# source: https://raw.githubusercontent.com/davidoggoo/Fund/9ae0b21f84e9950b17ee9e8f2d0a27eb50f2f13d/strategies/RTAIStrategy.py
"""
Enhanced RTAI Strategy - Microstructure Mean-Reversion
=====================================================

Advanced 1-minute mean-reversion strategy implementing the precise logic
from the context file:

ENTRY LOGIC:
- |OFI_z| > 2.25 AND sign(OFI_z) != sign(MPD_z) AND |MPD_z| > 1.5
- Risk Filters: TOBI ∈ [0.25,0.75], WallRatio < 0.25, VPIN < p98_threshold

EXIT LOGIC:  
- MPD_z crosses 0 OR Kyle λ spikes 5× OR LPI > 1.5

POSITION SIZING:
- S = tanh(0.6 × |OFI_z| × |MPD_z|) × sign(-OFI_z)

This strategy specifically fades short-term imbalances when microstructure 
shows price divergence from fair value.
"""

from freqtrade.strategy import IStrategy, DecimalParameter, IntParameter
from freqtrade.persistence import Trade
from typing import Dict, List, Optional, Tuple
import pandas as pd
import numpy as np
import talib.abstract as ta
import logging

# Import our enhanced indicators
try:
    from lib.rtai_indicators import add_all_rtai_indicators, joint_signal_score
except ImportError:
    try:
        from strategies.lib.rtai_indicators import add_all_rtai_indicators, joint_signal_score
    except ImportError:
        # Fallback to old location for backward compatibility
        from user_data.strategies.lib.rtai_indicators import add_all_rtai_indicators, joint_signal_score

logger = logging.getLogger(__name__)

class Github_davidoggoo_Fund__RTAIStrategy__20250809_225020(IStrategy):
    """
    RTAI Strategy for precise 1-minute microstructure mean-reversion.
    
    Implements the exact logic specified for optimal mean-reversion trading 
    based on order flow imbalance and micro-price divergence.
    """
    
    # === STRATEGY METADATA ===
    INTERFACE_VERSION = 3
    can_short: bool = True  # Enable short selling for mean-reversion
    
    # === TIMEFRAME & SETUP ===
    timeframe = '1m'  # Critical: Strategy designed for 1-minute bars
    startup_candle_count: int = 200  # Need history for robust Z-scores
    
    # === POSITION MANAGEMENT ===
    max_open_trades = 2  # Conservative for mean-reversion
    process_only_new_candles = True
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = True
    
    # === DISABLE BUILT-IN RISK MANAGEMENT ===
    # We use custom logic instead
    minimal_roi = {"0": 10}  # High value to effectively disable
    stoploss = -0.99  # Very low to disable (custom logic used)
    trailing_stop = False  # Custom adaptive stops used
    
    # === STRATEGY PARAMETERS (OPTIMIZABLE) ===
    
    # Entry thresholds (from context file)
    ofi_entry_threshold = DecimalParameter(2.0, 2.5, default=2.25, space='buy', optimize=True)
    mpd_entry_threshold = DecimalParameter(1.2, 1.8, default=1.5, space='buy', optimize=True)
    
    # Risk filter thresholds  
    tobi_min = DecimalParameter(0.20, 0.30, default=0.25, space='buy', optimize=True)
    tobi_max = DecimalParameter(0.70, 0.80, default=0.75, space='buy', optimize=True)
    wall_ratio_max = DecimalParameter(0.20, 0.30, default=0.25, space='buy', optimize=True)
    vpin_percentile = DecimalParameter(0.95, 0.99, default=0.98, space='buy', optimize=True)
    
    # Exit thresholds
    kyle_spike_multiplier = DecimalParameter(3.0, 7.0, default=5.0, space='sell', optimize=True)
    lpi_exit_threshold = DecimalParameter(1.2, 1.8, default=1.5, space='sell', optimize=True)
    
    # Position sizing
    max_position_percent = DecimalParameter(5.0, 10.0, default=8.0, space='buy', optimize=True)
    conviction_multiplier = DecimalParameter(0.5, 0.8, default=0.6, space='buy', optimize=True)
    
    # === PERFORMANCE TRACKING ===
    total_signals_generated = 0
    successful_exits = 0

    def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        """
        Populate all enhanced RTAI indicators for microstructure analysis.
        
