f67ede94fb
Smoke smoke-test-ftdjz (commitd9a4d98a3, Mech 6 + Mech 7) regressed both F0 (44 -> 38.82, per-element cap clipped legitimate outliers) and F1 (grad-collapse at step 2040 vs 3720 with Mech 6 alone). Slots 36-42 STILL fired — Mech 7's per-element clip didn't prevent Adam EMA saturation, just slowed training to grad-collapse. Re-analysis: Mech 6's 100x multiplier on the upper-bound formula was mismatched with the slot 36 threshold ratio. Per-element gradient max <= adaptive_clip = 100 x slow_ema x isv ~= 200. Adam m_X steady-state reaches 200, exceeding slot 36 threshold of 100*isv = 100. Mech 6 was doing its job but the bound was wider than the diagnostic threshold. Two coordinated changes (per feedback_no_partial_refactor): 1. REVERT Mech 7 (per-element clip in dqn_adam_update_kernel). The per-element approach was misdiagnosis — clipping post-global-clip gradients tighter than legitimate per-element variance harms F0 training without addressing Adam saturation root cause. Kernel returns to its post-Mech-6 state (blob546feee48). 2. TIGHTEN Mech 6's upper-bound multiplier from 100x to 5x. Standard DL practice (5-10x steady-state grad norm). Per-element gradient max becomes <= 5 x slow_ema x isv ~= 10, well below slot 36 threshold of 100. Adam m_X EMA stays bounded <= 10 — slots 36-42 should not fire. Why 5x and not 10x: slot 36 threshold is 100 x isv. 5x slow_ema (~=10 absolute) leaves 10x headroom against the threshold, providing robust margin. 10x slow_ema would be 20 absolute, only 5x margin — risk of fluctuations triggering slot 36. Why not tighter (e.g., 2x): per-step gradient norms can legitimately spike to 5x slow_ema in normal training (e.g., gradient resumption after warmup); tighter bounds would over-clip. F0 risk: low — F0 typical adaptive_clip values are bounded by Mech 6's upper anchor only when the EMA-driven clip exceeds 5x slow_ema, which is rare in steady F0 training. Cap should be a no-op for F0. F1 risk: prevents the saturation pathway diagnosed by smoke smoke-test-ftdjz. Validates by running the next smoke at this commit. 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;