{
  "sources": [
    {
      "url": "https://api.imd.fun/artifacts/149883fee797ad95a91e535b7a74453587a3eb19ca87ab95f7d8585db160a8f4",
      "quote": "from orderbook_pm_challenge.strategy import BaseStrategy\\nfrom orderbook_pm_challenge.types import CancelOrder, PlaceOrder, Side\\n\\nP = dict(lam_r=0.25, m=5.0, sig_n=1.2, lam_j=0.0019, sig_j=0.35, sig=0.02,\\n         qmax=3000.0, rmin=2, kA=0.7, kL=0.15, pL=0.05,\\n         ammo=1.0, ydem=0.4, W=200."
    },
    {
      "url": "https://api.imd.fun/artifacts/6b40ef6fdf9512700501e6b92fb5c920955a97b29111faada6d411074173b7cd",
      "quote": "from orderbook_pm_challenge.strategy import BaseStrategy\\nfrom orderbook_pm_challenge.types import CancelOrder, PlaceOrder, Side\\nP_s1 = dict(lam_r=0.25, m=5.0, sig_n=1.2, lam_j=0.0019, sig_j=0.35, sig=0.02, qmax=3000.0, rmin=2, kA=0.7, kL=0.15, pL=0.05, ammo=1.0, ydem=0.4, W=200.0, fy=0.6, ammo_q=6"
    },
    {
      "url": "https://api.imd.fun/artifacts/2d4b30bbc6bffbc500b37ce5e8a1ac821d6513c52a6a2377b4be3c11d97717fb",
      "quote": "from orderbook_pm_challenge.strategy import BaseStrategy\\nfrom orderbook_pm_challenge.types import CancelOrder, PlaceOrder, Side\\n\\ndef _spread1():\\n    P = dict(lam_r=0.25, m=5.0, sig_n=1.2, lam_j=0.0019, sig_j=0.35, sig=0.02, qmax=3000.0, rmin=2, kA=0.7, kL=0.15, pL=0.05, ammo=1.0, ydem=0.4, W=200"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/runner.py",
      "quote": "def sample_config(\n    base_config: ChallengeConfig,\n    variance: ParameterVariance,\n    *,\n    seed: int,\n) -> ChallengeConfig:\n    rng = random.Random(seed)\n    process = replace(\n        base_config.process,\n        initial_score=rng.uniform(variance.initial_score_min, variance.initial_score_max),\n        jump_intensity=rng.uniform(variance.jum"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/engine.py",
      "quote": "class SimulationEngine:\n    def __init__(self, config: ChallengeConfig, strategy_factory, *, seed: int) -> None:\n        self._config = config\n        self._strategy_factory = strategy_factory\n        self._seed = seed\n\n    def run(self) -> SimulationResult:\n        strategy = self._strategy_factory()\n        process = JumpDiffusionScoreProcess(sel"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/config.py",
      "quote": "n_steps: int = 2_000\n    initial_score: float = 0.0\n    diffusion_sigma: float = 0.02\n    jump_intensity: float = 0.001\n    jump_mean: float = 0.0\n    jump_sigma: float = 0.75\n    terminal_threshold: float = 0.0\n    poisson_tail_mass: float = 1e-12\n\n\n@dataclass(frozen=True)\nclass RetailFlowConfig:\n    \"\"\"Retail market-order flow parameters.\"\"\"\n\n   "
    }
  ]
}