Awesome-quant is a curated directory of open-source software libraries and tools designed for quantitative finance, algorithmic trading, and financial data analysis. It serves as a central hub for discovering resources that support the entire lifecycle of financial modeling, from raw data ingestion to complex statistical research. The repository organizes specialized tools into categorized collections, enabling users to identify solutions for high-performance numerical computing, technical indicator calculation, and derivative pricing. It highlights frameworks that facilitate the construction
Quantaxis is a quantitative trading framework designed for building, backtesting, and executing automated strategies across global equities, futures, and cryptocurrencies. It integrates an event-driven backtesting engine, a multi-market execution gateway for order routing, and a quantitative data pipeline for ingesting and storing multi-asset market data. The system features a Rust-accelerated financial library that utilizes Apache Arrow for high-performance technical indicator calculation and zero-copy data processing. It provides a containerized infrastructure model designed for orchestrati
TradingAgents-CN is a multi-agent framework designed for autonomous financial market analysis and automated trading execution. It functions as a containerized orchestrator that leverages large language models to perform complex reasoning, research, and decision-making tasks within financial environments. The platform distinguishes itself through a modular architecture that integrates diverse artificial intelligence providers and financial data sources into a unified pipeline. It provides granular control over agent behavior through prompt-driven logic configuration and multi-model orchestrati
This project is a Python library designed for the programmatic retrieval and analysis of diverse financial datasets. It functions as a comprehensive toolkit for quantitative research, providing a unified interface to fetch historical and real-time market data across asset classes including equities, futures, bonds, cryptocurrencies, and foreign exchange. By abstracting complex network requests into simple, parameter-driven functions, it enables users to integrate financial data into research workflows and automated trading systems. The library distinguishes itself through its scraper-based ag
This project is an automated trading and agentic workflow platform designed to orchestrate complex financial tasks through state-based graphs. It provides a comprehensive framework for building, deploying, and managing autonomous agents that execute multi-step analytical processes, monitor real-time market conditions, and perform high-speed trade execution.
Die Hauptfunktionen von wshobson/agents sind: Algorithmic Trading Engines, Stateful Agent Orchestration, Automated Trading Platforms, Financial Analysis Tools, AI Trading Insight Generators, Multi-Agent Orchestration, State-Based Workflow Engines, Stateful Agent Orchestrators.
Open-Source-Alternativen zu wshobson/agents sind unter anderem: wilsonfreitas/awesome-quant — Awesome-quant is a curated directory of open-source software libraries and tools designed for quantitative finance,… yutiansut/quantaxis — Quantaxis is a quantitative trading framework designed for building, backtesting, and executing automated strategies… hsliuping/tradingagents-cn — TradingAgents-CN is a multi-agent framework designed for autonomous financial market analysis and automated trading… akfamily/akshare — This project is a Python library designed for the programmatic retrieval and analysis of diverse financial datasets.… mementum/backtrader — Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading… ai4finance-foundation/finrl — FinRL is a reinforcement learning framework designed for the development, training, and backtesting of automated…