Polynomial Regression

Explanation & Rationale

The Polynomial Regression Strategy uses a polynomial regression model to capture non-linear trends in the past 14 days of price data, making it more adaptable to market fluctuations than simple linear regression. By transforming the time index into polynomial features, the model can better capture complex price movements, allowing for more accurate predictions of the next day’s price. Buy and sell signals are generated based on whether the predicted price indicates an uptrend or downtrend, helping to capture potential momentum shifts in the market.

Code

'''
Polynomial Regression Strategy.

Trains a Polynomial 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/polyreg
'''

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

def polynomial_regression(data, window=14, degree=2):

    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

        # Transform the features into polynomial features (degree=2 for quadratic)
        poly = PolynomialFeatures(degree)
        X_poly = poly.fit_transform(X)  # Transform the data to polynomial features

        # Fit the polynomial model (using LinearRegression)
        model = LinearRegression()
        model.fit(X_poly, y)

        # Predict the next value (for the current time period)
        prediction = model.predict(poly.transform(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):

    # Set Fit to Quadratic (Change if you wish)
    degree = 2

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

    return data