{
  "sources": [
    {
      "url": "https://api.imd.fun/artifacts/fe2320b5c091489337b3eb06bcbaea19bf4a8af09d959d67504ba63184f1dab6",
      "quote": "\"code_sha256\":\"dd53db1fbe42628e040c6c7b7a82689867ab601263c5e740506299a3a186b38c\",\"params\":{\"architecture\":\"19-8-2 as IA "
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    {
      "url": "https://api.imd.fun/artifacts/df0ddfdf122c88641513dd179980fce475f42a912a184f812d213f27db7c14df",
      "quote": "\"code_sha256\":\"956566e95f46dc1ed5980fae5e550de7b9da10f9b3ad8eed62f265682cfcecf2\",\"port_zb64\":\"eNqtPGtzGzmO3/tX9M1U3ertfk"
    },
    {
      "url": "https://api.imd.fun/artifacts/afd5183e9ff3235334e041c00b0cd19079599a2c6464db95f8c1586712105557",
      "quote": "\"code_sha256\":\"301941829a44bb60977bb28181a5e4bfa6544f3696763f987054789b2611d686\",\"contract_name\":\"inputs_b\",\"port_zb64\":"
    },
    {
      "url": "https://api.imd.fun/artifacts/e8a7a2af9039c24b8bb2d3b5fa1c9113606cbf5a480ac07d02ca26750f1752ea",
      "quote": "\"replica_source_sha256\": \"1e4c44b4bffbdeed65872ee15d46dbde209bf5aa11014329d13d100507b4040c\",\n  \"th_names\": [\n    \"ELAPSE"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_sim_rs/src/market/arbitrageur.rs",
      "quote": "/// Uses closed-form solutions for constant product AMMs.\n/// For reserves (x, y), k=xy, fee f (fee-on-input), \u03b3 = 1 - f, and fair price p (Y per X):\n/// - Buy X from AMM (AMM sells X): \u0394x_out = x - sqrt(k / (\u03b3\u00b7p)) (profit-maximizing)\n/// - Sell X to AMM (AMM buys X): \u0394x_in = (sqrt(k\u00b7\u03b3 / p) - x) / \u03b3 (profit-maximizing, \u0394x_in is gross input)\npub struct Arbitrageur;\n\nimpl Arbitrageur {\n    /// Creat"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_sim_rs/src/simulation/engine.rs",
      "quote": "// 1. Generate new fair price via GBM\n/// 2. Arbitrageur extracts profit from each AMM\n/// 3. Retail orders arrive and are routed to best AMM\npub struct SimulationEngine {\n    config: SimulationConfig,\n}\n\nimpl SimulationEngine {\n    /// Create a new simulation engine.\n    pub fn new(config: SimulationConfig) -> Self {\n        Self { config }\n    }\n\n    /// Run a complete simulation.\n    pub fn run"
    },
    {
      "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            retail_arrival_rate = (\n                rng.uniform(self.variance.retail_arrival_rate_min, sel"
    }
  ]
}