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QuantJourney Backtester
Open-source Python engine for defensible portfolio research
An open-source Python backtesting engine built for reproducible portfolio research—not just equity curves. Trace signals into weights, orders, fills, cash, positions and performance. Use fast vectorized research or order-aware simulation, with transaction costs, walk-forward testing, 80+ metrics, 25+ charts and 50 strategy examples. Runs locally; optional managed market data is available through the QJ API.
Hi Product Hunt — I’m Jakub, the founder of QuantJourney.
Writing a backtester with AI can look like a weekend project. Building one whose results you can inspect, reproduce and challenge is a very different problem.
Too many backtests stop at an attractive equity curve. They do not clearly show how signals became positions, when trades occurred, how transaction costs were applied, how cash and holdings changed, or whether the result survived out-of-sample testing.
We built QuantJourney Backtester to make that process more transparent.
It is an open-source Python research engine with two complementary paths:
• A fast, vectorized engine for portfolio and factor research, ranking models, long/short strategies, allocation, rebalancing and parameter exploration.
• An order-aware engine for strategies where orders, fills, execution assumptions, transaction costs, cash and position accounting matter.
The project currently includes:
• 50 strategy examples
• 80+ performance and risk metrics
• 25+ charts and diagnostics
• Walk-forward and out-of-sample testing
• Transaction-cost and execution modelling
• Reproducible reports and research outputs
• Local execution with your own data
• Optional managed market data through the QJ API
You do not need an account to install the open-source engine and run it with sample data or your own data.
This is an active beta, not a claim that every backtesting problem has been solved. There may still be bugs, incomplete documentation and unsupported edge cases. We would genuinely like people to test it, challenge the assumptions and tell us where it breaks.
Our longer-term objective is to support the full path from investment hypothesis, through validation and portfolio construction, to production and live execution.
I’d especially value feedback from quants, portfolio managers and Python developers:
1. What would stop you from trusting a backtest produced by this engine?
2. Which missing workflow would make it useful in your actual research process?
3. Where do the documentation or assumptions remain unclear?
Thanks for taking a look.
About QuantJourney Backtester on Product Hunt
“Open-source Python engine for defensible portfolio research”
QuantJourney Backtester was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #158 on the daily leaderboard. An open-source Python backtesting engine built for reproducible portfolio research—not just equity curves. Trace signals into weights, orders, fills, cash, positions and performance. Use fast vectorized research or order-aware simulation, with transaction costs, walk-forward testing, 80+ metrics, 25+ charts and 50 strategy examples. Runs locally; optional managed market data is available through the QJ API.
On the analytics side, QuantJourney Backtester competes within Open Source, Fintech, Developer Tools and GitHub — topics that collectively have 673k followers on Product Hunt. The dashboard above tracks how QuantJourney Backtester performed against the three products that launched closest to it on the same day.
Who hunted QuantJourney Backtester?
QuantJourney Backtester was hunted by Jakub Polec. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of QuantJourney Backtester including community comment highlights and product details, visit the product overview.
Jakub Polec