Ridge Regression

Explanation & Rationale

The Ridge Regression Strategy uses L2 regularization to fit a regression model to the past 14 days of price data, helping to prevent overfitting while capturing the relationship between time and price movements. By applying regularization, Ridge Regression reduces the impact of less significant features, leading to more robust predictions. The strategy generates buy and sell signals based on whether the predicted price suggests an uptrend or downtrend, helping traders capture market momentum while avoiding noise.

Code

'''
Ridge Regression Strategy.

Trains a Ridge Regression model using the past 14 days of data to predict the next day's price movement (up/down).
Learn more @ docs.ubacktest.com/examples/regression/ridgereg
'''

import pandas as pd
from sklearn.linear_model import Ridge
import numpy as np

def ridge_regression(data, window=14, alpha=1.0):

    signals = np.zeros(len(data))  # Initialize signals array
    predictions = np.zeros(len(data)) # Initialize predictions array

    # Iterate over the data starting from the window index
    for i in range(window, len(data)):
        # Prepare the features (X) and target (y) for the regression model
        X = np.array(range(i-window, i)).reshape(-1, 1)  # Time index for the last window days
        y = data['close'][i-window:i].values  # Last window closing prices

        # Fit the Ridge regression model (using L2 regularization)
        model = Ridge(alpha=alpha)  # 'alpha' is the regularization strength
        model.fit(X, y)

        # Predict the next value (for the current time period)
        prediction = model.predict(np.array([[i]]))  # Predict the next point (i.e., the 6th day)
        predictions[i] = prediction[0]

        # Signal generation based on prediction (uptrend or downtrend)
        if prediction > data['close'][i-1]:
            signals[i] = 1  # Buy signal
        else:
            signals[i] = -1  # Sell signal

    return signals, predictions

def strategy(data):

    # Call the ridge_regression function to get the signals
    data['signal'], data['prediction'] = ridge_regression(data)

    return data