        This implements the enhanced indicator suite from rtai_indicators_enhanced.py
        with all the optimizations specified in the context file.
        """
        
        pair = metadata.get('pair', 'UNKNOWN')
        logger.info(f"🔬 Calculating enhanced RTAI indicators for {pair} ({len(dataframe)} candles)")
        
        # === 1. MICROSTRUCTURE DATA INTEGRATION ===
        # Get tick-level data from RTAIDataProvider if available
        if hasattr(self, 'dp') and self.dp:
            try:
                # Enhanced data from our custom DataProvider
                enhanced_df = self.dp.get_pair_dataframe(pair=pair, timeframe=self.timeframe)
                if enhanced_df is not None and not enhanced_df.empty:
                    # Merge microstructure columns
                    microstructure_cols = [
                        'bid', 'ask', 'bid_size', 'ask_size',
                        'buy_volume', 'sell_volume', 'volume_imbalance',
                        'long_liquidations', 'short_liquidations', 'liquidation_imbalance',
                        'open_interest', 'funding_rate', 'index_price', 'mark_price'
                    ]
                    
                    for col in microstructure_cols:
                        if col in enhanced_df.columns:
                            dataframe[col] = enhanced_df[col]
                    
                    logger.info(f"✅ Merged {len([c for c in microstructure_cols if c in enhanced_df.columns])} microstructure columns")
                else:
                    logger.warning(f"⚠️ No enhanced microstructure data for {pair}")
            except Exception as e:
                logger.error(f"❌ Error getting microstructure data: {e}")
        
        # === 2. ENHANCED RTAI INDICATORS ===
        # This adds all the enhanced indicators from the context file
        dataframe = add_all_rtai_indicators(dataframe)
        
        # === 3. DYNAMIC THRESHOLDS ===
        # VPIN dynamic threshold (98th percentile, updated every 15 minutes)
        vpin_window = 15  # minutes
        dataframe['vpin_dynamic_threshold'] = dataframe['vpin'].rolling(
            window=vpin_window, min_periods=5
        ).quantile(self.vpin_percentile.value)
        
        # Kyle Lambda baseline for spike detection (5× median rule)
        kyle_window = 90  # 90 minutes for stable baseline
        dataframe['kyle_baseline'] = dataframe['kyle_lambda'].rolling(
            window=kyle_window, min_periods=10
        ).median()
        
        # === 4. MARKET REGIME DETECTION ===
        # ATR for volatility regime
        dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
        dataframe['atr_regime'] = dataframe['atr'].rolling(100).rank(pct=True)
        
        # Volume regime (for filtering low-volume periods)
        dataframe['volume_regime'] = (
            dataframe['volume'].rolling(30).mean() / 
            dataframe['volume'].rolling(120).mean()
        )
        
        # === 5. SIGNAL PREPARATION ===
        # Joint conviction score for position sizing (from context file)
        dataframe['conviction_score'] = joint_signal_score(
            dataframe['ofi_z'], dataframe['mpd_z']
        )
        
        # Risk-adjusted signal strength
        volatility_penalty = np.clip(dataframe['atr_regime'], 0.1, 1.0)
        dataframe['risk_adjusted_conviction'] = (
            dataframe['conviction_score'] / volatility_penalty
        )
        
        # === 6. SIGNAL VALIDATION ===
        # Check data quality for critical indicators
        critical_indicators = ['ofi_z', 'mpd_z', 'tobi', 'wall_ratio', 'vpin']
        missing_indicators = [ind for ind in critical_indicators if ind not in dataframe.columns]
        
        if missing_indicators:
            logger.warning(f"⚠️ Missing critical indicators: {missing_indicators}")
        else:
            logger.info(f"✅ All critical indicators present for {pair}")
        
        return dataframe

    def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        """
        Implement the precise mean-reversion entry logic from the context file.
        
