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

# 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 strategy import KQTStrategy

def calculate_ema(prices, span):
    # Using adjust=False mimics how many trading platforms calculate EMA
    return pd.Series(prices).ewm(span=span, adjust=False).mean().values
def calculate_rsi(prices, period=14):
    """Calculate Relative Strength Index"""
    deltas = np.diff(prices)
    seed = deltas[:period+1]
    
    up = seed[seed >= 0].sum()/period
    down = -seed[seed < 0].sum()/period
    
    if down == 0: return 100  # No downward movement means RSI == 100
    rs = up/down
    rsi = np.zeros_like(prices)
    rsi[:period] = 100. - 100./(1. + rs)
    
    for i in range(period, len(prices)):
        delta = deltas[i-1]
        
        if delta > 0:
            upval = delta
            downval = 0.
        else:
            upval = 0.
            downval = -delta
            
        up = (up * (period-1) + upval) / period
        down = (down * (period-1) + downval) / period
        
        rs = up/down if down != 0 else float('inf')
        rsi[i] = 100. - 100./(1. + rs)
        
    return rsi

def calculate_macd(prices, fast=12, slow=26, signal=9):
    """Calculate MACD line and histogram"""
    # Convert to numpy array if not already
    prices = np.array(prices)
    
    # Calculate EMAs
    ema_fast = pd.Series(prices).ewm(span=fast, adjust=False).mean().values
    ema_slow = pd.Series(prices).ewm(span=slow, adjust=False).mean().values
    
    # Calculate MACD line and signal line
    macd_line = ema_fast - ema_slow
    signal_line = pd.Series(macd_line).ewm(span=signal, adjust=False).mean().values
    
    # Calculate histogram
    histogram = macd_line - signal_line
    
    return macd_line, signal_line, histogram

def calculate_atr(high, low, close, period=14):
    """Calculate Average True Range"""
    if len(high) != len(low) or len(high) != len(close):
        raise ValueError("Input arrays must have the same length")
    
    tr = np.zeros(len(high))
    tr[0] = high[0] - low[0]  # Initial TR = high - low of first bar
    
    for i in range(1, len(tr)):
        tr[i] = max(
            high[i] - low[i],
            abs(high[i] - close[i-1]),
            abs(low[i] - close[i-1])
        )
    
    # Calculate ATR
    atr = np.zeros_like(tr)
    atr[0] = tr[0]
    for i in range(1, len(atr)):
        atr[i] = (atr[i-1] * (period-1) + tr[i]) / period
        
    return atr

class KQTAlgorithm(QCAlgorithm):
    def Initialize(self):
        """Initialize the algorithm"""
        # Set start date, end date, and cash
        self.SetStartDate(2019, 1, 1)
        self.SetEndDate(2024, 12, 31)
        self.SetCash(1000000)
        self.previous_portfolio_value = 0

        
        # Set benchmark to SPY
        self.SetBenchmark("SPY")
        
        # Initialize the KQT strategy
        self.strategy = KQTStrategy()
        self.lookback = 60  # Need enough data for technical indicators
        self.tickers = []
        self.symbols = {}
        self.sector_mappings = {}
        self.strategy.sector_mappings = self.sector_mappings  # Share the dictionary
        self._universe = self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)        

        # Add SPY for market data
        self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol
        
        # Storage for historical data and predictions
        self.stock_data = {}
        self.current_predictions = {}
        self.previous_positions = {}
        
        # Track stopped out positions
        self.stopped_out = set()
        
        # Schedule the trading function to run before market close
        self.Schedule.On(self.DateRules.EveryDay(), 
                self.TimeRules.At(10, 0),  # 10:00 AM Eastern
                self.TradeExecute)
        
        # Initialize model with feature count
        feature_count = 18  # Adjust based on your actual feature count
        
        # Load pre-trained model weights if available
        #self.TryLoadModelWeights()
        
    def CoarseSelectionFunction(self, coarse):
        sorted_by_dollar_volume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
        return [x.Symbol for x in sorted_by_dollar_volume[:500]]
    def FineSelectionFunction(self, fine):
        sorted_by_market_cap = sorted(fine, key=lambda x: x.MarketCap, reverse=True)
        selected = sorted_by_market_cap[:100]
        
        # Debug the first item to understand available properties
        if len(selected) > 0:
            f = selected[0]
            self.Debug(f"Available fundamental properties: {[attr for attr in dir(f) if not attr.startswith('_')]}")
            if hasattr(f, 'AssetClassification'):
                self.Debug(f"AssetClassification properties: {[attr for attr in dir(f.AssetClassification) if not attr.startswith('_')]}")
        
        for f in selected:
            ticker = f.Symbol.Value
            # Try multiple ways to get sector information
            sector = "Unknown"
            
            try:
                if hasattr(f, 'AssetClassification') and f.AssetClassification is not None:
                    # Try commonly used sector properties
                    if hasattr(f.AssetClassification, 'MorningstarSectorCode'):
                        sector = str(f.AssetClassification.MorningstarSectorCode)
                    elif hasattr(f.AssetClassification, 'MorningstarIndustryCode'):
                        sector = str(f.AssetClassification.MorningstarIndustryCode)
                    elif hasattr(f.AssetClassification, 'GicsCode'):
                        sector = str(f.AssetClassification.GicsCode)
                    # Additional fallbacks
                    elif hasattr(f.AssetClassification, 'Sector'):
                        sector = f.AssetClassification.Sector
                    elif hasattr(f.AssetClassification, 'Industry'):
                        sector = f.AssetClassification.Industry
            except Exception as e:
                self.Debug(f"Error getting sector for {ticker}: {str(e)}")
                
            self.sector_mappings[ticker] = sector
            
        return [f.Symbol for f in selected]
    def OnSecuritiesChanged(self, changes):
        self.Debug(f"Universe changed: Added {len(changes.AddedSecurities)}, Removed {len(changes.RemovedSecurities)}")
        for added in changes.AddedSecurities:
            self.Debug(f"Added: {added.Symbol.Value}")
        for removed in changes.RemovedSecurities:
            self.Debug(f"Removed: {removed.Symbol.Value}")
        for added in changes.AddedSecurities:
            ticker = added.Symbol.Value
            if ticker not in self.tickers:
                self.tickers.append(ticker)
            self.symbols[ticker] = added.Symbol
        for removed in changes.RemovedSecurities:
            ticker = removed.Symbol.Value
            if ticker in self.tickers:
                self.tickers.remove(ticker)
            if ticker in self.symbols:
                del self.symbols[ticker]
            if ticker in self.sector_mappings:
                del self.sector_mappings[ticker]
            if ticker in self.stock_data:
                del self.stock_data[ticker]
    def TryLoadModelWeights(self):
        """Try to load model weights from ObjectStore"""
        try:
            if self.ObjectStore.ContainsKey("kqt_model_weights"):
                self.Debug("Found model weights in ObjectStore, loading...")
                # Get base64 encoded string
                encoded_bytes = self.ObjectStore.Read("kqt_model_weights")
                
                # Decode back to binary
                import base64
                model_bytes = base64.b64decode(encoded_bytes)
                
                # Save temporarily to file
                import tempfile
                with tempfile.NamedTemporaryFile(delete=False, suffix='.pth') as temp:
                    temp_path = temp.name
                    temp.write(model_bytes)
                
                # Extract input size from saved weights and reinitialize model
                try:
                    state_dict = torch.load(temp_path)
                    input_shape = state_dict['embedding.0.weight'].shape
                    actual_input_size = input_shape[1]
                    self.Debug(f"Detected input size from weights: {actual_input_size}")
                    
                    # Reinitialize model with correct input size
                    self.Debug("Successfully loaded model weights")
                except Exception as inner_e:
                    self.Debug(f"Error examining weights: {str(inner_e)}")
                
                # Clean up
                import os
                os.unlink(temp_path)
            else:
                self.Debug("No model weights found in ObjectStore")
        except Exception as e:
            self.Debug(f"Error loading model weights: {str(e)}")
    
    def OnData(self, data):
        """OnData event is the primary entry point for your algorithm."""
        # We're using scheduled events instead of processing each data point
        pass
    
    def TradeExecute(self):
        """Execute trading logic with extreme safety measures to prevent leverage spikes"""
        # Check if we're in emergency safety mode
        in_emergency_mode = hasattr(self, 'EMERGENCY_SAFETY_MODE') and self.EMERGENCY_SAFETY_MODE
        if in_emergency_mode:
            days_since_transition = (self.Time - self.transition_date).days if hasattr(self, 'transition_date') else 0
            self.Debug(f"EMERGENCY SAFETY MODE: Day {days_since_transition+1} after transition. " +
                      f"Max allowed leverage: {self.MAX_LEVERAGE*100:.0f}%, Position cap: {self.POSITION_CAP*100:.0f}%")
            
            # Completely skip trading in the first 2 days for safety
            if days_since_transition < 2:
                self.Debug(f"EMERGENCY SAFETY: Skipping trading entirely for the first 2 days after transition")
                return
        
        # *** FIXED Market Open Check ***
        if not self.Securities.ContainsKey(self.spy) or not self.Securities[self.spy].Exchange.ExchangeOpen:
            return # Skip if market is closed or SPY data isn't loaded yet
        
        # Standard trade execution code continues...
        self.Debug(f"Current universe size: {len(self.tickers)}")
        self.Debug(f"Using sectors: {list(self.sector_mappings.keys())[:5]}...")
        
        self.Debug(f"TradeExecute running on {self.Time}")

        # 1. Update historical data for all stocks
        self.UpdateHistoricalData()
        
        # 2. Generate predictions for each stock
        self.current_predictions = self.GeneratePredictions()

        # 3. Check for stop losses
        self.ProcessStopLosses()
        
        # 4. Generate new position sizes
        market_returns = self.GetMarketReturns()
        target_positions = self.strategy.generate_positions(self.current_predictions, market_returns)
        self.Debug(f"Target positions before execution: {target_positions}")
        self.Debug(f"Market returns: {market_returns}")

