All the experimental answers for exercises from Advances in Financial Machine Learning by Dr Marcos López de Prado.
This book (A collection of research papers) can teach you necessary quant skills, the exercises provided in the book is a great way to ensure you will have a solid understanding of implementating quantitative strategy. From "A" to "Z".
"Every successful investment strategy is always supported by an equally sound theory that is practical, you either prove with results or you prove the theory first.
Either way one will always lead to another."
— "Project Cauldrons"
The book itself teaches very rational methods to quantitative finance, most of the concepts (especially triple barriers/ bet sizing) can be cross-reference to other strategies types (not just mean-reversion) such as volatilities, trends.
If you decide to write the code from scratch (Highly recommend!) and use my notebook answers as a reference, you will need the below:
- Python 3.7.4
- numpy 1.17.3
- scipy 1.3.1
- numba 0.49.1
- pandas 1.0.3
- matplotlib 3.1.1
- sklearn 0.23.1
- statsmodels 0.10.1
Disclaimer
Apart from the above modules, all the codes ran in these notebook were written from scratch (Private Repository).
Other References
If you are interested in quantitative research, but do not have high-frequency data. I created a code snippet for you to generate some synthetic HFT data to get you started.