# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np
import pandas as pd
from pandas import DataFrame
from datetime import datetime, timedelta
from typing import Optional, Union
import logging

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IntParameter, IStrategy, merge_informative_pair)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from scipy import stats
from scipy.signal import find_peaks, find_peaks_cwt
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
import warnings
warnings.filterwarnings('ignore')

logger = logging.getLogger(__name__)


class ClaudePricePrediction(IStrategy):
    """
    Advanced Price Prediction Strategy with Support/Resistance Detection
    
    This strategy combines machine learning with sophisticated technical analysis:
    - Multi-timeframe price prediction using FreqAI
    - Dynamic support and resistance level detection
    - Volume profile analysis for key price zones
    - Advanced pattern recognition
    - Statistical analysis of price movements
    - Risk-adjusted position sizing
    - Profitable entry/exit timing optimization
    
    Features:
    - Machine learning price targets
    - Support/resistance breakout detection
    - Volume-weighted price levels
    - Fibonacci retracement levels
    - Market regime detection
    - Risk parity position sizing
    - Advanced exit strategies
    """

    INTERFACE_VERSION = 3

    # Optimized ROI for ML predictions
    minimal_roi = {
        "0": 0.15,    # 15% at any time (ML confidence high)
        "10": 0.08,   # 8% after 10 minutes
        "20": 0.05,   # 5% after 20 minutes
        "40": 0.03,   # 3% after 40 minutes
        "80": 0.015,  # 1.5% after 80 minutes
        "120": 0.01   # 1% after 2 hours
    }

    # Dynamic stoploss based on volatility
    stoploss = -0.06  # 6% base stoploss

    # Multi-timeframe approach
    timeframe = '15m'
    
    # Informative timeframes for comprehensive analysis
    inf_5m = '5m'
    inf_1h = '1h'
    inf_4h = '4h'
    inf_1d = '1d'

    # Process only new candles
    process_only_new_candles = True

    # Use ML-enhanced exit signals
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = True

    # Extended startup for comprehensive analysis
    startup_candle_count: int = 400

    # === Strategy Parameters - Hyperopt Optimized ===
    
    # === ML Prediction Parameters ===
    prediction_confidence_threshold = DecimalParameter(0.6, 0.9, default=0.75, space='buy', optimize=True)
    prediction_horizon = IntParameter(12, 48, default=24, space='buy', optimize=True)
    ml_signal_weight = DecimalParameter(0.3, 0.8, default=0.5, space='buy', optimize=True)
    
    # === Support/Resistance Parameters ===
    sr_lookback_period = IntParameter(50, 200, default=100, space='buy', optimize=True)
    sr_touch_tolerance = DecimalParameter(0.002, 0.01, default=0.005, space='buy', optimize=True)
    sr_strength_threshold = IntParameter(3, 8, default=5, space='buy', optimize=True)
    sr_break_threshold = DecimalParameter(0.003, 0.015, default=0.008, space='buy', optimize=True)
    
    # === Volume Profile Parameters ===
    volume_profile_periods = IntParameter(100, 300, default=200, space='buy', optimize=True)
    poc_deviation_threshold = DecimalParameter(0.01, 0.05, default=0.02, space='buy', optimize=True)
    
    # === Fibonacci Parameters ===
    fib_lookback = IntParameter(20, 100, default=50, space='buy', optimize=True)
    fib_deviation = DecimalParameter(0.005, 0.02, default=0.01, space='buy', optimize=True)
    
    # === Trend Parameters ===
    trend_ema_fast = IntParameter(12, 26, default=20, space='buy', optimize=True)
    trend_ema_slow = IntParameter(40, 80, default=60, space='buy', optimize=True)
    trend_strength_threshold = DecimalParameter(0.02, 0.08, default=0.04, space='buy', optimize=True)
    
    # === Risk Parameters ===
    volatility_threshold = DecimalParameter(0.01, 0.05, default=0.03, space='buy', optimize=True)
    risk_reward_ratio = DecimalParameter(1.5, 4.0, default=2.5, space='buy', optimize=True)
    
    # === Exit Parameters ===
    take_profit_multiplier = DecimalParameter(1.5, 3.5, default=2.0, space='sell', optimize=True)
    trailing_stop_activation = DecimalParameter(0.02, 0.08, default=0.04, space='sell', optimize=True)
    
    # Position sizing
    max_open_trades = 3
    position_adjustment_enable = True

    def informative_pairs(self):
        """
        Define informative pairs for comprehensive market analysis
        """
        pairs = self.dp.current_whitelist()
        informative_pairs = []
        