        # 5. Execute trades to reach target positions
        self.ExecuteTrades(target_positions)
        
        # 6. Update portfolio return for regime detection
        daily_return = self.CalculatePortfolioReturn()
        self.strategy.update_portfolio_returns(daily_return)
        
        # 7. Add this line to store today's value for tomorrow's calculation
        self.previous_portfolio_value = self.Portfolio.TotalPortfolioValue
        self.Debug(f"Generated {len(target_positions)} positions: {target_positions}")
    
    def CalculatePortfolioReturn(self):
        """Calculate today's portfolio return"""
        # Get the portfolio value change
        current_value = self.Portfolio.TotalPortfolioValue
        
        # Use our stored previous value instead of the non-existent property
        if self.previous_portfolio_value > 0:
            return (current_value / self.previous_portfolio_value - 1) * 100  # as percentage
        
        # On first day, just store the value and return 0
        self.previous_portfolio_value = current_value
        return 0
    
    def UpdateHistoricalData(self):
        """Fetch and update historical data for all symbols"""
        for ticker in self.tickers:
            # Use the stored Symbol object instead of accessing Securities by ticker
            if ticker not in self.symbols:
                self.Debug(f"Symbol not found for ticker {ticker}, skipping")
                continue
                
            symbol = self.symbols[ticker]
            
            # Request sufficient history for features
            history = self.History(symbol, self.lookback, Resolution.Daily)
            
            if history.empty or len(history) < self.lookback:
                self.Debug(f"Not enough historical data for {ticker}, skipping")
                continue
                    
            # Store historical data
            if isinstance(history.index, pd.MultiIndex):
                history_reset = history.reset_index()
                symbol_data = history_reset[history_reset['symbol'] == symbol]
                self.stock_data[ticker] = symbol_data
            else:
                self.stock_data[ticker] = history
    
    def GetMarketReturns(self):
        """Get recent market returns for regime detection"""
        spy_history = self.History(self.spy, 10, Resolution.Daily)
        
        if spy_history.empty:
            return []
            
        # Handle both MultiIndex and regular index formats
        if isinstance(spy_history.index, pd.MultiIndex):
            spy_history_reset = spy_history.reset_index()
            spy_history_filtered = spy_history_reset[spy_history_reset['symbol'] == self.spy]
            spy_prices = spy_history_filtered['close'].values
        else:
            spy_prices = spy_history['close'].values
            
        # Calculate returns
        spy_returns = []
        for i in range(1, len(spy_prices)):
            daily_return = (spy_prices[i] / spy_prices[i-1] - 1) * 100
            spy_returns.append(daily_return)
            
        return spy_returns
    
    def GeneratePredictions(self):
        """Generate predictions for all stocks"""
        predictions = {}
        self.Debug(f"Generating predictions with {len(self.stock_data)} stocks in data")

        for ticker, history in self.stock_data.items():
            try:
                if len(history) < self.lookback:
                    self.Debug(f"Skipping {ticker}: Not enough history ({len(history)} < {self.lookback})")
                    continue

                # Prepare data for this stock
                X, stock_df = self.strategy.prepare_stock_data(history, ticker)
                if X is None or len(X) == 0:

                    # FALLBACK: Simple prediction if ML fails
                    if isinstance(history.index, pd.MultiIndex):
                        history_reset = history.reset_index()
                        closes = history_reset[history_reset['symbol'] == self.symbols[ticker]]['close'].values
                    else:
                        closes = history['close'].values
                    
                    if len(closes) > 20:
                        # Simple momentum strategy for fallback
                        short_ma = np.mean(closes[-5:])
                        long_ma = np.mean(closes[-20:])
                        momentum = closes[-1] / closes[-10] - 1 if len(closes) > 10 else 0
                        
                        pred_score = momentum + 0.5 * (short_ma/long_ma - 1)
                        predictions[ticker] = {
                            "pred_return": pred_score * 2,
                            "composite_score": pred_score * 5
                        }
                        self.Debug(f"Used fallback prediction for {ticker}: {pred_score}")
                    continue
                # If we reach here, X must be valid data for the ML model
                # Generate prediction with ML model
                pred_return = self.strategy.predict_returns(X, ticker) # Now X is guaranteed not to be None

                # Check if prediction itself returned None (handle potential issues in predict_returns)
                if pred_return is None:
                     self.Debug(f"ML prediction for {ticker} returned None. Skipping.")
                     continue # Skip this ticker if ML prediction failed internally

                # Store ML prediction
                predictions[ticker] = {
                    "pred_return": pred_return,
                    "composite_score": pred_return / self.strategy.adaptive_threshold if self.strategy.adaptive_threshold != 0 else pred_return # Avoid division by zero
                }
                self.Debug(f"ML prediction for {ticker}: {pred_return}")

            except Exception as e:
                # Catch errors during data prep or ML prediction call itself
                self.Debug(f"Error processing {ticker} in GeneratePredictions loop: {str(e)}")
                import traceback
                self.Debug(traceback.format_exc()) # Log full traceback for debugging
                continue # Skip to next ticker on any error in the main try block

        self.Debug(f"Generated {len(predictions)} predictions")
        return predictions
    
    def ProcessStopLosses(self):
        """Check and process stop loss orders"""
        stop_loss_level = self.strategy.get_stop_loss_level()
        
        for ticker in self.tickers:
            if ticker not in self.symbols or not self.Portfolio[self.symbols[ticker]].Invested:
                continue
                
            symbol = self.symbols[ticker]
            position = self.Portfolio[symbol]
            
            # Get today's return
            history = self.History(symbol, 2, Resolution.Daily)
            if history.empty or len(history) < 2:
                continue
                
            # Handle both MultiIndex and regular index formats
            if isinstance(history.index, pd.MultiIndex):
                history_reset = history.reset_index()
                symbol_data = history_reset[history_reset['symbol'] == symbol]
                if len(symbol_data) < 2:
                    continue
                close_prices = symbol_data['close'].values
            else:
                close_prices = history['close'].values
                
            daily_return = (close_prices[-1] / close_prices[-2] - 1) * 100
            
            position_type = "long" if position.Quantity > 0 else "short"
            hit_stop = False
            
            if position_type == "long" and daily_return < stop_loss_level:
                hit_stop = True
                self.Debug(f"Stop loss triggered for {ticker} (long): {daily_return:.2f}%")
            elif position_type == "short" and daily_return > -stop_loss_level:
                hit_stop = True
                self.Debug(f"Stop loss triggered for {ticker} (short): {daily_return:.2f}%")
                
            if hit_stop:
                self.stopped_out.add(ticker)
        
    def ExecuteTrades(self, target_positions):
        """Execute trades with extreme caution to prevent leverage spikes"""
        if not target_positions:
            self.Debug("No target positions received")
            return
        
        # FIRST STEP: Check for emergency safety mode
        in_emergency_mode = hasattr(self, 'EMERGENCY_SAFETY_MODE') and self.EMERGENCY_SAFETY_MODE
        max_leverage_allowed = 0.8  # Default conservative cap
        max_position_size = 0.15    # Default conservative position cap
        
        if in_emergency_mode:
            max_leverage_allowed = self.MAX_LEVERAGE
            max_position_size = self.POSITION_CAP
            self.Debug(f"EMERGENCY SAFETY: Limiting allocation to {max_leverage_allowed*100:.0f}% with {max_position_size*100:.0f}% position cap")
        
        # CRITICAL STEP 1: Calculate and log current leverage BEFORE making changes
        portfolio_value = self.Portfolio.TotalPortfolioValue
        current_holdings_value = sum(holding.AbsoluteHoldingsValue for holding in self.Portfolio.Values if holding.Invested)
        current_leverage = current_holdings_value / portfolio_value if portfolio_value > 0 else 0
        
        self.Debug(f"CURRENT LEVERAGE: {current_leverage:.3f}x - Holdings value: ${current_holdings_value:.2f}, Portfolio value: ${portfolio_value:.2f}")
        
        # CRITICAL STEP 2: Calculate total target allocation and compare to limit
        total_target_allocation = sum(abs(weight) for weight in target_positions.values())
        self.Debug(f"ALLOCATION CHECK: Target allocation {total_target_allocation:.3f}x vs limit {max_leverage_allowed:.3f}x")
        
        # CRITICAL STEP 3: Enforce more aggressive scaling if needed
        if total_target_allocation > max_leverage_allowed:
            scaling_factor = max_leverage_allowed / total_target_allocation
            original_positions = target_positions.copy()  # Save original for logging
            target_positions = {ticker: weight * scaling_factor for ticker, weight in target_positions.items()}
            
            # Log the scaling in detail
            self.Debug(f"LEVERAGE PROTECTION: Scaling down target allocation from {total_target_allocation:.3f}x to {max_leverage_allowed:.3f}x")
            # Log sample of position changes
            sample_tickers = list(original_positions.keys())[:5]  # First 5 positions
            for ticker in sample_tickers:
                if ticker in original_positions and ticker in target_positions:
                    self.Debug(f"  {ticker}: {original_positions[ticker]:.4f} → {target_positions[ticker]:.4f}")
        
        # STEP 4: Enforce position size cap
        for ticker in list(target_positions.keys()):
            if abs(target_positions[ticker]) > max_position_size:
                original = target_positions[ticker]
                target_positions[ticker] = max_position_size * (1 if original > 0 else -1)
                self.Debug(f"POSITION CAP: Limiting {ticker} from {original:.4f} to {target_positions[ticker]:.4f}")
        
        # STEP 5: Additional safety - enforce maximum number of positions in emergency mode
        if in_emergency_mode:
            max_positions = 10  # Severely limit number of positions during emergency
            if len(target_positions) > max_positions:
                # Keep only the highest conviction positions
                top_positions = sorted(target_positions.items(), key=lambda x: abs(x[1]), reverse=True)[:max_positions]
                target_positions = {ticker: weight for ticker, weight in top_positions}
                self.Debug(f"SAFETY LIMIT: Restricted to top {max_positions} positions during emergency mode")
        
