{"sources":[{"url":"https://api.imdnotdead.fun/artifacts/fd2f147e94f3523e4c4f91e43dc98b88e9ce8ef1060d2939367445711154a16a","quote":"{\"item\":\"copy-b\",\"batch\":\"e04-a-u1-give\",\"valid\":true,\"submission\":false,\"status\":\"all gates pass (isArb 11856/11856; closed loop max 4.58e-7 bps vs 0.01; sanity pass; validate; 21055 B; 6 of 32 slots; no assembly; engine afterSwap peak 207569, 0 failures)\",\"change\":\"One rule added to frozen lookup-u1 (Strategy.sol sha256 69c361ef...), in afterSwap only: on a callback not read as arbitrage, after "},{"url":"https://api.imdnotdead.fun/artifacts/8a6e303b662e1a5ea9bb47f31acedd3988ef65b3ddb66d074517aa59ad41ca40","quote":"```solidity\n// SPDX-License-Identifier: MIT\npragma solidity ^0.8.24;\nimport {AMMStrategyBase} from \"./AMMStrategyBase.sol\";\nimport {TradeInfo} from \"./IAMMStrategy.sol\";\n// Solved AMM policy by Taylor (gtmcknight), amm-sim 4812a638 research/solved: Gaussian filter + qm tables (qm_k1e-5.json),\n// fee rule of real.py's qm variant (sd level, phase, synced flag). All credit for the policy, filter and "},{"url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/setup.sh","quote":"echo \"aedb68280aca34c110968f00148387c95885207913fb2cf19a1ab4c5cfd6887a  $R/$W\"|sha256sum -c -"},{"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)\n\ndef _midpoint(min_val: float, max_val: float) -> float:\n    return (min_val + max_val) / 2\n\n\ndef baseline_nominal_sigma() -> fl"},{"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_r"},{"url":"https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/evm/compiler.py","quote":"                    \"optimizer\": {\n                        \"enabled\": True,\n                        \"runs\": 200,\n                    },\n                    \"viaIR\": True,\n                    \"evmVersion\": \"paris\",\n                    \"outputSelection\": {\n                        \"*\": {\n                            \"*\": ["},{"url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research/tell/README.md","quote":"Nothing above ran on the engine. Two checks, both on the unpatched wheel:\n\n- **The score.** The contract with the test against the contract on the board, each alone against VanillaStrategy on the same fresh seeds. About +3.2 says everything at once.\n- **The probe.** `run_batch` returns, for every step, `fair_price`, `spot_prices` and `fees` for the submission. A contract can therefore show a harness what it read: fixed 30 bps fees plus a few wei "}]}