        # Add all timeframes for current pairs
        for pair in pairs:
            informative_pairs.extend([
                (pair, self.inf_5m),
                (pair, self.inf_1h),
                (pair, self.inf_4h),
                (pair, self.inf_1d)
            ])
        
        # Add major pairs for correlation analysis
        major_pairs = ['BTC/USDT:USDT', 'ETH/USDT:USDT']
        for pair in major_pairs:
            if pair not in pairs:
                informative_pairs.extend([
                    (pair, self.timeframe),
                    (pair, self.inf_1h),
                    (pair, self.inf_4h)
                ])
        
        return informative_pairs

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Populate all indicators including ML features and S/R levels
        """
        
        # === Basic Price Data ===
        dataframe['hl2'] = (dataframe['high'] + dataframe['low']) / 2
        dataframe['hlc3'] = (dataframe['high'] + dataframe['low'] + dataframe['close']) / 3
        dataframe['ohlc4'] = (dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4
        
        # === Trend Analysis ===
        dataframe['ema_fast'] = ta.EMA(dataframe, timeperiod=self.trend_ema_fast.value)
        dataframe['ema_slow'] = ta.EMA(dataframe, timeperiod=self.trend_ema_slow.value)
        dataframe['ema_trend'] = dataframe['ema_fast'] > dataframe['ema_slow']
        dataframe['trend_strength'] = abs((dataframe['ema_fast'] - dataframe['ema_slow']) / dataframe['close'])
        
        # === Volatility ===
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        dataframe['volatility'] = dataframe['atr'] / dataframe['close']
        
        # === Volume Analysis ===
        dataframe['volume_sma'] = ta.SMA(dataframe['volume'], timeperiod=20)
        dataframe['volume_ratio'] = dataframe['volume'] / dataframe['volume_sma']
        dataframe['vwap'] = qtpylib.vwap(dataframe)
        
        # === Price Action Patterns ===
        dataframe = self.calculate_price_patterns(dataframe)
        
        # === Support/Resistance Levels ===
        dataframe = self.calculate_support_resistance(dataframe)
        
        # === Volume Profile ===
        dataframe = self.calculate_volume_profile(dataframe)
        
        # === Fibonacci Levels ===
        dataframe = self.calculate_fibonacci_levels(dataframe)
        
        # === Market Regime Detection ===
        dataframe = self.detect_market_regime(dataframe)
        
        # === Advanced Technical Indicators ===
        dataframe = self.calculate_advanced_indicators(dataframe)
        
        # === Higher Timeframe Analysis ===
        dataframe = self.populate_higher_timeframe_data(dataframe, metadata)
        
        # === FreqAI Features ===
        dataframe = self.populate_freqai_features(dataframe)
        
        return dataframe

    def calculate_support_resistance(self, dataframe: DataFrame) -> DataFrame:
        """
        Advanced support and resistance level detection
        """
        
        # Calculate pivot points using different methods
        dataframe = self.calculate_pivot_points(dataframe)
        
        # Find significant highs and lows
        high_peaks = find_peaks(dataframe['high'].values, distance=10, prominence=dataframe['atr'].mean())[0]
        low_peaks = find_peaks(-dataframe['low'].values, distance=10, prominence=dataframe['atr'].mean())[0]
        
        # Initialize S/R columns
        dataframe['resistance_level'] = np.nan
        dataframe['support_level'] = np.nan
        dataframe['resistance_strength'] = 0
        dataframe['support_strength'] = 0
        dataframe['at_resistance'] = False
        dataframe['at_support'] = False
        dataframe['resistance_break'] = False
        dataframe['support_break'] = False
        
        lookback = min(len(dataframe), self.sr_lookback_period.value)
        
        for i in range(lookback, len(dataframe)):
            current_price = dataframe.iloc[i]['close']
            tolerance = self.sr_touch_tolerance.value
            
            # Get recent peaks for resistance
            recent_highs = []
            for peak in high_peaks:
                if i - lookback <= peak < i:
                    peak_price = dataframe.iloc[peak]['high']
                    # Check if this level was tested multiple times
                    touches = 0
                    for j in range(max(0, peak - 20), min(len(dataframe), peak + 20)):
                        if abs(dataframe.iloc[j]['high'] - peak_price) / peak_price <= tolerance:
                            touches += 1
                    
                    if touches >= self.sr_strength_threshold.value:
                        recent_highs.append((peak_price, touches))
            
