import numpy as np
from AlgorithmImports import *

class AssetWeightCalculator:
    def __init__(self, algorithm: QCAlgorithm):
        
        self.algorithm = algorithm

        self.risk_free = self.algorithm.add_equity("BIL", Resolution.HOUR)
        
    def coarse_selection(self, coarse):
        """
        Selects stonks, first filter
        """

        # Sorts by dollar volume before taking top 200 
        sorted_by_volume = sorted([x for x in coarse if x.price > 10 and x.has_fundamental_data],
                                key=lambda x: x.dollar_volume, 
                                reverse=True)
        return [x.symbol for x in sorted_by_volume][:200]

    def fine_selection(self, fine):
        """
        Selects stonks, second filter
        """
        filtered = [x.symbol for x in fine if x.market_cap is not None and x.market_cap > 10e9]
        self.algorithm.debug(f"Fine Selection: {len(filtered)} symbols passed filters")

        # Doing it this way makes it so that stocks are ranked on each universe update and then the macds can be redone with the scheduler in main
        ranked_symbols = self.rank_stocks(filtered)
        return ranked_symbols

    def calculate_sharpe_ratio(self, symbol, period=4914): # This is 3 yrs worth of trading days
        """
        Calculates the sharpe
        """
        try:
            # If a KeyValuePair was recieved only take the symbol
            if hasattr(symbol, "Key"):
                symbol = symbol.Key

            history = self.algorithm.history([symbol], period, Resolution.HOUR) 

            if history.empty:
                self.algorithm.debug(f"No history for {symbol.value}")
                return None
            
            # Get risk-free rate
            rf_history = self.algorithm.history(self.risk_free.symbol, 1, Resolution.HOUR)
            risk_free_rate = rf_history['close'].iloc[-1]/100 if not rf_history.empty else 0.02  # Default to 2% if no data
            
            # Sharpe ratio logic
            returns = history['close'].pct_change().dropna()
            excess_returns = returns - (risk_free_rate/1638)
            mean_excess_return = excess_returns.mean() * 1638
            std_dev = excess_returns.std() * np.sqrt(1638)
            return mean_excess_return / std_dev if std_dev != 0 else None
            
        except Exception as e:
            self.algorithm.debug(f"Error calculating Sharpe for {symbol.value}: {str(e)}")
            return None

    def rank_stocks(self, symbols):
        """
        Ranks da top 50 stocks based on sharpe
        """
        if not symbols:
            self.algorithm.debug("No symbols to rank")
            return []
            
        self.algorithm.debug(f"Ranking {len(symbols)} symbols")

        # Converting from key pair if neccessary
        symbols = [s.Key if hasattr(s, 'Key') else s for s in symbols]
        scores = {symbol: self.calculate_sharpe_ratio(symbol) for symbol in symbols}
        valid_scores = {k: v for k, v in scores.items() if v is not None}
        
        self.algorithm.debug(f"Valid Sharpe ratios: {len(valid_scores)} out of {len(symbols)}")
        
        if not valid_scores:
            return []
            
        sorted_scores = sorted(valid_scores, key=valid_scores.get, reverse=True)[:20]

        self.algorithm.log(f"All symbols before ranking: {[s.value for s in symbols]}")
        self.algorithm.log(f"Symbols after filtering: {[s.value for s in valid_scores.keys()]}")

        return sorted_scores

    def normalize_scores(self, scores):
        """
        The list of scores from the ranking method are
        normalized using a z score so that an additive
        operation may be used in WeightCombiner()
        """
        values = np.array(list(scores.values()))
        mean = np.mean(values)
        std_dev = np.std(values)

        if std_dev == 0:
            # If no variation in scores, assign equal normalized scores
            return {symbol: 0 for symbol in scores.keys()}

        normalized_scores = {symbol: (score - mean) / std_dev for symbol, score in scores.items()}
        print(normalized_scores) #To see output for debugging
        return normalized_scores

from AlgorithmImports import *

class MACDSignalGenerator:

    def __init__(self, algorithm: QCAlgorithm, symbols: list, cash_buffer: float = 0.05):
        self.algorithm = algorithm
        self.symbols = symbols
        self.cash_buffer = cash_buffer
        self.macd_indicators = {}  # {symbol: {variant: MACD}}
            
        # Define MACD parameters for different variants
        self.macd_variants = {
            "slow": {"fast": 12, "slow": 26, "signal": 9},
            "slow-med": {"fast": 9, "slow": 19, "signal": 5},
            "med-fast": {"fast": 7, "slow": 15, "signal": 3},
            "fast": {"fast": 5, "slow": 12, "signal": 2},
        }

    def remove_symbols(self, symbols: list):
        """
        Removes MACD indicators for the specified symbols.
        """
        for symbol in symbols:

            # Liquidate position before removing indicator
            self.algorithm.liquidate(symbol)

            # Unregister and delete indicators tied to each symbol
            if symbol in self.macd_indicators:
                for macd in self.macd_indicators[symbol].values():  # Better: gets MACD objects directly
                    self.algorithm.unregister_indicator(macd)
                del self.macd_indicators[symbol]
                

    def add_symbols(self, new_symbols):
            """
            Add in the new symbols that are given by AssetWeightCalculator.
            """
            # Log initial attempt
            self.algorithm.debug(f"Attempting to add symbols: {[s.value for s in new_symbols]}")

            # Get historical data for new symbols
            history = self.algorithm.history([s for s in new_symbols], 
                                        35,  # Longest MACD period needed
                                        Resolution.HOUR)

            # Log history data availability
            self.algorithm.debug(f"History data available for: {history.index.get_level_values(0).unique()}")
            
            self.symbols.extend(new_symbols)
            for symbol in new_symbols:

                security = self.algorithm.securities[symbol]

                # Detailed security check logging
               # self.algorithm.debug(f"Security {symbol.value} check:"
                               # f" has_data={security.has_data},"
                               # f" is_tradable={security.is_tradable},"
                               # f" price={security.price}")

                # Checking if price is 0
                if not (security.has_data and security.is_tradable and security.price > 0):
                    self.algorithm.debug(f"Waiting for valid price data: {symbol.value}")
                    continue

                # Adding the symbol
                if symbol not in self.macd_indicators:
                    self.macd_indicators[symbol] = {}

                    # Get symbol's historical data
                    if symbol not in history.index.get_level_values(0):
                        self.algorithm.debug(f"No history data for: {symbol.value}")
                        continue
                        
                    symbol_history = history.loc[symbol]
                    self.algorithm.debug(f"History rows for {symbol.value}: {len(symbol_history)}")

                    for variant, params in self.macd_variants.items():
                        macd = self.algorithm.macd(
                            symbol=symbol,
                            fast_period=params["fast"], 
                            slow_period=params["slow"], 
                            signal_period=params["signal"], 
                            type=MovingAverageType.EXPONENTIAL,
                            resolution=Resolution.HOUR,
                            selector=Field.CLOSE
                        )
                        self.macd_indicators[symbol][variant] = macd

                        # Warm up MACD with historical data
                        for time, row in symbol_history.iterrows():
                            macd.update(time, row['close'])
                            
                        self.macd_indicators[symbol][variant] = macd

    def calculate_position_sizes(self):
        position_sizes = {}
        max_position_limit = 0.1

        # Check if we have any symbols to process
        if not self.symbols or not self.macd_indicators:
            self.algorithm.debug("No symbols available for position calculation")
            return position_sizes
        
        # Calculating the maximum one variant can be in size
        max_position = (1 - self.cash_buffer) / (len(self.symbols) * len(self.macd_variants))


        for symbol in self.macd_indicators:
            position_sizes[symbol] = {}

            for variant, macd in self.macd_indicators[symbol].items():
                if macd.is_ready:

                    security = self.algorithm.securities[symbol]

                    # Detailed security check logging
                    # self.algorithm.debug(f"Position Check for {symbol.value}:"
                                   # f" has_data={security.has_data},"
                                   # f" is_tradable={security.is_tradable},"
                                   # f" price={security.price},"
                                   # f" last_data={security.get_last_data() is not None},")
                    
                    # More comprehensive check
                    # if not (security.has_data and 
                           # security.is_tradable and 
                           # security.price > 0 and
                           # security.get_last_data() is not None):
                       # self.algorithm.debug(f"Security not ready: {symbol.value}")
                       # continue

