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Q18 Machine Learning on a Rolling Basis

This trading strategy is designed for the Quantiacs platform, which hosts competitions for trading algorithms. Detailed information about the competitions is available on the official Quantiacs website.

How to Run the Strategy

In an Online Environment

The strategy can be executed in an online environment using Jupiter or JupiterLab on the Quantiacs personal dashboard. To do this, clone the template in your personal account.

In a Local Environment

To run the strategy locally, you need to install the Quantiacs Toolbox.

Strategy Overview

This strategy leverages supervised learning, specifically Bayesian Ridge Regression, to predict the rise or fall of stock prices in the NASDAQ-100 index. The strategy is divided into two main parts:

  1. Global Training:

    • Utilizes all available time series data for training.
    • Disregards the sequential nature of data, leading to potential forward-looking bias.

  2. Rolling Training and Prediction:

    • Implements a rolling window approach for training and prediction, ensuring no forward-looking bias.
    • Employs a specialized Quantiacs backtester to optimize performance.

Features for Learning:

  • The strategy incorporates various technical indicators such as MACD, RSI, stochastic oscillators, and volatility measures to capture market trends and patterns.