Explore live open-source projects and AI models.

API for backtesting and trading automation

Desktop GUI and CLI toolkit for WorldQuant Brain API workflows, including datasets, operators, alpha generation, simulations, backtests, evolution, and Telegram worker automation.

Simplified Investment & Trading Toolkit with Python & C++

Hybrid multi–timeframe XAUUSD trading pipeline with feature engineering in Python, Parquet‑based datasets, and transformer‑style models for supervised learning and backtesting on MT5 data

An advanced crypto futures trading platform featuring multi-agent AI debate consensus, real-time backtesting, and automated execution on Binance.

📊 EasyQuant - Event-driven quantitative trading framework for China A-share market. Backtest strategies, analyze risk metrics, and deploy with eqlib core library.

Intraday trading strategy for futures calendar spreads. Uses crude oil futures and 1-minute bid/ask bars from Interactive Brokers with a Bollinger Band mean reversion strategy. Runs in Moonshot. Demonstrates using exchange native spreads for live/paper trading, and non-native spreads for backtest... Read more

The most comprehensive MCP server for Polymarket — 48 tools spanning direct trading, market discovery, smart money tracking, copy trading, backtesting, risk management, and portfolio optimization. Works with Claude Code, Cursor, or any MCP-compatible client.

Multi-factor quantitative research and paper-trading system for Chinese A-shares. The backtest and the simulated trader run through one shared code path. Walk-forward validated, zero-config, runs in about five minutes. Includes a log of falsified strategies. MIT. Simulation only — not financial a... Read more

📈 Machine-learning DCF — Ridge & Elastic Net forecast six value drivers from SEC XBRL filings, then FCFF → DCF → fair value, validated with leak-free chronological backtests.