[smnl]

Cool technical presentation, but trying to optimize moving average trading strategies is a fool's game - you might find a set of parameters that work in all out-sample/cross-validation tests, but there's still a good chance it'll lose money in actual trading going forward - market paradigms can shift, unforeseen world events, t-cost/slippage higher than what the model accounted for (especially with more frequent rebalancing), etc.

If you actually want to trade moving average strategies, your best best is just to diversify and run several different strategies across a variety of parameter sets and across various sectors/asset classes, without trying to overly optimize a single strategy

[twiecki]

Certainly that's always a risk. Zipline does however support simulating transaction-costs and slippage so those will be accounted for (as best as possible).

As for market-paradigm shifts, I totally agree. One way to deal with that is to constantly re-optimize the parameters based on recent data. This is also known as walk-forward optimization on which there is a slide at the very end (see http://blog.quantopian.com/parameter-optimization/ for a more information).

Finally, as to running multiple strategies with different parameter settings. The OLMAR paper that describes the algorithm (http://arxiv.org/abs/1206.4626) has a variation of this where they use a range of different-length moving averages, rather than just one.

[twiecki]

Also, this is not your standard dual moving average cross-over strategy. Rather, it rebalances its portfolio (optimally) under the assumption that the stocks will revert to their moving average. The eps parameter, which I explored in this talk, influences how aggressively the algo rebalances its portfolio -- smaller values will result in a more stable, conservative strategy.

[OldSchool]

++ You're right on all counts. You've obviously done this before...

To anyone newly thinking about this topic, it is certainly the most challenging project you can take on. There are no laws of the market like there are laws of physics. You can get your code to work perfectly, but all that means is that your results will be the same as your model's going forward. As for a model, it's likely to need to change fairly frequently, even becoming the opposite of a previously great model for example, and you can never truly account for the effect of politics on markets. One tip is that a sudden catastrophic crash is far more likely than a gigantic surprise to the upside so if you're playing with real money, you need to use options to insure against such an event. Luckily (or not luckily in the case of trading itself) markets are highly correlated, so index options will probably fill this need.