{"sources": [{"url": "https://api.imd.fun/artifacts/41fa8829432f502a00c5302c0e2200b475c5c2e100a2a16b086aaab0ce62a395", "quote": "Crossing-cover base with isolated, untuned spread overrides."}, {"url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/docs/orderbook_prediction_market_challenge.md", "quote": "Those values are aggregated\nacross all fills in the step."}, {"url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/runner.py", "quote": "config = sample_config(base, var, seed=seed)"}, {"url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/config.py", "quote": "retail_arrival_rate_min: float = 0.154\n    retail_arrival_rate_max: float = 0.352\n    retail_mean_notional_min: float = 2.64\n    retail_mean_notional_max: float = 6.336"}, {"url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/retail.py", "quote": "notional = self._rng.lognormvariate(mu_ln, sigma)"}, {"url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/types.py", "quote": "client_order_id: str | None = None"}, {"url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/engine.py", "quote": "market.record_participant_fills(step_fills, probability=probability)"}, {"url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/pyproject.toml", "quote": "dependencies = []"}, {"url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/config.py", "quote": "buy_probability: float = 0.5"}]}