Sonar Sciences
1720 winning strategies, forward and back tested.
What it does
No more losing trades, no more fake bots that are built on a single strategy. You can build your winning trading bot on a scientifically proven strategies build by the same world class quant teams that build for Wall Street for 2 decades. Sonar Sciences is a professional workspace for quantitative traders. Build a strategy, validate it against four years of SONAR cross-venue data, forward test it live, and run it on your own broker account.
Sonar Sciences is a professional workspace for quantitative traders. Build a strategy, validate it against four years of SONAR cross-venue data, forward test it live, and run it on your own broker account.
The professional workspace for building rule-based quantitative strategies. Validate against four years of SONAR cross-venue data, forward test in live market conditions, and run what survives on your own broker account. Build in the Studio against four years of SONAR cross-venue data - spreads, slippage, and funding included. If the edge isn't real out of sample, you find out before your money does. A backtest is a hypothesis. Run your strategy against live market data with no money at risk and watch it trade forward in public, day by day, so you can see whether the edge held up outside the sample it was built on. A strategy that survives emits a versioned trade-recommendation stream, and…from sonar-sci.com
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Hey-Traders Live Trading5d ago · hey-traders.com · ▲9Describe, backtest, and automate your trading strategy
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Life & fun · 10d ago · louisabraham.github.io


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Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io


I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com