{
  "sources": [
    {
      "url": "https://api.imdnotdead.fun/artifacts/528a6da78583c6142644a552c86670f9d25c20e39e92f4de44deae05ddc8cecd",
      "quote": "uint256 constant BASE_FEE = 4 * BPS;"
    },
    {
      "url": "https://api.imdnotdead.fun/artifacts/da313379a2e69e9286668865e06b18dfcae25732c82b934b0fd9c2ef0d8c2ff5",
      "quote": "// Interior retail routing equalizes fee-adjusted marginal prices.\\n        // Subsequent trades in a step cannot be the initial arbitrage trade."
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/competition/config.py",
      "quote": "BASELINE_SETTINGS = BaselineSimulationSettings(\n    n_simulations=1000,\n    n_steps=10000,\n    initial_price=100.0,\n    initial_x=100.0,\n    initial_y=10000.0,\n    gbm_mu=0.0,\n    gbm_dt=1.0,\n    retail_buy_prob=0.5,\n    retail_size_sigma=1.2,\n"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/competition/config.py",
      "quote": "BASELINE_VARIANCE = HyperparameterVariance(\n    retail_mean_size_min=19.0,\n    retail_mean_size_max=21.0,\n    vary_retail_mean_size=True,\n    retail_arrival_rate_min=0.6,\n    retail_arrival_rate_max=1.0,\n    vary_retail_arrival_rate=True,\n    gbm_sigma_min=0.000882,\n    gbm_sigma_max=0.001008,\n    vary_gbm_sigma=True,\n"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/competition/match.py",
      "quote": "rng = np.random.default_rng(seed=i)\n\n            retail_mean_size = (\n                rng.uniform(self.variance.retail_mean_size_min, self.variance.retail_mean_size_max)\n                if self.variance.vary_retail_mean_size\n                else self.base_config.retail_mean_size\n            )\n"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/competition/match.py",
      "quote": "gbm_sigma = (\n                rng.uniform(self.variance.gbm_sigma_min, self.variance.gbm_sigma_max)\n                if self.variance.vary_gbm_sigma\n                else self.base_config.gbm_sigma\n            )\n\n"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_sim_rs/src/simulation/engine.rs",
      "quote": "let seed = self.config.seed.unwrap_or(0);\n\n        // Initialize price process\n        let mut price_process = GBMPriceProcess::new(\n            self.config.initial_price,\n            self.config.gbm_mu,\n            self.config.gbm_sigma,\n            self.config.gbm_dt,\n            Some(seed),\n        );\n\n"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_sim_rs/src/simulation/engine.rs",
      "quote": "let mut retail_trader = RetailTrader::new(\n            self.config.retail_arrival_rate,\n            self.config.retail_mean_size,\n            self.config.retail_size_sigma,\n            self.config.retail_buy_prob,\n            Some(seed + 1),\n        );\n\n"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/evm/baseline.py",
      "quote": "def get_vanilla_bytecode_and_abi() -> Tuple[bytes, list]:\n    \"\"\"Compile VanillaStrategy.sol once and cache.\n\n    Returns:\n        Tuple of (bytecode, abi) for the VanillaStrategy contract.\n\n    Raises:\n        RuntimeError: If compilation fails.\n    \"\"\"\n    global _CACHED_BYTECODE, _CACHED_ABI\n"
    }
  ]
}