TradingAgents: Multi-Agents LLM Financial Trading Framework
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"Vibe-Trading: Your Personal Trading Agent"
Python Backtesting library for trading strategies
- python
- trading
- backtesting
- metaclass
Translatedstock. Get stock data, calculate stock indicators, chip distribution, identify stock patterns, comprehensive stock selection, stock selection strategies, stock verification and backtesting, automatic stock trading, supports PC and mobile devices.
- stock
- stocks
- backtest
- backtesting
The most complete open-source, self-hosted Agentic Trading OS—AI research, Python strategies, factor discovery, backtesting, optimization, paper/live multi-asset execution, risk controls, monitoring, MCP, and multi-tenant SaaS in one auditable stack.
- quantitative-finance
- quant
- python
- fintech
TranslatedQUANTAXIS: a purely local quantitative solution for stocks/futures/options that supports task scheduling and distributed deployment, covering data/backtesting/simulation/trading/visualization/multiple accounts
Python quantitative trading strategies including VIX Calculator, Pattern Recognition, Commodity Trading Advisor, Monte Carlo, Options Straddle, Shooting Star, London Breakout, Heikin-Ashi, Pair Trading, RSI, Bollinger Bands, Parabolic SAR, Dual Thrust, Awesome, MACD
- trading-strategies
- quantitative-trading
- trading-bot
- quantitative-finance
The research engine that gives you an unfair advantage. Run thousands of trading ideas before others finish one.
- backtesting
- trading
- algorithmic-trading
- trading-strategies
An advanced crypto trading bot written in Python
- cryptocurrency
- trading-bot
- trading-algorithms
- trading
TranslatedTSP: a self-hosted, zero-maintenance quantitative workbench for A-shares covering "custom strategies + monitoring + backtesting" | LLM capabilities drive strategy customization + individual stock analysis + post-trade review | Freely connect third-party data sources and personalized extended data | Personal open-source project
- a-stock
- ai-agent
- aigc
- backtesting
A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities
MCP server for market analysis in AI assistants: screeners, multi-timeframe technical analysis, backtests and sentiment for crypto, stocks, forex and futures. 39 tools. Self-host free, or use the hosted version, TradingMCP.
- claude-desktop
- cryptocurrency
- mcp-server
- mcp-tools
Python toolkit for quantitative finance: stock analysis, technical indicators, strategy backtesting, portfolio optimization, and financial modeling.
- stocks
- stock-market
- finance
- python
modular quant framework.
FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading
- deep-reinforcement-learning
- stock-trading
- a2c-algorithm
- ppo
Algorithmic Trading in Python with Machine Learning
- algotrading
- backtesting
- machine-learning
- python
QuantStart.com - QSTrader backtesting simulation engine.
- equities
- backtesting-engine
- systematic-trading-strategies
- quantstart
Statistical and Algorithmic Investing Strategies for Everyone
- machine-learning
- algorithmic-trading
- investment-portfolio
- portfolio-optimization
A comprehensive quantitative trading system with AI-powered analysis, real-time data processing, and advanced risk management
- machine-learning
- python
- quantitative-trading
基金投资管理回测引擎
- finance
- quantitative-trading
- backtest
- investment
AKQuant is a high-performance quantitative research and trading framework built on Rust and Python! 开源量化回测框架
Framework for quantitative trading. Complete framework for development, backtesting, and deploying automated trading algorithms and trading bots.
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.
qstock由“Python金融量化”公众号开发，试图打造成个人量化投研分析包，目前包括数据获取（data）、可视化(plot)、选股(stock)和量化回测（策略backtest）模块。 qstock将为用户提供简洁的数据接口和规整化后的金融市场数据。可视化模块为用户提供基于web的交互图形的简单接口； 选股模块提供了同花顺的选股数据和自定义选股，包括RPS、MM趋势、财务指标、资金流模型等； 回测模块为大家提供向量化（基于pandas）和基于事件驱动的基本框架和模型。 关注“Python金融量化“微信公众号，获取更多应用信息。