Building Better Strategies with Good Science

It was strangely comforting talking to Ernie Chan. Whilst I was completely out of my depth talking about AI and Machine Learning, I came away broadly reinforced in my own belief that great trading still requires a human touch, and that the best niche's in the market are best discovered by applying a certain kind of wisdom, experience and competitive approach. The machine learning techniques and computer power needed to make them work are, however, quickly catching up, so how long we have is anyone's guess.

For now, however, even Ernie is on the same page: that causal strategies (ones you can say 'why' they work) are still superior, more robust, easier to tweak if they should begin to decay. Furthermore, diversification across strategy types is key, merging long and short vol strategies, diversifying between trend and mean reversion. Avoiding over-fitting these strategies is best done by applying the scientific method: create a hypothesis of what should work in the market, then try to invalidate it with a logical analysis of the data. Well, that's nicely validating for my approach, so I'm happy.

More detail / notes over at www.thealgorithmicadvantage.com