Quickstart¶

Install¶

pip install "traderharness[llm,data,ui]"

git clone https://github.com/HephaestLab/TraderHarness
cd TraderHarness
python -m venv .venv
.venv\Scripts\python.exe -m pip install -e ".[all]"

docker compose up --build

Install market data¶

traderharness data download --full

The installer verifies file size and SHA-256 against the release manifest before atomically replacing ~/.traderharness/dataset.

Run the no-key replay¶

traderharness demo

The cassette contains a recorded masked LLM trajectory. No API key is required; the engine still re-evaluates it against local canonical market data.

Open the research console¶

traderharness ui

Open http://127.0.0.1:8000. The service binds to loopback by default and rejects accidental public exposure unless explicitly enabled.

Run a fresh model agent¶

$env:DEEPSEEK_API_KEY="..."
traderharness run `
  --agent trend-breakout `
  --start 2024-03-04 `
  --end 2024-03-29 `
  --mask-entities

Compare the four built-in reference cards under one market clock and isolated portfolios:

traderharness compare `
  --agent trend-breakout `
  --agent quality-compounder `
  --agent event-hawk `
  --agent quant-researcher `
  --start 2024-03-04 `
  --end 2024-03-29 `
  --mask-entities `
  --output showcase

Add --record-replay cassette.jsonl to save a deterministic, leakage-auditable replay cassette.