            # Get recent lows for support
            recent_lows = []
            for peak in low_peaks:
                if i - lookback <= peak < i:
                    peak_price = dataframe.iloc[peak]['low']
                    # Check if this level was tested multiple times
                    touches = 0
                    for j in range(max(0, peak - 20), min(len(dataframe), peak + 20)):
                        if abs(dataframe.iloc[j]['low'] - peak_price) / peak_price <= tolerance:
                            touches += 1
                    
                    if touches >= self.sr_strength_threshold.value:
                        recent_lows.append((peak_price, touches))
            
            # Find closest resistance and support
            if recent_highs:
                resistance_candidates = [(level, strength) for level, strength in recent_highs if level > current_price]
                if resistance_candidates:
                    resistance_level, resistance_strength = min(resistance_candidates, key=lambda x: x[0])
                    dataframe.iloc[i, dataframe.columns.get_loc('resistance_level')] = resistance_level
                    dataframe.iloc[i, dataframe.columns.get_loc('resistance_strength')] = resistance_strength
                    
                    # Check if at resistance
                    if abs(current_price - resistance_level) / resistance_level <= tolerance:
                        dataframe.iloc[i, dataframe.columns.get_loc('at_resistance')] = True
                    
                    # Check for resistance break
                    if current_price > resistance_level * (1 + self.sr_break_threshold.value):
                        dataframe.iloc[i, dataframe.columns.get_loc('resistance_break')] = True
            
            if recent_lows:
                support_candidates = [(level, strength) for level, strength in recent_lows if level < current_price]
                if support_candidates:
                    support_level, support_strength = max(support_candidates, key=lambda x: x[0])
                    dataframe.iloc[i, dataframe.columns.get_loc('support_level')] = support_level
                    dataframe.iloc[i, dataframe.columns.get_loc('support_strength')] = support_strength
                    
                    # Check if at support
                    if abs(current_price - support_level) / support_level <= tolerance:
                        dataframe.iloc[i, dataframe.columns.get_loc('at_support')] = True
                    
                    # Check for support break
                    if current_price < support_level * (1 - self.sr_break_threshold.value):
                        dataframe.iloc[i, dataframe.columns.get_loc('support_break')] = True
        
        return dataframe

    def calculate_pivot_points(self, dataframe: DataFrame) -> DataFrame:
        """
        Calculate various pivot point levels
        """
        # Standard Pivot Points
        dataframe['pivot'] = (dataframe['high'].shift(1) + dataframe['low'].shift(1) + dataframe['close'].shift(1)) / 3
        dataframe['r1'] = 2 * dataframe['pivot'] - dataframe['low'].shift(1)
        dataframe['s1'] = 2 * dataframe['pivot'] - dataframe['high'].shift(1)
        dataframe['r2'] = dataframe['pivot'] + (dataframe['high'].shift(1) - dataframe['low'].shift(1))
        dataframe['s2'] = dataframe['pivot'] - (dataframe['high'].shift(1) - dataframe['low'].shift(1))
        
        # Fibonacci Pivot Points
        dataframe['fib_r1'] = dataframe['pivot'] + 0.382 * (dataframe['high'].shift(1) - dataframe['low'].shift(1))
        dataframe['fib_s1'] = dataframe['pivot'] - 0.382 * (dataframe['high'].shift(1) - dataframe['low'].shift(1))
        dataframe['fib_r2'] = dataframe['pivot'] + 0.618 * (dataframe['high'].shift(1) - dataframe['low'].shift(1))
        dataframe['fib_s2'] = dataframe['pivot'] - 0.618 * (dataframe['high'].shift(1) - dataframe['low'].shift(1))
        
        return dataframe

    def calculate_volume_profile(self, dataframe: DataFrame) -> DataFrame:
        """
        Calculate Volume Profile and Point of Control (POC)
        """
        lookback = min(len(dataframe), self.volume_profile_periods.value)
        
        dataframe['poc_level'] = np.nan
        dataframe['vah_level'] = np.nan  # Value Area High
        dataframe['val_level'] = np.nan  # Value Area Low
        dataframe['near_poc'] = False
        
        for i in range(lookback, len(dataframe)):
            # Get price and volume data for the lookback period
            price_data = dataframe.iloc[i-lookback:i]['hlc3'].values
            volume_data = dataframe.iloc[i-lookback:i]['volume'].values
            
            # Create price bins
            price_min, price_max = price_data.min(), price_data.max()
            price_bins = np.linspace(price_min, price_max, 50)
            
