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Tool that backtests your strategy only from a plain English description

Hacker News

Tool that backtests your strategy only from a plain English description

Hi HN, I built a tool that lets traders backtest strategies by simply describing them in plain English. Why? Because manual backtesting takes hours (sometimes days) and TONS of effort, especially when you spot new filters or changes you want to test. Most traders either waste tons of time doing it manually or don’t have the coding skills to automate it. I wanted to fix that. You write: “Buy when price breaks the previous day high with volume above 1M, stop loss below the low, TP 2R” …and it runs a backtest over historical data and returns the trade results, metrics, entry screenshots, and even AI suggestions to improve your strategy. It’s still early and needs polish, but the core is working. I'd love feedback on the product, usability, or anything you think could make it better. You can try the MVP here: https://www.BacktestAI.com/ Happy to answer any questions!

Visit product View on Hacker NewsJul 30, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, coding, plain · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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