Asset Management

SSRN Paper: Note: the paper is still in draft form

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3420952

This paper investigates various machine learning trading and portfolio optimisation models and techniques. The notebooks to this paper are Python based. By last count there are about 15 distinct trading varieties and around 100 trading strategies. Code and data are made available where appropriate. The hope is that this paper will organically grow with future developments in machine learning and data processing techniques. All feedback, contributions and criticisms are highly encouraged.

status: in-progress; confidence: likely; importance: 8

Trading Strategies

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

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

3. Tiny VIX CMF
Resources:
Data, Code

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

5. Earnings Surprise
Resources:
Code, Paper

6. Bankruptcy Prediction
Resources:
Data, Code, Paper

7. Filing Outcomes
Resources:
Data

8. Credit Rating Arbitrage
Resources:
Code

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

10. Systematic Global Macro
Resources:
Data, Code

11. Mixture Models
Resources:
Data, Code

12. Evolutionary
Resources:
Code

13. Agent Strategy
Resources:
Code

14. Stacked Trading
Resources:
Code, Blog

15. Deep Trading
Resources:
Code

Weight Optimisation

1. Online Portfolio Selection (OLPS)
Resources:
Code

2. HRP
Resources:
Data, Code

3. Deep
Resources:
Data, Code, Paper

4. Linear Regression
Resources:
Code, Paper

5. PCA and Hierarchical
Resource:
Code

Other

1. GANVaR
Resources:
Code

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All Data and Code