89ece2e366
Adds a paired diagnostic to localise the eval-mode collapse mechanism.
The existing `val_dir_dist` line reads `actions_history_buf` which env_step
overwrites with the POST-physics `actual_dir` (signed position bucket). When
the policy picks Long but the Kelly cap zeroes target_position, the bar shows
up as `actual_dir=Flat` — indistinguishable in the existing diagnostic from a
case where the kernel itself produced a Flat pick.
Cluster runs `ddrpr` (without Kelly fix) and `txdz9` (with Kelly fix
2c97e0436) produced bit-identical val_dir_dist patterns — Boltzmann math
should give `P(best) ≤ 47.5%` and `P(any direction) ≥ 13.5%` for 4 actions
with `tau=q_range`, yet observed `P(Long) + P(Short) ≈ 0.0007`. One of
those bounds is being violated and the existing diagnostic can't tell which.
The new `val_picked_dir_dist` reads `chunked_actions_buf` — the kernel's
RAW Boltzmann action_idx output BEFORE env_step computes actual_dir.
Divergence between the two distributions answers the gating question:
- both flat → kernel itself produces collapsed picks (Q-values degenerate)
- val_picked diverse, val_dir_dist flat → env_step drains to Flat
(Kelly / margin / trail stop)
Sample is the most recent chunk (~64 bars on 1-window val). Noisier than
the full-window post-physics histogram but enough for qualitative
comparison against Boltzmann theory bounds.
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;