Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original
Machine Learning for Algorithmic Trading, Second Edition - published by Packt (by PacktPublishing)
research_public
Quantitative research and educational materials (by quantopian)
| Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original | research_public | |
|---|---|---|
| 33 | 5 | |
| 1,075 | 2,315 | |
| 8.3% | 2.0% | |
| 0.0 | 0.0 | |
| over 1 year ago | over 3 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original
Posts with mentions or reviews of Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-08-05.
research_public
Posts with mentions or reviews of research_public.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-08-05.
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Evaluating the risk of a portfolio using Covariance matrix
According to this lecture: https://github.com/quantopian/research_public/blob/master/notebooks/lectures/Position_Concentration_Risk/notebook.ipynb
- Resources on Kalman filter predictors?
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Beta of a trading system
I would use a Kalman filter approach and not just a regression because of the problem of the lookback time for the regression. Which one do you use? Past 10 years? Just last month? Kalman filters overcome this problem. This is also what hedge funds do, more reliable. Here is a saved notebook from Quantopian where they do exactly that: https://github.com/quantopian/research_public/blob/master/notebooks/lectures/Kalman_Filters/notebook.ipynb
- Advice for a beginner
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Getting into the game. What do I need to learn?
I would check out the open source notebook based lessons at quantoptions github, they're quite good: https://github.com/quantopian/research_public/tree/master/notebooks/lectures
What are some alternatives?
When comparing Machine-Learning-for-Algorithmic-Trading-Second-Edition_Original and research_public you can also consider the following projects:
Kalman-and-Bayesian-Filters-in-Python - Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.