athenara:~$ registry show agents/quantharness

QuantHarness

A LangGraph multi-agent system that chains Indicator, Pattern and Trend agents over OHLC candlestick data into a decision agent emitting a LONG or SHORT trade directive.

#multi-agent #langgraph #candlestick #technical-analysis #vision-llm

added 2026-08-17 · MIT · external

$ conda create -n quantharness python=3.11
$ conda activate quantharness
$ pip install -r requirements.txt
$ python web_interface.py

requires python 3.11

you also need: Vision-capable LLM API key (OpenAI, Anthropic, Qwen or MiniMax)

The released graph is strictly sequential: graph_setup.py runs an Indicator agent, then a Pattern agent, then a Trend agent, then a Decision Maker. Because the agents generate and read chart images, the system requires a vision-capable model; the defaults are gpt-4o-mini for the agents and gpt-4o for graph logic, with Anthropic, Qwen and MiniMax providers also selectable. The decision agent emits a structured directive — direction, entry and exit points, a stop-loss threshold, and a risk-reward ratio constrained to between 1.2 and 1.8. Both a Flask web interface, which pulls the most recent 30 candlesticks from Yahoo Finance, and a TradingGraph Python API are provided.

The repository bundles 1,600 CSV candlestick windows — 100 per instrument and timeframe, eight instruments at each of the 1h and 4h timeframes and nine distinct instruments in total (BTC, CL, DAX, DJI, ES, NQ, QQQ, SPX, VIX, with DAX only at 1h and VIX only at 4h). Their provenance is not documented, so their source should not be assumed. Note also a discrepancy between the paper and the code: the abstract describes four specialized agents, Indicator, Pattern, Trend and Risk, but no risk agent module exists in the repository — the shipped system is three analysis agents feeding a decision agent. Test coverage is thin, and recent commits have been documentation and asset changes rather than functional work.

The project and its paper were renamed from QuantAgent in July 2026, when arXiv 2509.09995 was retitled; older citations and forks use the old name, and unrelated projects share it, so match on the Y-Research-SBU organization or the arXiv identifier. That paper carries no venue, journal reference or external DOI: treat it as an unpublished preprint. The authors report higher predictive accuracy than baselines at 1-hour and 4-hour intervals — a self-reported claim about prediction accuracy rather than returns, without peer review or independent replication. The README states the software is for educational and research purposes only and is not financial advice.

trading [●●●··] moderate   ai [●●●··] moderate   programming [●●●··] moderate   setup [●●●··] moderate

authors Fei Xiong, Xiang Zhang, Aosong Feng, Siqi Sun, Chenyu You, Y-Research @SBU
origin external
license MIT
disclosure fully-open
frameworks langgraph, langchain
models gpt-4o-mini, gpt-4o
strategy technical-analysis, pattern-recognition
risk controls stop-loss, risk-reward-ratio
status maintained
markets multi-asset

described in quantharness-paper

athenara:~$