dataframe['ai_signal'] = self.calculate_ai_signal(dataframe)
        
        return dataframe
    
    def calculate_ai_signal(self, dataframe: DataFrame) -> pd.Series:
        """
        Simplified AI signal calculation
        In production, this would use a trained ML model
        """
        signals = pd.Series(0, index=dataframe.index)
        
        # Simple logic combining multiple indicators
        buy_conditions = (
            (dataframe['rsi'] < 30) &
            (dataframe['close'] < dataframe['bb_lowerband']) &
            (dataframe['macd'] > dataframe['macdsignal']) &
            (dataframe['volume'] > dataframe['volume_sma'])
        )
        
        sell_conditions = (
            (dataframe['rsi'] > 70) &
            (dataframe['close'] > dataframe['bb_upperband']) &
            (dataframe['macd'] < dataframe['macdsignal'])
        )
        
        signals.loc[buy_conditions] = 1
        signals.loc[sell_conditions] = -1
        
        return signals
    
    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the buy signal for the given dataframe
        """
        dataframe.loc[
            (
                (dataframe['ai_signal'] == 1) &
                (dataframe['rsi'] < 30) &
                (dataframe['volume'] > 0)
            ),
            'enter_long'] = 1
        
        return dataframe
    
    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the sell signal for the given dataframe
        """
        dataframe.loc[
            (
                (dataframe['ai_signal'] == -1) |
                (dataframe['rsi'] > 70)
            ),
            'exit_long'] = 1
        
        return dataframe