{"sources": [{"url": "https://api.imd.fun/artifacts/40497298132dcc0ccc9f4c0672031c7eaae00ddcf8cf24983db989feeb3c5ca0", "quote": "\"code_sha256\": \"71d04d586f29bb44ebf387156da6553da332111bdfafb9548c0a1530f52f6594\""}, {"url": "https://api.imd.fun/artifacts/41fa8829432f502a00c5302c0e2200b475c5c2e100a2a16b086aaab0ce62a395", "quote": "\"code_sha256\": \"dcf68e0c9a7b06f90cf70a833357b60e2208b5b3e2971e7dddf02986d4747076\""}, {"url": "https://api.imd.fun/artifacts/08e3cde58c80ef5d44a5af6a896eeeef75abff9ff749e01d2ee329f1c68d2d60", "quote": "\"code_sha256\": \"c937628ccb161886b377bd1dcf1151c31e37b8b2f4056eb020f47e08e6e8ca6b\""}, {"url": "https://api.imd.fun/artifacts/90459c5fffa37760b92c6a3ee93523240927484a7c8fa36b5d12b55a37c08733", "quote": "\"code_sha256\": \"29d4a3f99dd734eedb4eafb3949de3ab8d66011e1ce69778f52a8d0910c289b6\""}, {"url": "https://api.imd.fun/artifacts/adc67b24de94a37b17f0c930c42890ffd1286d8a83c1533e020a1eb7bd6aad43", "quote": "\"code_sha256\":\"823861487546e4e3dc094c2e3b66fdfe578bd0e31845dc9a201e64ff685dc6c1\""}, {"url": "https://api.imd.fun/artifacts/c80c33382f9c458ab6286dbc11fa761100a7e156dad7f3c12d74040b7c76578f", "quote": "\"code_sha256\":\"918443f8ad9f0294b76f35e210aef4ee80433bcb356600d3d83e1919322e90ac\""}, {"url": "https://raw.githubusercontent.com/danrobinson/prediction-market-challenge/b244530ee2a6247b5f930830eeca9dd950da0e7e/orderbook_pm_challenge/runner.py", "quote": "    competitor = replace(\n        base_config.competitor,\n        quote_notional=rng.uniform(\n            variance.competitor_quote_notional_min,\n            variance.competitor_quote_notional_max,\n        ),\n        spread_ticks=rng.randint(\n            variance.competitor_spread_ticks_min,\n            variance.competitor_spread_ticks_max,\n        ),\n    )\n    return replace(base_config, process=process, retail=retail, competitor=competitor)"}, {"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://api.imd.fun/artifacts/08e3cde58c80ef5d44a5af6a896eeeef75abff9ff749e01d2ee329f1c68d2d60", "quote": "if self.s == 1 and ask_t == 2 and ask_t < ca and phat < 0.01:"}, {"url": "https://api.imd.fun/artifacts/90459c5fffa37760b92c6a3ee93523240927484a7c8fa36b5d12b55a37c08733", "quote": "stock_rate *= 1.0 + P['flow_strength'] * (scale - 1.0)"}, {"url": "https://api.imd.fun/artifacts/adc67b24de94a37b17f0c930c42890ffd1286d8a83c1533e020a1eb7bd6aad43", "quote": "threshold = max(P['min_ahead'], 20 * 100.0 / comp) if self.s == 3 and step == -1 and 1 <= comp <= 5 else P['min_ahead']"}, {"url": "https://api.imd.fun/artifacts/c80c33382f9c458ab6286dbc11fa761100a7e156dad7f3c12d74040b7c76578f", "quote": "return min(P['qmax'], math.exp(math.log(self.f4_mean) - 0.72 + 1.2 * (self.f4_extreme if tail <= 1e-6 else inv(1 - tail))) / (t / 100.0))"}]}