                    # Distance between fast and slow
                    distance = macd.fast.current.value - macd.slow.current.value

                    # Normalize the distance as a percentage difference and then as a fraction of max position
                    position_size = max_position * (distance / macd.slow.current.value) * 70 # Scalar value of max_position, the scalar integer can be though of as a form of leverage setting
                    
                    # Only allow positive positions, cap at maximum
                    position_size = max(0, min(position_size, max_position_limit))
                    position_sizes[symbol][variant] = position_size
                    #self.algorithm.debug(f"Calculated position for {symbol.value} {variant}: {position_size}")
                
                else:
                  position_sizes[symbol][variant] = 0 

        # Running daily cause the logging is too heavy hourly 
        if self.algorithm.time.hour == 10 and self.algorithm.time.minute == 0:
            rounded_positions = [(s.value, {k: round(v, 5) for k, v in sizes.items()}) for s, sizes in position_sizes.items()]
            #self.algorithm.debug(f"Daily position sizes proposed: {rounded_positions}")

        return position_sizes

from AlgorithmImports import *
import numpy as np
from datetime import timedelta

class MarketCapWeightedSP500Tracker(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2019, 1, 1)
        self.SetEndDate(2025, 1, 1)
        self.SetCash(100000)

        self.UniverseSettings.Resolution = Resolution.Daily

        self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol
        self.bil = self.AddEquity("BIL", Resolution.Daily).Symbol

        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)

        self.selected_by_market_cap = []
        self.rebalance_flag = False
        self.spy_30day_window = RollingWindow[float](30)
        self.entry_prices = {}
        self.previous_bil_allocation = 0.0

        self.Schedule.On(self.DateRules.MonthStart(self.spy), 
                        self.TimeRules.AfterMarketOpen(self.spy, 30), 
                        self.SetRebalanceFlag)
        self.Schedule.On(self.DateRules.WeekStart(self.spy, DayOfWeek.Wednesday), 
                        self.TimeRules.AfterMarketOpen(self.spy, 30), 
                        self.MonthlyRebalance)

        # Initialize rolling window with historical data
        history = self.History(self.spy, 30, Resolution.Daily)
        if not history.empty:
            for time, row in history.loc[self.spy].iterrows():
                self.spy_30day_window.Add(row["close"])

        # Add simple tracking of market trend
        self.trend_lookback = 10
        self.spy_prices = {}
        self.max_spy_history = 60  # Days of price history to keep
        
        # Add dynamic stop-loss enhancement
        self.stop_loss_base = 0.04  # Reduced base stop-loss threshold
        self.dynamic_stop_weight = 0.5  # Blend 50% ATR signal with base threshold

        # Expanded list of inverse and defensive ETFs
        # Original inverse ETFs
        self.sh = self.AddEquity("SH", Resolution.Daily).Symbol    # Inverse S&P 500
        self.psq = self.AddEquity("PSQ", Resolution.Daily).Symbol  # Inverse Nasdaq-100
        self.dog = self.AddEquity("DOG", Resolution.Daily).Symbol  # Inverse Dow Jones
        self.rwm = self.AddEquity("RWM", Resolution.Daily).Symbol  # Inverse Russell 2000
        self.eum = self.AddEquity("EUM", Resolution.Daily).Symbol  # Inverse Emerging Markets
        self.myd = self.AddEquity("MYY", Resolution.Daily).Symbol  # Inverse Mid-Cap 400
        
        # Alternative defensive ETFs (not inverse but potentially good in downturns)
        self.gld = self.AddEquity("GLD", Resolution.Daily).Symbol  # Gold
        self.ief = self.AddEquity("IEF", Resolution.Daily).Symbol  # 7-10 Year Treasury
        self.bnd = self.AddEquity("BND", Resolution.Daily).Symbol  # Total Bond Market
        
        # Sector-based defensive ETFs (often outperform in bear markets)
        self.xlp = self.AddEquity("XLP", Resolution.Daily).Symbol  # Consumer Staples
        self.xlu = self.AddEquity("XLU", Resolution.Daily).Symbol  # Utilities
        self.xlv = self.AddEquity("XLV", Resolution.Daily).Symbol  # Healthcare
        self.vht = self.AddEquity("VHT", Resolution.Daily).Symbol  # Vanguard Healthcare
        self.vdc = self.AddEquity("VDC", Resolution.Daily).Symbol  # Vanguard Consumer Staples
        
        # Group all defensive ETFs together
        self.inverse_etfs = [self.sh, self.psq, self.dog, self.rwm, self.eum, self.myd]
        self.alternative_defensive = [self.gld, self.ief, self.bnd]
        self.sector_defensive = [self.xlp, self.xlu, self.xlv, self.vht, self.vdc]
        self.all_defensive = self.inverse_etfs + self.alternative_defensive + self.sector_defensive
        
        # Add diagnostic logging capability
        self.diagnostic_mode = True  # Enable detailed diagnostics
        
        # Initialize positions tracking and add weekly tactical adjustment
        self.defensive_positions = set()
        self.last_defensive_update = datetime(1900, 1, 1)
        
        # Add weekly defensive ETF evaluation schedule
        self.Schedule.On(self.DateRules.WeekStart(self.spy, DayOfWeek.Monday), 
                       self.TimeRules.AfterMarketOpen(self.spy, 60),  # After main rebalance
                       self.WeeklyDefensiveAdjustment)

        # Initialize positions tracking
        self.inverse_positions = set()
        
        # Add inverse ETF lookback windows for better momentum calculation
        self.inverse_lookback_short = 7   # 1 week momentum window
        self.inverse_lookback_med = 15    # Medium-term momentum
        # Add ATR indicators for enhanced volatility-based stop-loss calculation
        self.atr_period = 14
        self.atr = {}
        # Register ATR for key symbols (defensive ETFs, BIL, and SPY)
        for symbol in self.all_defensive + [self.bil, self.spy]:
            self.atr[symbol] = self.ATR(symbol, self.atr_period, Resolution.Daily)
            
    def CoarseSelectionFunction(self, coarse):
        filtered = [x for x in coarse if x.HasFundamentalData 
                   and x.Price > 5 
                   and x.Market == Market.USA]
        return [x.Symbol for x in filtered]

    def FineSelectionFunction(self, fine):
        filtered = [x for x in fine if x.MarketCap > 1e10
                   and x.SecurityReference.SecurityType == "ST00000001"]

        sorted_by_cap = sorted(filtered, key=lambda x: x.MarketCap, reverse=True)[:30]
        self.selected_by_market_cap = [(x.Symbol, x.MarketCap) for x in sorted_by_cap]
        return [x.Symbol for x in sorted_by_cap]

    def SetRebalanceFlag(self):
        if self.Time.weekday() == 2:  # Wednesday
            self.rebalance_flag = True

    def OnData(self, data):
        # Update price window
        if not data.Bars.ContainsKey(self.spy): return
        self.spy_30day_window.Add(data.Bars[self.spy].Close)
        
        # Track prices for trend calculation
        self.spy_prices[self.Time.date()] = data.Bars[self.spy].Close
        
        # Remove old prices
        dates_to_remove = []
        for date in self.spy_prices.keys():
            if (self.Time.date() - date).days > self.max_spy_history:
                dates_to_remove.append(date)
        for date in dates_to_remove:
            self.spy_prices.pop(date)
        
        market_trend = self._calculateMarketTrend()

        # Track if any stop-loss was triggered
        stop_loss_triggered = False
        
        # Check stop-loss triggers with improved dynamic thresholds
        for kvp in self.Portfolio:
            symbol = kvp.Key
            holding = kvp.Value

            if holding.Invested and symbol != self.bil:
                current_price = self.Securities[symbol].Price

                if symbol not in self.entry_prices:
                    self.entry_prices[symbol] = current_price

                price_drop = (self.entry_prices[symbol] - current_price) / self.entry_prices[symbol]

                # Start with the base threshold and adjust based on market trend
                stop_threshold = self.stop_loss_base
                if market_trend < -0.03:
                    stop_threshold *= 0.9  # tighten in downtrends
                elif market_trend > 0.03:
                    stop_threshold *= 1.1  # loosen in uptrends

