Create random_forest.py

algo/random_forest.py

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+from sklearn.ensemble import RandomForestRegressor
+import pandas as pd
+import numpy as np
+
+def create_lagged_features(data, n_lags=5):
+    """
+    Prepares the dataset with lagged features necessary for Random Forest regression.
+
+    Parameters:
+    - data: Pandas Series of historical closing prices.
+    - n_lags: The number of lagged observations to create as features.
+    
+    Returns:
+    - A tuple (X, y) where X is a DataFrame of lagged features and y is the original dataset shifted.
+    """
+    df = pd.DataFrame(data)
+    for lag in range(1, n_lags + 1):
+        df[f'lag_{lag}'] = df[data.name].shift(lag)
+    df.dropna(inplace=True)  # Drop rows with NaN values resulted from shifting
+    X = df.drop(columns=[data.name])
+    y = df[data.name]
+    return X, y
+
+def random_forest_forecast(data, forecast_horizon, n_lags=5):
+    """
+    Forecast future values using a Random Forest Regressor, with a dynamic forecast horizon.
+
+    Parameters:
+    - data: Pandas Series of historical closing prices.
+    - forecast_horizon: Integer specifying the number of days to forecast.
+    - n_lags: Number of past observations to use for forecasting.
+    
+    Returns:
+    - Pandas Series containing the forecasted values with a datetime index.
+    """
+    X, y = create_lagged_features(data, n_lags)
+    model = RandomForestRegressor(n_estimators=100, random_state=42)
+    model.fit(X, y)
+
+    # Prepare the data for forecasting
+    last_obs = data.tail(n_lags).values[::-1]  # Reverse to get the correct order (most recent first)
+    forecasts = []
+
+    for _ in range(forecast_horizon):
+        # Reshape last_obs to match model input shape
+        model_input = np.array(last_obs).reshape(1, -1)
+        forecast = model.predict(model_input)[0]
+        forecasts.append(forecast)
+
+        # Update last_obs with the forecasted value
+        last_obs = np.roll(last_obs, -1)
+        last_obs[-1] = forecast
+
+    future_dates = pd.date_range(start=data.index[-1] + pd.Timedelta(days=1), periods=forecast_horizon)
+    forecast_series = pd.Series(forecasts, index=future_dates)
+    
+    return forecast_series