Trading and managing portfolios with Zipline

In the previous chapter, we introduced Zipline to simulate the computation of alpha factors from trailing market, fundamental, and alternative data for a cross-section of stocks. In this section, we will start acting on the signals emitted by alpha factors. We'll do this by submitting buy and sell orders so we can enter long and short positions or rebalance the portfolio to adjust our holdings to the most recent trade signals.

We will postpone optimizing the portfolio weights until later in this chapter and, for now, just assign positions of equal value to each holding. As mentioned in the previous chapter, an in-depth introduction to the testing and evaluation of strategies that include ML models will follow in Chapter 6, The Machine Learning Process.

Scheduling signal generation and trade execution

We will use the custom MeanReversion factor developed in the previous chapter (see the implementation in...