{
  "sources": [
    {
      "url": "https://api.imdnotdead.fun/artifacts/151e0b1b16f90a1384b823bc528d6d1407f7b4bbfcc7966c12119fe483bfd326",
      "quote": "\"code_sha256\": \"4bbf720ff34be09b441e84a7e0c1bf47cba036b5211c4118e44a597f941453db\""
    },
    {
      "url": "https://api.imdnotdead.fun/artifacts/41fa8829432f502a00c5302c0e2200b475c5c2e100a2a16b086aaab0ce62a395",
      "quote": "\"code_sha256\": \"dcf68e0c9a7b06f90cf70a833357b60e2208b5b3e2971e7dddf02986d4747076\""
    },
    {
      "url": "https://api.imdnotdead.fun/artifacts/40497298132dcc0ccc9f4c0672031c7eaae00ddcf8cf24983db989feeb3c5ca0",
      "quote": "\"code_sha256\": \"71d04d586f29bb44ebf387156da6553da332111bdfafb9548c0a1530f52f6594\""
    },
    {
      "url": "https://api.imdnotdead.fun/artifacts/f7304b601a970104247074052cd75db749edc8d9e989fcce2a67c98bdcdc44bd",
      "quote": "\"code_sha256\": \"f627ba06a2fe47399e5170ab1885ddec5fd6c06e8a27f8293005b93daaaa32f7\""
    },
    {
      "url": "https://api.imdnotdead.fun/artifacts/fbaba62304b28380cd79b2de69f597deba95429638ed18a2bf5391077d025558",
      "quote": "\"code_sha256\":\"5948bf6227f4224bb2358dd267a562f6459693eb32f134ba30eeb6771f9a7f0a\""
    },
    {
      "url": "https://api.imdnotdead.fun/artifacts/ad3b2ad91284e519d0c80ecfe18eac37e0853b90703d564341744a9639e2a761",
      "quote": "\"code_sha256\": \"3bcbb5f3d400eb31c7ed5a1f45929987613cea9e96b2df850e5ed1af8335b448\""
    },
    {
      "url": "https://api.imdnotdead.fun/artifacts/46b20a51dbb98abf4c9f7832f362dc8320c2c3288dae986a8863a7455bd57402",
      "quote": "\"code_sha256\": \"d96399a0cd50e3e4f9e5121656b227f9f4d61e088500919b18073dfdd91bc864\""
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/runner.py",
      "quote": "        spread_ticks=rng.randint(\n            variance.competitor_spread_ticks_min,\n            variance.competitor_spread_ticks_max,\n        ),\n    )\n    return replace(base_config, process=process, retail=retail, competitor=competitor)"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/engine.py",
      "quote": "                actions = strategy.on_step(state)\n"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/results.py",
      "quote": "    def mean_edge(self) -> float:\n        successes = [result.total_edge for result in self.simulation_results if not result.failed]\n        return sum(successes) / len(successes) if successes else 0.0"
    },
    {
      "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/pyproject.toml",
      "quote": "dependencies = []"
    }
  ]
}