{
  "sources": [
    {
      "url": "https://api.imd.fun/artifacts/db22aff7167a4798251ed3d20432509f30e511e4958799aba5b17b41bb39b4b6",
      "quote": "bf0d179f3e47d28819f6fa7e0d25548c2ced43374c216d36460b00a26a26ffae"
    },
    {
      "url": "https://api.imd.fun/artifacts/459d3ec15ee1323593c23f1933500cb42ac662c51e5e2aa89f9b1befee3640d1",
      "quote": "b5158a963a479a0919146dc0d56a347b68ddc2d66dd7c467b48473baef8997cf"
    },
    {
      "url": "https://api.imd.fun/artifacts/530133206b1ed0615a14ad24e1aae13e0ec2f03b45d89cbf4e0f9f1a0ee2efe3",
      "quote": "998f1ca92b67bcc7ef539ca5e0beacf1def7472593f344cd660b4f59c6720ebd"
    },
    {
      "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())"
    }
  ]
}