{"sources":[{"url":"https://api.imdnotdead.fun/artifacts/2333ecf28d4356bd232b85c1e55842e94e1ada05e1cd067830b5625dfdbc5336","quote":"\"code_sha256\":\"cb6d945511e1e10af78baa64c2a6cb25806f0998403ce6440654bfc3c70092d9\",\"params\":{\"W1\":[[-0.122003,0.731454,-0.465368,2.998833,-2.325042,0.583646,-1.465719,2.330367,-5.296259,-1.804534,-0.852343,-0.171263,0.445759,-2.173843,-0.471323,-0.232548,-1.223397,-0.141278,1.469197],[0.723577,0.803271,2.012153,2.714905,-0.15748,-0.282939,-0.464975,0.413107,-0.374623,0.209898,-0.085672,-1.819724,0.155218,-0.150565,-0.090415,1.03029,0.352852,0.06097"},{"url":"https://api.imdnotdead.fun/artifacts/ebbd90c663c396598965b23341aea97a4501b4c971db183a8fe94a880f399979","quote":"\"port_sha256\":\"b29cd2a078b4c3fc57945a79e927624dd346c44d8cc149dae43f4929aa6d736b\",\"params\":{\"architecture\":\"19-8-2, ReLU clipped [0,4], inputs clipped [-4,4], output in bps added to T-path fees then clamped [0,MAX_FEE]; weights rounded to 1e-6, Solidity signed 1e6 fixed point\",\"feature_order\":[\"sigmaHat\",\"lambdaHat\",\"sizeHat\",\"toxEma\",\"actEma\",\"direction\",\"(spot-staleHat)/staleHat\",\"signed_trade_ratio\",\"arb_probability\",\"first_in_step\",\"uncertaint"},{"url":"https://api.imdnotdead.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_DECAY\",\n    \"TOX_DECAY\",\n    \"SIGMA_DECAY\",\n    \"LAMBDA_DECAY\",\n    \"SIZE_BLEND_DECAY\",\n    \"TOX_BLEND_DECAY\",\n    \"ACT_BLEND_DECAY\",\n    \"PHAT_ALPHA\",\n    \"PHAT_ALPHA_RETAIL\",\n    \"DIR_IMPACT_MULT\",\n    \"GATE_SIGMA_MULT\",\n    \"MIN_GATE\",\n    \"TRADE_TOX_"},{"url":"https://api.imdnotdead.fun/artifacts/2801efc8190e86318dd922d4a7179f63684d77a226047fc2f3c78232963eb690","quote":"\"sha256\":\"99e3a2eceae0e5543d27473859e10b005d0ce0aa7067f2e1a24d1e0e4a7390fe\",\"real\":{\"chunks\":[{\"start\":6020000,\"n\":64,\"mean_diff\":1.1425077821720162},{\"start\":6020064,\"n\":64,\"mean_diff\":1.2991317911389402},{\"start\":6020128,\"n\":64,\"mean_diff\":1.540802036087534},{\"start\":6020192,\"n\":64,\"mean_diff\":0.9597960607904676},{\"start\":6020256,\"n\":64,\"mean_diff\":1.213708554548524},{\"start\":6020320,\"n\":64,\"mean_diff\":1.1967016961108339},{\"start\":6020384,\"n\":6"},{"url":"https://api.imdnotdead.fun/artifacts/33f54e81f3372088e9eced52c0174f310ac348af98ef5727b73785c088679194","quote":"\"edge_decomposition\":{\"method\":\"Reconstruct negative optimal arb profit from step fair prices, preceding final spot and fees, constant k=initial_x*initial_y, and the pinned closed-form arb/quote formulas. Initial fee is 0.0004. Retail edge is reported total edge minus reconstructed arb edge. These are numerical reconstructions, not simulator-provided trade-type counters.\",\"check\":\"32 diagnostic seeds 11000..11031, 100 steps, arrival input 0 (simu"},{"url":"https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/competition/match.py","quote":"        configs = []\n        for i in range(self.n_simulations):\n            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.r"},{"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"}]}