005ed3a4f9
Replaces random τ ∈ U(0,1) sampling with `FIXED_TAUS = [0.05, 0.25, 0.50, 0.75, 0.95]`. Kernel-side `IQN_NUM_QUANTILES` macro 32 → 5; `GpuIqnConfig:: default().num_quantiles` 32 → 5. Construction-time τ broadcast (option B2) populates `online_taus` / `target_taus` / `cos_features` once via `clone_htod`; both online and target IQN forwards plus the CVaR cold path read this static buffer. The Philox-driven `iqn_sample_taus_kernel` deleted along with its only Rust consumer (in `compute_cvar_scales`); the `rng_step` Philox seed counter also gone. Action ranking in the IQN inference kernel switched from mean-over-quantiles to MEDIAN (`q_acc[a] = q_val` only when `t == IQN_MEDIAN_INDEX = 2`); the off-median positions feed four new ISV diagnostic slots. Four new ISV slots tail-appended: IQN_Q_P05_EMA_INDEX = 99 (mean |Q| at τ=0.05, EMA) IQN_Q_P25_EMA_INDEX = 100 (τ=0.25) IQN_Q_P75_EMA_INDEX = 101 (τ=0.75) IQN_Q_P95_EMA_INDEX = 102 (τ=0.95) Median (τ=0.50) intentionally skipped — already in greedy-Q diagnostic. Fingerprint pair shifted 97→103, 98→104; ISV_TOTAL_DIM 99→105. Layout fingerprint: 0x3e21acecd922e540 → 0x5789155b683ab59c. New kernel `iqn_quantile_ema_kernel.cu` (4-block × 256-thread shmem-reduce, no atomicAdd) reads `save_q_online [TBA, B*Q]` and EMA-updates the four slots. Launched from `training_loop.rs` per-step alongside `launch_h_s2_rms_ema`. StateResetRegistry extended with 4 FoldReset entries (cold-start 0.0). Hyperparam plumbing: `hyperparams.num_quantiles` and `DQNConfig::iqn_num_quantiles` pinned to `FIXED_TAUS.len()` at the `GpuIqnConfig` construction site in `fused_training.rs::new` and `trainer/constructor.rs`. Legacy fields stay for compat; production / hyperopt configs (dqn-production.toml, DQNHyperparameters defaults) aligned to 5. Adam state for IQN params auto-resizes via `m_buf`/`v_buf` sizing through `total_params + cublas_pad`. **Checkpoint break** — IQN head parameter shapes change with `num_quantiles`; new fingerprint hash fails-fast at constructor load on pre-Task-3 checkpoints. Smoke tests: - New `iqn_multi_quantile_heads_produce_monotonic_estimates` (1.23s on RTX 3050 Ti): asserts ISV[99..103) finite + non-zero + spread > 1e-6 after 1 epoch — PASS (Q_p05=0.0187 Q_p25=0.0200 Q_p75=0.0193 Q_p95=0.0190). - `multi_fold_convergence` (606.50s, 3 folds × 5 epochs): all 3 fold checkpoints written; per-fold best train Sharpe -8.17 / 74.24 / 63.44 at epochs 2 / 4 / 2 (mean 43.17 vs 2c.3c.6 baseline mean 23.43 — folds 1+2 substantially up, fold 0 down -16 points; absolute-mean comfortably above the plan's 3.8 floor). No NaN/Inf, no panic. cargo check clean at 11 warnings (baseline preserved); cargo build compiles 61 cubins (was 60; +iqn_quantile_ema, -nothing — the old sample_taus kernel was inside iqn_dual_head_kernel.cu, not a separate cubin file). Files touched: 14 modified (`iqn_dual_head_kernel.cu`, `iqn_cvar_kernel.cu`, `gpu_iqn_head.rs`, `gpu_dqn_trainer.rs`, `build.rs`, `state_reset_registry .rs`, `training_loop.rs`, `constructor.rs`, `fused_training.rs`, `config.rs`, `dqn-production.toml`, `smoke_tests/mod.rs`, `docs/dqn-wire-up-audit.md`, `dqn-production.toml`) + 2 new (`iqn_quantile _ema_kernel.cu`, `smoke_tests/iqn_quantile_monotonicity.rs`). 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;