In this tutorial, we will review some of the most parts of the Python programming language we don't use every day… but should! Using the motivating example of a portfolio construction backtester, we will build the rough outline of a library that allows users to design strategies to be automatically executed and evaluated for constructing a portfolio.
We will design this tool such that:
- it has moderate performance for low-frequency strategies (e.g., no intraday trading, no real-time, more mutual fund than hedge fund)
- it supports high generality in strategy construction
- it supports “foreign” data (i.e., it minimizes assumptions about pandas.Series or pandas.DataFrame representing market information or trades)
- it is reasonably easy for a pandas user to construct a strategy without knowing too many esoteric details of Python or pandas (except, of course, the rules of index alignment)
Along the way, we will look to answer the following questions:
- what are generators, generator coroutines, and decorators, and where do they they actually show up in analytical code?
- why are generators and generator coroutines so well-suited to the design of simulators, backtesters, model training, &c.?
- how do we write libraries that accept and return pandas.Series that do not lose generality?
- why is pandas often considered a “tail-end” analytical tool, and how might we solve the problem of writing libraries that may grow a pandas.Series?