{
  "sources": [
    {
      "url": "https://api.imdnotdead.fun/artifacts/79af781a0f1882f2a291e045f803714b52128f9938480d54fb43360417b381ac",
      "quote": "9fba1eb5659d5b0ac9b7273a968a520ef9a95551c697be22020993005af70192"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/runner.py",
      "quote": "def sample_config(\n    base_config: ChallengeConfig,\n    variance: ParameterVariance,\n    *,\n    seed: int,\n) -> ChallengeConfig:\n    rng = random.Random(seed)"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/engine.py",
      "quote": "    def run(self) -> SimulationResult:\n        strategy = self._strategy_factory()\n        process = JumpDiffusionScoreProcess(self._config.process, seed=self._seed)"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/loader.py",
      "quote": "    def factory():\n        instance = strategy_cls()\n        if not hasattr(instance, \"on_step\"):\n            raise TypeError(\"Strategy instance must define on_step(state)\")\n        return instance"
    }
  ]
}