Quant-Builder.ai - Simplifying Quant
Build Quant & Machine Learning stock models and trade them.
What it does
Quant trading has been locked behind PhDs and code. Quant-Builder.ai opens it up: pick a stock universe, train a machine learning model on 30 years of data, validate it walk-forward, and get ranked stock picks every morning. Connect your broker and trade the picks live with automatic stop losses and take profits - or start with a free demo model in minutes, no code and no credit card. Built by a solo founder who uses it to trade his own money every day.
No-code quant trading platform for retail traders. Build machine learning stock models, run walk-forward backtests, and get daily picks ranked by confidence.
The no-code quant trading platform for retail investors. Build machine learning stock prediction models, run walk-forward backtests, and get ranked daily picks — no coding required. Research predictions, not trading recommendations. Past results do not guarantee future performance. Quant-Builder.ai — Simplifying Quant Trading: Try a Free Demo at quant-builder.ai/learn Quant-Builder.ai is a quant trading platform that lets retail investors build, backtest, and automate machine learning stock prediction models — without writing a single line of code. Train models on 30 years of point-in-time daily data across 3,000+ US stocks, run walk-forward backtests to evaluate model performance on…from quant-builder.ai
Does the same job
all alternatives →
- QTQuant, the AI stock trading analystOct 2025 · ▲8
Today we're releasing Quant (https://sourcetable.com/quant), an AI analyst that connects to 600+ exchanges with 1000+ built-in analysis tools. Andrew, CTO, has a background building software at hedge funds so we put his knowledge and experience into this application. The core idea: if you already know spreadsheets, you shouldn't need to learn Python/R or set up complex infrastructure to do serious quantitative analysis. One way to think of Quant is a low-cost Bloomberg Terminal alternative. What's inside: Portfolio optimization (including Dalio's risk parity approach),…
More ai this month
the category →
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
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, August 2026
the whole month →- TL
Life & fun · 10d ago · louisabraham.github.io
- SA
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