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Babaika Strategy Lab
Reality-check your trading strategy before risking money
The project puts math first. Its calculations follow transparent rules and established research - not claims that AI can predict markets. Strategies are tested on unseen periods and account for trading costs, market stress, and survivorship bias. AI is used only to review news and double-check candidates; it does not replace the maths or pretend to know what the market will do next. A Reality Check then looks for lucky periods, unstable settings, and results that disappear after realistic costs.
This project began with a mistake that many people make when first testing a strategy.
I optimized a model on historical data and then evaluated it again using essentially the same information. The result looked incredible: roughly 40,000% profit. At that moment, it is easy to believe you have discovered the holy grail - and start thinking about investing real money.
But the result was largely an illusion. The strategy had already “seen” the answers.
I built Babaika Strategy Lab to make that mistake harder. It moves through history chronologically: the model learns only from earlier data, freezes its parameters, and then evaluates them on the next unavailable test period. An optional Reality Check looks for overfitting, survivorship bias, concentrated returns, severe drawdowns, sensitivity to costs, unstable parameters, and incomplete evidence.
The beta supports stocks, crypto, and historical options data, along with Monte Carlo stress testing, virtual portfolios, an end-of-day scanner, and optional AI-assisted review. AI supports the final analysis - it does not replace the mathematical model or pretend to predict the market.
This is an experimental research tool, not a trading bot, financial adviser, or promise of future returns.
I’d especially value feedback on whether the results and warnings are understandable without a quantitative-finance background. What would you like this tool to check before trusting the backtest results?
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About Babaika Strategy Lab on Product Hunt
“Reality-check your trading strategy before risking money”
Babaika Strategy Lab was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #56 on the daily leaderboard. The project puts math first. Its calculations follow transparent rules and established research - not claims that AI can predict markets. Strategies are tested on unseen periods and account for trading costs, market stress, and survivorship bias. AI is used only to review news and double-check candidates; it does not replace the maths or pretend to know what the market will do next. A Reality Check then looks for lucky periods, unstable settings, and results that disappear after realistic costs.
Babaika Strategy Lab was featured in Fintech (47.3k followers), Investing (26.7k followers) and Artificial Intelligence (475.9k followers) on Product Hunt. Together, these topics include over 139.7k products, making this a competitive space to launch in.
Who hunted Babaika Strategy Lab?
Babaika Strategy Lab was hunted by Babaika Strategy Lab. 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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Babaika Strategy Lab
Hi 👋
This project began with a mistake that many people make when first testing a strategy.
I optimized a model on historical data and then evaluated it again using essentially the same information. The result looked incredible: roughly 40,000% profit. At that moment, it is easy to believe you have discovered the holy grail - and start thinking about investing real money.
But the result was largely an illusion. The strategy had already “seen” the answers.
I built Babaika Strategy Lab to make that mistake harder. It moves through history chronologically: the model learns only from earlier data, freezes its parameters, and then evaluates them on the next unavailable test period. An optional Reality Check looks for overfitting, survivorship bias, concentrated returns, severe drawdowns, sensitivity to costs, unstable parameters, and incomplete evidence.
The beta supports stocks, crypto, and historical options data, along with Monte Carlo stress testing, virtual portfolios, an end-of-day scanner, and optional AI-assisted review. AI supports the final analysis - it does not replace the mathematical model or pretend to predict the market.
This is an experimental research tool, not a trading bot, financial adviser, or promise of future returns.
I’d especially value feedback on whether the results and warnings are understandable without a quantitative-finance background. What would you like this tool to check before trusting the backtest results?