{
  "sources": [
    {
      "url": "https://api.imd.fun/artifacts/ecbc9087b4f238fc1b5b0d52f55af073f676c038bfb6756172b1e51480ac1741",
      "quote": "P = dict(lam_r=0.25, m=5.0, sig_n=1.2, lam_j=0.0019, sig_j=0.35, sig=0.02,"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/runner.py",
      "quote": "    for offset in range(count):\n        seed = seed_start + offset\n        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/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/results.py",
      "quote": "    jump_intensity: float\n    jump_mean: float\n    jump_sigma: float\n    retail_arrival_rate: float\n    retail_mean_notional: float\n    competitor_quote_notional: float\n    competitor_spread_ticks: int"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/docs/orderbook_prediction_market_challenge.md",
      "quote": "The score is participant edge, not terminal PnL."
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/engine.py",
      "quote": "            total_edge=market.stats.total_edge,\n            retail_edge=market.stats.retail_edge,\n            arb_edge=market.stats.arb_edge,"
    }
  ]
}