from AlgorithmImports import *
from sklearn.cluster import KMeans  # K Means clustering model
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd

# Custom fee model for 0.1% per trade
class PercentageFeeModel(FeeModel):
    def GetOrderFee(self, parameters):
        security = parameters.Security
        order = parameters.Order
        fee = 0.001 * security.Price * abs(order.Quantity)
        currency = security.QuoteCurrency.Symbol
        return OrderFee(CashAmount(fee, currency))

class MLTradingAlgorithm(QCAlgorithm):
    
    def Initialize(self):
        # Algorithm Parameters
        self.SetStartDate(2019, 1, 1)         # Start date
        self.SetEndDate(2024, 12, 31)         # End date
        self.SetCash(10000)                   # Initial capital

        # Configurable ticker symbols and allocation percentage
        self.trading_ticker = self.GetParameter("trading_ticker", "MSTR")
        self.benchmark_ticker = self.GetParameter("benchmark_ticker", "SPY")
        self.allocation_percentage = self.GetParameter("allocation_percentage", 1)

        # Add trading equity with custom fee and slippage models
        trading_security = self.AddEquity(self.trading_ticker, Resolution.Daily)
        trading_security.SetFeeModel(PercentageFeeModel())
        trading_security.SetSlippageModel(ConstantSlippageModel(0))
        self.symbol = trading_security.Symbol
        
        # Add benchmark equity with custom fee and slippage models
        benchmark_security = self.AddEquity(self.benchmark_ticker, Resolution.Daily)
        benchmark_security.SetFeeModel(PercentageFeeModel())
        benchmark_security.SetSlippageModel(ConstantSlippageModel(0))
        self.benchmark_symbol = benchmark_security.Symbol

        # RollingWindow to store 200 days of TradeBar data for the trading asset
        self.data = RollingWindow[TradeBar](200)

        # Warm-up period
        self.SetWarmUp(200)

        # Initialize KMeans clustering model with 2 clusters
        self.model = KMeans(n_clusters=2, random_state=42)
        # Scaler for feature normalization
        self.scaler = StandardScaler()
        # Dictionary mapping each cluster to a trading signal (1: buy, 0: sell)
        self.cluster_to_signal = {}
        self.training_count = 0
        self.is_model_trained = False  # Tracks if the model is trained

        # Schedule training every Monday at 10:00 AM
        self.Schedule.On(self.DateRules.Every(DayOfWeek.Monday), 
                         self.TimeRules.At(10, 0), 
                         self.TrainModel)
        
        # Initialize variables for benchmarking
        self.beat_benchmark_count = 0
        self.trade_entry_price = None  # To store the entry price of a trade
        self.benchmark_entry_price = None  # To store the benchmark price at trade entry
    
    def OnData(self, data):
        # Ensure data exists for trading symbol
        if not data.ContainsKey(self.symbol):
            return
        
        trade_bar = data[self.symbol]
        if trade_bar is None:
            return
        
        # Add TradeBar to Rolling Window
        self.data.Add(trade_bar)

        # Check if RollingWindow is ready
        if not self.data.IsReady or self.data.Count < 200:
            return
        
        # Ensure model is trained before making predictions
        if not self.is_model_trained:
            self.Debug("Model is not trained yet. Skipping prediction.")
            return

        # Extract features for prediction
        df = self.GetFeatureDataFrame()
        if df is None or len(df) < 1:
            return
        
        # Latest features (all columns except the target)
        latest_features = df.iloc[-1, :-1].values.reshape(1, -1)
        try:
            # Scale the features using the fitted scaler
            latest_features_scaled = self.scaler.transform(latest_features)
            # Get the cluster label from KMeans
            cluster_label = self.model.predict(latest_features_scaled)[0]
            # Map the cluster label to a trading signal (1: buy, 0: sell)
            prediction = self.cluster_to_signal.get(cluster_label, 0)
        except Exception as e:
            self.Debug(f"Error: Model prediction failed. {e}")
            return
        
        # Trading logic
        holdings = self.Portfolio[self.symbol].Quantity
        
        # Buy if prediction = 1 and not currently invested
        if prediction == 1 and holdings <= 0:
            self.SetHoldings(self.symbol, self.allocation_percentage)
            # Record the entry prices for the trade and benchmark
            self.trade_entry_price = trade_bar.Close
            if self.benchmark_symbol in data and data[self.benchmark_symbol] is not None:
                self.benchmark_entry_price = data[self.benchmark_symbol].Close
            else:
                self.benchmark_entry_price = None
        
