awesome_ai4finance

Real-time and historical Hyperliquid and Lighter market data via REST and WebSocket APIs.

Free open-source A-share quant trading database with multi-source data fusion and dynamic proxy.

54-service x402-paid API platform for AI agents — crypto market data, onchain analytics, financial intelligence, and inference endpoints. MCP server with 37 tools. Pay-per-call via x402 on Base; no API keys required.

Korean listed-company data as static JSON and CSV with a remote MCP server: the previous trading day's settled closing prices from the government equity price feed (ETFs excluded), DART regulatory filings with the minute each one was received, earnings from periodic filings, and median market-adjusted returns after a filing grouped by filing type. No account and no API key — Non-commercial use with attribution; commercial redistribution is not permitted.

Elegant Python financial data interface covering A-shares, futures, options, funds, forex, bonds, indices, and crypto from authoritative Chinese sources.

Official Python SDK for Alpaca. Commission-free stock trading API with paper/live trading.

Python quant trading system supporting US/A/HK stocks, futures, options, and Bitcoin with ML-based strategy optimization.

Python-based platform for development, optimization, and deployment of automated trading systems.

Feature-rich Python framework for backtesting and live trading. Supports multiple data feeds and brokers.

Open-source Rust framework for building event-driven live-trading and backtesting systems.

Flexible backtesting framework for Python with modular algorithm stacks and portfolio-level strategy testing.

Python stock trading automation for Chinese brokers (Tonghuashun/MiniQMT/Xueqiu).

Experimental solutions to exercises from Advances in Financial Machine Learning .

Comprehensive AI stock trading platform covering LLMs, factor mining, ML/DL/RL, graph networks, and HFT strategies.

ML framework for both speculators and data scientists. Automated feature engineering and model selection.

Implementations of methods from Advances in Financial Machine Learning by Marcos Lopez De Prado.

Paper-backed PyTorch research stack for mask-aware multi-factor modeling, ML baselines, portfolio optimization, and vectorized backtesting.

Microsoft's AI-oriented quantitative investment platform with full ML pipeline. Supports alpha mining, model training, and backtesting.

Multi-agent AI hedge fund simulator with 18 agents modeled after legendary investors (Buffett, Munger, etc.) plus specialist agents.

Fully-automated agent-native trading platform from HKU. Cross-platform signal sync and one-click copy trading.

Generates synergistic formulaic alpha factors via reinforcement learning (KDD 2023).

Anthropic's official reference agents for finance: 10 specialized agents for earnings review, KYC screening, pitch generation, model building, and more.

Swarm-intelligence-powered autonomous hedge fund with Director, Quant, Risk, and Execution agents.

LLM-powered A/H/US stock analysis system. Multi-source market data + real-time news + LLM decision dashboard + multi-channel push notifications.

Azure high-performance computing for financial services — risk simulation, pricing, and analytics.

The fundamental package for scientific computing with Python. Foundation for most quantitative finance libraries.

AI-Powered Multi-Market Stock Analysis — transparent multi-factor scoring for 73 stocks across US, HK, and A-share markets. EU AI Act Art.50 compliant.

Identify and monitor social/historical cues for short-term stock movement.

Tongdaxin/Tonghuashun financial formula syntax ported to Python (e.g., , ).

Technical analysis library using Pandas and NumPy. Includes 40+ indicators out of the box.

Python wrapper for TA-Lib. 200+ technical analysis functions: candlestick patterns, momentum, volatility, and more.