                # Incorporate ATR if ready with adjustment to prevent overreaction in high volatility
                if symbol in self.atr and self.atr[symbol].IsReady:
                    current_atr = self.atr[symbol].Current.Value
                    atr_pct = current_atr / current_price
                    # If ATR is excessively high versus our base, use a lower weight to temper the effect
                    effective_weight = self.dynamic_stop_weight
                    if atr_pct > stop_threshold * 1.2:
                        effective_weight = min(self.dynamic_stop_weight, 0.3)
                    stop_threshold = ((1 - effective_weight) * stop_threshold +
                                      effective_weight * atr_pct)

                if price_drop >= stop_threshold:
                    self.Liquidate(symbol)
                    stop_loss_triggered = True
                    self.Debug(f"Stop-loss triggered for {symbol} at {current_price}, drop: {price_drop*100:.1f}%, threshold: {stop_threshold*100:.1f}%")
        # If any stop-loss was triggered, invest all available cash in BIL
        if stop_loss_triggered:
            available_cash = self.Portfolio.Cash + self.Portfolio.UnsettledCash
            if available_cash > 0:
                bil_price = self.Securities[self.bil].Price
                bil_quantity = available_cash / bil_price
                self.MarketOrder(self.bil, bil_quantity)
                self.Debug(f"Invested ${available_cash:0.2f} in BIL after stop-loss")

    def WeeklyDefensiveAdjustment(self):
        """Weekly check and adjustment for defensive ETF positions"""
        # Skip if we've done the monthly rebalance recently
        days_since_rebalance = (self.Time.date() - self.last_rebalance_date.date()).days if hasattr(self, 'last_rebalance_date') else 999
        if days_since_rebalance < 3:
            return
            
        # Skip if we've updated defensive positions recently
        days_since_update = (self.Time.date() - self.last_defensive_update.date()).days
        if days_since_update < 5:  # At most once a week
            return
            
        # Calculate current market conditions
        spy_price = self.Securities[self.spy].Price
        sma_30 = sum(self.spy_30day_window) / self.spy_30day_window.Count if self.spy_30day_window.Count > 0 else spy_price
        market_deviation = (spy_price / sma_30) - 1.0
        market_trend = self._calculateMarketTrend()
        
        # Skip in strong bull markets
        if market_deviation > 0.04 and market_trend > 0.03:
            return
        
        # Calculate total invested amount including all positions
        total_invested = sum(holding.HoldingsValue for holding in self.Portfolio.Values 
                             if holding.Invested) / self.Portfolio.TotalPortfolioValue
        
        # If we're already fully invested, can't add more defensive positions
        if total_invested >= 0.98:  # Allow small buffer for rounding errors
            self.Debug(f"Already fully invested ({total_invested:.2f}), skipping defensive adjustments")
            return
            
        # Calculate available room for defensive positions
        available_allocation = max(0, 0.99 - total_invested)  # Keep tiny buffer
        
        # Calculate how much is currently allocated to defensive positions
        current_defensive_value = sum(self.Portfolio[s].HoldingsValue 
                                    for s in self.defensive_positions
                                    if self.Portfolio.ContainsKey(s) and self.Portfolio[s].Invested)
        
        # Calculate current BIL allocation
        current_bil_value = self.Portfolio[self.bil].HoldingsValue if self.Portfolio[self.bil].Invested else 0
        bil_allocation = current_bil_value / self.Portfolio.TotalPortfolioValue
        
        # Limit potential allocation to available room
        max_defensive_pct = min(0.25, available_allocation / bil_allocation if bil_allocation > 0 else 0)
        potential_allocation = bil_allocation * max_defensive_pct
        
        # Make sure we don't exceed available room
        potential_allocation = min(potential_allocation, available_allocation)
        
        # Super detailed diagnostics for current defensive positions
        if self.diagnostic_mode and self.defensive_positions:
            self.Debug(f"WEEKLY CHECK - Current defensive positions:")
            for symbol in self.defensive_positions:
                if self.Portfolio.ContainsKey(symbol) and self.Portfolio[symbol].Invested:
                    position = self.Portfolio[symbol]
                    entry = self.entry_prices.get(symbol, position.AveragePrice)
                    current = self.Securities[symbol].Price
                    pnl_pct = (current / entry) - 1 if entry > 0 else 0
                    self.Debug(f"  {symbol}: PnL {pnl_pct*100:.2f}%, Value ${position.HoldingsValue:.2f}")
        
        # Evaluate current defensive positions and potential new ones
        self.Debug(f"WEEKLY CHECK - Market: Dev {market_deviation*100:.2f}%, Trend {market_trend*100:.2f}%")
        self.Debug(f"BIL allocation: {bil_allocation*100:.2f}%, Potential defensive: {potential_allocation*100:.2f}%")
        
        # Run the defensive ETF evaluation
        new_allocations = self._evaluateDefensiveETFs(market_deviation, market_trend, potential_allocation)
        
        # Calculate which positions to add, modify, or remove
        positions_to_add = {}
        positions_to_remove = set()
        
        # Process existing positions
        for symbol in self.defensive_positions:
            # If position should be kept but maybe at different allocation
            if symbol in new_allocations and new_allocations[symbol] > 0:
                current_pct = self.Portfolio[symbol].HoldingsValue / self.Portfolio.TotalPortfolioValue if self.Portfolio.ContainsKey(symbol) else 0
                target_pct = new_allocations[symbol]
                
                # If allocation difference is significant, adjust position
                if abs(target_pct - current_pct) > 0.01:
                    positions_to_add[symbol] = target_pct
                
                # Remove from new allocations dict to avoid double-processing
                new_allocations.pop(symbol)
            else:
                # Position should be removed
                positions_to_remove.add(symbol)
        
        # Add any remaining new positions
        for symbol, allocation in new_allocations.items():
            if allocation > 0.01:  # Minimum meaningful allocation
                positions_to_add[symbol] = allocation
        
        # Check if we'll exceed our allocation limits with new positions
        total_new_allocation = sum(positions_to_add.values())
        if total_new_allocation > available_allocation:
            # Scale back allocations to fit available space
            scale_factor = available_allocation / total_new_allocation
            for symbol in positions_to_add:
                positions_to_add[symbol] *= scale_factor
            self.Debug(f"Scaled defensive allocations to fit available space: {scale_factor:.4f}")
        
        # Execute trades if needed
        if positions_to_add or positions_to_remove:
            self.Debug(f"WEEKLY ADJUSTMENT - Making defensive position changes")
            
            # Remove positions no longer needed
            for symbol in positions_to_remove:
                self.Liquidate(symbol)
                self.defensive_positions.remove(symbol)
                self.Debug(f"Removed defensive position: {symbol}")
            
            # Add or adjust positions
            for symbol, allocation in positions_to_add.items():
                self.SetHoldings(symbol, allocation)
                self.defensive_positions.add(symbol)
                self.entry_prices[symbol] = self.Securities[symbol].Price
                self.Debug(f"Updated defensive position: {symbol} to {allocation*100:.2f}%")
            
            self.last_defensive_update = self.Time

    def MonthlyRebalance(self):
        if not self.rebalance_flag: return
        self.rebalance_flag = False
        self.entry_prices.clear()  # Reset entry prices at rebalance

        if self.spy_30day_window.Count < 30:
            self.Debug("Waiting for enough SPY history.")
            return

        spy_price = self.Securities[self.spy].Price
        sma_30 = sum(self.spy_30day_window) / 30

        # Calculate market deviation for better decisions
        market_deviation = (spy_price / sma_30) - 1.0
        market_trend = self._calculateMarketTrend()
        
        # Enhanced BIL allocation logic with lower caps
        bil_weight = 0.0
        if spy_price < sma_30:
            # Enhanced formula for better downside protection
            base_weight = (sma_30 - spy_price) / sma_30
            
            if base_weight > 0.08:  # Significant drop
                # Lower cap on BIL for significant drops
                bil_weight = min(base_weight * 1.1, 0.7)  # Cap at 70% (was 90%)
            else:
                bil_weight = min(base_weight, 0.6)  # Cap at 60% (was 80%)
        