        ENTRY CONDITION (ALL must be true):
        1. |OFI_z| > 2.25 (strong flow imbalance)
        2. sign(OFI_z) != sign(MPD_z) (divergence = mean-reversion opportunity)
        3. |MPD_z| > 1.5 (significant micro-price divergence)
        4. TOBI ∈ [0.25, 0.75] (balanced book, no extreme skew)
        5. WallRatio < 0.25 (no large walls blocking)
        6. VPIN < dynamic_p98 (flow not toxic)
        7. Minimum volume and volatility filters
        """
        
        # === CORE DIVERGENCE SIGNALS ===
        strong_ofi = np.abs(dataframe['ofi_z']) > self.ofi_entry_threshold.value
        strong_mpd = np.abs(dataframe['mpd_z']) > self.mpd_entry_threshold.value
        divergence = np.sign(dataframe['ofi_z']) != np.sign(dataframe['mpd_z'])
        
        # === RISK FILTERS ===
        balanced_book = (
            (dataframe['tobi'] >= self.tobi_min.value) & 
            (dataframe['tobi'] <= self.tobi_max.value)
        )
        no_walls = dataframe['wall_ratio'] < self.wall_ratio_max.value
        flow_not_toxic = dataframe['vpin'] < dataframe['vpin_dynamic_threshold']
        
        # === MARKET CONDITION FILTERS ===
        sufficient_volume = (
            dataframe['volume'] > dataframe['volume'].rolling(20).mean() * 0.5
        )
        normal_volatility = dataframe['atr_regime'] < 0.9  # Not in extreme vol regime
        
        # === COMBINED CONDITIONS ===
        base_conditions = (
            strong_ofi & strong_mpd & divergence & 
            balanced_book & no_walls & flow_not_toxic &
            sufficient_volume & normal_volatility
        )
        
        # === LONG ENTRIES: Fade sell pressure ===
        # OFI_z < -2.25 (sell pressure) AND MPD_z > 1.5 (price too low) → BUY
        long_specific = (
            (dataframe['ofi_z'] < -self.ofi_entry_threshold.value) &
            (dataframe['mpd_z'] > self.mpd_entry_threshold.value)
        )
        
        dataframe.loc[base_conditions & long_specific, 'enter_long'] = 1
        
        # === SHORT ENTRIES: Fade buy pressure ===
        # OFI_z > 2.25 (buy pressure) AND MPD_z < -1.5 (price too high) → SELL  
        short_specific = (
            (dataframe['ofi_z'] > self.ofi_entry_threshold.value) &
            (dataframe['mpd_z'] < -self.mpd_entry_threshold.value)
        )
        
        dataframe.loc[base_conditions & short_specific, 'enter_short'] = 1
        
        # === ENTRY TAGGING FOR ANALYSIS ===
        long_entries = dataframe['enter_long'] == 1
        short_entries = dataframe['enter_short'] == 1
        
        if long_entries.any():
            dataframe.loc[long_entries, 'enter_tag'] = (
                'FADE_SELL|OFI:' + dataframe.loc[long_entries, 'ofi_z'].round(2).astype(str) +
                '|MPD:' + dataframe.loc[long_entries, 'mpd_z'].round(2).astype(str) +
                '|CONV:' + dataframe.loc[long_entries, 'conviction_score'].round(3).astype(str)
            )
        
        if short_entries.any():
            dataframe.loc[short_entries, 'enter_tag'] = (
                'FADE_BUY|OFI:' + dataframe.loc[short_entries, 'ofi_z'].round(2).astype(str) +
                '|MPD:' + dataframe.loc[short_entries, 'mpd_z'].round(2).astype(str) +
                '|CONV:' + dataframe.loc[short_entries, 'conviction_score'].round(3).astype(str)
            )
        
        # === LOGGING ===
        total_long = long_entries.sum()
        total_short = short_entries.sum()
        if total_long > 0 or total_short > 0:
            self.total_signals_generated += total_long + total_short
            logger.info(f"🎯 Entry signals: {total_long} LONG, {total_short} SHORT (Total: {self.total_signals_generated})")
        
        return dataframe

    def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame:
        """
        Implement mean-reversion exit logic from the context file.
        
