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Show HN: How I Used Machine Learning to Optimize My Trading Algorithm

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Re: Show HN: How I Used Machine Learning to Optimize My Trading Algorithm

#24
post #6
post #4

sticking around for 97% drawdown? Sorry.. doing this in real life would be more stupid than anything

One point in the graph shows a -144% return. How do you even get a -144% return? Debt?

Clearly the simulation doesn't take into account forced de-leveraging at significant losses due to margin calls.

Re: Show HN: How I Used Machine Learning to Optimize My Trading Algorithm

#25

If someone really came up with some fool proof method of beating the market, wouldn't they keep it secret? Meanwhile, most folks should stick to asset class allocation and indexed funds and ETFs.

Yes, a heap of firms have found these methods, they don't post them on HN :)

One would hope that most people would do that, unfortunately too few people pay attention to saving at all, let alone allocations that fit their needs/profile.

Re: Show HN: How I Used Machine Learning to Optimize My Trading Algorithm

#26
"Past performance does not guarantee future results" is still the operative principle here. Data-mining discovers patterns, but it doesn't lead to deep insight into causes, and markets are perturbed by many events that you don't put into your training algorithm. "The market can remain irrational longer than you can remain solvent" is still important investment advice.

Re: Show HN: How I Used Machine Learning to Optimize My Trading Algorithm

#27
post #11
post #9

Earlier quoted context omitted.

How about risk and money management?

Cash management is built already. We track how much you have, dividend payments, all that stuff. We've built many risk measurements, too: alpha, beta, Sortino, Information Ratio, etc. Risk management is far more complex. Risk management is more a part of the algorithm itself than a feature that we can build. That said, we can add more risk tools. We're very open to suggestions, if you have some in mind.

I see cash and risk management both as separate domains in their own right equal to if not more important than the actual algorithm. I don't think they can be folded into the algorithm itself - there is a reason banks have separate risk departments.

Eg: For risk management I might not allow any trading whatsoever when the VIX is over 40, and the 5 day stddev of the S&P is above some threshold.

Similarly, I might scale my capital usage based on my risk metrics. Or scale the capital available to a particular algorithm based on its individual risk profile.

Recreating risk management in each algorithm seems like a bad idea. But even worse is pushing off risk to the user to do in an ad-hoc way.

Re: Show HN: How I Used Machine Learning to Optimize My Trading Algorithm

#29
The site is pretty slick, but the backtest I ran when I cloned the algo is pretty slow. It's been running for about 10+ mins now and it's only 40% done.

As well, in the logs, when I see stuff like:

2012-05-31handle_data:35INFO -63.520880 shares of Security(6109) sold.

it doesn't really inspire a lot of confidence. What does it mean that -63.520880 shares were sold? Does that mean they were bought? And the fact that you are purchasing fractional shares also doesn't inspire a lot of confidence.

Re: Show HN: How I Used Machine Learning to Optimize My Trading Algorithm

#30
post #17

Has anyone consistently beat the market with a home grown trading algorithm? I suspect it's possible but probably needs more signal than just stock price.

Given the number of people trying to beat the market with their personal algos, at least a few are certain to beat the market for many years, even if their strategies were no better than random.

Edit: I don't mean to suggest that it can't be done. I'm just suggesting that the existence of people beating the market may not be a good indicator of your ability to beat the market.

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