| In general, machine learning would probably not be a great approach to day trading. There are several problems: 1) Limited market data: As an individual, you have limited access to market data. At best, you probably have real time top of book data but without paying for it, you almost certainly aren't getting a real time depth feed. Going off of only last trade data gives you even less to work with. In most cases free quotes are delayed making them worthless to day traders (although not longer term investors). 2) Limitations of market data: Even if you have a lot of historical data with a full order book, that is still of limited utility for simulations. The problem is that everything you do affects the market so any trade you simulate needs to be doing small enough quantity to not make your simulation unreasonable. How to deal with simulating market-affecting trades is a complex problem and the only way to truly know is to run your trades in the market. 3) For day trading, there is a lot of profit to be captured simply from the microstructure of the market (its moves back and forth, etc.). To capture much of this it helps to be fast but you won't be able to colo boxes at an exchange with only 10K. Machine learning seems more suited to longer term investments and trading on a slower platform than making a quick profit arbing or scalping. You may want to consider this rather than day trading since not only does day trading require much larger amounts of capital, but its also extremely risky. Also, if you plan to trade equities, enjoy all of the ridiculous SEC regulation (order marking, etc.). |