            # Calculate volume at each price level
            volume_profile = []
            for j in range(len(price_bins)-1):
                bin_volume = 0
                bin_center = (price_bins[j] + price_bins[j+1]) / 2
                
                for k, price in enumerate(price_data):
                    if price_bins[j] <= price <= price_bins[j+1]:
                        bin_volume += volume_data[k]
                
                volume_profile.append((bin_center, bin_volume))
            
            if volume_profile:
                # Find Point of Control (highest volume level)
                poc_level = max(volume_profile, key=lambda x: x[1])[0]
                dataframe.iloc[i, dataframe.columns.get_loc('poc_level')] = poc_level
                
                # Calculate Value Area (70% of total volume)
                total_volume = sum([x[1] for x in volume_profile])
                target_volume = total_volume * 0.7
                
                # Sort by volume and find value area
                sorted_profile = sorted(volume_profile, key=lambda x: x[1], reverse=True)
                cumulative_volume = 0
                value_area_prices = []
                
                for price, volume in sorted_profile:
                    cumulative_volume += volume
                    value_area_prices.append(price)
                    if cumulative_volume >= target_volume:
                        break
                
                if value_area_prices:
                    vah = max(value_area_prices)
                    val = min(value_area_prices)
                    dataframe.iloc[i, dataframe.columns.get_loc('vah_level')] = vah
                    dataframe.iloc[i, dataframe.columns.get_loc('val_level')] = val
                
                # Check if current price is near POC
                current_price = dataframe.iloc[i]['close']
                if abs(current_price - poc_level) / poc_level <= self.poc_deviation_threshold.value:
                    dataframe.iloc[i, dataframe.columns.get_loc('near_poc')] = True
        
        return dataframe

    def calculate_fibonacci_levels(self, dataframe: DataFrame) -> DataFrame:
        """
        Calculate dynamic Fibonacci retracement levels
        """
        lookback = min(len(dataframe), self.fib_lookback.value)
        
        dataframe['fib_0'] = np.nan
        dataframe['fib_236'] = np.nan
        dataframe['fib_382'] = np.nan
        dataframe['fib_500'] = np.nan
        dataframe['fib_618'] = np.nan
        dataframe['fib_786'] = np.nan
        dataframe['fib_1000'] = np.nan
        dataframe['near_fib_level'] = False
        
        for i in range(lookback, len(dataframe)):
            # Find swing high and low in lookback period
            high_data = dataframe.iloc[i-lookback:i]['high']
            low_data = dataframe.iloc[i-lookback:i]['low']
            
            swing_high = high_data.max()
            swing_low = low_data.min()
            
            # Calculate Fibonacci levels
            diff = swing_high - swing_low
            
            dataframe.iloc[i, dataframe.columns.get_loc('fib_0')] = swing_high
            dataframe.iloc[i, dataframe.columns.get_loc('fib_236')] = swing_high - 0.236 * diff
            dataframe.iloc[i, dataframe.columns.get_loc('fib_382')] = swing_high - 0.382 * diff
            dataframe.iloc[i, dataframe.columns.get_loc('fib_500')] = swing_high - 0.5 * diff
            dataframe.iloc[i, dataframe.columns.get_loc('fib_618')] = swing_high - 0.618 * diff
            dataframe.iloc[i, dataframe.columns.get_loc('fib_786')] = swing_high - 0.786 * diff
            dataframe.iloc[i, dataframe.columns.get_loc('fib_1000')] = swing_low
            
            # Check if price is near any Fibonacci level
            current_price = dataframe.iloc[i]['close']
            fib_levels = [
                dataframe.iloc[i]['fib_236'],
                dataframe.iloc[i]['fib_382'],
                dataframe.iloc[i]['fib_500'],
                dataframe.iloc[i]['fib_618'],
                dataframe.iloc[i]['fib_786']
            ]
            
            for fib_level in fib_levels:
                if not pd.isna(fib_level):
                    if abs(current_price - fib_level) / fib_level <= self.fib_deviation.value:
                        dataframe.iloc[i, dataframe.columns.get_loc('near_fib_level')] = True
                        break
        
        return dataframe

    def detect_market_regime(self, dataframe: DataFrame) -> DataFrame:
        """
        Detect market regime (trending, ranging, volatile)
        """
        # Calculate regime indicators
        dataframe['regime_adx'] = ta.ADX(dataframe, timeperiod=14)
        dataframe['regime_atr_ratio'] = dataframe['atr'] / dataframe['close']
        
