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RLX Backtester
Fast Rust backtesting engine for quantitative traders
RLX Backtester is a native macOS app built for AI-powered quantitative research. Connect Claude, Cursor, or any MCP-compatible agent to design, backtest, optimize, and stress-test trading strategies locally. Unlike traditional backtesting tools, RLX combines a high-performance Rust engine, reinforcement learning workflows, and an AI-native interface that lets autonomous agents iterate on strategies in real time. Free tier included, with Pro for unlimited research.
👋 Hi Product Hunt!
I’m Serhii, the solo developer behind RLX Backtester.
I built RLX because I was frustrated with existing backtesting tools. They were either too slow, difficult to extend, or disconnected from the way modern AI agents work.
RLX started as my own high-performance research engine written in Rust. Over time it evolved into a native macOS application where Claude, Cursor, or any MCP-compatible agent can build, optimize, backtest, and stress-test trading strategies locally.
My goal wasn’t just to make another backtester. I wanted to build a tool that feels like a real development environment for quantitative research - where AI can iterate on ideas while developers stay in control.
This is only the first public release, and I’d genuinely love your feedback.
What feature would make this indispensable for your workflow?
Thanks for checking it out! 🚀
How does the Rust engine actually handle the reinforcement learning loop, is that training happening locally on my Mac or offloading to the cloud somehow?
Curious how the RL loop actually works in practice here, like is the agent running full training cycles on past data or more of a prompt-driven parameter sweep? Also wondering what kind of latency you are seeing from the Rust engine when iterating with Claude live.
Love the MCP integration angle, this feels like the right direction for quant tools right now. One thing I'd really want to see is a built-in walk-forward analysis mode that splits data into rolling in-sample and out-of-sample windows automatically, instead of just a single train/test split. Would make the RL workflow feel a lot more trustworthy out of the box.
How does the RLX engine handle walk-forward validation versus simple in-sample fitting, especially when the reinforcement learning loop starts overfitting to historical regimes?
About RLX Backtester on Product Hunt
“Fast Rust backtesting engine for quantitative traders”
RLX Backtester was submitted on Product Hunt and earned 6 upvotes and 9 comments, placing #159 on the daily leaderboard. RLX Backtester is a native macOS app built for AI-powered quantitative research. Connect Claude, Cursor, or any MCP-compatible agent to design, backtest, optimize, and stress-test trading strategies locally. Unlike traditional backtesting tools, RLX combines a high-performance Rust engine, reinforcement learning workflows, and an AI-native interface that lets autonomous agents iterate on strategies in real time. Free tier included, with Pro for unlimited research.
RLX Backtester was featured in Investing (26.7k followers), Artificial Intelligence (473.1k followers) and Development (6k followers) on Product Hunt. Together, these topics include over 116k products, making this a competitive space to launch in.
Who hunted RLX Backtester?
RLX Backtester was hunted by Serhii Ovsiienko. 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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Serhii Ovsiienko