A zipline algorithm will run for a specified period after an initial setup and executes its trading logic when specific events occur. These events are driven by the trading frequency and can also be scheduled by the algorithm, and result in zipline calling certain methods. The algorithm maintains state through a context dictionary and receives actionable information through a data variable containing point-in-time (PIT) current and historical data. The algorithm returns a DataFrame containing portfolio performance metrics if there were any trades, as well as user-defined metrics that can be used to record, for example, the factor values. 

You can execute an algorithm from the command line, in a Jupyter Notebook, and by using the run_algorithm() function.

An algorithm requires an initialize() method that is called once when the simulation starts. This method can be used to add properties to the context dictionary that is available to all other algorithm methods or register pipelines that perform more complex data processing, such as filtering securities based, for example, on the logic of alpha factors.

Algorithm execution occurs through optional methods that are either scheduled automatically by zipline or at user-defined intervals. The method before_trading_start() is called daily before the market opens and serves primarily to identify a set of securities the algorithm may trade during the day. The method handle_data() is called every minute.

The Pipeline API facilitates the definition and computation of alpha factors for a cross-section of securities from historical data. A pipeline defines computations that produce columns in a table with PIT values for a set of securities. It needs to be registered with the initialize() method and can then be executed on an automatic or custom schedule. The library provides numerous built-in computations such as moving averages or Bollinger Bands that can be used to quickly compute standard factors but also allows for the creation of custom factors as we will illustrate next. 

Most importantly, the Pipeline API renders alpha factor research modular because it separates the alpha factor computation from the remainder of the algorithm, including the placement and execution of trade orders and the bookkeeping of portfolio holdings, values, and so on.