Algorithmic Trading: Training for and by Traders
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      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.