        # STEP 6: Calculate expected final leverage after proposed changes
        # This helps us detect if our changes would still result in too much leverage
        expected_total_allocation = sum(abs(weight) for weight in target_positions.values())
        self.Debug(f"FINAL CHECK: Expected allocation after all safety measures: {expected_total_allocation:.3f}x")
        
        # STEP 7: Set a minimum cash buffer in emergency mode
        if in_emergency_mode:
            cash_buffer = 0.3  # Keep 30% in cash during emergency
            if expected_total_allocation > (1.0 - cash_buffer):
                final_scaling = (1.0 - cash_buffer) / expected_total_allocation
                target_positions = {ticker: weight * final_scaling for ticker, weight in target_positions.items()}
                self.Debug(f"CASH BUFFER: Added {cash_buffer*100:.0f}% cash buffer, final scaling: {final_scaling:.3f}x")
        
        # Now execute the trades with our ultra-safe target positions
        # Continue with the existing execution logic
        # Available buying power tracking - FIXED LINE
        available_buying_power = self.Portfolio.MarginRemaining  # Use MarginRemaining instead of GetBuyingPower()
        self.Debug(f"Starting available margin: ${available_buying_power:.2f}")
        
        # Execute trades to reach target positions
        for ticker, target_weight in target_positions.items():
            symbol = self.symbols[ticker]
            current_security = self.Securities[symbol]
            
            # Calculate target share amount
            price = current_security.Price
            target_value = portfolio_value * target_weight
            target_shares = int(target_value / price) if price > 0 else 0
            
            # Get current holdings
            holding = self.Portfolio[symbol]
            current_shares = holding.Quantity
            
            # Calculate shares to trade
            shares_to_trade = target_shares - current_shares
            
            # Skip tiny orders
            if abs(shares_to_trade) > 0:
                try:
                    # Check if we have enough buying power for this order
                    required_buying_power = abs(shares_to_trade) * price * 1.01  # Add 1% buffer
                    
                    if shares_to_trade < 0 or required_buying_power <= available_buying_power:
                        # Place the order
                        if shares_to_trade > 0:
                            self.MarketOrder(symbol, shares_to_trade)
                            self.Debug(f"BUY {shares_to_trade} shares of {ticker} (${shares_to_trade * price:.2f})")
                            available_buying_power -= required_buying_power
                        else:
                            self.MarketOrder(symbol, shares_to_trade)  # Negative for sell
                            self.Debug(f"SELL {abs(shares_to_trade)} shares of {ticker}")
                            # Selling increases buying power
                            available_buying_power += abs(shares_to_trade) * price * 0.98  # Assume 2% impact/fees
                    else:
                        # Not enough buying power, try reduced size
                        max_affordable = int(available_buying_power / (price * 1.01))
                        if max_affordable > 0:
                            self.MarketOrder(symbol, max_affordable)
                            self.Debug(f"REDUCED BUY {max_affordable} shares of {ticker} due to buying power constraints")
                            available_buying_power -= max_affordable * price * 1.01
                        else:
                            self.Debug(f"Skipped {ticker} order: Insufficient buying power")
                except Exception as e:
                    self.Debug(f"Order error for {ticker}: {str(e)}")
        
        # Store current positions for next day
        self.previous_positions = target_positions.copy()
        
        # After executing trades, verify total allocation and deleverage if needed
        current_allocation = sum(self.Portfolio[self.symbols[ticker]].AbsoluteHoldingsValue 
                             for ticker in target_positions if ticker in self.symbols 
                             and self.Portfolio[self.symbols[ticker]].Invested) / portfolio_value
        
        # FIX: Use max_leverage_allowed instead of max_allocation_limit
        if current_allocation > max_leverage_allowed * 1.05:  # 5% buffer
            self.Debug(f"EMERGENCY: Current allocation {current_allocation*100:.1f}% exceeds limit, executing deleveraging")
            self._ExecuteEmergencyDeleveraging(max_leverage_allowed)

    def _LimitSectorExposure(self, target_positions, max_sector_allocation):
        """Limit exposure to any single sector during post-transition period"""
        # Group positions by sector
        sector_allocations = {}
        
        for ticker, weight in target_positions.items():
            sector = self.sector_mappings.get(ticker, "Unknown")
            sector_allocations[sector] = sector_allocations.get(sector, 0) + abs(weight)
        
        # Identify sectors that exceed the limit
        for sector, allocation in sector_allocations.items():
            if allocation > max_sector_allocation:
                scaling_factor = max_sector_allocation / allocation
                self.Debug(f"Limiting {sector} sector from {allocation*100:.1f}% to {max_sector_allocation*100:.1f}%")
                
                # Scale down all positions in this sector
                for ticker in list(target_positions.keys()):
                    if self.sector_mappings.get(ticker, "Unknown") == sector:
                        target_positions[ticker] *= scaling_factor

    def _ExecuteEmergencyDeleveraging(self, target_allocation):
        """Emergency deleveraging procedure to quickly reduce position sizes"""
        self.Debug("EXECUTING EMERGENCY DELEVERAGING")
        
        # Get current total allocation
        portfolio_value = self.Portfolio.TotalPortfolioValue
        current_allocation = sum(holding.AbsoluteHoldingsValue for holding in self.Portfolio.Values 
                              if holding.Invested) / portfolio_value
        
        if current_allocation <= target_allocation:
            self.Debug(f"Current allocation {current_allocation*100:.1f}% already within target {target_allocation*100:.1f}%")
            return
        
        # Calculate how much we need to reduce positions by
        reduction_factor = target_allocation / current_allocation
        self.Debug(f"Reducing all positions by {(1-reduction_factor)*100:.1f}%")
        
        # Get all positions sorted by size (largest first)
        positions = sorted(
            [(kvp.Key, kvp.Value) for kvp in self.Portfolio if kvp.Value.Invested],
            key=lambda p: abs(p[1].HoldingsValue),
            reverse=True
        )
        
        # Reduce each position proportionally
        for symbol, position in positions:
            current_shares = position.Quantity
            
            # Calculate reduced position
            new_shares = int(current_shares * reduction_factor)
            shares_to_sell = current_shares - new_shares
            
            # Only execute if the change is significant
            if abs(shares_to_sell) > 0:
                self.MarketOrder(symbol, -shares_to_sell)  # Negative to reduce position
                self.Debug(f"DELEVERAGING: Reduced {symbol} by {abs(shares_to_sell)} shares")
        
        self.Debug(f"DELEVERAGING COMPLETE: Target allocation {target_allocation*100:.1f}%")

    def _ExecuteEmergencyCircuitBreaker(self, target_leverage):
        """Last-resort emergency circuit breaker to prevent extreme leverage"""
        self.Debug("!!! EXECUTING EMERGENCY CIRCUIT BREAKER !!!")
        
        # Calculate current leverage
        portfolio_value = self.Portfolio.TotalPortfolioValue
        holdings_value = sum(holding.AbsoluteHoldingsValue for holding in self.Portfolio.Values if holding.Invested)
        current_leverage = holdings_value / portfolio_value if portfolio_value > 0 else 0
        
        if current_leverage <= target_leverage:
            self.Debug(f"Circuit breaker check passed: {current_leverage:.2f}x <= {target_leverage:.2f}x")
            return
        
        # Calculate reduction needed
        reduction_factor = target_leverage / current_leverage
        self.Debug(f"EMERGENCY: Reducing all positions by {(1-reduction_factor)*100:.1f}%")
        
        # Get all positions sorted by size (largest first for biggest impact)
        positions = sorted(
            [(kvp.Key, kvp.Value) for kvp in self.Portfolio if kvp.Value.Invested],
            key=lambda p: abs(p[1].HoldingsValue),
            reverse=True
        )
        
        # AGGRESSIVE APPROACH: Liquidate the largest positions first until we're safe
        total_reduced = 0
        target_reduction = holdings_value - (target_leverage * portfolio_value)
        
        for symbol, position in positions:
            if total_reduced >= target_reduction:
                break
                
            # For largest positions, liquidate entirely for immediate effect
            self.Liquidate(symbol)
            total_reduced += position.AbsoluteHoldingsValue
            self.Debug(f"EMERGENCY LIQUIDATION: Completely liquidated {symbol} (${position.AbsoluteHoldingsValue:.2f})")
        
        self.Debug(f"CIRCUIT BREAKER COMPLETE: Liquidated ${total_reduced:.2f} in emergency response")

# PENDULUM SYSTEM for quant connect switching algos
from AlgorithmImports import *
from datetime import datetime, timedelta
import calendar
from kqtalgo import KQTAlgorithm
from riskcontrol import MarketCapWeightedSP500Tracker

class PendulumSystem(QCAlgorithm):
    def Initialize(self):
        """Initialize the pendulum system algorithm."""
        # Set start date, end date, and cash
        self.SetStartDate(2020, 1, 1)
        self.set_brokerage_model(BrokerageName.INTERACTIVE_BROKERS_BROKERAGE, AccountType.CASH)

        self.SetEndDate(2025, 1, 1)
        self.SetCash(1000000)
        
        # Add SPY for market data
        self.spy = self.AddEquity("SPY", Resolution.Daily).Symbol
        
        # Add VIX for monitoring only (not for trading)
        self.vix = self.AddIndex("VIX", Resolution.Hour).Symbol
        
        # Pre-add securities needed by RiskControl algorithm
        # Add BIL security needed by RiskControl
        self.bil = self.AddEquity("BIL", Resolution.Daily).Symbol
        
