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QuantJourneyOrg/quantjourney-bt

A Python backtesting engine for reproducible portfolio research, modeling signals, orders, fills, costs, NAV and walk-forward validation.

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Repolode describes open-source projects and does not give investment advice. Trading and investing can lose money: assess the risk yourself and follow the laws where you live before using this project.

What is it?

QuantJourney Backtester turns trading strategy ideas into auditable research runs. Signals become target weights or explicit orders, orders become simulated fills, fills update cash and positions, and NAV is rebuilt from portfolio state, so each assumption about execution, costs, slippage and rebalancing is visible rather than implicit.

It supports two modes: weight mode for portfolio research such as factor portfolios, rotation models, long/short books and volatility targeting, and order mode for execution-aware work with market, limit, stop, stop-limit, trailing, bracket and OCO orders. Each run can produce metrics, plots, equity curves, drawdowns, walk-forward results, CSV/JSON artifacts and a static HTML dashboard.

The repository includes 50 runnable example strategies and a deterministic sample dataset for trying the engine without an account. Real market data is fetched through QuantJourney Cloud, while strategy code and simulation stay local. Borrow fees, financing and market impact are simplified in some examples and documented as such.

Key features

  • 01Weight mode and order mode for portfolio and execution-aware backtests
  • 02Order types include market, limit, stop, trailing, bracket and OCO
  • 03Configurable commissions, slippage and volume participation
  • 04Walk-forward validation and Optuna-based parameter optimization
  • 05Run outputs include metrics, plots, CSV/JSON files and an HTML dashboard
  • 0650 runnable example strategies with a deterministic offline sample dataset

Use cases

  • Testing a factor or rotation strategy with realistic costs and rebalancing rules
  • Checking whether a strategy's edge survives out-of-sample walk-forward validation
  • Prototyping limit, stop or bracket order logic before connecting to a broker
  • Producing reproducible backtest reports for review by a research team

Quick start

  1. 1

    Install the package from PyPI

    pip install quantjourney-bt
  2. 2

    Check one strategy import without credentials

    ./strategy.sh example_weights_01_sma_daily --check
  3. 3

    Run a deterministic demo with bundled sample data

    ./strategy.sh example_weights_01_sma_daily --sample-data --output /tmp/qj-sample

Things to know

  • Real market data requires a QuantJourney API key and account credentials.
  • Requires Python 3.11 or newer.
  • This is a backtesting engine, not a broker or live trading system.
  • Some examples simplify assumptions such as borrow cost, financing, liquidity and market impact.

Topics

#backtesting#quantitative-finance#portfolio-optimization#walk-forward-analysis#trading-strategies#python#optimization#backtest-engine#backtester#backtester-python

FAQ

What is quantjourney-bt?
A Python backtesting engine for reproducible portfolio research, modeling signals, orders, fills, costs, NAV and walk-forward validation. QuantJourney Backtester turns trading strategy ideas into auditable research runs. Signals become target weights or explicit orders, orders become simulated fills, fills update cash and positions, and NAV is rebuilt from portfolio state, so each assumption about execution, costs, slippage and rebalancing is visible rather than implicit.
Is quantjourney-bt free to use? What license does it have?
quantjourney-bt is open source under the Apache-2.0 license. Check the license terms before using it in commercial products.
Is quantjourney-bt still maintained?
Actively maintained. The last commit to quantjourney-bt was on Nov 4, 2026.
What can I use quantjourney-bt for?
Testing a factor or rotation strategy with realistic costs and rebalancing rules Checking whether a strategy's edge survives out-of-sample walk-forward validation Prototyping limit, stop or bracket order logic before connecting to a broker Producing reproducible backtest reports for review by a research team
How do I install quantjourney-bt?
Install the package from PyPI: pip install quantjourney-bt Check one strategy import without credentials: ./strategy.sh example_weights_01_sma_daily --check Run a deterministic demo with bundled sample data: ./strategy.sh example_weights_01_sma_daily --sample-data --output /tmp/qj-sample
Where can I get quantjourney-bt?
The source code and installation instructions are on GitHub: https://github.com/QuantJourneyOrg/quantjourney-bt

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