fb24614f07
Per train-multi-seed-pfh9n post-mortem: structural-Q-bias hypothesis (Adam's m/sqrt(v) prefers low-variance Hold over noisy direction Q-targets) needs direct per-action Q observations to verify or refute. WR plateaued at ~0.46 across both folds while Hold% climbed 15% → 52% and Q-mean climbed 0.19 → 0.41 — but per-action Q was unobservable. Adds HEALTH_DIAG emit each epoch: HEALTH_DIAG[N]: q_by_action [hold=X long=Y short=Z flat=W] Reads via host-side averaging of `q_out_buf [B, total_actions]` direction-branch columns [0..b0=4]. No new kernel needed — q_out_buf is already populated row-major by compute_expected_q. Modeled directly on the existing Task 0.3 magnitude-bucket diagnostic (same dtoh-and- average cold path). Action-index ordering canonical, see state_layout.cuh:123-126: DIR_SHORT=0, DIR_HOLD=1, DIR_LONG=2, DIR_FLAT=3. Emit slot order is [hold, long, short, flat] (Hold first because the hypothesis is about Hold's ascent dominating direction Q-magnitudes). New surface: - gpu_dqn_trainer.rs: q_dir_means_cached field + update_q_dir_means_cached method + q_direction_action_means accessor + free static helper compute_q_dir_means_from_host_buf (factored for testability). - fused_training.rs: FusedTrainingCtx wrappers parallel to q_mag pair. - training_loop.rs: emit block adjacent to q_var_per_branch. Behavioral test: sp16_phase0_q_by_action_diagnostic_reads_four_action_means seeds known per-column constants, asserts canonical-dir-idx → emit-slot mapping at 1e-3 tolerance, and includes sentinel-leak guard (non-direction columns at 99.0 fail any wrong index→slot mapping with margin >12). 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;