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quant-wiki is a comprehensive knowledge base and structured reference for quantitative finance, financial engineering, and algorithmic trading. It serves as a centralized library of documentation covering mathematical models, financial instruments, and systematic trading strategies. The project integrates AI-driven capabilities through a modular retrieval-augmented generation framework that extracts structured data from research papers and news. It features a multi-agent workflow engine designed to discover and validate predictive alpha factors, alongside tools for local large language model
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
Vibe-Trading is a system for automated financial trading and algorithmic market research. It uses autonomous agents to manage financial assets and execute trades based on predefined rules and logic. The project features a multi-agent collaborative workflow that coordinates specialized agents to perform joint research and risk reviews. It utilizes large language model orchestration to map natural language prompts to executable data loaders and backtesting functions. The platform includes capabilities for quantitative strategy backtesting and alpha benchmarking using information coefficients t
Hikyuu is a quantitative trading framework designed for developing, backtesting, and executing systematic trading strategies. It functions as a high-speed system that combines a financial time-series library, a multi-factor analysis tool, and a quantitative backtesting engine to support comprehensive trading research. The framework is distinguished by its high-speed computing core, which utilizes multi-threaded execution to process large volumes of market data for technical indicator generation. It supports a modular strategy composition model where signal, risk, and fund management component
This is a containerized algorithmic trading system that connects to Interactive Brokers to execute high-frequency pairs trading strategies on forex instruments. The project implements a mean-reversion model that maintains long-short position pairs, continuously recalculating a beta hedge ratio to profit from temporary divergences in correlated price spreads.
The main features of jamesmawm/high-frequency-trading-model-with-ib are: Trading Broker APIs, Arbitrage Trading, Broker Connectivity, Interactive Brokers TWS Connections, Interactive Brokers Connectors, Automated Trading Execution, Pairs Trading Strategies, Tick-Driven Trading.
Projects with overlapping indexed features include: llmquant/quant-wiki — quant-wiki is a comprehensive knowledge base and structured reference for quantitative finance, financial engineering,… yutiansut/quantaxis — Quantaxis is a quantitative trading framework designed for building, backtesting, and executing automated strategies… hkuds/vibe-trading — Vibe-Trading is a system for automated financial trading and algorithmic market research. It uses autonomous agents to… fasiondog/hikyuu — Hikyuu is a quantitative trading framework designed for developing, backtesting, and executing systematic trading… mementum/backtrader — Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading… je-suis-tm/quant-trading — This project is a Python financial analytics framework and quantitative trading library. It provides a suite of…