{
  "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, sized at 0.6 times remaining steps divided by the competitor's near-zero ask price, up to 60% of free budget."
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/docs/orderbook_prediction_market_challenge.md",
      "quote": "- buy every resting ask with `ask_price < p_t`\n- sell into every resting bid with `bid_price > p_t`"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/market.py",
      "quote": "                    self._cash = round(self._cash + price * covered, 10)"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/results.py",
      "quote": "    def mean_edge(self) -> float:\n        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/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())"
    },
    {
      "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\n    jump_sigma_min: float = 0.20\n    jump_sigma_max: float = 0.60\n    jump_mean_min: float = -0.04\n    jump_mean_max: float = 0.04"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/engine.py",
      "quote": "                process.step()\n                probability = process.current_true_probability()\n\n                step_fills = []\n                step_fills.extend(market.execute_arbitrage(probability=probability, step=step))"
    },
    {
      "url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/cli.py",
      "quote": "    if args.json:\n        print(json.dumps(asdict(batch), indent=2))\n        return 0"
    }
  ]
}