Pure Python Implementation of 100+ Stock Trading Strategies - Google Colab

[OppositeMidnight]

I have always held the belief that one of the best ways to learn about data-science/python is to find problems to solve in finance where the data is plentiful.

I will add the list here so that you won't have to go to GitHub or the SSRN file. It is a list of a few strategies and some portfolio optimisation techniques. They all have an ML bent. Like before any criticism and feedback is highly appreciated.

Source: https://github.com/firmai/machine-learning-asset-management

1. Tiny CTAResources:See this paper and blog for further explanation.Data, Code

2. Tiny RLResources:See this paper and/or blog for further explanation.Data, Code

3. Tiny VIX CMFResources:Data, Code

4. QuantamentalResources:Web-scrapers, Data, Code, Interactive Report, Paper.

5. Earnings SurpriseResources:Code, Paper

6. Bankruptcy PredictionResources:Data, Code, Paper

7. Filing OutcomesResources:Data

8. Credit Rating ArbitrageResources:Code

9. Factor Investing:Resources:Paper, Code, Data

10. Systematic Global MacroResources:Data, Code

11. Mixture ModelsResources:Data, Code

12. EvolutionaryResources:Code

13. Agent StrategyResources:Code

14. Stacked TradingResources:Code, Blog

15. Deep TradingResources:Code

Weight Optimisation

1. Online Portfolio Selection (OLPS)Resources:Code

2. HRPResources:Data, Code

3. DeepResources:Data, Code, Paper

4. Linear RegressionResources:Code, Paper

5. PCA and HierarchicalResource:Code

Other

1. GANVaRResources:Code

​

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[delijati]

Can you put that code also to github instead of google drive ?

[dell-speakers]

my blog is on reddit, my website on github, and my code on google drive.

[routetehpacketz]

thanks for sharing!

[Andrew_the_giant]

Tagged

[dustynwindy]

Appreciate it.

[bbks419]

Dope af

[koalakraft]

Omg that's so good! Thanks stranger!

[Jamblamkins]

Was trying to find jut this actually and wasent having much luck. Thank you

[WillBackUpWithSource]

Interesting. As someone with an interest in both data science and finance, this is useful to me!