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The thing with backtesting is, unless you dug into the dirty details yourself,
you can't rely on execution correctness, and you risk losing your house.
In addition, everyone has their own preconveived ideas about how a mechanical
trading strategy should be conducted, so everyone (and their brother)
just rolls their own backtesting frameworks.
If after reviewing the docs and examples perchance you find
[_Backtesting.py_](https://kernc.github.io/backtesting.py) not your cup of tea,
kindly have a look at some similar alternative Python backtesting frameworks:
- [bt](http://pmorissette.github.io/bt/) -
a framework based on reusable and flexible blocks of
strategy logic that support multiple instruments and
output detailed statistics and useful charts.
- [vectorbt](https://polakowo.io/vectorbt/) -
a pandas-based library for quickly analyzing trading strategies at scale.
- [Backtrader](https://www.backtrader.com/) -
a pure-python feature-rich framework for backtesting