{
 "sources": [
  {
   "url": "https://api.imd.fun/artifacts/a88ff982eba7e078bc6ccb1555b90a211c00c9fd76ec90a62ab13a4074f2c3d0",
   "quote": "Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved: replica, Gaussian filter, fee rule, qm tables and the exact-arithmetic arb/retail tell."
  },
  {
   "url": "https://api.imd.fun/artifacts/a88ff982eba7e078bc6ccb1555b90a211c00c9fd76ec90a62ab13a4074f2c3d0",
   "quote": "x_i = clip((feat_i - MU_i)/SD_i, -3, 3). 42 weights: z_own = w[0] + sum_i w[1+i] x_i, z_other = w[21] + sum_i w[22+i] x_i."
  },
  {
   "url": "https://api.imd.fun/artifacts/a88ff982eba7e078bc6ccb1555b90a211c00c9fd76ec90a62ab13a4074f2c3d0",
   "quote": "Own side (bid if is_buy else ask) += 8e-4*tanh(z_own); other side += 8e-4*tanh(z_other)."
  },
  {
   "url": "https://api.imd.fun/artifacts/a88ff982eba7e078bc6ccb1555b90a211c00c9fd76ec90a62ab13a4074f2c3d0",
   "quote": "weights=None skips the hook; all-zero weights return (0.0, 0.0) and reproduce LEANBASE bit for bit"
  },
  {
   "url": "https://api.imd.fun/artifacts/a88ff982eba7e078bc6ccb1555b90a211c00c9fd76ec90a62ab13a4074f2c3d0",
   "quote": "pi_last (feature 2) is checked only with zero weight against the CPython hook oracle, because the reference keeps no pi; the other 19 features are checked with non-zero weights."
  },
  {
   "url": "https://api.imd.fun/artifacts/a88ff982eba7e078bc6ccb1555b90a211c00c9fd76ec90a62ab13a4074f2c3d0",
   "quote": "\"mean_edge_fast\": 527.8724874783702"
  },
  {
   "url": "https://api.imd.fun/artifacts/a88ff982eba7e078bc6ccb1555b90a211c00c9fd76ec90a62ab13a4074f2c3d0",
   "quote": "\"hook_on_linear_42_weights\": 128.2"
  },
  {
   "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research/solved/SHA256SUMS",
   "quote": "efbb5ee102db4d312f64565d9bf7232b6becbe02b624164f744a402bbbf2983b  pol_mid.json"
  },
  {
   "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research/solved/SHA256SUMS",
   "quote": "5ff53e42ca87390e5f65d1caa3f7a480e67a8212fb0670274dfd562b05ba1afd  qm_k1e-5.json"
  },
  {
   "url": "https://pypi.org/pypi/cma/json",
   "quote": "CMA-ES, Covariance Matrix Adaptation Evolution Strategy for non-linear numerical optimization in Python"
  }
 ]
}