        EXIT CONDITIONS (ANY can trigger):
        1. MPD_z crosses zero (mean-reversion complete)
        2. Kyle λ spikes 5× median (high impact risk)  
        3. LPI_z > 1.5 (liquidation cascade risk)
        4. Extreme VPIN (>0.99, toxic flow)
        """
        
        # === PRIMARY EXIT: Mean-reversion complete ===
        # For LONG: MPD was positive (price too low), now crosses to negative/zero
        mpd_long_reversion = (
            (dataframe['mpd_z'].shift(1) > 0) & 
            (dataframe['mpd_z'] <= 0)
        )
        
        # For SHORT: MPD was negative (price too high), now crosses to positive/zero  
        mpd_short_reversion = (
            (dataframe['mpd_z'].shift(1) < 0) &
            (dataframe['mpd_z'] >= 0)
        )
        
        # === RISK MANAGEMENT EXITS ===
        # Kyle Lambda spike (market impact risk)
        kyle_spike = (
            dataframe['kyle_lambda'] > 
            dataframe['kyle_baseline'] * self.kyle_spike_multiplier.value
        )
        
        # Liquidation cascade risk
        liquidation_cascade = dataframe['lpi_z'] > self.lpi_exit_threshold.value
        
        # Extreme toxic flow
        toxic_flow = dataframe['vpin'] > 0.99
        
        # === COMBINED EXIT CONDITIONS ===
        risk_exits = kyle_spike | liquidation_cascade | toxic_flow
        
        # === APPLY EXITS ===
        dataframe.loc[mpd_long_reversion | risk_exits, 'exit_long'] = 1
        dataframe.loc[mpd_short_reversion | risk_exits, 'exit_short'] = 1
        
        # === EXIT TAGGING FOR ANALYSIS ===
        long_exits = dataframe['exit_long'] == 1
        short_exits = dataframe['exit_short'] == 1
        
        # Determine exit reason
        def get_exit_reason(row):
            if row['vpin'] > 0.99:
                return 'TOXIC_FLOW'
            elif row['lpi_z'] > self.lpi_exit_threshold.value:
                return 'LIQ_CASCADE'  
            elif row['kyle_lambda'] > row.get('kyle_baseline', 0) * self.kyle_spike_multiplier.value:
                return 'KYLE_SPIKE'
            else:
                return 'MPD_REVERSION'
        
        if long_exits.any():
            exit_reasons = dataframe.loc[long_exits].apply(get_exit_reason, axis=1)
            dataframe.loc[long_exits, 'exit_tag'] = exit_reasons
        
        if short_exits.any():
            exit_reasons = dataframe.loc[short_exits].apply(get_exit_reason, axis=1)
            dataframe.loc[short_exits, 'exit_tag'] = exit_reasons
        
        # === LOGGING ===
        total_long_exits = long_exits.sum()
        total_short_exits = short_exits.sum()
        if total_long_exits > 0 or total_short_exits > 0:
            self.successful_exits += total_long_exits + total_short_exits
            logger.info(f"🚪 Exit signals: {total_long_exits} LONG, {total_short_exits} SHORT")
        
        return dataframe

    def custom_stake_amount(self, pair: str, current_time, current_rate: float,
                          proposed_stake: float, min_stake: float, max_stake: float,
                          leverage: float, entry_tag: str, side: str, **kwargs) -> float:
        """
        Dynamic position sizing based on signal conviction from context file.
        
