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Quant Workflow & Algorithmic Trading R&D with the DARWIN API

Good morning everyone,

Yesterday we published the first tutorial in a planned series to follow, geared exclusively towards providing a detailed introduction to a typical Quant’s workflow and approach to algorithmic trading strategy development with the DARWIN API.

  1. If you’ve ever wondered how we conduct alpha research in Darwinex Labs, this tutorial gives you a rich bird’s eye view of the process, including everything from process to source code :slight_smile:

  2. If you’ve ever used any assessment criteria at all for selecting DARWINs, this tutorial shows you how you can quickly backtest your ideas in Python using the DARWIN API.

  3. Finally, it also caters to algorithmic traders at different levels of Python fluency - regardless of where you think you stand in terms of ability to code strategies, this tutorial also serves as a crash course in Python for Algorithmic Trading :muscle:

It is structured as follows:

1) Developing a hypothesis
2) Assessing data requirements
3) Creating the required dataset
4) Generating hypothesis factors
5) Calculating strategy returns
6) Evaluating results
7) Running statistical tests e.g. what is P(μ != 0) ? (we’ll cover this in detail in future tutorials)

Tutorial Video:

Source code is pre-written for you at each step, available in the DARWIN API Tutorials GitHub repository.

For this tutorial we’ve presented everything in a Jupyter Notebook, making it much simpler and more interactive to follow the tutorial - please click on the following link to open it in your web browser:

Quant Workflow & Algorithmic Trading R&D - Jupyter Notebook


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Thank you!


Congratulations and thank you very much for such deep work !