3ddcfb8868
K=3 sparse cross-entropy reduce over the trade-outcome softmax tile produced by `aux_trade_outcome_forward` (Phase A3). Mirrors the K=2 sibling `aux_next_bar_loss_reduce` structurally: single-block shmem-tree reduce, two parallel partial strips (loss_numer + valid_count) reduced lockstep, fmaxf(p_tgt, 1e-30) numerical floor, fmaxf(valid, 1.0) all- skip-batch guard, valid_count_out[1] save-for-backward. Kept as SEPARATE kernel from the K=2 sibling: - Diagnostic isolation (distinct HEALTH_DIAG slot, distinct cubin in profiles for clean per-loss-source attribution) - Sparse-label semantic clarity (~95-99% mask=-1 vs ~50-100% valid for the K=2 next-bar head) - Future per-class weighting headroom (Profit/Stop/Timeout 3:1-10:1 imbalance will likely need class-weighted CE — surgical mod here without touching the K=2 head's contract) Phase A4 (this commit) is dead code — no Rust launcher yet. Phase A5 lands backward; Phase B wires the full forward→loss→backward chain. Discipline: feedback_no_atomicadd (single-block tree-reduce), feedback_ cpu_is_read_only (pure GPU), pearl_first_observation_bootstrap (sentinel 0 valid_count produces zero gradients gracefully on cold start). Audit: docs/dqn-wire-up-audit.md Phase A4 section. Cubin: aux_trade_outcome_loss_reduce_kernel.cubin (9.9 KB) compiles clean. 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;