Learn backtesting and algorithmic trading in Python

Eight libraries compared on what their users praise and complain about: engine, multi-asset, execution realism, live trading, licence, maintenance. Checked September 2026.

A step-by-step Python walkthrough: from raw bars to a realistic backtest and a tearsheet.

What backtesting is, why it works, where it lies to you, and how to do it honestly in Python.

The libraries, the workflow, and a runnable parameter sweep to research systematic strategies.

Optimize in-sample, validate out-of-sample, fold by fold, the honest way to tune parameters.

The workflow where a coding agent writes and runs your backtest, what breaks when it writes from memory, and the measured difference a reference makes.

Two Rust engines, benchmarked head to head: both fast on one call, but Manifold-BT is 25x faster on the parameter sweeps that validate a strategy.

An honest comparison: fast, realistic backtesting versus live crypto trading, and when to use each.

Rust engine with vectorized signals and a sequential fills pass, versus vectorized NumPy: speed, realism, path-dependent logic.

A fast, realistic research engine versus a mature pure-Python framework, and when each fits.

A research backtesting library with fast parameter sweeps and GPU, versus a full event-driven platform built for live trading and backtest-to-live parity.

A local Python research library versus a hosted platform: pip install and every core of your machine, against cloud nodes, credits and live deployment, with the pricing compared.

The simplest Python backtester versus a Rust research engine: one instrument and a commission rate, against a universe in one account, funding, partial fills and walk-forward.

Slippage, market impact, funding, partial fills, and signal delay: why most backtests lie, and how to fix it.

Look-ahead, optimistic fills, grid cherry-picking: how an agent-written backtest goes wrong while every number looks fine, and the check that catches each one.