Exponential Regression

Explanation & Rationale

The Exponential Regression Strategy fits a regression model to the past 14 days of log-transformed prices, capturing exponential trends in market movements. By predicting the next day’s price and comparing it to the most recent closing price, the strategy generates buy signals for expected uptrends and sell signals for anticipated downtrends. This approach helps identify non-linear price trends more effectively than linear models, making it useful for markets that exhibit compounding growth or decay.

Code

'''
Exponential Regression Strategy.

Trains an Exponential 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/expreg
'''

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

def exponential_regression(data, window=5):

    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 = np.log(data['close'][i-window:i].values)  # Apply log transformation to the closing prices

        # Fit the model
        model = LinearRegression()
        model.fit(X, y)

        # Predict the next value (for the current time period) in the transformed space
        prediction_log = model.predict(np.array([[i]]))  # Predict the next point (i.e., the 6th day)

        # Transform the prediction back to the original space
        prediction = np.exp(prediction_log[0])  # Apply exponential to get back to the original scale
        predictions[i] = prediction

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

    return data