        # Price efficiency (how much price moved vs total movement)
        dataframe['price_efficiency'] = abs(dataframe['close'] - dataframe['close'].shift(20)) / (
            dataframe['atr'].rolling(20).sum()
        )
        
        # Market regime classification
        dataframe['market_regime'] = 'ranging'  # Default
        
        for i in range(20, len(dataframe)):
            adx = dataframe.iloc[i]['regime_adx']
            efficiency = dataframe.iloc[i]['price_efficiency']
            volatility = dataframe.iloc[i]['regime_atr_ratio']
            
            if adx > 25 and efficiency > 0.3:
                dataframe.iloc[i, dataframe.columns.get_loc('market_regime')] = 'trending'
            elif volatility > self.volatility_threshold.value:
                dataframe.iloc[i, dataframe.columns.get_loc('market_regime')] = 'volatile'
            else:
                dataframe.iloc[i, dataframe.columns.get_loc('market_regime')] = 'ranging'
        
        return dataframe

    def calculate_price_patterns(self, dataframe: DataFrame) -> DataFrame:
        """
        Detect important price patterns
        """
        # Higher highs and higher lows (uptrend)
        dataframe['higher_high'] = (
            (dataframe['high'] > dataframe['high'].shift(1)) &
            (dataframe['high'].shift(1) > dataframe['high'].shift(2))
        )
        
        dataframe['higher_low'] = (
            (dataframe['low'] > dataframe['low'].shift(1)) &
            (dataframe['low'].shift(1) > dataframe['low'].shift(2))
        )
        
        # Lower highs and lower lows (downtrend)
        dataframe['lower_high'] = (
            (dataframe['high'] < dataframe['high'].shift(1)) &
            (dataframe['high'].shift(1) < dataframe['high'].shift(2))
        )
        
        dataframe['lower_low'] = (
            (dataframe['low'] < dataframe['low'].shift(1)) &
            (dataframe['low'].shift(1) < dataframe['low'].shift(2))
        )
        
        # Engulfing patterns
        dataframe['bullish_engulfing'] = (
            (dataframe['close'].shift(1) < dataframe['open'].shift(1)) &  # Previous red
            (dataframe['close'] > dataframe['open']) &  # Current green
            (dataframe['open'] < dataframe['close'].shift(1)) &  # Opens below previous close
            (dataframe['close'] > dataframe['open'].shift(1))  # Closes above previous open
        )
        
        dataframe['bearish_engulfing'] = (
            (dataframe['close'].shift(1) > dataframe['open'].shift(1)) &  # Previous green
            (dataframe['close'] < dataframe['open']) &  # Current red
            (dataframe['open'] > dataframe['close'].shift(1)) &  # Opens above previous close
            (dataframe['close'] < dataframe['open'].shift(1))  # Closes below previous open
        )
        
        return dataframe

    def calculate_advanced_indicators(self, dataframe: DataFrame) -> DataFrame:
        """
        Calculate advanced technical indicators
        """
        # RSI with multiple periods
        dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_21'] = ta.RSI(dataframe, timeperiod=21)
        
        # MACD
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']
        
        # Stochastic
        stoch = ta.STOCH(dataframe)
        dataframe['stoch_k'] = stoch['slowk']
        dataframe['stoch_d'] = stoch['slowd']
        
        # Williams %R
        dataframe['willr'] = ta.WILLR(dataframe, timeperiod=14)
        
        # Commodity Channel Index
        dataframe['cci'] = ta.CCI(dataframe, timeperiod=20)
        
        # Money Flow Index
        dataframe['mfi'] = ta.MFI(dataframe, timeperiod=14)
        
        # Bollinger Bands
        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
        dataframe['bb_lower'] = bollinger['lower']
        dataframe['bb_middle'] = bollinger['mid']
        dataframe['bb_upper'] = bollinger['upper']
        dataframe['bb_percent'] = (dataframe['close'] - dataframe['bb_lower']) / (dataframe['bb_upper'] - dataframe['bb_lower'])
        
        return dataframe

    def populate_higher_timeframe_data(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Add higher timeframe analysis
        """
        # 1H timeframe
        inf_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
        inf_1h['trend_1h'] = ta.EMA(inf_1h, timeperiod=20) < inf_1h['close']
        inf_1h['rsi_1h'] = ta.RSI(inf_1h, timeperiod=14)
        inf_1h['volume_1h'] = inf_1h['volume']
        
        dataframe = merge_informative_pair(dataframe, inf_1h, self.timeframe, self.inf_1h, ffill=True)
        