        # Add defensive ETFs required by RiskControl
        # Inverse ETFs
        self.sh = self.AddEquity("SH", Resolution.Daily).Symbol
        self.psq = self.AddEquity("PSQ", Resolution.Daily).Symbol
        self.dog = self.AddEquity("DOG", Resolution.Daily).Symbol
        self.rwm = self.AddEquity("RWM", Resolution.Daily).Symbol
        self.eum = self.AddEquity("EUM", Resolution.Daily).Symbol
        self.myd = self.AddEquity("MYY", Resolution.Daily).Symbol
        
        # Alternative defensive ETFs
        self.gld = self.AddEquity("GLD", Resolution.Daily).Symbol
        self.ief = self.AddEquity("IEF", Resolution.Daily).Symbol
        self.bnd = self.AddEquity("BND", Resolution.Daily).Symbol
        
        # Sector defensive ETFs
        self.xlp = self.AddEquity("XLP", Resolution.Daily).Symbol
        self.xlu = self.AddEquity("XLU", Resolution.Daily).Symbol
        self.xlv = self.AddEquity("XLV", Resolution.Daily).Symbol
        self.vht = self.AddEquity("VHT", Resolution.Daily).Symbol
        self.vdc = self.AddEquity("VDC", Resolution.Daily).Symbol
        
        # Set benchmark to SPY
        self.SetBenchmark("SPY")
        
        # Set universe settings
        self.UniverseSettings.Resolution = Resolution.Daily
        self.UniverseSettings.ExtendedMarketHours = False
        
        # Add universe selection for KQT algorithm
        self._universe = self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
        
        # Add reference to all ETFs for RiskControl to use
        self.all_defensive_etfs = [self.sh, self.psq, self.dog, self.rwm, self.eum, self.myd,
                              self.gld, self.ief, self.bnd, self.xlp, self.xlu, self.xlv, 
                              self.vht, self.vdc]
        
        # Initialize both algorithms as member variables
        self.kqt_algo = self._InitializeKQTAlgorithm()
        self.risk_control_algo = self._InitializeRiskControlAlgorithm()
        
        # Initialize active algorithm to KQT by default
        self.active_algo = self.kqt_algo
        self.current_algo_name = "KQT"
        
        # Initialize tracking variables for algorithm switching
        self.in_risk_control_mode = False
        self.risk_control_end_date = None
        self.months_in_risk_control = 0
        self.vix_trigger_months = set()  # Track months where VIX has triggered
        
        # Add a flag to track if we've done the initial setup for each algorithm
        self.risk_control_initialized = False
        self.kqt_initialized = False
        
        # Add a flag to record when switching occurred
        self.switch_date = None
        
        # Add comprehensive error tracking
        self.error_count = 0
        self.max_errors = 10  # Exit after 10 serious errors
        
        # Add a flag to track if we should force a rebalance
        self.force_rebalance = False
        self.force_rebalance_date = None
        
        # Add flag to directly execute RiskControl's MonthlyRebalance function after switch
        self.force_immediate_rebalance = False
        
        # Add a counter to track days since algorithm switch to ensure rebalancing happens
        self.days_since_switch = 0
        
        # Add a force allocation flag to ensure RiskControl properly allocates
        self.force_deep_allocation = False
        
        # Add a helper dictionary to store current market metrics for debugging
        self.market_metrics = {}
        
        # Schedule hourly VIX check
        self.Schedule.On(self.DateRules.EveryDay(), 
                          self.TimeRules.Every(TimeSpan.FromHours(1)),
                          self.CheckVIX)
        
        # Schedule daily notification of which algorithm is active
        self.Schedule.On(self.DateRules.EveryDay(), 
                          self.TimeRules.At(9, 31),  # 9:31 AM
                          self.NotifyActiveAlgorithm)
        
        # Add daily logic execution for active algorithm
        self.Schedule.On(self.DateRules.EveryDay(), 
                         self.TimeRules.At(10, 0),  # Same time as KQTAlgorithm.TradeExecute
                         self.ExecuteActiveAlgorithmLogic)
        
        self.Debug(f"PendulumSystem initialized with default algorithm: {self.current_algo_name}")

    def _InitializeKQTAlgorithm(self):
        """Initialize the KQT algorithm."""
        kqt = KQTAlgorithm()
        # Set the QCAlgorithm properties from this algorithm
        self._ShareAlgorithmProperties(kqt)
        # Call Initialize to set up the algorithm
        kqt.Initialize()
        return kqt
    
    def _InitializeRiskControlAlgorithm(self):
        """Initialize the Risk Control algorithm correctly with proper market data"""
        try:
            # Create a new instance with the correct classname
            risk_control = MarketCapWeightedSP500Tracker()
            
            # Share common properties
            self._ShareAlgorithmProperties(risk_control)
            
            # Explicitly set required properties before Initialize
            # Share ETF symbols directly to member variables
            risk_control.spy = self.spy
            risk_control.bil = self.bil
            risk_control.sh = self.sh
            risk_control.psq = self.psq
            risk_control.dog = self.dog
            risk_control.rwm = self.rwm
            risk_control.eum = self.eum
            risk_control.myd = self.myd
            risk_control.gld = self.gld
            risk_control.ief = self.ief
            risk_control.bnd = self.bnd
            risk_control.xlp = self.xlp
            risk_control.xlu = self.xlu
            risk_control.xlv = self.xlv
            risk_control.vht = self.vht
            risk_control.vdc = self.vdc
            
            # Set the securities dictionaries
            risk_control.Securities = self.Securities
            risk_control.Portfolio = self.Portfolio
            
            # Initialize RiskControl with correct state
            risk_control.rebalance_flag = True  # Force rebalance at start
            risk_control.diagnostic_mode = True  # Enable detailed logging
            
            # Initialize the proper ATR tracking for all symbols
            risk_control.atr = {}
            for symbol in self.all_defensive_etfs + [self.bil, self.spy]:
                risk_control.atr[symbol] = self.ATR(symbol, 14, Resolution.Daily)
            
            # Initialize tracking collections
            risk_control.spy_30day_window = RollingWindow[float](30)
            risk_control.entry_prices = {}
            risk_control.previous_bil_allocation = 0.0
            risk_control.inverse_positions = set()
            risk_control.defensive_positions = set()
            risk_control.last_defensive_update = datetime(1900, 1, 1)
            
            # Do explicit pre-fill of historical data BEFORE calling Initialize
            history = self.History(self.spy, 60, Resolution.Daily)
            
            # Pre-populate SPY price history data
            if not history.empty:
                self.Debug(f"Loading {len(history)} days of historical SPY data into spy_30day_window")
                
                # Handle different index formats
                if isinstance(history.index, pd.MultiIndex):
                    spy_prices = history.loc[self.spy]['close'].values
                else:
                    spy_prices = history['close'].values
                    
                # IMPORTANT: Organize prices in correct chronological order
                # Must add OLDEST prices first, then move to newest
                prices_to_add = spy_prices[-30:]  # Get the 30 most recent prices
                
                # Add prices to the window in the correct order (oldest first)
                for price in prices_to_add:
                    risk_control.spy_30day_window.Add(price)
                    
                self.Debug(f"Filled spy_30day_window with {risk_control.spy_30day_window.Count} days of data")
                
                # Also fill market price dictionary for trend calculation
                risk_control.spy_prices = {}
                
                # FIX: Properly extract dates based on index type
                if isinstance(history.index, pd.MultiIndex):
                    # For MultiIndex, properly extract dates from the level containing timestamps
                    # This works with format: (symbol, timestamp)
                    dates = []
                    for idx in history.index:
                        if idx[0] == self.spy:  # Ensure we're getting dates for SPY
                            # The timestamp is the second element, regardless of index structure
                            dates.append(idx[1].date())
                else:
                    # For DatetimeIndex, convert each timestamp to date directly
                    dates = [idx.date() for idx in history.index]
                
                # Make sure we have matching lengths
                min_len = min(len(dates), len(spy_prices))
                
                # Add recent prices to the spy_prices dictionary
                for i in range(min_len):
                    risk_control.spy_prices[dates[i]] = spy_prices[i]
                    
                self.Debug(f"Added {len(risk_control.spy_prices)} prices to spy_prices dictionary")
            
            # Initialize algorithm but catch any exceptions
            try:
                # Call the original Initialize method
                risk_control.Initialize()
                
                # CRITICAL FIX: Calculate and store current market metrics for debugging
                if risk_control.spy_30day_window.Count >= 30:
                    spy_price = self.Securities[self.spy].Price
                    sma_30 = sum(risk_control.spy_30day_window) / 30
                    market_deviation = (spy_price / sma_30) - 1.0
                    market_trend = risk_control._calculateMarketTrend()
                    
                    self.market_metrics = {
                        "spy_price": spy_price,
                        "sma_30": sma_30,
                        "market_deviation": market_deviation,
                        "market_trend": market_trend
                    }
                    
                    self.Debug(f"Market metrics: SPY={spy_price:.2f}, SMA30={sma_30:.2f}, " +
                              f"Deviation={market_deviation*100:.2f}%, Trend={market_trend*100:.2f}%")
                
                # Override rebalance flag to force a rebalance
                risk_control.rebalance_flag = True
                risk_control.force_rebalance_override = True
                
                # VERY IMPORTANT: Set the date properly
                risk_control.last_rebalance_date = self.Time - timedelta(days=60)  # Force a rebalance
                
                self.risk_control_initialized = True
                
            except Exception as e:
                self.Error(f"Error initializing RiskControl algorithm: {str(e)}")
                import traceback
                self.Debug(traceback.format_exc())
                
            return risk_control
        except Exception as e:
            self.Error(f"Error creating RiskControl algorithm: {str(e)}")
            import traceback
            self.Debug(traceback.format_exc())
            # Return a placeholder risk control algorithm that won't crash
            placeholder = QCAlgorithm()
            placeholder.Debug = self.Debug
            return placeholder
    
    def _ShareAlgorithmProperties(self, algo):
        """Share essential QCAlgorithm properties with better error handling."""
        try:
            # Share the broker, trader, and other essential properties
            algo.Securities = self.Securities
            algo.Portfolio = self.Portfolio
            algo.Transactions = self.Transactions
            algo.BrokerageModel = self.BrokerageModel
            algo.Debug = self.Debug
            algo.Error = self.Error if hasattr(self, 'Error') else self.Debug
            
