Transparent and Efficient Financial Analysis
- financial-statements
- stock-data
- fundamental-analysis
- fundamentals
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Transparent and Efficient Financial Analysis
Free, open source, a high frequency trading and market making backtesting and trading bot, which accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books(Level-2 and Level-3), with real-world crypto trading examples for Binance and Bybit
MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.
Portfolio Optimization in Python
TranslatedWe are committed to the open-sourcing quantitative knowledge, aiming to bridge the information gap between the domestic and international quantitative finance industries. We are dedicated to open-sourcing quantitative knowledge and translating it into Chinese (汉化), breaking the information gap between the domestic and international quantitative finance industries.
Python toolkit for quantitative finance: stock analysis, technical indicators, strategy backtesting, portfolio optimization, and financial modeling.
modular quant framework.
FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
An open-source, high-performance quantitative trading framework in C++/Python focused on strategy analysis and backtesting Trading model R&D · Ultra-fast engine · Efficient backtesting
Algorithmic Trading in Python with Machine Learning
Python library for portfolio optimization, factor model construction, and risk management built on scikit-learn
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.
Python trading framework with AI agents that actually place the trade. Backtest, paper and live trade on 12 brokers: stocks, options, futures, forex, crypto and prediction markets.
A股 AI 投研
📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.
Open-source construction ERP - BOQ, PDF/CAD/BIM takeoff, AI cost matching, 293 guided cases. 120,000+ priced positions, 44 languages, 190+ modules, cost data for 40+ markets. AGPL-3.0. pip install openconstructionerp
Scalable, event-driven, deep-learning-friendly backtesting library
QuantJourney Backtester
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.
A composable, real time, market data and trade execution toolkit. Built with Elixir, runs on the Erlang virtual machine
Open Source Algorithmic Trading Engine
TranslatedMulti-market intelligent quantitative workbench (A-shares/Hong Kong stocks/US stocks) — secondary development based on shy3130/tickflow-stock-panel, adding multi-market support for Hong Kong and US stocks. Self-hosted stock screening + monitoring + backtesting. MIT.
Self-hosted, invisible CAPTCHA that detects AI agents driving real browsers. GDPR, WCAG 2.2 AA, and EAA: no cookies, no third-party data sharing, and no visual or audio puzzle. With a false-positive budget enforced in CI. Proof of work, drop-in for Turnstile and reCAPTCHA.
TranslatedLow-code quant backtesting for China A-shares