        # Sell if prediction = 0 and currently invested
        elif prediction == 0 and holdings > 0:
            # Calculate trade return and benchmark return
            if self.trade_entry_price is not None and self.benchmark_entry_price is not None:
                trade_exit_price = trade_bar.Close
                trade_return = (trade_exit_price - self.trade_entry_price) / self.trade_entry_price
                
                if self.benchmark_symbol in data and data[self.benchmark_symbol] is not None:
                    benchmark_exit_price = data[self.benchmark_symbol].Close
                    benchmark_return = (benchmark_exit_price - self.benchmark_entry_price) / self.benchmark_entry_price
                    
                    # Compare trade return with benchmark return
                    if trade_return > benchmark_return:
                        self.beat_benchmark_count += 1
            
            # Reset entry prices after the trade is closed
            self.trade_entry_price = None
            self.benchmark_entry_price = None
            
            # Execute the sell order
            self.Liquidate(self.symbol)

    def TrainModel(self):
        # Prepare training data
        df = self.GetFeatureDataFrame()
        if df is None or len(df) < 50:  # Require enough data to train
            self.Debug("Insufficient data for training.")
            return

        # Separate features and target; features are all columns except 'Target'
        X = df.iloc[:, :-1]  # Features
        y = df.iloc[:, -1]   # Target (0 or 1)

        # Scale the features
        X_scaled = self.scaler.fit_transform(X)
        
        # Split data chronologically (no shuffle) for evaluation purposes
        X_train, X_test, y_train, y_test = train_test_split(
            X_scaled, y, test_size=0.2, shuffle=False, random_state=42
        )

        # Train KMeans clustering on the training data
        self.model.fit(X_train)
        
        # Determine the trading signal for each cluster:
        # For each cluster, calculate the average target value of samples assigned to it.
        # If the mean > 0.5, we assign a signal 1 (buy); otherwise, signal 0 (sell).
        train_labels = self.model.labels_
        cluster_to_signal = {}
        for cluster in np.unique(train_labels):
            indices = np.where(train_labels == cluster)
            cluster_mean = np.mean(np.array(y_train)[indices])
            cluster_to_signal[cluster] = 1 if cluster_mean > 0.5 else 0
        self.cluster_to_signal = cluster_to_signal

        # Evaluate model performance on the training set
        train_cluster_labels = self.model.predict(X_train)
        train_signals = np.array([self.cluster_to_signal[label] for label in train_cluster_labels])
        train_accuracy = accuracy_score(y_train, train_signals)

        # Evaluate on the test set
        test_cluster_labels = self.model.predict(X_test)
        test_signals = np.array([self.cluster_to_signal[label] for label in test_cluster_labels])
        test_accuracy = accuracy_score(y_test, test_signals)
        
        self.is_model_trained = True
        self.training_count += 1
        self.Debug(f"Training #{self.training_count}: "
                   f"Train Accuracy: {train_accuracy:.2%}, "
                   f"Test Accuracy: {test_accuracy:.2%}")

    def GetFeatureDataFrame(self):
        # Wait until we have 200 data points in the rolling window
        if self.data.Count < 200:
            return None
        
        # Convert rolling window data to a DataFrame
        close_prices = [bar.Close for bar in self.data]
        df = pd.DataFrame(close_prices, columns=["Close"])
        
        # Feature Engineering
        df["SMA_10"] = df["Close"].rolling(window=10).mean()
        df["SMA_60"] = df["Close"].rolling(window=60).mean()
        
        # RSI Calculation
        delta = df["Close"].diff()
        gain = (delta.where(delta > 0, 0)).rolling(14).mean()
        loss = (-delta.where(delta < 0, 0)).rolling(14).mean()
        rs = gain / loss
        df["RSI"] = 100 - (100 / (1 + rs))
        
        # MACD Calculation
        df["MACD"] = df["Close"].ewm(span=12, adjust=False).mean() - df["Close"].ewm(span=26, adjust=False).mean()
        df["MACD_Signal"] = df["MACD"].ewm(span=9, adjust=False).mean()

        # Historical Volatility (HV_30)
        df["HV_30"] = df["Close"].pct_change().rolling(window=30).std() * np.sqrt(252)

        # Define Target: 1 if next day's Close > today's Close, else 0
        df["Target"] = (df["Close"].shift(-1) > df["Close"]).astype(int)
        
        # Remove rows with NaN values
        df.dropna(inplace=True)

        return df
    
    def OnEndOfAlgorithm(self):
        # Print the number of times the strategy beat the benchmark
        self.Log(f"Number of times strategy beat {self.benchmark_ticker}: {self.beat_benchmark_count}")