            # Share needed symbols - check first to avoid errors
            if hasattr(self, 'spy'):
                algo.spy = self.spy
            if hasattr(self, 'bil'):
                algo.bil = self.bil
            
            # Share all defensive ETFs with RiskControl algorithm more carefully
            if isinstance(algo, MarketCapWeightedSP500Tracker):
                for etf in ['sh', 'psq', 'dog', 'rwm', 'eum', 'myd', 'gld', 'ief', 'bnd', 
                           'xlp', 'xlu', 'xlv', 'vht', 'vdc']:
                    if hasattr(self, etf):
                        setattr(algo, etf, getattr(self, etf))
            
            # Only share properties that exist on the parent algorithm
            for prop in ['SubscriptionManager', 'OptionChainProvider', 'FutureChainProvider']:
                if hasattr(self, prop):
                    setattr(algo, prop, getattr(self, prop))
            
            # This allows child algorithms to place orders through the main algorithm
            def wrapped_market_order(symbol, quantity, tag=""):
                try:
                    # Don't allow trading of VIX (index)
                    if symbol == self.vix:
                        self.Debug(f"Blocked attempt to trade VIX index")
                        return None
                    return self.MarketOrder(symbol, quantity, asynchronous=False, tag=tag)
                except Exception as e:
                    self.Error(f"Error in wrapped_market_order for {symbol}: {str(e)}")
                    return None
            
            algo.MarketOrder = wrapped_market_order
            
            # Safe SetHoldings wrapper
            def wrapped_set_holdings(symbol, percentage, liquidateExistingHoldings=False, tag=""):
                try:
                    if symbol == self.vix:
                        self.Debug(f"Blocked attempt to set holdings for VIX index")
                        return None
                    return self.SetHoldings(symbol, percentage, liquidateExistingHoldings, tag)
                except Exception as e:
                    self.Error(f"Error in SetHoldings for {symbol}: {str(e)}")
                    return None
                    
            algo.SetHoldings = wrapped_set_holdings
            
            # Safe Liquidate wrapper
            def wrapped_liquidate(symbol=None, tag=""):
                try:
                    return self.Liquidate(symbol, tag)
                except Exception as e:
                    self.Error(f"Error in Liquidate for {symbol}: {str(e)}")
                    return None
                    
            algo.Liquidate = wrapped_liquidate
            
            # Make other relevant methods available
            algo.History = self.History
            
            # Create a custom getter method for Time instead of direct assignment
            algo.GetCurrentTime = lambda: self.Time
            algo.GetUtcTime = lambda: self.UtcTime
            
            # Access to UniverseSettings
            algo.UniverseSettings = self.UniverseSettings
            
            # Pass ObjectStore access
            algo.ObjectStore = self.ObjectStore
            
            return algo
            
        except Exception as e:
            self.Error(f"Error in _ShareAlgorithmProperties: {str(e)}")
            import traceback
            self.Debug(traceback.format_exc())
            return algo  # Return the algorithm even if property sharing failed
    
    def OnSecuritiesChanged(self, changes):
        """Handle securities universe changes."""
        # Forward to active algorithm
        if hasattr(self.active_algo, "OnSecuritiesChanged"):
            self.active_algo.OnSecuritiesChanged(changes)
    
    def OnData(self, data):
        """Main event for handling market data."""
        try:
            # Track days since algorithm switch
            if self.in_risk_control_mode and hasattr(self, 'switch_date'):
                self.days_since_switch = (self.Time - self.switch_date).days
            
            # Update RiskControl algorithm's spy_30day_window if in RiskControl mode
            if self.in_risk_control_mode and self.spy in data.Bars:
                spy_price = data.Bars[self.spy].Close
                # Add to the RiskControl's price window directly
                self.active_algo.spy_30day_window.Add(spy_price)
                
                # Also update the price history dictionary
                if hasattr(self.active_algo, 'spy_prices'):
                    self.active_algo.spy_prices[self.Time.date()] = spy_price
                    
                    # Clean up old prices (keep only the last 60 days)
                    dates_to_remove = []
                    for date in self.active_algo.spy_prices.keys():
                        if (self.Time.date() - date).days > 60:
                            dates_to_remove.append(date)
                    for date in dates_to_remove:
                        self.active_algo.spy_prices.pop(date)
                        
            # Forward data to the active algorithm
            self.active_algo.OnData(data)
            
            # Force immediate rebalance after switching to RiskControl
            if self.force_immediate_rebalance and self.in_risk_control_mode:
                # Check if market is open and we have SPY data
                if self.Securities.ContainsKey(self.spy) and self.Securities[self.spy].Exchange.ExchangeOpen:
                    self.Debug(f"Executing immediate rebalance {self.days_since_switch} days after switching to RiskControl")
                    # Force flag to be set
                    self.active_algo.rebalance_flag = True
                    # Call the method directly
                    self.ForceRiskControlRebalance()
                    self.force_immediate_rebalance = False
                    self.Debug("Immediate rebalance completed")
                    
                    # Verify portfolio state after rebalance
                    self.LogRiskControlPortfolio()
        except Exception as e:
            self.Error(f"Error in OnData: {str(e)}")
            import traceback
            self.Debug(traceback.format_exc())
    
    def CheckVIX(self):
        """Check VIX levels hourly with enhanced error handling."""
        try:
            if not self.Securities.ContainsKey(self.vix):
                self.Debug("VIX data not available")
                return
            
            current_vix = self.Securities[self.vix].Price
            current_month_year = (self.Time.year, self.Time.month)
            
            # Log VIX level for monitoring
            if self.Time.hour == 10 and self.Time.minute == 0:  # Log once per day at 10:00 AM
                self.Debug(f"Current VIX: {current_vix:.2f}, Current Algorithm: {self.current_algo_name}")
                
                # Add extra diagnostics if in risk control mode
                if self.in_risk_control_mode:
                    days_since_switch = (self.Time - self.switch_date).days if self.switch_date else "unknown"
                    self.Debug(f"Days since switch to RiskControl: {days_since_switch}, Error count: {self.error_count}")
            
            # Check if we need to switch to Risk Control
            if current_vix > 29 and not self.in_risk_control_mode:
                self.Debug(f"VIX TRIGGER: VIX at {current_vix:.2f} > 30, switching to Risk Control algorithm")
                self.SwitchToRiskControl()
                # Mark this month as having triggered VIX
                self.vix_trigger_months.add(current_month_year)
            
            # Check if VIX exceeded 30 in a new month while already in risk control mode
            elif current_vix > 29 and self.in_risk_control_mode:
                if current_month_year not in self.vix_trigger_months:
                    self.vix_trigger_months.add(current_month_year)
                    self.ExtendRiskControlPeriod()
            
            # Check if it's time to switch back to KQT
            if self.in_risk_control_mode and self.Time >= self.risk_control_end_date:
                self.Debug("Risk Control period ended, switching back to KQT algorithm")
                self.SwitchToKQT()
                
        except Exception as e:
            self.Error(f"Error in CheckVIX: {str(e)}")
            import traceback
            self.Debug(traceback.format_exc())
    
    def SwitchToRiskControl(self):
        """Switch from KQT to Risk Control algorithm, ensuring proper initialization and execution"""
        try:
            self.Debug("Starting switch to RiskControl...")
            
            # Liquidate all positions before switching
            self.Liquidate()
            self.Debug("Positions liquidated")
            
            # Re-initialize the risk control algorithm to ensure a clean state
            self.Debug("Reinitializing RiskControl algorithm...")
            self.risk_control_algo = self._InitializeRiskControlAlgorithm()
            
            # CRITICAL ADDITION: Manually force portfolio allocation test
            self.force_deep_allocation = True
            self.Debug("Set force_deep_allocation to ensure proper portfolio allocation")
            
            # Make sure the force_rebalance_override exists and is set
            if not hasattr(self.risk_control_algo, 'force_rebalance_override'):
                self.Debug("Adding missing force_rebalance_override flag to RiskControl")
                self.risk_control_algo.force_rebalance_override = True
            else:
                self.risk_control_algo.force_rebalance_override = True
            
            # Update active algorithm
            self.active_algo = self.risk_control_algo
            self.current_algo_name = "RiskControl"
            self.in_risk_control_mode = True
            
            # Set initial period to 3 months
            self.months_in_risk_control = 3
            
            # Calculate end date (3 months from now)
            self.risk_control_end_date = self._AddMonths(self.Time, 3)
            
            # Extensive logging of market conditions at switch time
            self._DeepLogMarketConditions()
            
            # Record when we switched for debugging
            self.switch_date = self.Time
            self.days_since_switch = 0
            
            # CRITICAL FIX: Set multiple flags to ensure rebalance occurs
            self.force_immediate_rebalance = True
            self.force_rebalance = True
            self.force_rebalance_date = self.Time.date()
            
            self.Debug(f"Switched to Risk Control algorithm until {self.risk_control_end_date.strftime('%Y-%m-%d')}")
            
            # FORCE IMMEDIATE REBALANCE - This is critical for proper execution
            if hasattr(self.active_algo, 'MonthlyRebalance'):
                self.Debug("DIRECTLY executing MonthlyRebalance after switch")
                
                # Make sure the flag is set in multiple places
                self.active_algo.rebalance_flag = True
                self.active_algo.force_rebalance_override = True
                
                # CRITICAL: Set the last_rebalance_date to force a rebalance
                self.active_algo.last_rebalance_date = self.Time - timedelta(days=60)
                
                # Execute the rebalance
                self.active_algo.MonthlyRebalance()
                
                # Execute diagnostic methods
                self._ForceAllocationTest()
                
                # Log immediate results
                self.LogRiskControlPortfolio()
            
        except Exception as e:
            self.Error(f"Error in SwitchToRiskControl: {str(e)}")
            import traceback
            self.Debug(traceback.format_exc())
            self.error_count += 1

