Quantopian zipline trading algorithm parameter optimization with Spearmint Bayesian Optimizer - Part 1

Quantopian’s zipline - a Pythonic Algorithmic Trading Library – is a powerful platform for creating automated trading algorithms.  Algorithms almost always have tuning parameters that control the entry or exit rules for trades. 

As an example, a trading algorithm using Bollinger Bands (referred to as BBANDS) has three free parameters.  The first is an integer time period for the look-back.  The other two are floating point numbers for the “up” and “down” deviation for the trading signal.  To optimize such an algorithm using a grid scan to explore all combinations is an immense task.

Currently, neither Quantopian nor zipline offer a built in method of optimizing tuning parameters. 

Quantopian’s blog entry on this problem listed a few alternatives, one of which is the Spearmint Bayesian Optimizer open source tool kit.  The results to me were very impressive.

Read more to see how I did it


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