        Formula: pos_size = S × min(max_pos_pct, account_nav / ADRV)
        Where: S = tanh(0.6 × |OFI_z| × |MPD_z|) × sign(-OFI_z)
        """
        
        try:
            # Get current market data
            dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
            if dataframe.empty:
                return proposed_stake
            
            latest_row = dataframe.iloc[-1]
            
            # === CONVICTION CALCULATION ===
            ofi_z = latest_row.get('ofi_z', 0)
            mpd_z = latest_row.get('mpd_z', 0)
            
            # Signal strength from context file formula
            conviction_raw = self.conviction_multiplier.value * abs(ofi_z) * abs(mpd_z)
            signal_strength = np.tanh(conviction_raw)  # Normalized 0-1
            
            # === BASE POSITION SIZE ===
            total_capital = self.wallets.get_total_stake_amount()
            base_position_pct = self.max_position_percent.value / 100.0
            base_stake = total_capital * base_position_pct
            
            # === RISK ADJUSTMENTS ===
            # Reduce size in high volatility
            atr_regime = latest_row.get('atr_regime', 0.5)
            volatility_adjustment = 1.0 / (1.0 + atr_regime)
            
            # Reduce size if VPIN is elevated (risky regime)
            vpin = latest_row.get('vpin', 0.5)
            vpin_adjustment = 1.0 if vpin < 0.7 else 0.7
            
            # === FINAL CALCULATION ===
            adjusted_stake = (
                base_stake * 
                signal_strength * 
                volatility_adjustment * 
                vpin_adjustment
            )
            
            # Apply limits
            final_stake = max(min_stake, min(adjusted_stake, max_stake))
            
            logger.info(f"💰 Position sizing for {pair}: "
                       f"conviction={signal_strength:.3f}, "
                       f"vol_adj={volatility_adjustment:.3f}, "
                       f"stake=${final_stake:.2f}")
            
            return final_stake
            
        except Exception as e:
            logger.error(f"❌ Error in position sizing: {e}")
            return proposed_stake

    def custom_exit(self, pair: str, trade: Trade, current_time, current_rate: float,
                   current_profit: float, **kwargs) -> Optional[str]:
        """
        Custom exit logic with adaptive stop-loss from context file.
        
        Adaptive Stop: ATR(60s) × 2, tighten to 1× when Kyle > threshold
        """
        
        try:
            # Get current market data
            dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
            if dataframe.empty:
                return None
            
            latest_row = dataframe.iloc[-1]
            
            # === ADAPTIVE STOP LOSS ===
            atr = latest_row.get('atr', 0.001)
            kyle_lambda = latest_row.get('kyle_lambda', 0)
            kyle_baseline = latest_row.get('kyle_baseline', 1e-6)
            
            # Adaptive stop multiplier based on Kyle Lambda
            kyle_ratio = abs(kyle_lambda) / (abs(kyle_baseline) + 1e-9)
            if kyle_ratio > 3.0:
                stop_multiplier = 1.0  # Tight stop when impact risk high
            else:
                stop_multiplier = 2.0  # Normal stop
            
            # Calculate stop distance
            stop_distance_pct = (atr * stop_multiplier) / current_rate
            
            # Apply stop based on trade direction
            if trade.is_short:
                stop_price = trade.open_rate * (1 + stop_distance_pct)
                if current_rate >= stop_price:
                    return "adaptive_stop_loss"
            else:
                stop_price = trade.open_rate * (1 - stop_distance_pct)
                if current_rate <= stop_price:
                    return "adaptive_stop_loss"
            
            # === RISK MANAGEMENT EXITS ===
            vpin = latest_row.get('vpin', 0.5)
            lpi_z = latest_row.get('lpi_z', 0)
            
            # Exit on toxic flow
            if vpin > 0.99:
                return "toxic_flow_exit"
            
            # Exit on liquidation cascade
            if abs(lpi_z) > self.lpi_exit_threshold.value:
                return "liquidation_cascade_exit"
            
            # === PROFIT TAKING ===
            # Take profits when mean-reversion is significant
            mpd_z = latest_row.get('mpd_z', 0)
            min_profit_threshold = 0.003  # 0.3% minimum profit
            
            if current_profit > min_profit_threshold:
                if trade.is_short and mpd_z > 1.0:
                    return "mean_reversion_profit"
                elif not trade.is_short and mpd_z < -1.0:
                    return "mean_reversion_profit"
            
            return None
            
        except Exception as e:
            logger.error(f"❌ Error in custom exit: {e}")
            return None

    def leverage(self, pair: str, current_time, current_rate: float,
                 proposed_leverage: float, max_leverage: float, entry_tag: str,
                 side: str, **kwargs) -> float:
        """
        Conservative leverage for mean-reversion strategy.
        Higher leverage only for very high conviction signals.
        """
        base_leverage = 1.5  # Conservative base
        
        try:
            # Get signal strength from entry tag
            if entry_tag and 'CONV:' in entry_tag:
                conv_str = entry_tag.split('CONV:')[1].split('|')[0]
                conviction = float(conv_str)
                