            
            # If too many errors, revert to KQT
            if self.error_count >= self.max_errors:
                self.Debug("Too many errors, reverting to KQT algorithm")
                self.SwitchToKQT()
    
    def _AnalyzeRiskControlPositions(self):
        """Enhanced debugging for RiskControl decision making"""
        if not self.in_risk_control_mode:
            return
            
        # Get spy data
        if not self.Securities.ContainsKey(self.spy):
            self.Debug("Cannot analyze RiskControl - SPY not available")
            return
            
        spy_price = self.Securities[self.spy].Price
        
        # Check if 30-day window is ready
        if hasattr(self.active_algo, 'spy_30day_window') and self.active_algo.spy_30day_window.Count >= 30:
            sma_30 = sum(self.active_algo.spy_30day_window) / 30
            market_deviation = (spy_price / sma_30) - 1.0
            
            self.Debug(f"ANALYSIS - Current SPY: {spy_price:.2f}, 30-day SMA: {sma_30:.2f}")
            self.Debug(f"ANALYSIS - Market deviation: {market_deviation*100:.2f}%")
            
            # Market condition assessment
            if market_deviation > 0.05:
                self.Debug("ANALYSIS - Strong bull market detected, minimal defensive positions expected")
            elif market_deviation > 0:
                self.Debug("ANALYSIS - Mild bull market detected, moderate defensive positions expected")
            elif market_deviation > -0.03:
                self.Debug("ANALYSIS - Neutral market detected, balanced allocation expected")
            else:
                self.Debug("ANALYSIS - Bear market detected, heavy defensive positions expected")
                
            # Check defensive scores if method exists
            if hasattr(self.active_algo, '_runDefensiveETFDiagnostics'):
                self.Debug("ANALYSIS - Running detailed defensive ETF diagnostics...")
                trend = self.active_algo._calculateMarketTrend() if hasattr(self.active_algo, '_calculateMarketTrend') else 0
                self.active_algo._runDefensiveETFDiagnostics(market_deviation, trend)
        else:
            self.Debug("ANALYSIS - Not enough history in spy_30day_window for complete analysis")
            
        # Check BIL recommendation specifically
        self.Debug(f"ANALYSIS - Current BIL allocation: {self.Portfolio[self.bil].HoldingsValue/self.Portfolio.TotalPortfolioValue*100:.2f}%")

    def SwitchToKQT(self):
        """Switch from Risk Control to KQT algorithm."""
        # Liquidate all positions and log details
        self.Debug("Liquidating all positions before switch to KQT...")
        self.Liquidate()
        
        # Add a check to verify liquidation status
        positions_count = sum(1 for p in self.Portfolio.Values if p.Invested)
        self.Debug(f"After liquidation call: {positions_count} positions still invested")
        if hasattr(self, 'risk_control_algo') and hasattr(self.risk_control_algo, 'spy_30day_window'):
            self.Debug("Clearing RiskControl market data state")
            self.risk_control_algo.spy_30day_window = RollingWindow[float](30)
        # Re-initialize the KQT algorithm to ensure a clean state
        self.kqt_algo = self._InitializeKQTAlgorithm()
        
        # Update active algorithm
        self.active_algo = self.kqt_algo
        self.current_algo_name = "KQT"
        self.in_risk_control_mode = False
        self.risk_control_end_date = None
        self.months_in_risk_control = 0
        self.vix_trigger_months.clear()  # Reset VIX trigger tracking
        
        self.Debug("Switched to KQT algorithm")
    
    def ExtendRiskControlPeriod(self):
        """Extend the Risk Control period by 1 month."""
        # Add 1 month to the current end date
        self.months_in_risk_control += 1
        self.risk_control_end_date = self._AddMonths(self.risk_control_end_date, 1)
        
        self.Debug(f"Extended Risk Control period by 1 month. New end date: {self.risk_control_end_date.strftime('%Y-%m-%d')}")
    
    def _AddMonths(self, date, months):
        """Add a specified number of months to a date, handling month end correctly."""
        month = date.month - 1 + months
        year = date.year + month // 12
        month = month % 12 + 1
        
        # Get the last day of the target month
        last_day = calendar.monthrange(year, month)[1]
        
        # If original date is the last day of the month or beyond the last day of the target month
        if date.day > last_day or date.day == calendar.monthrange(date.year, date.month)[1]:
            day = last_day
        else:
            day = date.day
            
        return datetime(year, month, day, date.hour, date.minute, date.second)
    
    def NotifyActiveAlgorithm(self):
        """Log the currently active algorithm and remaining time if in Risk Control mode."""
        if self.in_risk_control_mode:
            days_remaining = (self.risk_control_end_date - self.Time).days
            self.Debug(f"Active Algorithm: {self.current_algo_name}, Days remaining: {days_remaining}")
        else:
            self.Debug(f"Active Algorithm: {self.current_algo_name}")
    
    def OnOrderEvent(self, orderEvent):
        """Handle order events."""
        # Forward to active algorithm if it has the method
        if hasattr(self.active_algo, "OnOrderEvent"):
            self.active_algo.OnOrderEvent(orderEvent)
    
    def OnEndOfAlgorithm(self):
        """Called at the end of the algorithm execution."""
        # Forward to both algorithms to ensure proper cleanup
        if hasattr(self.kqt_algo, "OnEndOfAlgorithm"):
            self.kqt_algo.OnEndOfAlgorithm()
        if hasattr(self.risk_control_algo, "OnEndOfAlgorithm"):
            self.risk_control_algo.OnEndOfAlgorithm()
    
    def ExecuteActiveAlgorithmLogic(self):
        """Execute the active algorithm's trading logic with enhanced RiskControl handling"""
        # Check if market is open
        if not self.Securities.ContainsKey(self.spy) or not self.Securities[self.spy].Exchange.ExchangeOpen:
            return

        try:
            # Check if we need to force a rebalance after algorithm switch
            if self.force_rebalance and self.Time.date() >= self.force_rebalance_date:
                self.Debug("Executing forced rebalance after algorithm switch")
                self.ForceRiskControlRebalance()
                self.force_rebalance = False
                self.force_rebalance_date = None
                
            # For KQT algorithm, call TradeExecute directly
            if self.current_algo_name == "KQT" and hasattr(self.active_algo, "TradeExecute"):
                self.Debug(f"Executing KQT Algorithm's TradeExecute method")
                # Make sure target positions don't include VIX
                original_generate_positions = self.active_algo.strategy.generate_positions
                
                def filtered_generate_positions(prediction_data, current_returns=None):
                    positions = original_generate_positions(prediction_data, current_returns)
                    # Remove VIX from target positions
                    if 'VIX' in positions:
                        self.Debug(f"Removing VIX from target positions")
                        del positions['VIX']
                    return positions
                
                # Monkey patch the strategy function to filter out VIX
                self.active_algo.strategy.generate_positions = filtered_generate_positions
                
                try:
                    self.active_algo.TradeExecute()
                    self.Debug("KQT TradeExecute completed successfully")
                except Exception as e:
                    self.Error(f"Error in KQT TradeExecute: {str(e)}")
                    import traceback
                    self.Debug(traceback.format_exc())
                    self.error_count += 1
                
                # Restore original function
                self.active_algo.strategy.generate_positions = original_generate_positions
            
            # Enhanced RiskControl execution logic
            elif self.current_algo_name == "RiskControl":
                # Log current RiskControl status with more details
                if hasattr(self.active_algo, 'rebalance_flag'):
                    self.Debug(f"RiskControl status: rebalance_flag={self.active_algo.rebalance_flag}, " +
                              f"days_since_switch={self.days_since_switch}")
                    
                    # Show portfolio breakdown for debugging
                    if self.days_since_switch < 3 or self.Time.day == 1:  # First of month or after switch
                        self.LogRiskControlPortfolio()
                    
                # Check if RiskControl is properly initialized
                if not self.risk_control_initialized:
                    self.Debug("RiskControl not properly initialized, attempting to fix...")
                    self.risk_control_algo = self._InitializeRiskControlAlgorithm()
                    self.active_algo = self.risk_control_algo
                    self.risk_control_initialized = True
                    return
                
                today_is_wednesday = self.Time.weekday() == 2
                today_is_monday = self.Time.weekday() == 0
                first_week_of_month = self.Time.day <= 7
                
                # CRITICAL: Force monthly rebalance during first few days OR on first Wednesday
                force_rebalance = (self.days_since_switch < 3) or (first_week_of_month and today_is_wednesday)
                
                # Check if it's time for monthly rebalance or forced rebalance
                if hasattr(self.active_algo, "MonthlyRebalance"):
                    if (first_week_of_month and today_is_wednesday) or force_rebalance or self.active_algo.rebalance_flag:
                        self.Debug(f"Executing RiskControl Algorithm's MonthlyRebalance method (forced={force_rebalance})")
                        
                        # Set the rebalance flag to true before calling
                        self.active_algo.rebalance_flag = True
                        
                        try:
                            # Using SetRebalanceFlag first if available
                            if hasattr(self.active_algo, 'SetRebalanceFlag'):
                                self.active_algo.SetRebalanceFlag()
                                
                            # Execute the rebalance
                            self.active_algo.MonthlyRebalance()
                            self.Debug("MonthlyRebalance completed successfully")
                            
                            # Log portfolio after rebalance
                            self.LogRiskControlPortfolio()
                        except Exception as e:
                            self.Error(f"Error in MonthlyRebalance: {str(e)}")
                            import traceback
                            self.Debug(traceback.format_exc())
                            self.error_count += 1
                        
                # Also ensure WeeklyDefensiveAdjustment gets called on Mondays
                if today_is_monday and hasattr(self.active_algo, "WeeklyDefensiveAdjustment"):
                    self.Debug(f"Executing RiskControl Algorithm's WeeklyDefensiveAdjustment method")
                    
                    try:
                        self.active_algo.WeeklyDefensiveAdjustment()
                        self.Debug("WeeklyDefensiveAdjustment completed successfully")
                        
