{
  "sources": [
    {
      "url": "https://api.imdnotdead.fun/artifacts/5ec4e47200eb5e16cec281f133e5e7fbfd73b3157fcf5f2c60d013ffa9300b76",
      "quote": "So the strategy now rests one large bid at tick 1 while its estimate of p is between 1c and 6c"
    },
    {
      "url": "https://api.imdnotdead.fun/artifacts/d3ff83a7fc00bc1b69254d96e49cb9ea9f0d570b9b4a494787a977ee2e4d55c0",
      "quote": "Each step propagates a diffusion/jump Gaussian mixture, maps integer-price bounds through the compound-Poisson terminal-probability formula"
    },
    {
      "url": "https://api.imdnotdead.fun/artifacts/341d096827c6ba4069d8400581e8497fcb8d1a1cc519d18f579b2dd621d49a12",
      "quote": "New uncovered asks must leave a $100 cash reserve, released linearly over the final 50 steps"
    },
    {
      "url": "https://api.imdnotdead.fun/artifacts/b6188d966df3be38f92205625f63960acd2cd14fb76c8463c0a9e768ec6ea9e9",
      "quote": "the strategy queues a 2-cent ask behind it and uses the normal YES inventory target to support that ask."
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/docs/orderbook_prediction_market_challenge.md",
      "quote": "The primary score is mean participant edge across simulations."
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/__main__.py",
      "quote": "from .cli import main"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/engine.py",
      "quote": "process.step()\n                probability = process.current_true_probability()"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/runner.py",
      "quote": "# Serial in-process execution (original behaviour)"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/cli.py",
      "quote": "print(json.dumps(asdict(batch), indent=2))"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/results.py",
      "quote": "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": "initial_score_min: float = -0.75\n    initial_score_max: float = 0.75\n    jump_intensity_min: float = 0.0008\n    jump_intensity_max: float = 0.0030"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/loader.py",
      "quote": "strategy_cls = getattr(module, \"Strategy\", None)"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/types.py",
      "quote": "competitor_best_bid_ticks: int | None\n    competitor_best_ask_ticks: int | None\n    buy_filled_quantity: float\n    sell_filled_quantity: float\n    own_orders: tuple[OwnOrderView, ...]"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/market.py",
      "quote": "if self.free_cash() + EPSILON < reserved_cash:\n            raise OrderBookError(\"Insufficient free cash for order collateral\")"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/sandbox.py",
      "quote": "def run_sandboxed_simulation("
    }
  ]
}