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Lower-priority combinations were deferred to preserve the full fresh-seed comparison and delivery margin."},"next":["Include the post-retail price displacement and subsequent fee update in the expected arbitrage loss, then solve a small joint transition table.","Condition the risk horizon on within-step retail count using the Poisson residual-arrival distribution; validate on newly reserved seeds.","Track uncertainty in the normalizer estimate when orders route only to the normalizer, and compare an uncertainty-weighted router quote against this frozen baseline."],"code":"// SPDX-License-Identifier: MIT\npragma solidity ^0.8.24;\n\nimport {AMMStrategyBase} from \"./AMMStrategyBase.sol\";\nimport {TradeInfo} from \"./IAMMStrategy.sol\";\n\n/// @notice Router marginal-edge objective with a future-arbitrage penalty.\ncontract Strategy is AMMStrategyBase {\n    uint256 private constant RISK_WEIGHT = 2500;\n    uint256 private constant RISK_SUPPORT = 60 * BPS;\n    uint256 private constant MEAN_ORDER = 20 * WAD;\n\n    // slots: 0 filtered fair price; 1 last timestamp + 1; 2 bid; 3 ask.\n    function afterInitialize(uint256 initialX, uint256 initialY)\n        external override returns (uint256 bidFee, uint256 askFee)\n    {\n        uint256 fair = wdiv(initialY, initialX);\n        slots[0] = fair;\n        slots[1] = 0;\n        slots[4] = fair;\n        (bidFee, askFee) = quote(fair, fair, initialY, fair);\n        slots[2] = bidFee;\n        slots[3] = askFee;\n    }\n\n    function afterSwap(TradeInfo calldata trade)\n        external override returns (uint256 bidFee, uint256 askFee)\n    {\n        if (trade.reserveX == 0 || trade.reserveY == 0) return (MAX_FEE, MAX_FEE);\n        uint256 spot = wdiv(trade.reserveY, trade.reserveX);\n        uint256 fair = slots[0];\n        uint256 implied = trade.isBuy\n            ? wmul(spot, WAD - slots[2])\n            : wdiv(spot, WAD - slots[3]);\n        uint256 endpoint = implied;\n        bool first = trade.timestamp + 1 != slots[1];\n        uint256 weight = first ? 26 : 5;\n        // An arbitrage endpoint reveals fair price. Retail endpoints are noisy;\n        // use a bounded filter and less weight after the first fill of a step.\n        uint256 limit = fair * 60 / 10000;\n        implied = clamp(implied, fair - limit, fair + limit);\n        fair = (fair * (100 - weight) + implied * weight) / 100;\n        slots[0] = fair;\n        slots[1] = trade.timestamp + 1;\n        // Both pools keep k constant. At fair, normalizer Y is sqrt(k * fair).\n        // This arithmetic approximation has only second-order spot error.\n        uint256 depth = (trade.reserveY + wmul(trade.reserveX, fair)) / 2;\n        uint256 normalizer = slots[4];\n        if (first) {\n            normalizer = clamp(normalizer, wmul(fair, WAD - 30 * BPS), wdiv(fair, WAD - 30 * BPS));\n        }\n        // A retail split equates fee-adjusted post-trade marginal prices.\n        uint256 routed = trade.isBuy\n            ? wdiv(endpoint, WAD - 30 * BPS)\n            : wmul(endpoint, WAD - 30 * BPS);\n        uint256 inferred = trade.isBuy\n            ? (normalizer < routed ? normalizer : routed)\n            : (normalizer > routed ? normalizer : routed);\n        if (!first) normalizer = inferred;\n        else normalizer = inferred;\n        slots[4] = normalizer;\n        (bidFee, askFee) = quote(spot, fair, depth, normalizer);\n        slots[2] = bidFee;\n        slots[3] = askFee;\n    }\n\n    function marginFor(int256 competitorCost, uint256 depth) private pure returns (uint256 margin) {\n        int256 raw = (competitorCost + int256(2 * wdiv(MEAN_ORDER, depth))) / 3;\n        margin = raw > int256(5 * BPS) ? uint256(raw) : 5 * BPS;\n        margin = clamp(margin, 5 * BPS, 300 * BPS);\n        if (margin < RISK_SUPPORT && RISK_WEIGHT != 0) {\n            uint256 gap = RISK_SUPPORT - margin;\n            margin += 2 * RISK_WEIGHT * gap * gap\n                / (3 * 8000 * RISK_SUPPORT + 4 * RISK_WEIGHT * gap);\n        }\n    }\n\n    function quote(uint256 spot, uint256 fair, uint256 depth, uint256 normalizer)\n        private pure returns (uint256 bidFee, uint256 askFee)\n    {\n        fair = (fair * 3 + spot) / 4;\n        uint256 ratio = wdiv(normalizer, fair);\n        uint256 bidMargin = marginFor(int256(WAD) - int256(wmul(ratio, WAD - 30 * BPS)), depth);\n        uint256 askMargin = marginFor(int256(wdiv(ratio, WAD - 30 * BPS)) - int256(WAD), depth);\n        uint256 bidRatio = fair * (WAD - bidMargin) / spot;\n        uint256 askRatio = spot * WAD / wmul(fair, WAD + askMargin);\n        bidFee = bidRatio < WAD ? clampFee(WAD - bidRatio) : 0;\n        askFee = askRatio < WAD ? clampFee(WAD - askRatio) : 0;\n    }\n\n    function getName() external pure override returns (string memory) {\n        return \"RouterOptimal\";\n    }\n}\n","code_sha256":"8b6afc7a21c7941e48163b07bd5a1ad5c3364c77c2269ec131d95aa9744dddd1","score":{"n":1000,"mean_edge":519.2870871962983},"paired":{"reference":"contracts/src/StarterStrategy.sol @ 6b6921f8d463314aa7c96234e1be25a25245f704","seed_start":100000,"n":2000,"mean_diff":149.4622701598869,"se":0.9187921101518638,"worst":[{"seed":101573,"diff":72.3124792087005},{"seed":100645,"diff":73.25149226788972},{"seed":100236,"diff":77.90360237419097},{"seed":100631,"diff":78.45157749956604},{"seed":100818,"diff":78.99583854847123}]},"seconds_per_simulation":0.17589508567436132,"validate_ok":true,"citations":[{"url":"https://github.com/benedictbrady/amm-challenge/blob/6b6921f8d463314aa7c96234e1be25a25245f704/amm_sim_rs/src/market/router.rs","use":"Exact fee-on-input router split and marginal-price equality."},{"url":"https://github.com/benedictbrady/amm-challenge/blob/6b6921f8d463314aa7c96234e1be25a25245f704/amm_sim_rs/src/market/arbitrageur.rs","use":"Arbitrage band endpoints and profit-maximizing trade sizes."},{"url":"https://github.com/benedictbrady/amm-challenge/blob/6b6921f8d463314aa7c96234e1be25a25245f704/amm_sim_rs/src/simulation/engine.rs","use":"Actual edge signs, step ordering, and separate retail/arbitrage edge accounting."},{"url":"https://github.com/benedictbrady/amm-challenge/blob/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/competition/match.py","use":"Canonical seed and randomized parameter construction."},{"url":"https://medium.com/@crypto_algebra/amm-fee-challenge-notes-from-our-run-on-optimization-arena-4e980dbbd699","use":"Public write-up: asymmetric fees, latent-state estimates, and uncertain remaining retail flow."},{"url":"https://github.com/dipankar/amm-challenge/blob/main/ANALYSIS.md","use":"Public account contrasting trade classification with fair-price state estimation."},{"url":"https://github.com/jiayaoqijia/amm-challenge-yq/blob/main/contracts/src/Strategy.sol","use":"Public endpoint-filter pattern and initial EMA weights; complete strategy not copied."}],"changed":["candidates/router-optimal/Strategy.sol","candidates/router-optimal/result.json","candidates/router-optimal/NOTES.md","artifacts/goal/batches/r1/items/router-optimal/result.json"],"checks":[{"command":"git checkout --detach 6b6921f8d463314aa7c96234e1be25a25245f704","result":"Pinned simulator checkout; scoring engine source was not modified."