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(2012, 1, 1)
        self.SetEndDate(2025, 1, 1)
        self.SetCash(1000000)
        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"])

    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 if any stop-loss was triggered
        stop_loss_triggered = False
        
        # Check for stop-loss triggers on all holdings
        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]
                
                # Slightly more nuanced stop-loss threshold
                stop_threshold = 0.05
                
                # For larger positions, use slightly tighter stop-loss to protect portfolio
                position_value = holding.HoldingsValue
                portfolio_pct = position_value / self.Portfolio.TotalPortfolioValue
                if portfolio_pct > 0.08:  # If position is over 8% of portfolio
                    stop_threshold = 0.048  # Slightly tighter stop
                
                if price_drop >= stop_threshold:
                    stop_loss_triggered = True
                    # Liquidate the position
                    self.Liquidate(symbol)
                    self.Debug(f"Stop-loss triggered for {symbol} at {current_price}, drop: {price_drop*100:.2f}%")
        
        # If any stop-loss was triggered, invest all available cash in BIL
        if stop_loss_triggered:
            # Calculate total available cash (including unsettled cash from sales)
            available_cash = self.Portfolio.Cash + self.Portfolio.UnsettledCash
            if available_cash > 0:
                # Calculate how much BIL we can buy with available cash
                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 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 new BIL allocation based on SMA
        bil_weight = 0.0
        if spy_price < sma_30:
            # Original formula with slight adjustment to react faster to deeper drops
            base_weight = (sma_30 - spy_price) / sma_30
            
            # Use a slightly more aggressive allocation when significantly below SMA
            if base_weight > 0.08:  # Slightly earlier trigger (8% below vs 10%)
                bil_weight = min(base_weight * 1.2, 1.0)  # 20% more aggressive
            else:
                bil_weight = min(base_weight, 1.0)

        # Apply reduction rule from previous month (maintain original approach)
        min_bil_allocation = self.previous_bil_allocation * 0.8
        bil_weight = max(bil_weight, min_bil_allocation)
        
        # Cap at 80% to ensure some equity exposure remains even in severe downturns
        bil_weight = min(bil_weight, 0.8)

        # If market is rising strongly, reduce BIL allocation more aggressively
        if spy_price > sma_30 * 1.03:  # More responsive to uptrends (3% above SMA)
            bil_weight = min(bil_weight, self.previous_bil_allocation * 0.75)  # Slightly faster reduction
        
        # Add an even stronger reduction in very strong uptrends
        if spy_price > sma_30 * 1.08:  # Very strong uptrend
            bil_weight = min(bil_weight, self.previous_bil_allocation * 0.65)  # Much faster reduction
        
        equity_weight = 1.0 - bil_weight

        if not self.selected_by_market_cap:
            self.Debug("No stocks selected for investment.")
            return

        total_market_cap = sum([x[1] for x in self.selected_by_market_cap])
        weights = {x[0]: (x[1] / total_market_cap) * equity_weight for x in self.selected_by_market_cap}
        
        # Limit individual stock exposure to improve diversification
        capped_weights = {}
        for symbol, weight in weights.items():
            # Slightly higher weight cap for largest companies
            market_cap = next((cap for sym, cap in self.selected_by_market_cap if sym == symbol), 0)
            if market_cap > 5e11:  # Over $500B market cap
                capped_weights[symbol] = min(weight, equity_weight * 0.12)  # Allow up to 12%
            else:
                capped_weights[symbol] = min(weight, equity_weight * 0.10)  # Standard 10% cap
        
        # Normalize the weights after capping
        total_capped_weight = sum(capped_weights.values())
        if total_capped_weight > 0:
            normalized_weights = {symbol: (weight / total_capped_weight) * equity_weight 
                                 for symbol, weight in capped_weights.items()}
        else:
            normalized_weights = weights

        invested = set()
        for symbol, weight in normalized_weights.items():
            if weight > 0.005:  # Small minimum position threshold (0.5%)
                self.SetHoldings(symbol, weight)
                invested.add(symbol)
                self.entry_prices[symbol] = self.Securities[symbol].Price

        if bil_weight > 0:
            self.SetHoldings(self.bil, bil_weight)
            invested.add(self.bil)
        else:
            self.Liquidate(self.bil)

        # 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}%)")
        
        # Add conditional logging to help track performance
        if spy_price < sma_30:
            self.Debug(f"Market below 30d SMA by {((sma_30 - spy_price) / sma_30 * 100):0.2f}%")
        else:
            self.Debug(f"Market above 30d SMA by {((spy_price - sma_30) / sma_30 * 100):0.2f}%")
            
        # Add portfolio statistics to logs
        self.Debug(f"Total invested: {self.Portfolio.Invested:0.2f}, Cash: {self.Portfolio.Cash: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:
                self.Liquidate(symbol)

# 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 = []
        self.entry_prices = {}
        

    
        

    
    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 adjust_for_market_conditions(self, positions, spy_sma_signal, defensive_mode):
        """Adjust positions based on market conditions from SPY moving average"""
        adjusted_positions = positions.copy()
        
