Machine Learning in Algorithmic Trading Systems: Opportunities and Pitfalls

Last night it was my pleasure to present at the Tyro Fintech Hub in Sydney on the topic of using machine learning in algorithmic trading systems.
Here you can download the presentation
Many thanks to all who attended and particularly for the engaging questions. I thoroughly enjoyed myself!
In particular, thanks to Andrien Juric for oraganising the event and Sharon Lu from Tyro for making available such a great space!!

5 thoughts on “Machine Learning in Algorithmic Trading Systems: Opportunities and Pitfalls”

  1. Mixture of good tools is vital. There are too many possible trading models. Tossing a coin is a stupid trading system but it’s a trading system. We need Data Mining to find the gold. Good tools are easy to get so good luck with the mining.

    • Hey there, not sure I agree that we ‘need’ data mining to find the gold, but it can certainly be a useful tool if used appropriately.

  2. The indicators that were most useful were all relatively simple and were based on recent events in the market I was trading as well as the markets of correlated securities. Having indicators that simply predicted an up or down price movement wasn t enough. I needed to know exactly how much price movement was predicted by each possible value of each indicator. I needed a formula that would convert an indicator value to a price prediction.


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