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How to (ACTUALLY) Learn Machine Learning (and use it for trading).
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Motivated by @BeingHorizontal& #39;s noble albeit suboptimal thread on the same. https://twitter.com/BeingHorizontal/status/1248870718168690691">https://twitter.com/BeingHori...
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Motivated by @BeingHorizontal& #39;s noble albeit suboptimal thread on the same. https://twitter.com/BeingHorizontal/status/1248870718168690691">https://twitter.com/BeingHori...
Learn a programming language that lends itself well to data science (like Python, R or Julia).
Also, go beyond the data science libraries - learn how to build software and data infrastructure. If you don& #39;t, you probably won& #39;t be able to do much with your ML model.
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Also, go beyond the data science libraries - learn how to build software and data infrastructure. If you don& #39;t, you probably won& #39;t be able to do much with your ML model.
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Understand what machine learning is and isn& #39;t. There is a lot of hype surrounding ML/AI and that has skewed people& #39;s expectations of it.
It& #39;s not magic - just clever mathematics that harnesses our access to computing power and lots of good data.
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It& #39;s not magic - just clever mathematics that harnesses our access to computing power and lots of good data.
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If you do not understand the mathematics behind ML algorithms, you are very likely to fuck up, especially when working with something as chaotic/noisy as price data.
Study Linear Algebra, Calculus, Probability & Statistics. Use @MIT OCW lectures for this.
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Study Linear Algebra, Calculus, Probability & Statistics. Use @MIT OCW lectures for this.
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Data Science/ML has more to it than just throwing algorithms at a dataset hoping something fits. You need to understand the dataset you& #39;re working with.
Study the nature of financial time series data and its statistical properties.
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Study the nature of financial time series data and its statistical properties.
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An often overlooked but essential part of the ML pipeline is feature engineering i.e. modifying input data to make it more amenable to modeling. This requires a knowledge of data mining and data transformation.
Remember, bad input features can derail your ML project.
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Remember, bad input features can derail your ML project.
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Using ML in trading, particularly for signal detection/strategy design is far more challenging than you probably realize, particularly because most ML algorithms suck at dealing with the kind of properties financial time series data exhibits (chaotic/noisy, non-stationary).
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Most blogs you& #39;ll read showcasing great results on price data are trash (at least their results are; the blog itself might be useful to learn the concept).
Realize that the odds of someone blogging on Medium about their (truly) profitable ML trading model are nil.
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Realize that the odds of someone blogging on Medium about their (truly) profitable ML trading model are nil.
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On a similar note, do not conflate accuracy with effectiveness when using ML for trading. If anything, less accurate ML-based trading signals perform better than ones with very high (~90%) accuracy. Think hard about how you will evaluate the performance of your model.
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Know what ML algorithm suits the task at hand. Trying to use deep neural networks for a problem you have 100 data points for will only yield trash.
You& #39;ll probably get much more mileage out of simpler models if you understand the process and your data [SEE ATTACHED PICTURE]
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![Know what ML algorithm suits the task at hand. Trying to use deep neural networks for a problem you have 100 data points for will only yield trash.You& #39;ll probably get much more mileage out of simpler models if you understand the process and your data [SEE ATTACHED PICTURE]9/n](https://pbs.twimg.com/media/EVUbBjMUYAI2p47.png "Know what ML algorithm suits the task at hand. Trying to use deep neural networks for a problem you have 100 data points for will only yield trash.You& #39;ll probably get much more mileage out of simpler models if you understand the process and your data [SEE ATTACHED PICTURE]9/n")
You& #39;ll probably get much more mileage out of simpler models if you understand the process and your data [SEE ATTACHED PICTURE]
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Go through JP Morgan& #39;s guide on Machine Learning and Big Data (this is also a great starting point if you know ML but aren& #39;t sure how to apply it to financial datasets).
Link: https://www.cfasociety.org/cleveland/Lists/Events%20Calendar/Attachments/1045/BIG-Data_AI-JPMmay2017.pdf
10/n">https://www.cfasociety.org/cleveland...
Link: https://www.cfasociety.org/cleveland/Lists/Events%20Calendar/Attachments/1045/BIG-Data_AI-JPMmay2017.pdf
10/n">https://www.cfasociety.org/cleveland...
Read @lopezdeprado& #39;s paper "THE 10 REASONS MOST MACHINE LEARNING FUNDS FAIL". It& #39;s highly likely to foreshadow the mistakes you& #39;re going to make using ML for trading.
Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3104816
11/n">https://papers.ssrn.com/sol3/pape...
Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3104816
11/n">https://papers.ssrn.com/sol3/pape...
Read the following books (in order):
- 100 Page Machine Learning Book by @burkov
- Introduction to Statistical Learning
- Elements of Statistical Learnings
- Deep Learning by @goodfellow_ian
- Reinforcement Learning by Sutton
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- 100 Page Machine Learning Book by @burkov
- Introduction to Statistical Learning
- Elements of Statistical Learnings
- Deep Learning by @goodfellow_ian
- Reinforcement Learning by Sutton
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Most importantly, quant trading is a multi-faceted endeavor and trying to simultaneously learn a new skill set and apply it to a challenging problem is going to be painful.
Find good teams/groups/people/mentors to work with. It& #39;ll vastly accelerate your progress.
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Find good teams/groups/people/mentors to work with. It& #39;ll vastly accelerate your progress.
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