# Y-Research-SBU/QuantHarness

Official Repository for QuantHarness

Repository: https://github.com/Y-Research-SBU/QuantHarness
Canonical: https://ross.abutalabs.com/products/quantharness
Language: HTML
License: MIT
License Family: permissive
Topics: agentic-ai, large-language-models
Last push: 2026-08-18T08:35:31+00:00

## Health v2 (maintenance only)
Score: 62/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 35, longevity 30
- inputs: {"age_days": 420, "days_push": 15, "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 2839, forks 612 (observed 2026-08-28T04:07:24.700843+00:00)

## What it is
QuantHarness is a multi-agent LLM framework for high-frequency trading analysis, built on LangChain and LangGraph. Specialized agents compute technical indicators, recognize chart patterns, and analyze trends from price data, with both a web interface and programmatic API.

## Use cases
- analyze stock price charts with LLM agents
- compute technical indicators like RSI and MACD from OHLC data
- detect chart patterns in K-line data
- build multi-agent trading analysis pipelines
- research price-driven LLM agents for trading
- get plain-language trend analysis of market data

## When to choose
- you want LLM-driven technical analysis of price data
- you're researching multi-agent systems for financial markets
- you need indicator, pattern, and trend agents orchestrated with LangGraph

## When to avoid
- you need production algorithmic trading execution or low-latency order routing
- you want a battle-tested commercial trading platform
- you need backtesting with historical data at scale

## Facets
- artifact type: framework
- maturity: active
- function: agent-framework, llm-inference, machine-learning, trading, data-visualization
- domain: artificial-intelligence, large-language-models, fintech, data-visualization
- platform: python, cross-platform
- tags: multi-agent, high-frequency-trading, langchain, langgraph, technical-analysis, k-line, research-paper, ai-agents

## Member repositories
- Y-Research-SBU/QuantHarness (main) score 62

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:24.700843+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 2026-08-30T07:37:17.212041+00:00, confidence not recorded.
  - readme: https://github.com/Y-Research-SBU/QuantHarness (fetched 2026-08-28T04:07:24.700843+00:00, sha cdd10da1a0af)
- Data as of 2026-08-30T08:39:29.467469+00:00.