        # Enhanced reduction rule for better returns in bull markets
        if market_deviation > 0.05:  # Strong bull market
            min_bil_allocation = self.previous_bil_allocation * 0.7  # 30% reduction
        elif market_deviation > 0.02:  # Modest bull market
            min_bil_allocation = self.previous_bil_allocation * 0.75  # 25% reduction
        else:
            min_bil_allocation = self.previous_bil_allocation * 0.8  # Standard 20% reduction
            
        bil_weight = max(bil_weight, min_bil_allocation)
        
        # Lower caps on BIL in all market conditions
        if market_deviation > 0.08:  # Very strong bull
            bil_weight = min(bil_weight, 0.15)  # Cap at 15% (was 20%)
        elif market_deviation > 0.05:  # Strong bull
            bil_weight = min(bil_weight, 0.25)  # Cap at 25% (was 30%)
        elif market_deviation > 0.0:   # Mild bull
            bil_weight = min(bil_weight, 0.4)   # Cap at 40% (new tier)
        elif market_deviation > -0.03: # Neutral
            bil_weight = min(bil_weight, 0.5)   # Cap at 50% (new tier)
        else:                          # Bear
            bil_weight = min(bil_weight, 0.6)   # Cap at 60% (new tier)
            
        # Calculate how much of the original BIL allocation to potentially use for inverse ETFs
        original_bil = bil_weight
        # Use only a portion of BIL for inverse ETFs, keeping some as BIL
        inverse_etf_potential = original_bil * 0.4  # Use 40% of BIL allocation for inverse ETFs
        bil_weight = original_bil - inverse_etf_potential
        
        # Run diagnostics on defensive ETFs
        if self.diagnostic_mode:
            self._runDefensiveETFDiagnostics(market_deviation, market_trend)
        
        # Evaluate inverse ETFs for possible allocation
        inverse_allocations = self._evaluateInverseETFs(market_deviation, market_trend, inverse_etf_potential)
        
        # Include alternative defensive ETFs in evaluation
        all_defensive_allocations = self._evaluateDefensiveETFs(market_deviation, market_trend, inverse_etf_potential)
        
        # Calculate total allocation to defensive ETFs
        total_defensive_allocation = sum(all_defensive_allocations.values())
        
        # Set aside remainder as cash (won't be allocated)
        cash_reserve = inverse_etf_potential - total_defensive_allocation
        
        # Calculate weight for equity portion
        equity_weight = 1.0 - total_defensive_allocation
        
        # Ensure total allocation never exceeds 100%
        total_allocation = bil_weight + total_defensive_allocation + equity_weight
        if total_allocation > 1.0:
            # Scale back components proportionally
            scale_factor = 1.0 / total_allocation
            bil_weight *= scale_factor
            equity_weight *= scale_factor
            # Scale each defensive allocation
            for symbol in all_defensive_allocations:
                all_defensive_allocations[symbol] *= scale_factor
            
            total_defensive_allocation = sum(all_defensive_allocations.values())
            self.Debug(f"Scaled allocations to prevent leverage: {scale_factor:.4f}")
        
        self.Debug(f"Allocation breakdown: Equity {equity_weight*100:.1f}%, BIL {bil_weight*100:.1f}%, " +
                  f"Defensive ETFs {total_defensive_allocation*100:.1f}%, Cash {cash_reserve*100:.1f}%")

        # Enhance stock selection with simple momentum filter
        momentum_scores = self._calculateSimpleMomentum()
        
        # Filter out worst momentum stocks
        filtered_stocks = []
        for symbol, mcap in self.selected_by_market_cap:
            score = momentum_scores.get(symbol, 1.0)
            if score >= 0.9:  # Keep only neutral or positive momentum stocks
                filtered_stocks.append((symbol, mcap))
        
        # If we filtered too many, revert to original list
        if len(filtered_stocks) < 20:
            filtered_stocks = self.selected_by_market_cap
        
        # Calculate weights using the filtered stocks
        total_market_cap = sum([x[1] for x in filtered_stocks])
        weights = {x[0]: (x[1] / total_market_cap) * equity_weight for x in filtered_stocks}

        invested = set()
        for symbol, weight in weights.items():
            if weight > 0:
                self.SetHoldings(symbol, weight)
                invested.add(symbol)
                self.entry_prices[symbol] = self.Securities[symbol].Price

        # Set BIL position
        if bil_weight > 0:
            self.SetHoldings(self.bil, bil_weight)
            invested.add(self.bil)
        else:
            self.Liquidate(self.bil)
            
        # Set defensive ETF positions
        for symbol, weight in all_defensive_allocations.items():
            if weight > 0:
                self.SetHoldings(symbol, weight)
                invested.add(symbol)
                self.defensive_positions.add(symbol)  # Using renamed set
                self.entry_prices[symbol] = self.Securities[symbol].Price
                self.Debug(f"Allocated {weight*100:.2f}% to defensive ETF {symbol}")
            elif symbol in self.defensive_positions:
                self.Liquidate(symbol)
                self.defensive_positions.remove(symbol)
                
        # Update last rebalance date tracker
        self.last_rebalance_date = self.Time

        # Store current BIL allocation for next month's minimum
        self.previous_bil_allocation = self.Portfolio[self.bil].HoldingsValue / self.Portfolio.TotalPortfolioValue
        self.Debug(f"New BIL allocation: {bil_weight*100:0.2f}% (Minimum was {min_bil_allocation*100:0.2f}%)")

        # Liquidate positions not in current selection
        for kvp in self.Portfolio:
            symbol = kvp.Key
            if (kvp.Value.Invested and symbol not in invested 
                and symbol != self.spy and symbol not in self.defensive_positions):
                self.Liquidate(symbol)

    def _calculateMarketTrend(self):
        """Calculate recent market trend using price history"""
        if len(self.spy_prices) < self.trend_lookback + 1:
            return 0  # Not enough data
            
        dates = sorted(self.spy_prices.keys())
        if len(dates) <= self.trend_lookback:
            return 0
            
        recent_price = self.spy_prices[dates[-1]]
        older_price = self.spy_prices[dates[-self.trend_lookback]]
        
        return (recent_price / older_price) - 1.0

    def _calculateSimpleMomentum(self):
        """Calculate simple momentum scores for stock filtering"""
        momentum_scores = {}
        
        symbols = [sym for sym, _ in self.selected_by_market_cap]
        if not symbols:
            return momentum_scores
            
        # Get 30 days of history for all stocks
        history = self.History(symbols, 30, Resolution.Daily)
        if history.empty:
            return momentum_scores
            
        # Calculate simple momentum (30-day price change)
        for symbol in symbols:
            if symbol in history.index.get_level_values(0):
                prices = history.loc[symbol]['close']
                
                if len(prices) >= 30:
                    # 30-day momentum
                    mom = prices.iloc[-1] / prices.iloc[0] - 1
                    
                    # Convert to a score between 0.7 and 1.3
                    # Center around 1.0, with range based on 15% move
                    momentum_scores[symbol] = min(1.3, max(0.7, 1 + (mom * 2)))
        
        return momentum_scores

    def _evaluateInverseETFs(self, market_deviation, market_trend, max_allocation):
        """Enhanced evaluation of inverse ETFs with more sensitive criteria"""
        allocations = {symbol: 0 for symbol in self.inverse_etfs}
        
        # More permissive consideration of inverse ETFs
        if market_deviation > 0.04 and market_trend > 0.02:
            return allocations  # Only skip in very strong bull markets
        
        # Get more history for better momentum calculation
        history = self.History(self.inverse_etfs, 45, Resolution.Daily)
        if history.empty:
            return allocations
        
        # Enhanced momentum scoring
        momentum_scores = {}
        volatility_scores = {}
        
        for symbol in self.inverse_etfs:
            if symbol in history.index.get_level_values(0):
                prices = history.loc[symbol]['close']
                
                if len(prices) >= 30:
                    # Multiple timeframe momentum - more emphasis on recent performance
                    mom_7d = prices.iloc[-1] / prices.iloc[-7] - 1 if len(prices) >= 7 else 0
                    mom_15d = prices.iloc[-1] / prices.iloc[-15] - 1 if len(prices) >= 15 else 0
                    mom_30d = prices.iloc[-1] / prices.iloc[0] - 1
                    
                    # Weight recent momentum much more heavily
                    momentum = (mom_7d * 0.5) + (mom_15d * 0.3) + (mom_30d * 0.2)
                    
