# 0xemmkty/QuantMuse

A comprehensive quantitative trading system with AI-powered analysis, real-time data processing, and advanced risk management

Repository: https://github.com/0xemmkty/QuantMuse
Canonical: https://ross.abutalabs.com/products/quantmuse
Language: Python
License: MIT
License Family: permissive
Topics: machine-learning, python, quantitative-trading
Last push: 2025-07-29T00:34:31+00:00

## Health v2 (maintenance only)
Score: 36/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 34, release rhythm 35, longevity 42
- inputs: {"age_days": 595, "days_push": 401, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2900, forks 602 (observed 2026-08-28T04:07:28.874576+00:00)

## What it is
A Python-based quantitative trading system combining factor analysis, ML models, and LLM-powered market analysis with real-time market data streaming and risk management. It includes an extensible strategy framework, backtesting, portfolio optimization, and interactive dashboards.

## Use cases
- build algorithmic trading strategies in python
- backtest quantitative trading strategies
- analyze market sentiment with llm
- stream real-time crypto market data
- optimize a portfolio with risk parity
- screen stocks using multi-factor models

## When to choose
- you want an end-to-end quant pipeline from data ingestion to strategy execution
- you need AI/LLM-driven market analysis integrated with trading workflows
- you want built-in risk management and portfolio optimization
- you prefer a Python stack with dashboard visualization

## When to avoid
- you need guaranteed low-latency HFT execution
- you require broker certification or regulatory compliance out of the box
- you want a fully managed cloud trading service rather than self-hosted software

## Facets
- artifact type: application
- maturity: active
- function: machine-learning, nlp, data-visualization, streaming, caching, trading, llm-inference, rag
- domain: fintech, machine-learning, data-science, analytics
- platform: python, windows, self-hosted
- tags: quantitative-trading, algorithmic-trading, factor-models, backtesting, risk-management, portfolio-optimization, sentiment-analysis, market-data, binance, streamlit-dashboard, real-time, linux, macos, docker

## Member repositories
- 0xemmkty/QuantMuse (main) score 36

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:28.874576+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:35:10.383337+00:00, confidence not recorded.
  - readme: https://github.com/0xemmkty/QuantMuse (fetched 2026-08-28T04:07:28.874576+00:00, sha 88c6de8507c6)
- Data as of 2026-08-30T08:39:29.467469+00:00.