        # 4H timeframe
        inf_4h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_4h)
        inf_4h['trend_4h'] = ta.EMA(inf_4h, timeperiod=20) < inf_4h['close']
        inf_4h['rsi_4h'] = ta.RSI(inf_4h, timeperiod=14)
        inf_4h['atr_4h'] = ta.ATR(inf_4h, timeperiod=14)
        
        dataframe = merge_informative_pair(dataframe, inf_4h, self.timeframe, self.inf_4h, ffill=True)
        
        # Daily timeframe
        inf_1d = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1d)
        inf_1d['trend_1d'] = ta.EMA(inf_1d, timeperiod=20) < inf_1d['close']
        inf_1d['volatility_1d'] = ta.ATR(inf_1d, timeperiod=14) / inf_1d['close']
        
        dataframe = merge_informative_pair(dataframe, inf_1d, self.timeframe, self.inf_1d, ffill=True)
        
        return dataframe

    def populate_freqai_features(self, dataframe: DataFrame) -> DataFrame:
        """
        Create features specifically for FreqAI ML model
        """
        
        # === Price-based features ===
        for period in [5, 10, 20, 50]:
            dataframe[f'price_change_{period}'] = dataframe['close'].pct_change(period)
            dataframe[f'high_low_ratio_{period}'] = (
                dataframe['high'].rolling(period).max() / dataframe['low'].rolling(period).min() - 1
            )
        
        # === Volume-based features ===
        for period in [5, 10, 20]:
            dataframe[f'volume_sma_{period}'] = dataframe['volume'].rolling(period).mean()
            dataframe[f'volume_ratio_{period}'] = dataframe['volume'] / dataframe[f'volume_sma_{period}']
        
        # === Momentum features ===
        for period in [7, 14, 21]:
            dataframe[f'rsi_{period}'] = ta.RSI(dataframe, timeperiod=period)
            dataframe[f'roc_{period}'] = ta.ROC(dataframe, timeperiod=period)
        
        # === Volatility features ===
        for period in [10, 20, 30]:
            dataframe[f'atr_{period}'] = ta.ATR(dataframe, timeperiod=period)
            dataframe[f'volatility_{period}'] = dataframe[f'atr_{period}'] / dataframe['close']
        
        # === Pattern features ===
        dataframe['doji'] = (
            abs(dataframe['close'] - dataframe['open']) / 
            (dataframe['high'] - dataframe['low']) < 0.1
        ).astype(int)
        
        dataframe['hammer'] = (
            ((dataframe['high'] - dataframe['low']) > 3 * (dataframe['open'] - dataframe['close'])) &
            ((dataframe['close'] - dataframe['low']) / (0.001 + dataframe['high'] - dataframe['low']) > 0.6) &
            ((dataframe['open'] - dataframe['low']) / (0.001 + dataframe['high'] - dataframe['low']) > 0.6)
        ).astype(int)
        
        # === Support/Resistance features ===
        dataframe['distance_to_support'] = (
            (dataframe['close'] - dataframe['support_level']) / dataframe['close']
        ).fillna(0)
        
        dataframe['distance_to_resistance'] = (
            (dataframe['resistance_level'] - dataframe['close']) / dataframe['close']
        ).fillna(0)
        
        # === Fibonacci features ===
        dataframe['near_fib'] = dataframe['near_fib_level'].astype(int)
        
        # === Market regime features ===
        dataframe['regime_trending'] = (dataframe['market_regime'] == 'trending').astype(int)
        dataframe['regime_ranging'] = (dataframe['market_regime'] == 'ranging').astype(int)
        dataframe['regime_volatile'] = (dataframe['market_regime'] == 'volatile').astype(int)
        
        # === Target variable for ML ===
        # Predict future price movement
        future_periods = self.prediction_horizon.value
        dataframe['future_price'] = dataframe['close'].shift(-future_periods)
        dataframe['price_change_future'] = (
            (dataframe['future_price'] - dataframe['close']) / dataframe['close']
        )
        
        # Classification target (up/down/sideways)
        dataframe['future_direction'] = 0  # sideways
        dataframe.loc[dataframe['price_change_future'] > 0.02, 'future_direction'] = 1  # up
        dataframe.loc[dataframe['price_change_future'] < -0.02, 'future_direction'] = -1  # down
        
        # Regression target (actual price change)
        dataframe['target_price_change'] = dataframe['price_change_future']
        
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        ML-enhanced entry signals with support/resistance confirmation
        """
        
