ac9bcab94a
Lands the static-configuration branch of Plan 1 C.6: cql_alpha, conviction_floor (no-op if IQL_BRANCH_SCALE_FLOOR already serves), plan_threshold written to dedicated ISV slots at constructor. Consumer kernels (CQL, backtest_plan, experience) read from ISV instead of config fields / hardcoded literals. Also pre-allocates ISV slots for the 6 upcoming GPU-kernel tasks (atoms, gamma, kelly_cap, tau, epsilon outputs + CPU-born epoch inputs). Those slots start at 0; GPU kernels in follow-up commits fill them. New ISV slots: - EPOCH_IDX_INDEX=39, TOTAL_EPOCHS_INDEX=40 (CPU-born inputs) - EPSILON_EFF_INDEX=41, TAU_EFF_INDEX=42, GAMMA_EFF_INDEX=43, KELLY_CAP_EFF_INDEX=44 (GPU-written in follow-up tasks) - CQL_ALPHA_INDEX=45, PLAN_THRESHOLD_INDEX=46 (static config; this commit) - Task 15 confirmed no-op: IQL_BRANCH_SCALE_FLOOR_INDEX=36 already serves conviction-floor role (constructor + ISV read in iql kernel). Layout fingerprint auto-updated via seed-byte edits; fingerprint re-tail at [47..49). ISV_TOTAL_DIM 39 -> 49. GpuDqnTrainConfig gains total_epochs field; fused_training.rs passes hyperparams.epochs at construction; default 0 for smoke tests. write_isv_signal_at bound extended from ISV_DIM(23) to ISV_TOTAL_DIM(49) so tail slots are writable by CPU. cql_alpha consumer: compute_cql_logit_gradients reads base from ISV[CQL_ALPHA_INDEX] instead of config.cql_alpha; adaptive formula (base x health x (1-regime_stability)) unchanged. plan_threshold consumers: experience_kernels.cu (experience_state_gather, experience_action_select, experience_env_step) and backtest_plan_kernel.cu (backtest_plan_state_isv) read plan_thr from ISV[ISV_PLAN_THRESHOLD_IDX=46] via isv_signals pointer already present in both kernels; null-guard defaults to 0.5f for smoke-scale runs without ISV warmup. ISV_PLAN_THRESHOLD_IDX=46 defined in state_layout.cuh (included by both plan kernel files); value must match PLAN_THRESHOLD_INDEX in gpu_dqn_trainer.rs. StateResetRegistry entries added for all new slots: SchemaContract for TOTAL_EPOCHS/CQL_ALPHA/PLAN_THRESHOLD; FoldReset for EPOCH_IDX and GPU-written per-fold slots. No behavioural change: all threshold/base values remain at their prior defaults; consumers now read adaptive values from ISV instead of baked-in config or hardcoded literals. Plan 1 Tasks 12, 15, 16 + pre-allocation for 9, 10, 11, 13, 14. Spec §4.C.6 (2026-04-24 GPU-drives-CPU-reads revision). 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;