Backtesting in Python
Python backtesting frameworks
4 min
You can write a backtest from scratch, but mature frameworks handle the bookkeeping — positions, cash, costs, orders — so you focus on the strategy.
The main options
- backtrader — a popular, full-featured event-driven framework. Rich, mature, with built-in indicators and broker simulation. A gentle on-ramp, though development has slowed.
- vectorbt — built on NumPy/pandas for vectorized backtesting. Extremely fast for sweeping thousands of parameter combinations, at the cost of a steeper, array-oriented mental model.
- backtesting.py — small, clean and beginner-friendly. Great for a single strategy with quick, readable output and built-in plots. We use its style in the next lesson.
- Zipline — the engine that powered Quantopian; still used (the community zipline-reloaded fork). Strong for daily equities research, heavier to set up.
- QuantConnect — a cloud platform (the LEAN engine) with hosted data across asset classes, backtesting and live deployment. Less setup, but you work inside their environment.
How to choose
- Learning or one strategy at a time: backtesting.py or backtrader.
- Large parameter sweeps and research speed: vectorbt.
- Hosted data and a path to live without managing infrastructure: QuantConnect.
The catch that no framework fixes
A framework guarantees correct accounting, not correct assumptions. It will faithfully simulate a look-ahead-biased, cost-free, overfit strategy and hand you a beautiful equity curve. The pitfalls from the previous lesson are yours to avoid — the tool will not do it for you.
This content is for educational and informational purposes only and is not investment, financial, tax or legal advice. Trading and investing carry risk, including the possible loss of capital. Any performance shown by third-party tools is hypothetical and not a promise of future results. Do your own research and consider professional advice before making any decision.