Parameter optimization is a feature request we frequently receive and we're heading in the direction to make it easier. We wrote a couple posts on our blog about this topic that you may find useful.
Backtest performance is always a big consideration for us, and we're constantly investing in it. We recently increased the size of the universe you can test, from 100 securities to 200 securities in a single backtest, and these improvements make for faster testing. If you have a large universe, over a long period of time, with complex calculations, the backtest performance may slow down. To speed it up, I'd suggest to use a smaller universe, over a shorter time period, with fast performing functions. To the last point, I'd suggest using history() instead of batch_transform, and pandas' rolling transformations of .mean() and .stdev(), instead of the built-in .mavg() and .stdev() functions.
If you'd like help, I can take a look at your code to see what improvements can be made for faster backtesting. You can invite me via collaboration - my email is [email protected].
You can use Zipline to develop your strategy offline and fine-tune your parameters. If you're coding offline you will need another data source, like Yahoo Finance. And if you have any questions, you can post directly to the Zipline Google Group: https://groups.google.com/forum/#!forum/zipline
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