I open-sourced HedgeVision — a full-stack stat-arb engine built in Python (FastAPI + React). Here's what I built and why.
After months of building in private, I just open-sourced HedgeVision — a full-stack statistical arbitrage platform.
GitHub: https://github.com/ayush108108/hedgevision
What it is:
HedgeVision is the quantitative core of a much larger project I'm building — SuperIntel, a fully autonomous, agentic trading system. HedgeVision is the first piece going public.
What it does:
- Cointegration-based pairs discovery (Engle-Granger & Johansen)
- Backtesting engine with z-score thresholds and configurable lookbacks
- Paper trading simulation before going live
- FastAPI backend + React/Vite dashboard with live analytics
- Local-first: runs on SQLite, zero external dependencies to start
- Production mode: Supabase/PostgreSQL + CCXT for live exchange trading
- LLM-powered market intelligence (Ollama, OpenAI, Anthropic)
- Docker + CLI + MCP server
Why I'm open-sourcing it:
I want other builders and quant developers to use it, stress-test it, and contribute. Stat-arb is a well-studied strategy but there aren't many clean, accessible open-source implementations with a proper UI and backtesting layer.
What's next:
More OSS projects from the SuperIntel ecosystem are dropping soon. If you're into algo trading, AI agents, or just want to follow the build — watch the repo or reach out.
Happy to answer any questions on the architecture or strategy.
open sourcing a full stat-arb engine is bold. most people in quant keep everything proprietary. the fastapi + react stack is a practical choice too — ive been running a similar python backend for a different kind of data pipeline (scraping agency contacts at scale).
whats your backtesting showing in terms of sharpe? and are you targeting retail traders or more of a dev audience who wants to build on top of it?