Example of implementing a backtesting framework from scratch.
Implementing backtesting is seen as something very complicated, but actually it is not! My main motivation for writing this backtesting framework from scratch was flexibility and optimization. I found it very difficult to work with existing backtesting for mutli-stocks strategies and they were pretty slow.
This code implements a general purpose trading environment through trading_env in backtesting.py.
This trading enviroment has access to the OHLC price dataframe passed during initialization. You interact with it
to place buy/sell orders. The enviroment keeps track of all the data during backtesting that is required for computing
the final performance metrics.
The trade function in backtesting.py implements my trading strategy which is a multi-stock strategy.
examples.py shows how to use these to run backtesting on my trading strategy and the benchmark strategy.
I wouldn't recommend using this code as is for your backtesting purpose. I would definitely try out existing frameworks and if they don't work, only then I would go for writing one from scratch like this. I landed on this piece of code after going through several iterations. I tried out several ways to optimize the code by vectorization and parallelization. But, ultimately the simple idea of iterating through each trading day worked the best. The final piece of code doesn't seem that complicated, but my thought process took a fairly long and winding road to reach there :D.
It was fun though! And that is what matters the most.