},{"command":"maturin develop --release --manifest-path test/scratch/router-optimal/amm-challenge/amm_sim_rs/Cargo.toml","result":"Release engine build passed, Python bindings installed."},{"command":"amm-match validate candidates/router-optimal/Strategy.sol","result":"Validating strategy...\nCompiling strategy...\nStrategy 'RouterOptimal' validated successfully!","passed":true},{"command":"python test/scratch/router-optimal/check_strategy.py candidates/router-optimal/Strategy.sol","result":{"validate":true,"compile":true,"canonical_configs_match":20,"direct_evm_callbacks":3078,"callback_gas_limit_tested":100000,"initialization_gas_limit_tested":250000,"runtime_bytecode_bytes":2265},"passed":true},{"command":"python test/scratch/router-optimal/check_math.py","result":{"router_kkt_cases":10000,"analytic_optimum_cases":1000,"table_knots_checked":24,"max_grid_objective_regret":0.0},"passed":true},{"command":"python test/scratch/router-optimal/measure.py <candidate.sol> --n 2000 --out <candidate_train.json> --reference test/scratch/router-optimal/starter_train.json","result":"Every reported candidate completed seeds 0..1999. Score on 0..999 is the exact prefix of those runs. At most eight workers per batch; chunks of eight or twenty. Worker sharing was used for later concurrent tuning jobs."},{"command":"python test/scratch/router-optimal/measure.py candidates/router-optimal/Strategy.sol --start 100000 --n 2000 --workers 8 --fixed-workers --out test/scratch/router-optimal/selected_fresh.json --reference test/scratch/router-optimal/starter_fresh.json","result":"Frozen candidate and unchanged starter each completed all 2000 fresh seeds against the fixed 30 bps normalizer."}],"promotion":{"promoted":true,"previous_best":"contracts/src/StarterStrategy.sol @ 6b6921f8d463314aa7c96234e1be25a25245f704","fresh_mean_edge":518.9635715972508,"reference_mean_edge":369.50130143736396,"criterion":"fresh paired mean gain > 2 * paired standard error","paired_over_previous_best":{"reference":"contracts/src/StarterStrategy.sol @ 6b6921f8d463314aa7c96234e1be25a25245f704","seed_start":100000,"n":2000,"mean_diff":149.4622701598869,"se":0.9187921101518638,"worst":[{"seed":101573,"diff":72.3124792087005},{"seed":100645,"diff":73.25149226788972},{"seed":100236,"diff":77.90360237419097},{"seed":100631,"diff":78.45157749956604},{"seed":100818,"diff":78.99583854847123}]}},"measurement":{"commit":"6b6921f8d463314aa7c96234e1be25a25245f704","tuning_seed_start":0,"tuning_n":2000,"score_seed_start":0,"workers":8,"batch_size":8,"fresh_candidate_seconds":351.79017134872265,"fresh_reference_seconds":224.21240616543218,"seconds_definition":"Batch wall time including config construction, run_batch, result extraction and collection, divided by simulations; compilation and report writing excluded.","initial_20_seconds_per_simulation":0.2159280349034816,"final_n_reduced":false},"limitations":["Local paired results do not certify a hidden leaderboard rank.","The retail objective uses equal-depth/near-fair approximations; the uniform shock support is a calibrated proxy for price-estimation and arrival uncertainty.","Training comparisons selected the candidate; only the final frozen candidate was judged on the reserved fresh seeds."],"setup_issues_resolved":["System Python venv creation failed because ensurepip was unavailable; installed a checkout-local Python 3.12.12 environment with uv.","First maturin develop attempt could not find pip; installed pip locally, then the release build succeeded with Rust 1.99.1.","A CLI invocation before the engine installation finished failed at amm_competition/competition/match.py:7 with ModuleNotFoundError: amm_sim_rs; the post-build and final validations passed."]}