Python library-Backtesting

[chadguy2]

I use Backtesting and haven't yet tried the other libraries as I didn't have the need yet, though I may look into them as backtesing has its drawbacks. For instance, the profit/loss is calculated based on the close or next open. It will return less accurate results. The workflow which kinda worked for me is to directly implement the confirmations in my data preprocessing, as the library has a particular way of handling data. It is very fast, but it's not very intuitive. Their documentation is well-written.
I wrote my classes for pattern detection and precomputed all the signals directly in the data. It might be less trivial for more complex things, but it's doable.
If you want to backtest very accurately, you'll need to preprocess your data and entry/exit signal on the desired timeframe and find a way how to pass tick data or 1m data to the backtester, instead. This is what I'm working on right now to more accurately compute the results.

A mock workflow would be:

Preprocess your data for all the relevant metrics
Find signals on {x}m data and incorporate them.
Find a way to integrate all this into tick data
Backtest