Create linear_regression.py

algo/linear_regression.py

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+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LinearRegression
+
+def linear_regression_forecast(data, forecast_horizon):
+    """
+    Forecast future values using Linear Regression, with a dynamic forecast horizon.
+
+    Parameters:
+    - data: Pandas Series of historical closing prices.
+    - forecast_horizon: Integer specifying the number of days to forecast.
+    
+    Returns:
+    - Pandas Series containing the forecasted values with a datetime index.
+    """
+    # Prepare the features (time) and target (data values)
+    X = np.arange(len(data)).reshape(-1, 1)  # Time as the feature
+    y = data.values  # Stock prices as the target
+    
+    # Fit the Linear Regression model
+    model = LinearRegression()
+    model.fit(X, y)
+    
+    # Prepare future time points for prediction based on the forecast horizon
+    future_X = np.arange(len(data), len(data) + forecast_horizon).reshape(-1, 1)
+    
+    # Forecast future stock prices
+    forecast = model.predict(future_X)
+    
+    # Create a pandas Series for the forecasted values with a date index
+    future_dates = pd.date_range(start=data.index[-1] + pd.Timedelta(days=1), periods=forecast_horizon)
+    forecast_series = pd.Series(forecast, index=future_dates)
+    
+    return forecast_series