1a3bcf97b8
Per train-multi-seed-pfh9n post-mortem: observed_hold_rate climbed 0.25 → 0.52 across training while cost penalty (~0.006) was 100× smaller than per-bar reward magnitudes (popart=0.97, cf=0.65). Hold action was effectively free, allowing Q(Hold) to dominate via structural low-variance bias. Fix: scale Hold cost adaptively. ISV[HOLD_COST_SCALE_INDEX=461] tracks: scale = clamp(1.0 + 24.0 × max(0, observed - target) / max(target, 0.01), 1.0, 25.0) Effective cost at 100% overrun (observed=2× target): 0.006 × 25 = 0.15, competitive with per-bar reward magnitudes (~0.01-0.1). At/below target: scale = 1.0 (no extra penalty). Pearl-A bootstrap + Welford slow EMA (α=0.05). Mirrors T1's MIN_HOLD_TEMPERATURE pattern (same input signals: ISV[382] observed, ISV[381] target). Producer: hold_cost_scale_update_kernel.cu — single-thread cold-path, per-epoch boundary, AFTER MIN_HOLD_TEMPERATURE in training_loop.rs. Consumer migration (atomic per feedback_no_partial_refactor): 3 sites in experience_kernels.cu — segment_complete branch (line ~3089), per-bar positioned-Hold branch (line ~3553), per-bar flat-Hold branch (line ~3617). Cold-start fallback: scale=1.0 when slot ≤ 0 or out-of-bounds (bit-identical pre-Phase-2 cost magnitude). ISV_TOTAL_DIM: 461 → 462. Behavioral tests (5/5 PASS on RTX 3050): - sp16_phase2_hold_cost_scale_climbs_with_overrun - sp16_phase2_hold_cost_scale_at_target_is_one - sp16_phase2_hold_cost_scale_under_target_is_one - sp16_phase2_hold_cost_scale_bounds_clamp - sp16_phase2_hold_cost_scale_pearl_a_bootstrap Regression: SP14 oracle suite 30/30 PASS, SP15 phase 1 oracle suite 36/36 PASS. Instrumentation: HEALTH_DIAG[N]: hold_cost_scale_diag obs/tgt/norm/scale. Per feedback_isv_for_adaptive_bounds + feedback_no_partial_refactor. 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;