{
  "sources": [
    {
      "url": "https://api.imd.fun/artifacts/ecbc9087b4f238fc1b5b0d52f55af073f676c038bfb6756172b1e51480ac1741",
      "quote": "if P['ydf1'] > 0 and P['m1'] < ptick < P['g1'] + 1.0 and amt != 1:"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/config.py",
      "quote": "class JumpDiffusionConfig:\n    \"\"\"Latent score-process parameters.\"\"\"\n\n    n_steps: int = 2_000"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/runner.py",
      "quote": "    results = []\n    for offset in range(count):\n        seed = seed_start + offset\n        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/engine.py",
      "quote": "                actions = strategy.on_step(state)\n                market.apply_actions(actions, step=step)\n\n                process.step()\n                probability = process.current_true_probability()"
    },
    {
      "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"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/market.py",
      "quote": "            edge = fill.quantity * (probability - price) if fill.side is Side.BUY else fill.quantity * (price - probability)"
    },
    {
      "url": "https://pypy.org/checksums.html",
      "quote": "1410db3a7ae47603e2b7cbfd7ff6390b891b2e041c9eb4f1599f333677bccb3e  pypy3.11-v7.3.20-linux64.tar.bz2"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/__init__.py",
      "quote": "from .runner import run_batch, sample_config"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/process.py",
      "quote": "class JumpDiffusionScoreProcess:"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/retail.py",
      "quote": "class RetailFlow:"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/results.py",
      "quote": "class SimulationResult:"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/utils.py",
      "quote": "def tick_to_price(price_ticks: int) -> float:"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/loader.py",
      "quote": "def load_strategy_factory(strategy_path: str):"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/strategy.py",
      "quote": "class BaseStrategy:"
    }
  ]
}