9fb980da2b
Post-commit kernel-formula audit of `compute_min_hold_penalty` at
trade_physics.cuh:567-577 showed the original `temp = TEMP_MIN +
(TEMP_MAX - TEMP_MIN) × skill` mapping was BACKWARDS for the
WR-plateau scenario this 8-commit chain targets.
Formula recap:
soft_factor = deficit / (deficit + T)
HIGH T → soft_factor → 0 → forgiving (no exit penalty)
LOW T → soft_factor → 1 → sharp (max exit penalty)
The deleted schedule's `START=50 → END=5` therefore meant
"permissive early, strict late" (classic explore-then-exploit), not
"permissive when committed, strict when uncertain" as the
substituted mapping assumed.
WR-plateau scenario: dir_acc ≈ 0.46-0.48 (model committed but
wrong) — the model needs a permissive (HIGH T) exit ramp to bail
out of bad committed directions, not maximum exit penalty (LOW T)
locking it into them.
Inverted the mapping using `confusion = 1 - skill`:
- dir_acc ≈ 0.5 (random / plateau) → confusion=1 → temp=50
(permissive, plateau exit ramp)
- dir_acc → 1.0 (saturated skill) → confusion=0 → temp=5
(strict, force informed commitment)
This preserves the deleted schedule's epoch-0 anchor (T=50
cold-start) while reacting to actual realized skill instead of an
epoch-time anchor.
Files touched (atomic per `feedback_no_partial_refactor`):
- min_hold_temperature_update_kernel.cu — formula + header doctring.
- sp14_isv_slots.rs — slot-doc comment expanded with new mapping
+ fixup-rationale paragraph.
- gpu_aux_trunk.rs — `MinHoldTemperatureUpdateOps` docstring.
- gpu_dqn_trainer.rs — cubin docstring + ISV_TOTAL_DIM doc-comment.
- tests/sp14_oracle_tests.rs — Tests 3 + 4 flipped (high dir_acc
now expects temp=TEMP_MIN; low dir_acc now expects TEMP_MAX);
Tests 1, 2, 5 numerically unchanged (sentinel guard fires before
the mapping; midpoint dir_acc=0.75 has skill=confusion=0.5 so
pre/post-fixup numerics match).
- docs/dqn-wire-up-audit.md — Item 4 entry rewritten with the
fixup rationale + mapping table updated.
Verification:
cargo check -p ml --tests --all-targets PASS
cargo test sp14_audit_4b (lib) 4/4 PASS (slot layout
locks unchanged)
GPU oracle re-run deferred to next L40S smoke (kernel was rebuilt
in-place so cubin contents change; the 5 oracles encode the new
mapping and will exercise the new cubin).
Per `pearl_first_observation_bootstrap.md` (sentinel→target
replace; sentinel anchors unchanged) +
`pearl_symmetric_clamp_audit.md` (bilateral clamp on target_temp
preserved) + `feedback_no_partial_refactor.md`.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;