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," presents a sophisticated approach to trend following in the futures market with customizable arguments. It leverages a variety of Python libraries such as xarray, numpy, pandas, and Plotly, along with specific quantiacs (qnt) modules for data loading, technical analysis, statistical evaluation, graph plotting, and backtesting.
The notebook begins by importing essential libraries and loading data for 75 global derivatives across currencies, indices, bonds, energy, and metals from leading futures exchanges. It details the process of downloading 20 years of data using qndata.futures.load_data, visualizing historical trends, and implementing a weight allocation strategy based on weighted moving averages (WMA) and rate of change (ROC) as trend indicators.
Further, it introduces a function to calculate positions for multiple instruments with different parameters, showcasing the flexibility in strategy testing across various assets. The document proceeds with a multi-pass backtesting approach, ensuring rigorous validation of the strategy without forward-looking bias.
Ideal for quant developers and traders, this notebook combines data analysis, technical indicators, and backtesting to form a comprehensive futures trading strategy, emphasizing trend following with adjustable parameters for tailored strategy optimization.