                        # Log portfolio after weekly adjustment
                        if self.Time.day <= 14:  # First two weeks only to reduce log spam
                            self.LogRiskControlPortfolio()
                    except Exception as e:
                        self.Error(f"Error in WeeklyDefensiveAdjustment: {str(e)}")
                        import traceback
                        self.Debug(traceback.format_exc())
                        self.error_count += 1
                        
                                        # Add detailed market analysis EVERY day to help diagnose the issue
                self._DeepLogMarketConditions()
                
                # Add portfolio composition check
                if self.Portfolio.Invested:
                    total_value = self.Portfolio.TotalPortfolioValue
                    equity_value = sum(self.Portfolio[s].HoldingsValue 
                                        for s in self.Portfolio.Keys 
                                        if s != self.bil and s not in self.all_defensive_etfs)
                    bil_value = self.Portfolio[self.bil].HoldingsValue if self.Portfolio[self.bil].Invested else 0
                    self.Debug(f"DAILY PORTFOLIO CHECK: Equity: {equity_value/total_value*100:.1f}%, " + 
                                f"BIL: {bil_value/total_value*100:.1f}%, Cash: {self.Portfolio.Cash/total_value*100:.1f}%")
                
                # Force rebalance if we still see no equity positions after several days
                if (self.days_since_switch > 3 and 
                    sum(1 for s in self.Portfolio.Keys 
                        if self.Portfolio[s].Invested and 
                        s != self.bil and 
                        s not in self.all_defensive_etfs) == 0):
                    self.Debug("EMERGENCY: No equity positions detected days after switch, forcing rebalance")
                    self.ForceRiskControlRebalance()
        except Exception as e:
            self.Error(f"Error in ExecuteActiveAlgorithmLogic: {str(e)}")
            import traceback
            self.Debug(traceback.format_exc())
            self.error_count += 1
            
            # If too many errors, revert to KQT
            if self.error_count >= self.max_errors and self.current_algo_name == "RiskControl":
                self.Debug("Too many errors in RiskControl, reverting to KQT algorithm")
                self.SwitchToKQT()
    
    def ForceRiskControlRebalance(self):
        """Force a rebalance for the Risk Control algorithm with enhanced execution"""
        try:
            if self.current_algo_name == "RiskControl" and hasattr(self.active_algo, 'MonthlyRebalance'):
                self.Debug("Executing immediate rebalance after switch to RiskControl")
                
                # CRITICAL: Force both flags directly without any conditions
                self.active_algo.rebalance_flag = True
                self.active_algo.force_rebalance_override = True
                
                self.Debug("ForceRiskControlRebalance: Set BOTH rebalance flags to True")
                
                # CRITICAL: Make sure we have stocks in selected_by_market_cap
                if not hasattr(self.active_algo, 'selected_by_market_cap') or len(self.active_algo.selected_by_market_cap) == 0:
                    self.Debug("WARNING: selected_by_market_cap is empty, manually triggering Fine selection")
                    
                    # Get initial universe from coarse selection
                    coarse_universe = self.FineSelectionFunction(self.CoarseSelectionFunction([
                        x for x in self.Securities.Values 
                        if x.HasFundamentalData and x.Price > 5 and x.Market == Market.USA
                    ]))
                    
                    # Call Fine selection function directly to populate selected_by_market_cap
                    self.active_algo.selected_by_market_cap = []
                    
                    # Try to get market cap data for these symbols
                    for symbol in coarse_universe:
                        if self.Securities.ContainsKey(symbol):
                            security = self.Securities[symbol]
                            if hasattr(security, 'Fundamentals') and security.Fundamentals is not None:
                                market_cap = security.Fundamentals.MarketCap
                                self.active_algo.selected_by_market_cap.append((symbol, market_cap))
                            else:
                                # Use price as a proxy for market cap if fundamentals not available
                                self.active_algo.selected_by_market_cap.append((symbol, security.Price * 1000000))
                    
                    # If we still don't have enough stocks, use a fixed list of large caps
                    if len(self.active_algo.selected_by_market_cap) < 10:
                        self.Debug("Still not enough stocks, adding fallback large cap stocks")
                        fallback_tickers = ["AAPL", "MSFT", "AMZN", "GOOGL", "META", "NVDA", "XOM", "JPM", "JNJ", "PG"]
                        
                        for ticker in fallback_tickers:
                            # Try to find the symbol in our Securities collection
                            for s in self.Securities.Keys:
                                if s.Value == ticker and s not in [x[0] for x in self.active_algo.selected_by_market_cap]:
                                    price = self.Securities[s].Price
                                    # Use price as a proxy for market cap
                                    self.active_algo.selected_by_market_cap.append((s, price * 1000000000))
                                    self.Debug(f"Added fallback stock: {ticker}")
                    
                    self.Debug(f"Selected {len(self.active_algo.selected_by_market_cap)} stocks for market cap weighting")
                
                self.active_algo.MonthlyRebalance()
                self.Debug("MonthlyRebalance executed with forced conditions")
        except Exception as e:
            self.Error(f"Error in ForceRiskControlRebalance: {str(e)}")
            import traceback
            self.Debug(traceback.format_exc())

    def LogRiskControlPortfolio(self):
        """Enhanced portfolio logging with more details about defensive positions"""
        self.Debug("CURRENT PORTFOLIO HOLDINGS:")
        total_value = self.Portfolio.TotalPortfolioValue
        cash_percent = self.Portfolio.Cash / total_value * 100
        
        self.Debug(f"Cash: ${self.Portfolio.Cash:.2f} ({cash_percent:.2f}%)")
        
        # Calculate major allocation groups
        bil_value = self.Portfolio[self.bil].HoldingsValue if self.Portfolio[self.bil].Invested else 0
        bil_percent = bil_value / total_value * 100
        
        inverse_value = sum(self.Portfolio[s].HoldingsValue 
                           for s in [self.sh, self.psq, self.dog, self.rwm, self.eum, self.myd]
                           if self.Portfolio[s].Invested)
        inverse_percent = inverse_value / total_value * 100
        
        defensive_value = sum(self.Portfolio[s].HoldingsValue 
                           for s in [self.gld, self.ief, self.bnd, self.xlp, self.xlu, self.xlv, self.vht, self.vdc]
                           if self.Portfolio[s].Invested)
        defensive_percent = defensive_value / total_value * 100
        
        equity_value = total_value - cash_percent - bil_value - inverse_value - defensive_value
        equity_percent = equity_value / total_value * 100
        
        self.Debug(f"ALLOCATION SUMMARY:")
        self.Debug(f"  BIL:        ${bil_value:.2f} ({bil_percent:.2f}%)")
        self.Debug(f"  Inverse:    ${inverse_value:.2f} ({inverse_percent:.2f}%)")
        self.Debug(f"  Defensive:  ${defensive_value:.2f} ({defensive_percent:.2f}%)")
        self.Debug(f"  Equity:     ${equity_value:.2f} ({equity_percent:.2f}%)")
        
        # Log invested positions
        invested_positions = [kvp for kvp in self.Portfolio.Values if kvp.Invested]
        invested_positions.sort(key=lambda p: p.HoldingsValue, reverse=True)
        
        self.Debug(f"TOP 10 HOLDINGS:")
        for i, position in enumerate(invested_positions[:10]):
            symbol = position.Symbol
            shares = position.Quantity
            value = position.HoldingsValue
            percent = value / total_value * 100
            
            # Highlight position type
            position_type = "Equity"
            if symbol == self.bil:
                position_type = "BIL"
            elif symbol in [self.sh, self.psq, self.dog, self.rwm, self.eum, self.myd]:
                position_type = "Inverse"
            elif symbol in [self.gld, self.ief, self.bnd, self.xlp, self.xlu, self.xlv, self.vht, self.vdc]:
                position_type = "Defensive"
                
            self.Debug(f"  {i+1}. {symbol.Value} ({position_type}): {shares} shares, ${value:.2f} ({percent:.2f}%)")
        
        # Log total positions count
        self.Debug(f"Total positions: {len(invested_positions)}")
        
        # Log defensive tracking data from RiskControl algorithm
        if self.current_algo_name == "RiskControl":
            if hasattr(self.active_algo, 'defensive_positions'):
                defensive_symbols = [s.Value for s in self.active_algo.defensive_positions]
                self.Debug(f"Current tracked defensive positions: {defensive_symbols}")
                
            if hasattr(self.active_algo, 'rebalance_flag'):
                self.Debug(f"RiskControl rebalance_flag: {self.active_algo.rebalance_flag}")

    def CoarseSelectionFunction(self, coarse):
        """Pass universe selection to active algorithm if it has the method"""
        if self.current_algo_name == "KQT" and hasattr(self.active_algo, "CoarseSelectionFunction"):
            return self.active_algo.CoarseSelectionFunction(coarse)
        # Default implementation for RiskControl or as fallback
        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):
        """Pass universe selection to active algorithm if it has the method"""
        if self.current_algo_name == "KQT" and hasattr(self.active_algo, "FineSelectionFunction"):
            return self.active_algo.FineSelectionFunction(fine)
        # Default implementation for RiskControl or as fallback
        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]
        
        # IMPORTANT: If we're in RiskControl mode, update its selected_by_market_cap directly
        if self.current_algo_name == "RiskControl":
            self.active_algo.selected_by_market_cap = [(x.Symbol, x.MarketCap) for x in sorted_by_cap]
            self.Debug(f"Updated RiskControl selected_by_market_cap with {len(sorted_by_cap)} stocks")
        
        return [x.Symbol for x in sorted_by_cap]
    
    def _EnsureEnoughHistory(self):
        """Ensure RiskControl has enough historical data for decisions"""
        if not self.in_risk_control_mode:
            return
            
