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BacktestLoop turns plain-English trading ideas into editable Python backtests. Generate strategy code, run it on historical stock, futures, forex, or crypto data, then inspect trades, equity curves, logs, drawdowns, and risk metrics. It is built for traders and quant builders who want a faster research loop without setting up their own data and backtesting pipeline.
Hey Product Hunt 👋
I built BacktestLoop because testing trading ideas is still way more annoying than it should be.
Usually, you either need to write the whole strategy yourself, wire up historical data, handle indicators/timeframes/fees, debug the backtest engine, and then figure out whether the result is actually trustworthy — or you use a no-code tool that hides too much of the logic.
BacktestLoop is my attempt at a faster research loop:
1. Describe a trading strategy in plain English
2. Generate editable Python strategy code
3. Run the backtest on historical stocks, futures, forex, or crypto data
4. Inspect the trades, logs, equity curve, drawdown, and risk metrics
5. Iterate from there
The key thing is that it is not a black box. The generated code is visible and editable, so you can inspect what is actually being tested.
Important note: BacktestLoop is research software only. It is not a signal service, not a brokerage, not copy trading, and not investment advice.
I’d love feedback from traders, Python users, quant builders, and anyone who has tried to backtest strategies before.
the part i'd worry about is overfitting from the iteration loop itself. if someone tweaks the prompt and reruns against the same historical window ten times until the equity curve looks good, that's curve fitting even though each individual run is honest backtrader code. does backtestloop do anything to nudge people toward out-of-sample testing, or is that on the user to enforce themselves
Would love to see a way to compare two strategy runs side by side in the same view. Right now I can run a backtest, tweak the prompt, and run again, but flipping between tabs to compare equity curves and drawdowns makes it hard to judge if a change actually helped. An overlay or a split comparison would make iteration much faster.
How does the generated Python code actually hold up if I want to swap in my own custom dataset or tweak the strategy logic after the fact, is it cleanly structured or more of a black box?
About BacktestLoop on Product Hunt
“Turn trading ideas into real Python backtests”
BacktestLoop was submitted on Product Hunt and earned 11 upvotes and 7 comments, placing #66 on the daily leaderboard. BacktestLoop turns plain-English trading ideas into editable Python backtests. Generate strategy code, run it on historical stock, futures, forex, or crypto data, then inspect trades, equity curves, logs, drawdowns, and risk metrics. It is built for traders and quant builders who want a faster research loop without setting up their own data and backtesting pipeline.
BacktestLoop was featured in Fintech (47.2k followers), Developer Tools (515.4k followers) and Artificial Intelligence (473.1k followers) on Product Hunt. Together, these topics include over 198k products, making this a competitive space to launch in.
Who hunted BacktestLoop?
BacktestLoop was hunted by Gabor Magyari. 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.
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Gabor Magyari