{"sources": [{"url": "https://api.imd.fun/artifacts/7236701c55bd1d2e0674854545ae0bbe4d4b54508c7f7a7c914261696b02e158", "quote": "Defends against the arbitrageur by knowing where p is before quoting."}, {"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"}, {"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/retail.py", "quote": "count = _sample_poisson(self._rng, self._config.arrival_rate)"}, {"url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/market.py", "quote": "quantity = quantize_down(\n            self._config.competitor.quote_notional / price,\n            self._config.share_quantum,\n        )"}, {"url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/process.py", "quote": "future_variance = (\n            horizon * config.diffusion_sigma * config.diffusion_sigma\n            + n_jumps * config.jump_sigma * config.jump_sigma\n        )"}, {"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/runner.py", "quote": "arrival_rate=rng.uniform(variance.retail_arrival_rate_min, variance.retail_arrival_rate_max),\n        mean_notional=rng.uniform(variance.retail_mean_notional_min, variance.retail_mean_notional_max),"}]}