{
  "sources": [
    {
      "url": "https://api.imd.fun/artifacts/ecbc9087b4f238fc1b5b0d52f55af073f676c038bfb6756172b1e51480ac1741",
      "quote": "baef955ec10728839f7c3357016ec9eb0550e7ec689bbd5171b1cdd5c9e37273"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/config.py",
      "quote": "    jump_intensity_min: float = 0.0008\n    jump_intensity_max: float = 0.0030\n    jump_sigma_min: float = 0.20\n    jump_sigma_max: float = 0.60"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/runner.py",
      "quote": "        config = sample_config(base, var, seed=seed)\n        engine = SimulationEngine(config, strategy_factory, seed=seed)\n        results.append(engine.run())"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/engine.py",
      "quote": "        strategy = self._strategy_factory()\n        process = JumpDiffusionScoreProcess(self._config.process, seed=self._seed)"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/docs/orderbook_prediction_market_challenge.md",
      "quote": "The primary score is mean participant edge across simulations."
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/process.py",
      "quote": "    - Gaussian diffusion over the remaining steps\n    - plus a compound Poisson number of Gaussian jumps"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/loader.py",
      "quote": "    def factory():\n        instance = strategy_cls()"
    }
  ]
}