Learn backtesting and algorithmic trading in Python
Step-by-step guides, runnable strategy walkthroughs, and honest tool comparisons, all built around realistic, reproducible backtests with Manifold-BT.
Guides
- How to Backtest a Trading Strategy
A step-by-step Python walkthrough: from raw bars to a realistic backtest and a tearsheet.
- What Is Backtesting?
What backtesting is, why it works, where it lies to you, and how to do it honestly in Python.
- Algorithmic Trading in Python
The libraries, the workflow, and a runnable parameter sweep to research systematic strategies.
- Walk-Forward Optimization in Python
Optimize in-sample, validate out-of-sample, fold by fold, the honest way to tune parameters.
Comparisons
- Manifold-BT vs Freqtrade
An honest comparison: fast, realistic backtesting versus live crypto trading, and when to use each.
- Manifold-BT vs vectorbt
Rust engine with vectorized signals and event-driven execution, versus vectorized NumPy: speed, realism, path-dependent logic.
- Manifold-BT vs Backtrader
A fast, realistic research engine versus a mature pure-Python framework, and when each fits.
Deep dives
Strategies
15 strategy walkthroughs with runnable Python and a sample tearsheet.
Browse all strategies →