                    # Calculate volatility (lower is better for inverse ETFs)
                    returns = [prices.iloc[i+1]/prices.iloc[i]-1 for i in range(min(20, len(prices)-1))]
                    volatility = np.std(returns) if returns else 0
                    
                    # Calculate short-term rate of change (acceleration)
                    if len(prices) >= 10:
                        recent_5d_change = prices.iloc[-1] / prices.iloc[-5] - 1
                        prev_5d_change = prices.iloc[-6] / prices.iloc[-10] - 1
                        acceleration = recent_5d_change - prev_5d_change
                    else:
                        acceleration = 0
                    
                    # Momentum score adds weight for accelerating performance
                    momentum_scores[symbol] = momentum + (acceleration * 0.5)
                    volatility_scores[symbol] = volatility
        
        # More aggressive filtering - consider even small positive momentum
        positive_momentum_etfs = {s: score for s, score in momentum_scores.items() if score > -0.005}
        
        # No allocation if no ETFs have at least neutral momentum
        if not positive_momentum_etfs:
            self.Debug("No inverse ETFs showing acceptable momentum - keeping as cash")
            return allocations
            
        # Enhanced selection: favor momentum but consider volatility too
        best_candidates = []
        for symbol, score in positive_momentum_etfs.items():
            volatility = volatility_scores.get(symbol, 1.0)
            # Adjust score: higher momentum is good, lower volatility is good
            adjusted_score = score - (volatility * 0.5)  
            best_candidates.append((symbol, score, adjusted_score))
        
        # Sort by adjusted score
        best_candidates.sort(key=lambda x: x[2], reverse=True)
        
        # More aggressive allocation model
        allocation_pct = 0.0
        
        # Allocate based on market conditions with more sensitivity
        if market_deviation < -0.05:
            allocation_pct = 1.0  # Use 100% of available inverse allocation
        elif market_deviation < -0.03:
            allocation_pct = 0.8  # Use 80% of available inverse allocation
        elif market_deviation < -0.01:
            allocation_pct = 0.6  # Use 60% of available inverse allocation
        elif market_deviation < 0.01:  # Even in slight bull market if momentum is positive
            allocation_pct = 0.4  # Use 40% of available inverse allocation
        else:
            allocation_pct = 0.2  # Use 20% only if momentum is strong enough
        
        # No candidates or market conditions don't justify allocation
        if not best_candidates or allocation_pct < 0.1:
            return allocations
            
        # Take top 1-2 ETFs depending on market conditions
        num_etfs = 1
        if market_deviation < -0.04 and len(best_candidates) > 1:
            num_etfs = 2  # Use two ETFs in stronger downtrends
            
        # Allocate to best ETF(s)
        remaining_allocation = max_allocation * allocation_pct
        
        for i in range(min(num_etfs, len(best_candidates))):
            symbol, raw_score, _ = best_candidates[i]
            
            # Allocate proportionally to momentum strength, with a minimum threshold
            etf_weight = min(1.0, max(0.3, raw_score * 3)) if raw_score > 0 else 0.3
            
            # Calculate allocation for this ETF
            etf_allocation = remaining_allocation * etf_weight / num_etfs
            
            # Only allocate if it's a meaningful amount
            if etf_allocation >= 0.01:  # At least 1% allocation
                allocations[symbol] = etf_allocation
                self.Debug(f"Selected inverse ETF {symbol} with momentum {raw_score:.2%}, allocating {etf_allocation*100:.2f}%")
                
        return allocations

    def _runDefensiveETFDiagnostics(self, market_deviation, market_trend):
        """Run detailed diagnostics on all defensive ETFs"""
        # Get extensive history for analysis
        history = self.History(self.all_defensive + [self.spy], 90, Resolution.Daily)
        if history.empty:
            return
            
        spy_perf = {}
        if self.spy in history.index.get_level_values(0):
            spy_prices = history.loc[self.spy]['close']
            if len(spy_prices) >= 30:
                spy_perf = {
                    "7d": spy_prices.iloc[-1] / spy_prices.iloc[-7] - 1 if len(spy_prices) >= 7 else 0,
                    "15d": spy_prices.iloc[-1] / spy_prices.iloc[-15] - 1 if len(spy_prices) >= 15 else 0,
                    "30d": spy_prices.iloc[-1] / spy_prices.iloc[-30] - 1
                }
        
        # Log market conditions
        self.Debug(f"DIAGNOSTIC - Market: Deviation {market_deviation*100:.2f}%, " + 
                  f"Trend {market_trend*100:.2f}%, SPY 30d: {spy_perf.get('30d', 0)*100:.2f}%")
        
        # Analyze each ETF
        for symbol in self.all_defensive:
            if symbol in history.index.get_level_values(0):
                prices = history.loc[symbol]['close']
                
                if len(prices) >= 30:
                    # Calculate multiple timeframe performance
                    perf_7d = prices.iloc[-1] / prices.iloc[-7] - 1 if len(prices) >= 7 else 0
                    perf_15d = prices.iloc[-1] / prices.iloc[-15] - 1 if len(prices) >= 15 else 0
                    perf_30d = prices.iloc[-1] / prices.iloc[-30] - 1
                    
                    # Calculate recent acceleration
                    recent_5d = prices.iloc[-1] / prices.iloc[-5] - 1 if len(prices) >= 5 else 0
                    prev_5d = prices.iloc[-6] / prices.iloc[-10] - 1 if len(prices) >= 10 else 0
                    accel = recent_5d - prev_5d
                    
                    # Calculate relative performance vs SPY
                    rel_perf = {}
                    for period, spy_val in spy_perf.items():
                        if period == "7d":
                            rel_perf[period] = perf_7d - spy_val
                        elif period == "15d":
                            rel_perf[period] = perf_15d - spy_val
                        elif period == "30d":
                            rel_perf[period] = perf_30d - spy_val
                    
                    # Log detailed ETF statistics
                    self.Debug(f"  {symbol}: 7d: {perf_7d*100:.2f}%, 15d: {perf_15d*100:.2f}%, " +
                              f"30d: {perf_30d*100:.2f}%, Accel: {accel*100:.2f}%, " +
                              f"Rel30d: {rel_perf.get('30d', 0)*100:.2f}%")

    def _evaluateDefensiveETFs(self, market_deviation, market_trend, max_allocation):
        """Enhanced defensive ETF evaluation with sector rotation"""
        allocations = {symbol: 0 for symbol in self.all_defensive}
        
        # Skip if market is very bullish
        if market_deviation > 0.04 and market_trend > 0.02:
            return allocations
            
        # Get history for all defensive options and SPY
        history = self.History(self.all_defensive + [self.spy], 60, Resolution.Daily)
        if history.empty:
            return allocations
            
        # Detailed diagnostics on all ETFs
        self.Debug(f"DEFENSIVE ETF PERFORMANCE DETAILS:")
        
        # Calculate SPY performance for relative comparisons
        spy_perf = {}
        if self.spy in history.index.get_level_values(0):
            spy_prices = history.loc[self.spy]['close']
            if len(spy_prices) >= 30:
                spy_perf = {
                    "5d": spy_prices.iloc[-1] / spy_prices.iloc[-5] - 1 if len(spy_prices) >= 5 else 0,
                    "10d": spy_prices.iloc[-1] / spy_prices.iloc[-10] - 1 if len(spy_prices) >= 10 else 0,
                    "20d": spy_prices.iloc[-1] / spy_prices.iloc[-20] - 1 if len(spy_prices) >= 20 else 0,
                    "30d": spy_prices.iloc[-1] / spy_prices.iloc[-30] - 1
                }
                self.Debug(f"  SPY: 5d: {spy_perf['5d']*100:.1f}%, 10d: {spy_perf['10d']*100:.1f}%, " +
                           f"20d: {spy_perf['20d']*100:.1f}%, 30d: {spy_perf['30d']*100:.1f}%")
                
