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Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.
11 Claude skills for day trading: scan thousands of charts, track breaking news, time every entry and exit, and replay every outcome before you risk a dollar.
Python library for portfolio optimization, factor model construction, and risk management built on scikit-learn
Oracle3: open-source trading engine and MCP server for prediction markets. Finds fee-adjusted no-arbitrage violations across related event contracts and trades them live on Kalshi, Polymarket and Solana, or on paper, under pre-trade risk limits. 13 MCP tools, JSON CLI, agent skills, 600+ tests.
Predicting price trends in cryptomarkets using an lstm-RNN for the use of a trading bot
Real-time HFT bot for Polymarket crypto Up/Down markets combining CLOB order books, Chainlink feeds and multi-exchange prices with TWAP and Black-Scholes probability models; paper mode and backtests.
Passive market-making bot for Predict.fun: quotes from live order books, replaces stale orders and cancels on unsafe price/depth; Windows desktop app and web console for Ubuntu servers.
AI crypto trading bot with deep neural network (84.9% accuracy, 25 coins). BiLSTM + Attention trained on GPU. Bybit, Binance, OKX, Gate.io. Free cloud or self-hosted.
Stock price prediction using a deterministic algorithm inspired by LSTM, focusing on pattern recognition in historical data.
Decentralized agent memory for the Lightning economy. Nostr identity, L402 payments, MCP server.
Financial Market Intelligence MCP Server — stock quotes, technical analysis, crypto data, and portfolio insights for AI agents
Contingency Random Number Generator — numbers with controllable fat tails, volatility clustering, and scale convergence
Simple, modern ETH backtesting project using Pandas/Numpy: data prep, MA signals, portfolio simulation, and result visualization.