Backtesting

Backtest trading strategies in Python and see exactly what they would have done. This section is for traders and researchers who want to turn signals, orders, or custom logic into a portfolio, model how orders fill and what they cost, and account for every position and cash flow. Each page covers one part of that workflow: the simulation engine, signals and compiled callbacks, continuing a backtest on new data, intraday fills, exits, sizing, costs, and the checks that keep a backtest honest.

Start here

Begin with the Backtesting engine page for the three ways to simulate and how many backtests run in one call. Or choose a starting point based on what you already have:

Signals can also use callbacks, so you can keep built-in stops and limits while adding custom decisions. The Event-driven backtesting page shows how to choose between extending signal simulation and writing your own order logic.

Explore backtesting features

features/backtesting pages

Backtesting engine

Simulate orders, signals, and callbacks across many assets and parameters at once

Signal backtesting

Turn entry and exit signals into long, short, reversing, and pyramided positions

Event-driven backtesting

Write compiled callbacks for cooldowns, position limits, and custom simulators

Live simulation

Continue backtests on new bars, chain runs, and feed an external trading system

Intraday and tick backtesting

Backtest minute bars, ticks, and sessions, and resolve stops on finer data

Realistic backtests

Avoid look-ahead bias, impossible fills, and survivorship bias in your backtests

Orders and execution

Simulate limit and stop-entry orders, fill prices, timing, rejections, and real fills

Stop loss and take profit

Backtest stop losses, trailing stops, take profits, time stops, and exit ladders

Position sizing

Size trades in units, cash, percent of equity, target weights, or risk per trade

Fees, slippage, and leverage

Model commissions, slippage, leverage, futures multipliers, and negative prices

Portfolio accounting

Track shared cash, deposits, dividends, positions, records, and exposure per bar

Model the account you want to trade

For a multi-asset strategy, start with shared cash and position sizing to decide how assets share the capital. Add fees, slippage, and leverage and track deposits, dividends, and existing holdings with Portfolio accounting.

For strategies where the fill matters, explore Orders and execution, Stop loss and take profit, and Intraday and tick backtesting. Realistic backtests brings together signal timing, price assumptions, and checks for look-ahead bias.

Inspect results and compare strategies

Follow a run into Trade analytics to inspect its trades, or Performance and risk metrics to compare returns and risk. When you are ready to explore variations, Parameter optimization covers testing parameter combinations, and Walk-forward and cross-validation covers testing on unseen periods.

For a closer look at the code, read the Portfolio documentation for the simulation lifecycle, or work through the Stop signals tutorial for a signal-driven example.