Eiten is an AI-powered market analysis platform and quantitative toolset designed to translate statistical market data and options flow into investment strategies. It provides a suite of specialized financial tools, including an analysis platform driven by large language models, a quantitative portfolio optimizer, and a trading strategy backtester.
Les fonctionnalités principales de tradytics/eiten sont : Financial Market Analysis Platforms, LLM-Based Data Transformations, Market Analysis Agents, Portfolio Optimization Algorithms, Options Flow Analytics, Trading Strategy Backtesters, Order Flow Monitoring, Covariance Noise Filtering.
Les alternatives open-source à tradytics/eiten incluent : llmquant/quant-wiki — quant-wiki is a comprehensive knowledge base and structured reference for quantitative finance, financial engineering,… 0xemmkty/quantmuse — QuantMuse is an algorithmic trading platform and quantitative trading framework that integrates large language models… letianzj/quantresearch — QuantResearch is a quantitative research framework and specialized toolkit for algorithmic simulation, financial… je-suis-tm/quant-trading — This project is a Python financial analytics framework and quantitative trading library. It provides a suite of… mementum/backtrader — Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading… hudson-and-thames/mlfinlab — mlfinlab is a Python machine learning library for finance designed for building and validating models used in…
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
QuantMuse is an algorithmic trading platform and quantitative trading framework that integrates large language models with mathematical analysis to automate market insights and trading strategies. It functions as a system for building, backtesting, and executing strategies using both historical and real-time market data. The framework is distinguished by its use of large language models for financial analysis and sentiment extraction from news and social media. It utilizes autonomous agents with chain-of-thought reasoning to generate market intelligence and strategic reports, while employing
QuantResearch is a quantitative research framework and specialized toolkit for algorithmic simulation, financial time-series analysis, and systematic trading. It provides an event-driven backtesting environment for validating strategies against historical tick and bar data, alongside a dedicated portfolio optimization engine for calculating asset weights and risk metrics. The project distinguishes itself through a machine learning finance toolkit that implements recurrent neural networks for price prediction and reinforcement learning for derivative pricing. It also features advanced statisti
This project is a Python financial analytics framework and quantitative trading library. It provides a suite of mathematical tools for asset pricing, statistical market analysis, and the development of algorithmic trading strategies. The library is distinguished by its focus on currency and commodity correlation modeling, using regression and normalization to identify exchange rate drivers. It features a specialized portfolio optimization engine that applies graph theory, such as clique centrality and degeneracy ordering, alongside quadratic programming to balance risk-adjusted returns. The