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I've finished a university course in statistical machine learning that covered topics such as regression, classification, neural networks, SVM, PCA etc. The class was quite tough and rigorous (we had Bishop as a textbook) and we implemented the algorithms, but I'd like to solidify my knowledge by actually going out and applying the techniques to more real world material, preferably in Python. The goal of this is to have something to show employers aside from just a class.

I have no illusions of spending a month working and being able to beat currency markets or whatever, but while I'm doing this, I'd like to test it on markets that aren't flooded with professionals and see what happens. I'm looking for resources/guides to get started - I'm not sure if I need data scrapers, need to set up a database etc. Basically just good resources to applying the stats I've learnt and possibly a primer on the finance I need to know.

Totally open to different directions I should go in. Apologies if this is a duplicate, I did try searching but mostly saw people hoping for a 'magic bullet' to crush markets. I'd be super happy with (optimistic I know) setting up some sort of auto trader for crypto markets even if it lost a bit, just for the experience.

edit: I should also add that I've done 5 computer science classes so am comfortable with programming in general, and have done a bit of stochastics through our stats department

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closed as too broad by LocalVolatility, Forgottenscience, Daneel Olivaw, amdopt, JejeBelfort Aug 2 '17 at 7:18

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

  • $\begingroup$ You may start by opening an account with a broker like Interactive Brokers. I don't know if they offer the possibility of using their API with a free money account, but keep in mind that achieving some kind of nonlinear separability in higher dimensional spaces with noisy financial data is not easy at all, and requires lots of smart feature engineering $\endgroup$ – james42 Aug 1 '17 at 13:18
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Try to find online forecasting challenges on sites like kaggle.com They would often provide you with the data, so you don't need to worry about it. Here's a good example (finished): https://www.kaggle.com/c/two-sigma-financial-modeling#description

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I would do the following:

  1. Use a free autotrading platform (Interactive Brokers API, metatrader etc)
  2. Get data from Quandl
  3. Upload any trading algorithm to github.
  4. Create a blog and write about the algorithm you have come up with.

These will give you something to show to potential employers.

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