        # Enhanced scoring system with different criteria for different ETF types
        etf_scores = {}
        
        # Process each ETF by type
        for group_name, group in [("Inverse", self.inverse_etfs), 
                                 ("Alternative", self.alternative_defensive),
                                 ("Sector", self.sector_defensive)]:
            self.Debug(f"  {group_name} ETFs:")
            
            for symbol in group:
                if symbol in history.index.get_level_values(0):
                    prices = history.loc[symbol]['close']
                    
                    if len(prices) >= 30:
                        # Calculate absolute momentum components
                        perf = {}
                        perf["5d"] = prices.iloc[-1] / prices.iloc[-5] - 1 if len(prices) >= 5 else 0
                        perf["10d"] = prices.iloc[-1] / prices.iloc[-10] - 1 if len(prices) >= 10 else 0
                        perf["20d"] = prices.iloc[-1] / prices.iloc[-20] - 1 if len(prices) >= 20 else 0
                        perf["30d"] = prices.iloc[-1] / prices.iloc[-30] - 1
                        
                        # Calculate relative outperformance vs SPY
                        rel_perf = {}
                        for period, spy_val in spy_perf.items():
                            rel_perf[period] = perf[period] - spy_val
                        
                        # Log detailed performance
                        self.Debug(f"    {symbol}: 5d: {perf['5d']*100:.1f}% (rel: {rel_perf['5d']*100:+.1f}%), " +
                                 f"10d: {perf['10d']*100:.1f}% (rel: {rel_perf['10d']*100:+.1f}%), " +
                                 f"30d: {perf['30d']*100:.1f}% (rel: {rel_perf['30d']*100:+.1f}%)")
                        
                        # Inverse ETFs need to show positive momentum in down markets
                        if symbol in self.inverse_etfs:
                            # In downtrends, rising inverse ETFs are good
                            if market_deviation < -0.02:
                                score = (perf["5d"] * 0.4) + (perf["10d"] * 0.4) + (perf["30d"] * 0.2)
                                # Bonus for relative outperformance
                                score += (rel_perf["5d"] + rel_perf["10d"]) * 0.15
                            else:
                                # Less emphasis on long-term performance in neutral markets
                                score = (perf["5d"] * 0.6) + (perf["10d"] * 0.3) + (perf["30d"] * 0.1)
                                
                        # Alternative defensive (bonds, gold) - focus on absolute return
                        elif symbol in self.alternative_defensive:
                            # Less dramatic movements, need lower thresholds
                            score = (perf["5d"] * 0.3) + (perf["10d"] * 0.4) + (perf["30d"] * 0.3)
                            
                            # In downtrends, emphasize relative performance more
                            if market_deviation < -0.03:
                                score += rel_perf["10d"] * 0.2  # Bonus for outperformance
                                
                        # Sector ETFs - focus on relative outperformance
                        else:
                            # These should have positive absolute returns and outperform SPY
                            abs_score = (perf["5d"] * 0.3) + (perf["10d"] * 0.3) + (perf["30d"] * 0.4)
                            rel_score = (rel_perf["5d"] * 0.3) + (rel_perf["10d"] * 0.3) + (rel_perf["30d"] * 0.4)
                            
                            # Balance absolute and relative performance
                            if market_deviation < -0.02:
                                # In downtrends, relative outperformance is more important
                                score = (abs_score * 0.4) + (rel_score * 0.6)
                            else:
                                # In neutral markets, absolute performance matters more
                                score = (abs_score * 0.6) + (rel_score * 0.4)
                                
                        etf_scores[symbol] = score
        
        # Find candidates with appropriate momentum based on market conditions
        threshold = -0.007  # Default threshold
        if market_deviation < -0.03:
            threshold = -0.01  # More permissive in stronger downturns
            
        candidates = {s: score for s, score in etf_scores.items() if score > threshold}
        
        if not candidates:
            self.Debug("No defensive ETFs showed sufficient momentum - keeping as cash")
            return allocations
            
        # Sort and log candidate scores
        sorted_candidates = sorted(candidates.items(), key=lambda x: x[1], reverse=True)
        self.Debug(f"Top 5 defensive candidates:")
        for symbol, score in sorted_candidates[:5]:
            group = "Inverse" if symbol in self.inverse_etfs else "Alternative" if symbol in self.alternative_defensive else "Sector"
            self.Debug(f"  {symbol} ({group}): Score {score*100:.2f}%")
        
        # Set allocation percent based on market conditions and trend
        allocation_pct = 0.0
        if market_deviation < -0.05 or market_trend < -0.04:
            allocation_pct = 0.95  # Almost all available allocation
        elif market_deviation < -0.03 or market_trend < -0.02:
            allocation_pct = 0.8
        elif market_deviation < -0.01 or market_trend < -0.01:
            allocation_pct = 0.6
        else:
            allocation_pct = 0.4
            
        # Adjust allocation based on strength of best candidate
        best_score = sorted_candidates[0][1] if sorted_candidates else 0
        allocation_pct *= min(1.0, max(0.5, (best_score + 0.02) * 4))
            
        # Determine number of ETFs to use - more in stronger downtrends
        num_etfs = 1
        if (market_deviation < -0.04 or market_trend < -0.03) and len(sorted_candidates) > 1:
            num_etfs = min(2, len(sorted_candidates))
        
        # Allocate to best candidates
        remaining_allocation = max_allocation * allocation_pct
        total_score = sum(score for _, score in sorted_candidates[:num_etfs])
        
        if total_score > 0:
            for i in range(num_etfs):
                symbol, score = sorted_candidates[i]
                
                # Weight by relative score
                weight = score / total_score if total_score > 0 else 1.0/num_etfs
                
                # Calculate allocation
                etf_allocation = remaining_allocation * weight
                
                # Only allocate if meaningful
                if etf_allocation >= 0.02:  # 2% minimum allocation
                    allocations[symbol] = etf_allocation
                    etf_type = "Inverse" if symbol in self.inverse_etfs else "Alternative" if symbol in self.alternative_defensive else "Sector"
                    self.Debug(f"Selected {etf_type} ETF {symbol} with score {score*100:.2f}%, allocating {etf_allocation*100:.2f}%")
                
        return allocations

# region imports
from AlgorithmImports import *
# endregion
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Data import *
from QuantConnect.Indicators import *
from datetime import timedelta
import numpy as np
import pandas as pd
import torch
import os
import torch.nn as nn
from sklearn.preprocessing import RobustScaler


class KQTStrategy:
    def __init__(self):
        self.model = None
        self.lookback = 30
        self.scalers = {}
        self.feature_cols = []
        self.stock_to_id = {}
        self.sector_mappings = {}

        self.adaptive_threshold = 0.2
        self.pred_std = 1.0
        self.current_regime = "neutral"
        self.portfolio_returns = []
        self.defensive_mode = False
        self.previous_day_hit_stops = []
        

    
        

    
    def create_sliding_sequences(self, df, feature_cols, lookback, stride=1):
        X = []
        for i in range(0, len(df) - lookback + 1, stride):
            X.append(df.iloc[i:i+lookback][feature_cols].values.astype(np.float32))
        return np.array(X)
    
    def clip_outliers(self, df, cols, lower=0.01, upper=0.99):
        df_copy = df.copy()
        for col in cols:
            if col in df_copy.columns:
                q_low = df_copy[col].quantile(lower)
                q_high = df_copy[col].quantile(upper)
                df_copy.loc[df_copy[col] < q_low, col] = q_low
                df_copy.loc[df_copy[col] > q_high, col] = q_high
        return df_copy
    
    def filter_features_to_match_model(self, df, feature_cols, required_count=5):
        """Ensure we have exactly the required number of features"""
        if len(feature_cols) == required_count:
            return feature_cols
            
        # First, prioritize the lag returns (most important)
        lag_features = [col for col in feature_cols if 'return_lag' in col]
        
        # Next, add in the most predictive technical features in a fixed order
        tech_priority = ['roc_5', 'volatility_10', 'ma_cross', 'dist_ma20', 'momentum_1m',
                        'oversold', 'overbought', 'roc_diff', 'volatility_regime']
                        
        prioritized_features = lag_features.copy()
        for feat in tech_priority:
            if feat in feature_cols and len(prioritized_features) < required_count:
                prioritized_features.append(feat)
        
        # If still not enough, add remaining features
        remaining = [col for col in feature_cols if col not in prioritized_features]
        while len(prioritized_features) < required_count and remaining:
            prioritized_features.append(remaining.pop(0))
        
