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.
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.
To run the strategy locally, you need to install the Quantiacs Toolbox.
The Jupyter Notebook presents a strategy for forecasting financial time series using machine learning, specifically tailored for the Quantiacs platform. This strategy is focused on the retraining of a model to adapt to new data on a rolling basis, using BTC Futures Contracts as the primary asset. The core of the model is a Voting Classifier that combines Ridge Classifiers and Stochastic Gradient Descent Classifiers from the scikit-learn library. The strategy employs a specialized version of the Quantiacs backtester, optimized for scenarios where models need frequent retraining.