{
  "sources": [
    {
      "url": "https://api.imd.fun/artifacts/e714896bd9db6c7290b97fbeed9b16f5fb9d8d58d63e7a83376c3b3f560c48f2",
      "quote": "{\"credit\":\"qt1 policy, Gaussian filter and qm tables: Taylor (gtmcknight), https://github.com/gtmcknight/amm-sim, commit 4812a6382311624e1506aca131ca35c378f930d9.\",\"status\":\"complete; test only; no new Solidity and no submission\",\"patch\":{\"base_repos"
    },
    {
      "url": "https://api.imd.fun/artifacts/d56e3af5cc1e32dcc52be27046df89fb403d770a7fabf39e1a0ef94827349a5a",
      "quote": "{\"item\":\"e06-tell-port/o1-b\",\"credit\":\"Taylor (gtmcknight): replica2.py, the Gaussian filter, fee rule and qm tables (github.com/gtmcknight/amm-sim @4812a6382311624e1506aca131ca35c378f930d9, research/solved). COMBO/TwoComp/qt1-ref are IdentityMD item"
    },
    {
      "url": "https://api.imd.fun/artifacts/9242f3ebbe1ef8d23e590c9784f147655714aaf8a6da7a841273170f20c9ad9b",
      "quote": "{\n \"item\": \"e06-tell-port/o1-a\",\n \"credit\": \"Taylor (gtmcknight): replica2.py (config, price and order streams, the reference run) and the policies scored on it, github.com/gtmcknight/amm-sim @4812a6382311624e1506aca131ca35c378f930d9, research/solved"
    },
    {
      "url": "https://api.imd.fun/artifacts/8a6e303b662e1a5ea9bb47f31acedd3988ef65b3ddb66d074517aa59ad41ca40",
      "quote": "# qt1: Taylor's qm tables (qm-spline compression) in trim-2's contract\n\n**Summary.** I replaced trim-2's network with the qm-spline lookup and kept trim-2's filter, 3-slot state and fee conversion. The new contract reproduces the reference's fees: on"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_sim_rs/src/market/arbitrageur.rs",
      "quote": "let x_virtual = (k * gamma / fair_price).sqrt();\n        let net_x = x_virtual - rx;\n        let amount_x = net_x / gamma;\n\n        if amount_x <= 0.0 {\n            return None;\n        }\n\n        // Use fast quote to compute profit\n        let (y_ou"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_sim_rs/src/types/wad.rs",
      "quote": "pub fn from_f64(value: f64) -> Self {\n        Wad((value * WAD as f64) as i128)\n    }\n\n    /// Convert WAD to floating point.\n    #[inline]\n    pub fn to_f64(self) -> f64 {\n        self.0 as f64 / WAD as f64\n    }\n\n    /// Create a WAD representing a"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_sim_rs/src/amm/cfmm.rs",
      "quote": "pub fn execute_buy_x(&mut self, amount_x: f64, timestamp: u64) -> Option<TradeResult> {\n        let (y_out, fee_amount) = self.quote_buy_x(amount_x);\n        if y_out <= 0.0 {\n            return None;\n        }\n\n        // Update reserves - fees go t"
    },
    {
      "url": "https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_sim_rs/src/market/router.rs",
      "quote": "fn split_sell_two_amms(&self, amm1: &CFMM, amm2: &CFMM, total_x: f64) -> (f64, f64) {\n        let (x1, y1) = amm1.reserves();\n        let (x2, y2) = amm2.reserves();\n        let f1 = amm1.fees().bid_fee.to_f64();\n        let f2 = amm2.fees().bid_fee."
    },
    {
      "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.def"
    },
    {
      "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_sig"
    },
    {
      "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 overrid"
    },
    {
      "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/README.md",
      "quote": "python3 -I gates.py\nseed   perfect-info      gaussian+plain    gaussian+qm       lattice+qm\n10000  448.261189  443.455598  443.522608  443.663520\n10001  576.779199  571.463409  571.623551  571.977293\n10002  531.886497  525.168028  525.865859  526.411"
    }
  ]
}