        # If too many, truncate
        return prioritized_features[:required_count]

    def add_technical_features(self, df):
        if 'Close' not in df.columns:
            return df
            
        df['ma5'] = df['Close'].rolling(5).mean() / df['Close'] - 1  # Relative to price
        df['ma20'] = df['Close'].rolling(20).mean() / df['Close'] - 1
        df['ma_cross'] = df['ma5'] - df['ma20']  # Moving average crossover signal
        
        df['volatility_10'] = df['Close'].pct_change().rolling(10).std()
        df['volatility_ratio'] = df['Close'].pct_change().rolling(5).std() / df['Close'].pct_change().rolling(20).std()
        
        df['roc_5'] = df['Close'].pct_change(5)
        df['roc_10'] = df['Close'].pct_change(10)
        df['roc_diff'] = df['roc_5'] - df['roc_10']
        
        df['dist_ma20'] = (df['Close'] / df['Close'].rolling(20).mean() - 1)
        
        return df.fillna(0)
    
    def add_enhanced_features(self, df):
        """Add enhanced technical features"""
        df['volatility_trend'] = df['volatility_10'].pct_change(5)
        df['volatility_regime'] = (df['volatility_10'] > df['volatility_10'].rolling(20).mean()).astype(int)
        
        if 'volume' in df.columns:
            df['vol_ma_ratio'] = df['volume'] / df['volume'].rolling(20).mean()
            df['vol_price_trend'] = df['vol_ma_ratio'] * df['roc_5']
        
        df['momentum_1m'] = df['Close'].pct_change(20)
        df['momentum_3m'] = df['Close'].pct_change(60)
        df['momentum_breadth'] = (
            (df['roc_5'] > 0).astype(int) + 
            (df['momentum_1m'] > 0).astype(int) + 
            (df['momentum_3m'] > 0).astype(int)
        ) / 3
        
        df['mean_rev_signal'] = -1 * df['dist_ma20'] * df['volatility_10']
        df['oversold'] = (df['dist_ma20'] < -2 * df['volatility_10']).astype(int)
        df['overbought'] = (df['dist_ma20'] > 2 * df['volatility_10']).astype(int)
        
        df['regime_change'] = (np.sign(df['ma_cross']) != np.sign(df['ma_cross'].shift(1))).astype(int)
        
        df['risk_adj_momentum'] = df['roc_5'] / (df['volatility_10'] + 0.001)
        
        return df

    def prepare_stock_data(self, stock_data, ticker, is_training=False):
        """Prepare data for a single stock"""
        if len(stock_data) < self.lookback + 5:  # Need enough data
            return None, None
        
        stock_df = pd.DataFrame({
            'Close': stock_data['close'].values,
            'time': stock_data['time'].values
        })
        
        if 'volume' in stock_data.columns:
            stock_df['volume'] = stock_data['volume'].values
            
        stock_df = stock_df.sort_values('time').reset_index(drop=True)
        
        stock_df['pct_return'] = stock_df['Close'].pct_change().shift(-1) * 100
        
        # In prepare_stock_data, replace the feature cols section with:
        feature_cols = []

        # Add basic lag features
        for i in range(1, 6):
            col_name = f'return_lag{i}'
            stock_df[col_name] = stock_df['pct_return'].shift(i)
            feature_cols.append(col_name)

        # Add technical features
        stock_df = self.add_technical_features(stock_df)
        stock_df = self.add_enhanced_features(stock_df)

        # Add all potential features
        additional_features = ['ma_cross', 'volatility_10', 'roc_5', 'roc_diff', 'dist_ma20']
        enhanced_features = ['volatility_trend', 'volatility_regime', 'momentum_1m', 
                            'momentum_breadth', 'mean_rev_signal', 'oversold', 
                            'overbought', 'regime_change', 'risk_adj_momentum']

        for col in additional_features + enhanced_features:
            if col in stock_df.columns:
                feature_cols.append(col)

        # Filter to the exact number of features expected by the model
        model_feature_count = 5  # Use the exact count from your model
        feature_cols = self.filter_features_to_match_model(stock_df, feature_cols, model_feature_count)

        if not self.feature_cols:
            self.feature_cols = feature_cols.copy()
        
        stock_df = stock_df.dropna().reset_index(drop=True)
        
        # Handle outliers
        stock_df = self.clip_outliers(stock_df, feature_cols)
        
        # Replace the scaling code in prepare_stock_data with this:
        # Scale features
        if ticker not in self.scalers or is_training:
            # Check if we have data
            if len(stock_df) == 0 or len(feature_cols) == 0:
                return None, stock_df  # Return early if no data
                
            # Check if any features are empty/nan
            if stock_df[feature_cols].isna().any().any() or stock_df[feature_cols].empty:
                # Fill NaNs with zeros
                stock_df[feature_cols] = stock_df[feature_cols].fillna(0)
                
            # Ensure we have data
            if len(stock_df[feature_cols]) > 0:
                try:
                    scaler = RobustScaler()
                    stock_df[feature_cols] = scaler.fit_transform(stock_df[feature_cols])
                    self.scalers[ticker] = scaler
                except Exception as e:
                    print(f"Scaling error for {ticker}: {str(e)}")
                    # Use a simple standardization as fallback
                    for col in feature_cols:
                        mean = stock_df[col].mean()
                        std = stock_df[col].std()
                        if std > 0:
                            stock_df[col] = (stock_df[col] - mean) / std
                        else:
                            stock_df[col] = 0
            else:
                return None, stock_df  # Return early if empty after processing
        else:
            # Use existing scaler
            scaler = self.scalers[ticker]
            try:
                stock_df[feature_cols] = scaler.transform(stock_df[feature_cols])
            except Exception as e:
                print(f"Transform error for {ticker}: {str(e)}")
                # Simple standardization fallback
                for col in feature_cols:
                    if col in stock_df.columns and len(stock_df[col]) > 0:
                        mean = stock_df[col].mean()
                        std = stock_df[col].std()
                        if std > 0:
                            stock_df[col] = (stock_df[col] - mean) / std
                        else:
                            stock_df[col] = 0
        
        # Create sequences for prediction
        X = self.create_sliding_sequences(stock_df, feature_cols, self.lookback, stride=1)
        
        if len(X) == 0:
            return None, stock_df
            
        return X, stock_df
    
        # Add to strategy.py in KQTStrategy class
    def calculate_portfolio_risk_score(self, market_returns):
        """Calculate a portfolio risk score (0-100) to scale overall exposure"""
        risk_score = 50  # Neutral starting point
        
        # VIX-like volatility measurement using SPY returns
        if len(market_returns) >= 5:
            recent_vol = np.std(market_returns[-5:]) * np.sqrt(252)  # Annualized
            longer_vol = np.std(market_returns[-10:]) * np.sqrt(252) if len(market_returns) >= 10 else recent_vol
            
            # Volatility spike detection
            vol_ratio = recent_vol / longer_vol if longer_vol > 0 else 1
            if vol_ratio > 1.5:  # Sharp volatility increase
                risk_score -= 30
            elif vol_ratio > 1.2:
                risk_score -= 15
                
        # Consecutive negative days
        if len(market_returns) >= 3:
            neg_days = sum(1 for r in market_returns[-3:] if r < 0)
            if neg_days == 3:  # Three consecutive down days
                risk_score -= 20
            elif neg_days == 2:
                risk_score -= 10
                
        # Trend direction
        if len(market_returns) >= 10:
            avg_recent = np.mean(market_returns[-5:])
            avg_older = np.mean(market_returns[-10:-5])
            trend_change = avg_recent - avg_older
            
            # Declining trend
            if trend_change < -0.3:
                risk_score -= 15
            # Accelerating uptrend
            elif trend_change > 0.3 and avg_recent > 0:
                risk_score += 10
                
        return max(10, min(100, risk_score))  # Constrain between 10-100
            
    def predict_returns(self, X, ticker):
        """Make predictions for a single stock"""
        if self.model is None:
            return 0
            
        if ticker not in self.stock_to_id:
            self.stock_to_id[ticker] = len(self.stock_to_id)
            
        stock_id = self.stock_to_id[ticker]
        
        try:
            X_tensor = torch.tensor(X, dtype=torch.float32)
            stock_ids = torch.tensor([stock_id] * len(X), dtype=torch.long)
            
            with torch.no_grad():
                predictions = self.model(X_tensor, stock_ids)
                
