My search of an ideal backtesting tool (my definition of 'ideal' is described in the earlier 'Backtesting dilemmas' posts) did not result in something that I could use right away. However, reviewing the available options helped me to understand better what I really want. Of the options I've looked at, pybacktest was the one I liked most because of its simplicity and speed. After going through the source code, I've got some ideas to make it simpler and a bit more elegant. From there, it was only a small step to writing my own backtester, which is now available in the TradingWithPython library.
I have chosen an approach where the backtester contains functionality which all trading strategies share and that often gets copy-pasted. Things like calculating positions and pnl, performance metrics and making plots.
Strategy specific functionality, like determining entry and exit points should be done outside of the backtester. A typical workflow would be:
find entry and exits -> calculate pnl and make plots with backtester -> post-process strategy data
At this moment the module is very minimal (take a look at the source here), but in the future I plan on adding profit and stop-loss exits and multi-asset portfolios.
Usage of the backtesting module is shown in this example notebook
