{"sources":[{"url":"https://api.imd.fun/artifacts/2333ecf28d4356bd232b85c1e55842e94e1ada05e1cd067830b5625dfdbc5336","quote":"\"weights\":\"w = w0 + 0.03 * rms(block) * z, blocks W1,b1,W2,b2\",\"constants\":\"c = c0 * 2^(0.02 z), exponent clipped to [-1,1]; decays d = 1-(1-d0)*2^(0.02 z); ELAPSED_CAP, DIR_IMPACT_MULT, STEP_COUNT_CAP, ANCHOR_COUNT_CAP rounded\""},{"url":"https://api.imd.fun/artifacts/ebbd90c663c396598965b23341aea97a4501b4c971db183a8fe94a880f399979","quote":"\"port_sha256\":\"b29cd2a078b4c3fc57945a79e927624dd346c44d8cc149dae43f4929aa6d736b\""},{"url":"https://api.imd.fun/artifacts/e8a7a2af9039c24b8bb2d3b5fa1c9113606cbf5a480ac07d02ca26750f1752ea","quote":"\"replica_source_sha256\": \"1e4c44b4bffbdeed65872ee15d46dbde209bf5aa11014329d13d100507b4040c\""},{"url":"https://api.imd.fun/artifacts/2801efc8190e86318dd922d4a7179f63684d77a226047fc2f3c78232963eb690","quote":"\"sha256\":\"99e3a2eceae0e5543d27473859e10b005d0ce0aa7067f2e1a24d1e0e4a7390fe\""},{"url":"https://api.imd.fun/artifacts/33f54e81f3372088e9eced52c0174f310ac348af98ef5727b73785c088679194","quote":"Fees are outside reserves, so k stays fixed up to floating-point roundoff.\\n    # The arb acts before retail, using the preceding step's final spot and fees.\\n"},{"url":"https://api.imd.fun/artifacts/6ac5e013b7b1fb4c5e7bdcdfec0aba28b16824e18ccf39500f258533879faffe","quote":"\"sha256\":\"ebd9de9bf6af6e55d5e308fd15dbb88edec11fd684891d02a30aec185acd19b3\""},{"url":"https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/competition/config.py","quote":"BASELINE_SETTINGS = BaselineSimulationSettings(\n    n_simulations=1000,\n    n_steps=10000,\n    initial_price=100.0,\n    initial_x=100.0,\n    initial_y=10000.0,\n    gbm_mu=0.0,\n    gbm_dt=1.0,\n    retail_buy_prob=0.5,\n    retail_size_sigma=1.2,\n)"},{"url":"https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/competition/match.py","quote":"        batch_result = amm_sim_rs.run_batch(\n            list(strategy_a._bytecode),\n            list(strategy_b._bytecode),\n            configs,\n            self.n_workers,\n        )"},{"url":"https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_sim_rs/src/simulation/engine.rs","quote":"                    // AMM edge is the negative of arbitrageur profit at true price\n                    *entry += -arb_result.profit;\n                }\n            }"},{"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)"},{"url":"https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/competition/match.py","quote":"        for i in range(self.n_simulations):\n            rng = np.random.default_rng(seed=i)"}]}