            # Convert to standard Python float for safety
            return float(predictions.detach().numpy().flatten()[-1])
        except Exception as e:
            print(f"Prediction error for {ticker}: {e}")
            return 0  # Return neutral prediction on error
        
    def detect_market_regime(self, daily_returns, lookback=10):
        """Detect current market regime based on portfolio returns"""
        if len(daily_returns) >= 1:
            market_return = np.mean(daily_returns)
            market_vol = np.std(daily_returns)
            
            if len(self.portfolio_returns) >= 3:
                recent_returns = self.portfolio_returns[-min(lookback, len(self.portfolio_returns)):]
                avg_recent_return = np.mean(recent_returns)
                
                if len(self.portfolio_returns) >= 5:
                    very_recent = np.mean(self.portfolio_returns[-3:])
                    less_recent = np.mean(self.portfolio_returns[-min(8, len(self.portfolio_returns)):-3])
                    trend_change = very_recent - less_recent
                    
                    if trend_change > 0.5 and avg_recent_return > 0.2:
                        return "breakout_bullish"
                    elif trend_change < -0.5 and avg_recent_return < -0.2:
                        return "breakdown_bearish"
                
                if avg_recent_return > 0.15:
                    if market_return > 0:
                        return "bullish_strong"
                    else:
                        return "bullish_pullback"
                elif avg_recent_return < -0.3:
                    if market_return < -0.2:
                        return "bearish_high_vol"
                    else:
                        return "bearish_low_vol"
                elif avg_recent_return > 0 and market_return > 0:
                    return "bullish"
                elif avg_recent_return < 0 and market_return < 0:
                    return "bearish"
            
            if market_return > -0.05:
                return "neutral"
            else:
                return "bearish"
        
        return "neutral"
        
    def detect_bearish_signals(self, recent_returns):
        """Detect early warning signs of bearish conditions"""
        bearish_signals = 0
        signal_strength = 0
        
        if len(self.portfolio_returns) >= 5:
            recent_portfolio_returns = self.portfolio_returns[-5:]
            pos_days = sum(1 for r in recent_portfolio_returns if r > 0)
            neg_days = sum(1 for r in recent_portfolio_returns if r < 0)
            
            if neg_days > pos_days:
                bearish_signals += 1
                signal_strength += 0.2 * (neg_days - pos_days)
        
        if len(self.portfolio_returns) >= 10:
            recent_vol = np.std(self.portfolio_returns[-5:])
            older_vol = np.std(self.portfolio_returns[-10:-5])
            if recent_vol > older_vol * 1.3:  # 30% volatility increase
                bearish_signals += 1
                signal_strength += 0.3 * (recent_vol/older_vol - 1)
        
        
        if len(self.portfolio_returns) >= 5:
            if self.portfolio_returns[-1] < 0 and self.portfolio_returns[-2] > 0.3:
                bearish_signals += 1
                signal_strength += 0.3
        
        return bearish_signals, signal_strength
            
    def generate_positions(self, prediction_data, current_returns=None):
        """Generate position sizing based on predictions with improved diversification"""
        if not prediction_data:
            return {}
            
        # Update market regime
        if current_returns is not None:
            self.current_regime = self.detect_market_regime(current_returns)
            bearish_count, bearish_strength = self.detect_bearish_signals(current_returns)
            self.defensive_mode = bearish_count >= 2 or bearish_strength > 0.5
        
        # Calculate portfolio risk score (0-100)
        portfolio_risk_score = self.calculate_portfolio_risk_score(current_returns if current_returns else [])
        # Convert to a scaling factor (0.1 to 1.0)
        risk_scaling = portfolio_risk_score / 100
        
        base_threshold = 0.25 * self.pred_std
        
        if self.current_regime in ["bullish_strong", "breakout_bullish"]:
            self.adaptive_threshold = base_threshold * 0.4
        elif self.current_regime in ["bearish_high_vol", "breakdown_bearish"]:
            self.adaptive_threshold = base_threshold * 2.5
        elif self.current_regime in ["bearish", "bearish_low_vol"]:
            self.adaptive_threshold = base_threshold * 1.6
        elif self.current_regime in ["bullish_pullback"]:
            self.adaptive_threshold = base_threshold * 0.9
        else:  # neutral or other regimes
            self.adaptive_threshold = base_threshold * 0.75
        
        positions = {}
        
        # Group stocks by sector
        sector_data = {}
        for ticker, data in prediction_data.items():
            pred_return = data["pred_return"]
            sector = self.sector_mappings.get(ticker, "Unknown")
            
            if sector not in sector_data:
                sector_data[sector] = []
                
            sector_data[sector].append({
                "ticker": ticker,
                "pred_return": pred_return,
                "composite_score": pred_return / self.adaptive_threshold
            })
        
        # Rank sectors by predicted return
        sector_avg_scores = {}
        for sector, stocks in sector_data.items():
            sector_avg_scores[sector] = np.mean([s["pred_return"] for s in stocks])
        
        # CHANGE: Include more sectors (3-4 instead of just 2)
        ranked_sectors = sorted(sector_avg_scores.keys(), key=lambda x: sector_avg_scores[x], reverse=True)
        top_sector_count = 3 if portfolio_risk_score > 60 else 2  # More diversification in lower risk periods
        top_sectors = ranked_sectors[:min(top_sector_count, len(ranked_sectors))]
        
        # CHANGE: Allow more stocks per sector in bull markets
        stocks_per_sector = 3 if self.current_regime in ["bullish_strong", "breakout_bullish"] else 2
        
        # Allocate within top sectors - focus on stocks with strongest signals
        for sector in top_sectors:
            sector_stocks = sorted(sector_data[sector], key=lambda x: x["pred_return"], reverse=True)
            
            # Take top N stocks in each selected sector
            top_stocks = sector_stocks[:min(stocks_per_sector, len(sector_stocks))]
                        
            # CHANGE: Make position size proportional to signal strength but limited by volatility
            for stock in top_stocks:
                ticker = stock["ticker"]
                signal_strength = stock["pred_return"] / (0.2 * self.pred_std)
                
                # Base size calculation
                base_size = min(0.3, max(0.05, 0.15 * signal_strength))
                
                # Scale by portfolio risk
                final_size = base_size * risk_scaling
                
                positions[ticker] = final_size
        
        # Defensive adjustments
        if self.defensive_mode or self.current_regime in ["bearish_high_vol", "bearish_low_vol", "breakdown_bearish"]:
            # 1. Reduce overall position sizes
            scaling_factor = 0.5 if self.defensive_mode else 0.7  # More aggressive reduction
            for ticker in positions:
                positions[ticker] *= scaling_factor
            
            # 2. Add inverse positions (shorts) as hedges if we have bearish predictions
            if len(positions) > 0 and portfolio_risk_score < 40:  # Only hedge in higher risk environments
                negative_preds = {t: data["pred_return"] for t, data in prediction_data.items() 
                                if data["pred_return"] < 0 and t not in positions}
                
                if negative_preds:
                    worst_stocks = sorted(negative_preds.items(), key=lambda x: x[1])[:2]
                    for ticker, pred in worst_stocks:
                        hedge_size = -0.15 if self.defensive_mode else -0.1
                        positions[ticker] = hedge_size
        
        return positions

    def get_stop_loss_level(self):
        """Get appropriate stop-loss level based on market regime"""
        if self.current_regime in ["bullish_strong", "breakout_bullish"]:
            if self.defensive_mode:
                return -2.0  # Tighter in defensive mode
            else:
                return -3.5  # More room for positions to breathe
        elif self.current_regime in ["bearish_high_vol", "breakdown_bearish"]:
            return -1.5  # Tighter stop-loss in bearish regimes
        else:
            if self.defensive_mode:
                return -1.8
            else:
                return -2.5
    
    def update_portfolio_returns(self, daily_return):
        """Update portfolio return history"""
        self.portfolio_returns.append(daily_return)
        if len(self.portfolio_returns) > 60:  # Keep a rolling window
            self.portfolio_returns = self.portfolio_returns[-60:]
    
    def update_model_calibration(self, all_predictions):
        """Update prediction standard deviation for threshold calibration"""
        all_pred_values = [p for p in all_predictions.values()]
        if len(all_pred_values) > 5:
            self.pred_std = np.std(all_pred_values)