                # Higher leverage for higher conviction
                if conviction > 0.8:
                    leverage_multiplier = 1.5  # Up to 2.25x
                elif conviction > 0.6:
                    leverage_multiplier = 1.2  # Up to 1.8x
                else:
                    leverage_multiplier = 1.0  # Base 1.5x
                
                final_leverage = base_leverage * leverage_multiplier
                return min(final_leverage, max_leverage)
        
        except Exception:
            pass
        
        return min(base_leverage, max_leverage)

    def confirm_trade_entry(self, pair: str, order_type: str, amount: float,
                          rate: float, time_in_force: str, current_time,
                          entry_tag: str, side: str, **kwargs) -> bool:
        """
        Final confirmation before entering trade.
        Last-minute validation of all conditions.
        """
        
        try:
            # Get fresh market data
            dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
            if dataframe.empty:
                return False
            
            latest_row = dataframe.iloc[-1]
            
            # === DOUBLE-CHECK CORE CONDITIONS ===
            ofi_z = latest_row.get('ofi_z', 0)
            mpd_z = latest_row.get('mpd_z', 0)
            
            # Ensure signals are still strong
            if (abs(ofi_z) < self.ofi_entry_threshold.value or 
                abs(mpd_z) < self.mpd_entry_threshold.value):
                logger.warning(f"⚠️ Signal weakened for {pair}, skipping entry")
                return False
            
            # Ensure divergence still exists
            if np.sign(ofi_z) == np.sign(mpd_z):
                logger.warning(f"⚠️ No divergence for {pair}, skipping entry")
                return False
            
            # === RISK CHECKS ===
            vpin = latest_row.get('vpin', 1.0)
            wall_ratio = latest_row.get('wall_ratio', 1.0)
            tobi = latest_row.get('tobi', 0.5)
            
            if vpin > self.vpin_percentile.value:
                logger.warning(f"⚠️ Toxic flow for {pair} (VPIN: {vpin:.3f}), skipping")
                return False
                
            if wall_ratio > self.wall_ratio_max.value:
                logger.warning(f"⚠️ Large walls detected for {pair}, skipping")
                return False
                
            if not (self.tobi_min.value <= tobi <= self.tobi_max.value):
                logger.warning(f"⚠️ Unbalanced book for {pair} (TOBI: {tobi:.3f}), skipping")
                return False
            
            # === FINAL CONFIRMATION ===
            logger.info(f"✅ Trade entry confirmed for {pair}: "
                       f"OFI_z={ofi_z:.2f}, MPD_z={mpd_z:.2f}, "
                       f"VPIN={vpin:.3f}, TOBI={tobi:.3f}")
            
            return True
            
        except Exception as e:
            logger.error(f"❌ Error in trade confirmation: {e}")
            return False

    def informative_pairs(self) -> List[Tuple[str, str]]:
        """
        Define informative pairs for additional market context.
        """
        return []  # Mean-reversion strategy focuses on single pair

    def bot_loop_start(self, **kwargs) -> None:
        """
        Called at the start of each bot loop.
        Use for logging and monitoring.
        """
        if self.total_signals_generated > 0:
            success_rate = (self.successful_exits / self.total_signals_generated) * 100
            logger.info(f"📊 RTAI Strategy Stats: "
                       f"Signals: {self.total_signals_generated}, "
                       f"Exits: {self.successful_exits}, "
                       f"Success Rate: {success_rate:.1f}%")

# Export the enhanced strategy
# Export only the main strategy class for Freqtrade discovery
# Removed __all__ to prevent duplicate detection issues