{
  "sources": [
    {
      "url": "https://api.imd.fun/artifacts/afd5183e9ff3235334e041c00b0cd19079599a2c6464db95f8c1586712105557",
      "quote": "\"code_sha256\":\"301941829a44bb60977bb28181a5e4bfa6544f3696763f987054789b2611d686\",\"contract_name\":\"inputs_b\",\"port_zb64\":\"eNq1PGtz28au3/kreNuZe/Q2HxJFpbXnKrZSe44j51hO09OMRkPbTMRGr4qUHefXXwD7wC5JpT1z53a"
    },
    {
      "url": "https://api.imd.fun/artifacts/2801efc8190e86318dd922d4a7179f63684d77a226047fc2f3c78232963eb690",
      "quote": "\"hidden_units\":8,\"W1\":[[-0.042655,1.389981,-0.435022,2.131939,-2.307678,-0.134711,-1.563002,0.838218,-3.867643,-0.287527,-0.014871,0.138925,0.547082],[0.840878,1.015656,1.905167,4.344855,1.49152,0.813"
    },
    {
      "url": "https://api.imd.fun/artifacts/e8a7a2af9039c24b8bb2d3b5fa1c9113606cbf5a480ac07d02ca26750f1752ea",
      "quote": "\"replica_source_sha256\": \"1e4c44b4bffbdeed65872ee15d46dbde209bf5aa11014329d13d100507b4040c\",\n  \"th_names\": [\n    \"ELAPSED_CAP\",\n    \"SIGNAL_THRESHOLD\",\n    \"DIR_DECAY\",\n    \"ACT_DECAY\",\n    \"SIZE_DECA"
    },
    {
      "url": "https://api.imd.fun/artifacts/d845a4b94eab87f23956fd1939c0135d955df212d1f3037f2a761c178cef7a0e",
      "quote": "\"port_sha256\":\"9eac3553357528b06eeae3ed8d18283d83401e1968145ceba16be356a147e271\",\"rs2_source_url\":\"https://api.imd.fun/artifacts/19f2ec857df536e8ab62ab1da89ec6623332521ce46849f15bccff402c63e1e5\",\"rs2_"
    },
    {
      "url": "https://api.imd.fun/artifacts/48cd6a10f39fc22a79c5f12398474bd65282803d59314819acd586f187015808",
      "quote": "\"decode_instructions\":\"Inside the pinned checkout, for each api.files entry k -> filename, b=zlib.decompress(base64.b64decode(result[k+\\\"_zb64\\\"])); assert hashlib.sha256(b).hexdigest()==result[k+\\\"_s"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/competition/match.py",
      "quote": "    def _build_configs(self) -> list[amm_sim_rs.SimulationConfig]:\n        \"\"\"Build simulation configs with optional variance.\"\"\"\n        import numpy as np\n\n        configs = []\n        for i in range(self.n_simulations):\n            rng = np.random.default_rng(seed=i)\n\n            retail_mean_size"
    },
    {
      "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)\n\n\nBASELINE_VARIANCE = HyperparameterVariance(\n    reta"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/contracts/src/VanillaStrategy.sol",
      "quote": "contract VanillaStrategy is AMMStrategyBase {\n    /// @notice Fixed fee in WAD (30 bps = 0.30% = 30e14)\n    uint256 public constant FEE = 30 * BPS;\n\n    /// @inheritdoc IAMMStrategy\n    function afterInitialize(uint256, uint256) external pure override returns (uint256 bidFee, uint256 askFee) {\n     "
    }
  ]
}