{
  "sources": [
    {
      "url": "https://api.imdnotdead.fun/artifacts/ecbc9087b4f238fc1b5b0d52f55af073f676c038bfb6756172b1e51480ac1741",
      "quote": "sd1 = math.sqrt(max(1.0, H - 1.0) * var)"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/runner.py",
      "quote": "def run_batch(\n    strategy_factory=None,\n    *,\n    strategy_path: str | None = None,\n    base_config: ChallengeConfig | None = None,\n    variance: ParameterVariance | None = None,\n    n_simulations: int | None = None,\n    seed_start: int = 0,\n    workers: int = 1,\n    sandbox: bool = False,\n) -> BatchResult:"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/config.py",
      "quote": "    n_steps: int = 2_000"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/loader.py",
      "quote": "def load_strategy_factory(strategy_path: str):"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/results.py",
      "quote": "    total_edge: float\n    retail_edge: float\n    arb_edge: float"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/pyproject.toml",
      "quote": "requires-python = \">=3.10\"\ndependencies = []"
    }
  ]
}