AlphaLens: The Quant Engine Thinks While You Sleep

Quant hedge funds spend millions staffing teams to track alternative data signals — hiring velocity, pricing pressure, app sentiment, and regulatory filings — because the edge is in the data that doesn't appear in earnings calls. AlphaLens makes that edge accessible. It's a fully autonomous, continuously-learning signal engine powered by Bright Data, Claude, Cognee graph memory, and TriggerWare live-data triggers. Each night (or the moment an anomaly is detected intraday), AlphaLens wakes up, pulls live data across four alternative sources, runs each dataset through purpose-built Claude AI chains, and synthesizes everything into a composite earnings surprise score — from 0 to 100 — complete with analyst-style bullet points and a confidence interval. What makes v2 genuinely intelligent is its memory. Cognee replaces a flat signals database with a persistent knowledge graph: every signal event is linked to its company, its context, and its actual outcome. When AlphaLens analyzes NVIDIA this quarter, it recalls that the same GPU-validation hiring spike preceded a 16% beat three months ago — and weights the signal accordingly. The system gets sharper with every run. Built in six phases over ~48 hours on a $250 cloud budget, AlphaLens surfaces in a clean Next.js dashboard with watchlist rankings, sparkline trends, per-source evidence panels, and one-click CSV export for any backtesting platform.