        # === ML Prediction Conditions ===
        ml_bullish_prediction = (
            (dataframe['do_predict'] == 1) &
            (dataframe['DI_values'] > self.prediction_confidence_threshold.value) &
            (dataframe['prediction'] > 0.02)  # Predicting >2% price increase
        )
        
        ml_bearish_prediction = (
            (dataframe['do_predict'] == 1) &
            (dataframe['DI_values'] > self.prediction_confidence_threshold.value) &
            (dataframe['prediction'] < -0.02)  # Predicting >2% price decrease
        )
        
        # === Technical Confirmation ===
        technical_bullish = (
            (dataframe['trend_1h'] == 1) &
            (dataframe['trend_4h'] == 1) &
            (dataframe['ema_trend'] == 1) &
            (dataframe['trend_strength'] > self.trend_strength_threshold.value) &
            (dataframe['rsi_14'] > 30) & (dataframe['rsi_14'] < 70) &
            (dataframe['volume_ratio'] > 1.2)
        )
        
        technical_bearish = (
            (dataframe['trend_1h'] == 0) &
            (dataframe['trend_4h'] == 0) &
            (dataframe['ema_trend'] == 0) &
            (dataframe['trend_strength'] > self.trend_strength_threshold.value) &
            (dataframe['rsi_14'] > 30) & (dataframe['rsi_14'] < 70) &
            (dataframe['volume_ratio'] > 1.2)
        )
        
        # === Support/Resistance Confirmation ===
        sr_bullish = (
            (dataframe['at_support']) |
            (dataframe['support_break'] == False) |
            (dataframe['resistance_break'] == True) |
            (dataframe['near_poc'] & (dataframe['close'] > dataframe['vwap']))
        )
        
        sr_bearish = (
            (dataframe['at_resistance']) |
            (dataframe['resistance_break'] == False) |
            (dataframe['support_break'] == True) |
            (dataframe['near_poc'] & (dataframe['close'] < dataframe['vwap']))
        )
        
        # === Pattern Confirmation ===
        pattern_bullish = (
            (dataframe['bullish_engulfing']) |
            (dataframe['hammer'] == 1) |
            (dataframe['higher_low'] & dataframe['higher_high'])
        )
        
        pattern_bearish = (
            (dataframe['bearish_engulfing']) |
            (dataframe['lower_high'] & dataframe['lower_low'])
        )
        
        # === Risk Management Filters ===
        risk_acceptable = (
            (dataframe['volatility'] < self.volatility_threshold.value) &
            (dataframe['market_regime'] != 'volatile')
        )
        
        # === Entry Conditions ===
        
        # Long entries
        dataframe.loc[
            (ml_bullish_prediction) &
            (technical_bullish) &
            (sr_bullish) &
            (pattern_bullish) &
            (risk_acceptable) &
            (dataframe['near_fib_level'] | dataframe['at_support']),
            'enter_long'] = 1

        # Short entries
        dataframe.loc[
            (ml_bearish_prediction) &
            (technical_bearish) &
            (sr_bearish) &
            (pattern_bearish) &
            (risk_acceptable) &
            (dataframe['near_fib_level'] | dataframe['at_resistance']),
            'enter_short'] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        ML-enhanced exit signals
        """
        
        # === ML-based exits ===
        ml_exit_long = (
            (dataframe['do_predict'] == 1) &
            (dataframe['DI_values'] > 0.6) &
            (dataframe['prediction'] < -0.01)  # Predicting price decline
        )
        
        ml_exit_short = (
            (dataframe['do_predict'] == 1) &
            (dataframe['DI_values'] > 0.6) &
            (dataframe['prediction'] > 0.01)  # Predicting price increase
        )
        
        # === Technical exits ===
        technical_exit_long = (
            (dataframe['rsi_14'] > 75) |
            (dataframe['at_resistance']) |
            (dataframe['bearish_engulfing']) |
            (dataframe['ema_trend'] == 0) |
            (dataframe['macd'] < dataframe['macdsignal'])
        )
        
        technical_exit_short = (
            (dataframe['rsi_14'] < 25) |
            (dataframe['at_support']) |
            (dataframe['bullish_engulfing']) |
            (dataframe['ema_trend'] == 1) |
            (dataframe['macd'] > dataframe['macdsignal'])
        )
        
        # === Support/Resistance exits ===
        sr_exit_long = (
            (dataframe['resistance_break'] == False) |
            (dataframe['support_break'] == True)
        )
        
        sr_exit_short = (
            (dataframe['support_break'] == False) |
            (dataframe['resistance_break'] == True)
        )
        
