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:
- Develop a hypothesis
- Assess data requirements
- Create the dataset
- Generate factors
- Calculate strategy returns
- Evaluate results
- 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.