prediction_models.py

import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from statsmodels.tsa.arima.model import ARIMA
from scipy.stats import norm

class MarketPredictor:
    def __init__(self):
        self.scaler = MinMaxScaler(feature_range=(0, 1))
        self.rf_model = None
        self.exp_model = None
        self.arima_model = None
        
    def prepare_data(self, data, lookback=60):
        """Prepare data for Random Forest model"""
        scaled_data = self.scaler.fit_transform(data.values.reshape(-1, 1))
        X, y = [], []
        for i in range(lookback, len(scaled_data)):
            X.append(scaled_data[i-lookback:i, 0])
            y.append(scaled_data[i, 0])
        return np.array(X), np.array(y)

    def train_rf(self, data, lookback=60):
        """Train Random Forest model"""
        X, y = self.prepare_data(data, lookback)
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
        
        self.rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
        self.rf_model.fit(X_train, y_train)
        return self.rf_model.score(X_test, y_test)

    def predict_rf(self, data, days_ahead=30):
        """Generate Random Forest predictions"""
        last_sequence = data[-60:].values.reshape(-1, 1)
        last_sequence = self.scaler.transform(last_sequence)
        
        predictions = []
        current_sequence = last_sequence.copy()
        
        for _ in range(days_ahead):
            current_features = current_sequence[-60:].reshape(1, -1)
            predicted_value = self.rf_model.predict(current_features)
            predictions.append(predicted_value[0])
            current_sequence = np.append(current_sequence, predicted_value)
            
        predictions = self.scaler.inverse_transform(np.array(predictions).reshape(-1, 1))
        return predictions

    def train_exp(self, data):
        """Train Exponential Smoothing model"""
        self.exp_model = ExponentialSmoothing(
            data,
            seasonal_periods=5,
            trend='add',
            seasonal='add',
        ).fit()
        return self.exp_model

    def predict_exp(self, days_ahead=30):
        """Generate Exponential Smoothing predictions"""
        forecast = self.exp_model.forecast(days_ahead)
        return forecast

    def calculate_var(self, data, confidence_level=0.95):
        """Calculate Value at Risk"""
        returns = data.pct_change().dropna()
        var = norm.ppf(1-confidence_level) * returns.std()
        return var

    def monte_carlo_simulation(self, data, n_simulations=1000, days_ahead=30):
        """Perform Monte Carlo simulation"""
        returns = data.pct_change().dropna()
        mu = returns.mean()
        sigma = returns.std()
        
        simulations = np.zeros((days_ahead, n_simulations))
        last_price = data.iloc[-1]
        
        for sim in range(n_simulations):
            prices = [last_price]
            for day in range(days_ahead):
                price = prices[-1] * (1 + np.random.normal(mu, sigma))
                prices.append(price)
            simulations[:, sim] = prices[1:]
            
        return pd.DataFrame(simulations, columns=[f'sim_{i}' for i in range(n_simulations)])

    def detect_patterns(self, data):
        """Detect basic chart patterns"""
        patterns = {}
        close = data['Close']
        
        # Moving averages for trend detection
        sma20 = close.rolling(window=20).mean()
        sma50 = close.rolling(window=50).mean()
        
        # Detect trend changes
        patterns['uptrend'] = sma20 > sma50
        patterns['downtrend'] = sma20 < sma50
        
        # Support/Resistance levels
        patterns['support'] = close.rolling(window=20).min()
        patterns['resistance'] = close.rolling(window=20).max()
        
        return patterns

    def predict_breakouts(self, data, window=20):
        """Predict potential breakout points using Bollinger Bands logic"""
        close = data['Close']
        bb = pd.DataFrame()
        
        # Calculate Bollinger Bands manually
        bb['middle'] = close.rolling(window=window).mean()
        std = close.rolling(window=window).std()
        bb['upper'] = bb['middle'] + (std * 2)
        bb['lower'] = bb['middle'] - (std * 2)
        
        # Calculate distance from bounds
        bb['distance_upper'] = (bb['upper'] - data['Close']) / data['Close']
        bb['distance_lower'] = (data['Close'] - bb['lower']) / data['Close']
        
        # Identify potential breakout points
        bb['breakout_probability'] = 1 - (bb['distance_upper'] + bb['distance_lower'])
        
        return bb