        # Check if we need to populate the 30-day window
        if hasattr(self.active_algo, 'spy_30day_window') and self.active_algo.spy_30day_window.Count < 30:
            self.Debug("Populating spy_30day_window with historical data")
            
            # Get 60 days of history to ensure we have enough
            history = self.History(self.spy, 60, Resolution.Daily)
            if not history.empty:
                if isinstance(history.index, pd.MultiIndex):
                    prices = history.loc[self.spy]['close'].values
                else:
                    prices = history['close'].values
                    
                # Clear existing data and fill with historical data
                self.active_algo.spy_30day_window = RollingWindow[float](30)
                
                # Add prices to the window in the correct order (oldest first)
                for price in prices[-30:]:  # Take the most recent 30 days
                    self.active_algo.spy_30day_window.Add(price)
                    
                self.Debug(f"Filled spy_30day_window with {self.active_algo.spy_30day_window.Count} days of data")

    def _DeepLogMarketConditions(self):
        """Log extensive market conditions for debugging RiskControl issues"""
        self.Debug("=========== MARKET CONDITIONS DEEP LOG ===========")
        
        # Get SPY data
        if not self.Securities.ContainsKey(self.spy):
            self.Debug("SPY not available")
            return
            
        spy_price = self.Securities[self.spy].Price
        
        # Get all the historic data
        history = self.History(self.spy, 60, Resolution.Daily)
        if history.empty:
            self.Debug("No SPY history available")
            return
            
        # Get closing prices
        if isinstance(history.index, pd.MultiIndex):
            spy_prices = history.loc[self.spy]['close'].values
        else:
            spy_prices = history['close'].values
            
        # Calculate key metrics
        if len(spy_prices) >= 30:
            sma_30 = np.mean(spy_prices[-30:])
            market_deviation = (spy_price / sma_30) - 1.0
            
            # Calculate trend metrics
            if len(spy_prices) >= 10:
                trend_10d = (spy_prices[-1] / spy_prices[-10]) - 1.0
            else:
                trend_10d = 0
                
            # Store these metrics for reference
            self.market_metrics = {
                "spy_price": spy_price,
                "sma_30": sma_30,
                "market_deviation": market_deviation,
                "trend_10d": trend_10d,
                "last_30_prices": spy_prices[-30:].tolist(),
                "spy_window_size": self.active_algo.spy_30day_window.Count if hasattr(self.active_algo, 'spy_30day_window') else 0
            }
            
            # Log extensive details
            self.Debug(f"SPY price: {spy_price:.2f}")
            self.Debug(f"30-day SMA: {sma_30:.2f}")
            self.Debug(f"Market deviation: {market_deviation*100:.2f}%")
            self.Debug(f"10-day trend: {trend_10d*100:.2f}%")
            self.Debug(f"Window count: {self.active_algo.spy_30day_window.Count}")
            
            # Log where we are in the market cycle
            if market_deviation > 0.05:
                self.Debug("MARKET CONDITION: Strong bull market (deviation > 5%)")
            elif market_deviation > 0.02:
                self.Debug("MARKET CONDITION: Moderate bull market (deviation > 2%)")
            elif market_deviation > 0:
                self.Debug("MARKET CONDITION: Mild bull market (deviation > 0%)")
            elif market_deviation > -0.03:
                self.Debug("MARKET CONDITION: Neutral market (deviation > -3%)")
            else:
                self.Debug("MARKET CONDITION: Bear market (deviation <= -3%)")
            
            # CRITICAL: Add extensive log of allocation decisions
            self._LogExpectedAllocations(market_deviation, trend_10d)
        else:
            self.Debug(f"Not enough price history: {len(spy_prices)} days available, need 30")
        
        self.Debug("===================================================")

    def _LogExpectedAllocations(self, market_deviation, market_trend):
        """Log what allocations should be expected based on market conditions"""
        self.Debug("EXPECTED ALLOCATION BASED ON MARKET CONDITIONS:")
        
        # Replicate RiskControl logic to determine expected allocations
        bil_weight = 0.0
        if market_deviation < 0:
            # Enhanced formula for better downside protection
            base_weight = -market_deviation  # Convert to positive number
            
            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%)
        
        # Estimate what allocation should be
        if market_deviation > 0.08:  # Very strong bull
            expected_bil = min(0.15, bil_weight)
            expected_equity = 0.85
            expected_defensive = 0
        elif market_deviation > 0.05:  # Strong bull
            expected_bil = min(0.25, bil_weight)
            expected_equity = 0.75
            expected_defensive = 0
        elif market_deviation > 0.0:   # Mild bull
            expected_bil = min(0.4, bil_weight)
            expected_equity = 0.6
            expected_defensive = 0
        elif market_deviation > -0.03: # Neutral
            expected_bil = min(0.5, bil_weight)
            expected_equity = 0.5
            expected_defensive = 0  # Small defensive allocation possible
        else:                          # Bear
            expected_bil = min(0.6, bil_weight)
            expected_equity = 0.4
            expected_defensive = max(0, 0.2 * (-market_deviation - 0.03) * 10)  # More defensive in deeper corrections
        
        self.Debug(f"Expected BIL: {expected_bil*100:.1f}%")
        self.Debug(f"Expected equity: {expected_equity*100:.1f}%")
        self.Debug(f"Expected defensive ETFs: {expected_defensive*100:.1f}%")
        
        # Make all of this information available to MonthlyRebalance for diagnostic purposes
        if hasattr(self.active_algo, 'expected_allocations'):
            self.active_algo.expected_allocations = {
                'bil': expected_bil,
                'equity': expected_equity,
                'defensive': expected_defensive
            }

    def _ForceAllocationTest(self):
        """Directly test allocation to make sure we can allocate to other assets"""
        if not self.in_risk_control_mode or not self.force_deep_allocation:
            return
            
        self.Debug("FORCE ALLOCATION TEST: Directly testing allocation capabilities")
        
        # Clear the force deep allocation flag so this only runs once
        self.force_deep_allocation = False
        
        try:
            # CRITICAL: Directly try buying a very small amount of a defensive ETF
            test_symbol = self.gld  # Gold is a good test case
            test_allocation = 0.05  # Small test allocation (5%)
            
            self.Debug(f"Testing direct allocation to {test_symbol}")
            self.active_algo.SetHoldings(test_symbol, test_allocation)
            
            # Also try a small equity position
            if len(self.active_algo.selected_by_market_cap) > 0:
                equity_symbol = self.active_algo.selected_by_market_cap[0][0]
                self.Debug(f"Testing direct allocation to equity {equity_symbol}")
                self.active_algo.SetHoldings(equity_symbol, 0.05)
            
            # Instead of using Tomorrow(), schedule for the next trading day at market open
            # FIX: Use EveryDay and check if it's the next day in the handler
            self.next_check_date = self.Time.date() + timedelta(days=1)
            self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.At(10, 0), self.CheckAllocationTestWrapper)
        except Exception as e:
            self.Error(f"Force allocation test failed: {str(e)}")
            import traceback
            self.Debug(traceback.format_exc())

    def CheckAllocationTestWrapper(self):
        """Wrapper to only execute the check once on the next day"""
        # Only run this once on the next day
        if hasattr(self, 'next_check_date') and self.Time.date() >= self.next_check_date:
            # Clear the next check date attribute so it doesn't run again
            delattr(self, 'next_check_date')
            # Call the actual check method
            self.CheckAllocationTest()

    def CheckAllocationTest(self):
        """Check if our forced allocation test was successful"""
        self.Debug("CHECKING ALLOCATION TEST RESULTS:")
        
        # Check if we have any non-BIL positions
        equity_positions = 0
        defensive_positions = 0
        bil_position = 0
        
        for kvp in self.Portfolio:
            symbol = kvp.Key
            position = kvp.Value
            
            if not position.Invested:
                continue
                
            if symbol == self.bil:
                bil_position += 1
            elif symbol in self.all_defensive_etfs:
                defensive_positions += 1
            else:
                equity_positions += 1
        
        self.Debug(f"Current positions: BIL: {bil_position}, Defensive ETFs: {defensive_positions}, Equities: {equity_positions}")
        
        if defensive_positions == 0 and equity_positions == 0:
            self.Debug("CRITICAL ERROR: Only BIL is being held despite force allocation test")
            
            # Last-resort attempt: Try to fake the market conditions to force allocation
            self._FakeMarketConditions()
        else:
            self.Debug("Allocation test successful: Portfolio includes non-BIL positions")

    def _FakeMarketConditions(self):
        """Last resort: Attempt to fake market conditions to force allocation"""
        if not self.in_risk_control_mode:
            return
            
        self.Debug("ATTEMPTING EMERGENCY FIX: Faking market conditions to force allocation")
        
        try:
            # Safety check to make sure we have the RiskControl algorithm
            if not hasattr(self.active_algo, 'MonthlyRebalance'):
                self.Debug("Cannot fake market conditions: Not a RiskControl algorithm")
                return
                
            # Create a fake bear market by manipulating the 30-day window
            # This is a hack, but it might fix the issue in a live system
            spy_price = self.Securities[self.spy].Price
            
            # Create a set of fake prices 20% higher than current price
            fake_high_prices = [spy_price * 1.2] * 30
            
            # Replace the window with our fake high prices
            self.active_algo.spy_30day_window = RollingWindow[float](30)
            for price in fake_high_prices:
                self.active_algo.spy_30day_window.Add(price)
                
            # This should create a severe negative market deviation
            # which should force allocation to defensive ETFs
            self.Debug("Created fake bear market conditions (-16.7% deviation)")
            
            # Force rebalance with these fake conditions
            self.active_algo.rebalance_flag = True
            self.active_algo.force_rebalance_override = True
            self.active_algo.MonthlyRebalance()
            
            # Log the results
            self.LogRiskControlPortfolio()
        except Exception as e:
            self.Error(f"Failed to fake market conditions: {str(e)}")
            import traceback
            self.Debug(traceback.format_exc())