        # === Exit Conditions ===
        dataframe.loc[
            (ml_exit_long) |
            (technical_exit_long) |
            (sr_exit_long),
            'exit_long'] = 1

        dataframe.loc[
            (ml_exit_short) |
            (technical_exit_short) |
            (sr_exit_short),
            'exit_short'] = 1

        return dataframe

    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                 proposed_leverage: float, max_leverage: float, entry_tag: Optional[str],
                 side: str, **kwargs) -> float:
        """
        Dynamic leverage based on ML confidence and volatility
        """
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        
        # Base leverage on ML confidence
        base_leverage = 5.0
        
        if 'DI_values' in last_candle and not pd.isna(last_candle['DI_values']):
            confidence = last_candle['DI_values']
            # Higher confidence = higher leverage
            leverage_multiplier = 1 + (confidence - 0.5) * 2
            base_leverage *= leverage_multiplier
        
        # Adjust for volatility
        if 'volatility' in last_candle and not pd.isna(last_candle['volatility']):
            volatility = last_candle['volatility']
            if volatility > 0.03:  # High volatility
                base_leverage *= 0.7
            elif volatility < 0.01:  # Low volatility
                base_leverage *= 1.3
        
        return min(base_leverage, max_leverage, 10.0)  # Max 10x leverage

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                       current_rate: float, current_profit: float, **kwargs) -> float:
        """
        Dynamic stoploss based on support/resistance levels
        """
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        
        # Default stoploss
        stop_loss = self.stoploss
        
        # Adjust based on support/resistance
        if not trade.is_short:  # Long position
            if 'support_level' in last_candle and not pd.isna(last_candle['support_level']):
                support_distance = (current_rate - last_candle['support_level']) / current_rate
                # Set stop just below support
                stop_loss = max(self.stoploss, -support_distance * 1.1)
        else:  # Short position
            if 'resistance_level' in last_candle and not pd.isna(last_candle['resistance_level']):
                resistance_distance = (last_candle['resistance_level'] - current_rate) / current_rate
                # Set stop just above resistance
                stop_loss = max(self.stoploss, -resistance_distance * 1.1)
        
        # Trailing stop based on ML predictions
        if current_profit > self.trailing_stop_activation.value:
            if 'prediction' in last_candle and not pd.isna(last_candle['prediction']):
                # If ML predicts continued favorable movement, trail closer
                if (not trade.is_short and last_candle['prediction'] > 0) or \
                   (trade.is_short and last_candle['prediction'] < 0):
                    trail_distance = self.trailing_stop_activation.value * 0.5
                else:
                    trail_distance = self.trailing_stop_activation.value
                
                stop_loss = max(stop_loss, trail_distance - current_profit)
        
        return stop_loss

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float,
                           rate: float, time_in_force: str, current_time: datetime,
                           entry_tag: Optional[str], side: str, **kwargs) -> bool:
        """
        Final confirmation before entering trade
        """
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        
        # Ensure ML model is providing predictions
        if 'do_predict' not in last_candle or last_candle['do_predict'] != 1:
            return False
        
        # Ensure confidence is above threshold
        if 'DI_values' not in last_candle or last_candle['DI_values'] < self.prediction_confidence_threshold.value:
            return False
        
        # Check risk/reward ratio
        if side == "long":
            if 'resistance_level' in last_candle and not pd.isna(last_candle['resistance_level']):
                potential_profit = (last_candle['resistance_level'] - rate) / rate
                potential_loss = abs(self.stoploss)
                if potential_profit / potential_loss < self.risk_reward_ratio.value:
                    return False
        
        return True

    def custom_exit(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float,
                   current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
        """
        Custom exit logic based on ML predictions and S/R levels
        """
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        
        # ML-based exit
        if 'prediction' in last_candle and 'DI_values' in last_candle:
            if last_candle['DI_values'] > 0.7:  # High confidence prediction
                if not trade.is_short and last_candle['prediction'] < -0.015:  # Predicting 1.5% decline
                    return "ml_reversal_prediction"
                elif trade.is_short and last_candle['prediction'] > 0.015:  # Predicting 1.5% increase
                    return "ml_reversal_prediction"
        
        # Profit taking at resistance/support
        if current_profit > 0.03:  # 3% profit
            if not trade.is_short and last_candle.get('at_resistance', False):
                return "resistance_profit_taking"
            elif trade.is_short and last_candle.get('at_support', False):
                return "support_profit_taking"
        
        # Risk management exit
        if last_candle.get('market_regime') == 'volatile' and current_profit < -0.02:
            return "volatile_market_exit"
        
        return None