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.
This Jupyter notebook, titled strategy.ipynb, outlines a trend-following trading strategy focusing on futures
markets. The strategy progresses from a simple, single-pass implementation to a more sophisticated multi-pass
version. It utilizes the xarray library for handling multidimensional datasets, making extensive use of labels
such as "time", "field", and "asset". This approach is particularly well-suited for financial data analysis.
Technical analysis indicators like the Weighted Moving Average (WMA) and the Rate of Change Percentage ( ROCP) are leveraged to discern market trends. Initially, the strategy is applied to single assets, then scaled to multiple assets using a single-pass approach for efficiency and a multi-pass approach for comprehensive backtesting.
The notebook includes comprehensive code snippets for data loading, indicator calculation, trading signal
definition, and result visualization with libraries such as Plotly. It integrates Quantiacs
libraries (qnt) for tasks such as data loading, statistical analysis, graphical representation, and
technical analysis. The strategy highlights the criticality of choosing suitable parameters and assets to
optimize performance.