{
  "sources": [
    {
      "url": "https://api.imdnotdead.fun/artifacts/149883fee797ad95a91e535b7a74453587a3eb19ca87ab95f7d8585db160a8f4",
      "quote": "\"code_sha256\": \"5d1bd5272884f513b19f0e66a7c235f6c188a08e4f7787ac9346f1614dda3ed3\""
    },
    {
      "url": "https://api.imdnotdead.fun/artifacts/6b40ef6fdf9512700501e6b92fb5c920955a97b29111faada6d411074173b7cd",
      "quote": "\"code_sha256\":\"4f9167dddd457f394e988bf9c9e2576dd6120834bfb8d93573ce9451fd587598\""
    },
    {
      "url": "https://api.imdnotdead.fun/artifacts/2d4b30bbc6bffbc500b37ce5e8a1ac821d6513c52a6a2377b4be3c11d97717fb",
      "quote": "\"code_sha256\": \"3c07bdcd221ac25729c3ebc92b21ac9e2014510ecb85816c4391ed81e933291f\""
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/runner.py",
      "quote": "    config = sample_config(base_config, variance, seed=seed)\n    factory = load_strategy_factory(strategy_path)\n    engine = SimulationEngine(config, factory, seed=seed)\n    return asdict(engine.run())"
    },
    {
      "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/engine.py",
      "quote": "            for step in range(self._config.process.n_steps):"
    }
  ]
}