        # If we're below SPY SMA, reduce position sizing
        if spy_sma_signal < 0 or defensive_mode:
            # Calculate how far below SMA we are (0 to 1 scale)
            reduction_factor = min(0.5, max(0.2, abs(spy_sma_signal))) if spy_sma_signal < 0 else 0.25
            
            # Reduce all position sizes
            for ticker in adjusted_positions:
                adjusted_positions[ticker] *= (1 - reduction_factor)
                
        return adjusted_positions

    def update_stopped_out_tickers(self, stopped_out_today):
        """Update the list of recently stopped out tickers"""
        # Add new stopped out tickers to the list
        self.previous_day_hit_stops.extend(stopped_out_today)
        
        # Keep only the most recent 10 stopped out tickers
        if len(self.previous_day_hit_stops) > 10:
            self.previous_day_hit_stops = self.previous_day_hit_stops[-10:]

    def get_stop_loss_level(self):
        """Get appropriate stop-loss level based on market regime"""
        # Use fixed 5% stop-loss from Drawdownminimize.py
        return -5.0

    def check_stop_losses(self, symbol_data):
        """Check if any positions should be stopped out based on 5% drop from entry"""
        # This would be called from the algorithm with current price data
        # Return list of tickers that hit stop-loss
        stopped_out = []
        for symbol, price_data in symbol_data.items():
            if symbol in self.entry_prices:
                entry_price = self.entry_prices[symbol]
                current_price = price_data['current_price']
                
                # 5% drop from entry
                if (entry_price - current_price) / entry_price >= 0.05:
                    stopped_out.append(symbol)
        
        return stopped_out

    def detect_market_regime(self, daily_returns, lookback=10, spy_ma_signal=0):
        """Detect current market regime based on portfolio returns and SPY MA"""
        # Make regime detection more sensitive to negative SPY MA signals
        
        # CRITICAL CHANGE: Immediately shift to bearish on SPY below MA
        if spy_ma_signal < 0:
            # How far below MA determines the severity
            if spy_ma_signal < -0.05:  # More than 5% below MA
                return "bearish_high_vol"
            elif spy_ma_signal < -0.02:  # More than 2% below MA
                return "bearish"
            else:
                # Slight MA crossover shifts neutral regimes bearish and bullish to neutral
                if self.current_regime == "bullish":
                    return "neutral"
                elif self.current_regime == "neutral":
                    return "bearish_low_vol"
        
        if len(daily_returns) >= 1:
            market_return = np.mean(daily_returns)
            market_vol = np.std(daily_returns)
            
                    # Incorporate SPY moving average signal (negative when below MA)
            if spy_ma_signal < -0.02:  # SPY at least 2% below 30-day MA
                if self.current_regime == "neutral":
                    return "bearish"
                elif self.current_regime in ["bullish", "bullish_pullback"]:
                    return "neutral"
                elif self.current_regime == "bearish":
                    return "bearish_high_vol"  # Increase bearish conviction
            
            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, spy_sma_signal=0):
        """Generate position sizing based on predictions with improved defensive capabilities"""
        if not prediction_data:
            return {}
            
        # Update market regime - now including SPY MA signal in the detection
        if current_returns is not None:
            self.current_regime = self.detect_market_regime(current_returns, 10, spy_sma_signal)
            bearish_count, bearish_strength = self.detect_bearish_signals(current_returns)
            # Set defensive mode if bearish signals or below SPY MA
            self.defensive_mode = (bearish_count >= 1 or  # Reduced from 2 to 1
                                  bearish_strength > 0.3 or  # Reduced from 0.5 to 0.3
                                  spy_sma_signal < 0)  # ANY negative signal from MA
        
        # 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
        
        # Adjust threshold based on regime
        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
        
        # Apply unified defensive logic at the end (remove redundant sections)
        if self.defensive_mode or self.current_regime in ["bearish_high_vol", "bearish_low_vol", "breakdown_bearish"] or spy_sma_signal < 0:
            # STRONGER SCALING: Adjust scaling based on severity
            if spy_sma_signal < -0.05 or self.current_regime == "bearish_high_vol":
                scaling_factor = 0.3  # Reduced from 0.4 to 0.3
            elif spy_sma_signal < -0.02 or self.current_regime == "bearish_low_vol":
                scaling_factor = 0.5  # Reduced from 0.6 to 0.5
            else:
                scaling_factor = 0.7
                
            for ticker in positions:
                positions[ticker] *= scaling_factor
        
        # Remove tickers that recently hit stop losses
        for ticker in list(positions.keys()):
            if ticker in self.previous_day_hit_stops:
                positions.pop(ticker, None)
        
        return positions

    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)

    def calculate_bil_allocation(self, spy_price, sma_30):
        """Calculate BIL allocation based on SPY price and SMA."""
        bill_drop_intensity = (sma_30 - spy_price) / sma_30

        if spy_price < sma_30:
            bil_weight = min(bill_drop_intensity * 1.5, 1.0)  # Stronger response
            if bill_drop_intensity > 0.05:  # SPY >5% below MA
                bil_weight = min(0.7, bil_weight * 1.3)  # Even stronger response
        else:
            bil_weight = 0.0

        return bil_weight