Linear Regression

Explanation & Rationale

The Simple Linear Regression Strategy fits a linear model to the past 14 days of closing prices to forecast the next day’s movement. By identifying the overall trend, it generates buy signals when the predicted price is higher than the previous close and sell signals when it is lower. This method provides a straightforward approach to trend forecasting, though it assumes a consistent linear relationship, which may not always hold in volatile markets.

Code

'''
Simple Linear Regression.

Trains an Linear 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/linreg
'''

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

def linear_regression(data, window=14):

    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]  # Last window closing prices

        # Fit the model
        model = LinearRegression()
        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 15th 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 linear_regression function to get the signals
    data['signal'], data['prediction'] = linear_regression(data)

    return data