{
  "sources": [
    {
      "url": "https://api.imd.fun/artifacts/149883fee797ad95a91e535b7a74453587a3eb19ca87ab95f7d8585db160a8f4",
      "quote": "\"code_sha256\": \"5d1bd5272884f513b19f0e66a7c235f6c188a08e4f7787ac9346f1614dda3ed3\""
    },
    {
      "url": "https://api.imd.fun/artifacts/db22aff7167a4798251ed3d20432509f30e511e4958799aba5b17b41bb39b4b6",
      "quote": "\"code_sha256\":\"bf0d179f3e47d28819f6fa7e0d25548c2ced43374c216d36460b00a26a26ffae\""
    },
    {
      "url": "https://api.imd.fun/artifacts/2194012e1ad22d8ec3c929865387870137484f68b7dc438c15bee80be81a9661",
      "quote": "\"key\": \"c-s3-retreat-strength\",\n      \"code_sha256\": \"40829d7aec82edcfcc6eaf2ff9c5c53aeacbe39a70183aab89a687e1b2ec297f\""
    },
    {
      "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/config.py",
      "quote": "    n_steps: int = 2_000"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/engine.py",
      "quote": "                actions = strategy.on_step(state)\n                market.apply_actions(actions, step=step)"
    }
  ]
}