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  3. Algorithmic Trading: Training for Traders, Created by Traders

Quant Workflow

Algorithmic Trading Strategy R&D in Python

This tutorial presents a step-by-step approach to algorithmic / quant trading research and development, breaking down the process as follows:

  1. Develop a hypothesis
  2. Assess data requirements
  3. Create the dataset
  4. Generate factors
  5. Calculate strategy returns
  6. Evaluate results
  7. Run statistical tests e.g. what is P(μ != 0) ? (we'll cover this in future tutorials)

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

HANDS-ON learning with source code examples provided during the tutorial.

All source code available on GitHub.