d9fee6ef8d
Composes the Kelly safety_multiplier from TWO orthogonal adaptive
signals instead of one:
safety = max(health_safety, conviction)
where:
health_safety = 0.5 + 0.5 × learning_health [training stability]
conviction ∈ [0, 1] [per-sample confidence]
Health measures training stability globally. Conviction measures per-
state policy certainty in the taken direction. These are orthogonal —
a policy can be confident on a given state before training globally
stabilises, and a stable training regime can still produce low-
conviction per-state decisions. max() composes them conservatively:
the cap uses whichever signal says "trust more" at this sample.
Bounded to [0.5, 1.0] by the health floor.
Both signals are already adaptive / temporal (health=ISV[12] EMA,
conviction=per-sample Q-spread normalised by q_dir_abs_ref ISV EMA).
No static tuning knobs. Per feedback_adaptive_not_tuned.md.
Motivation (per project_magnitude_eval_collapse_kelly_capped.md): at
typical smoke-test health=0.49, health_safety = 0.745 sits coincid-
entally on the Half/Full decoder boundary (abs_pos < 0.75). That
prevented Full from ever being realised at smoke horizon regardless
of adaptive warmup_floor. Letting conviction drive safety unblocks
Full realisation for confident actions without requiring health
graduation which 20-epoch smokes structurally can't reach.
Empirical result (local smoke, 2 runs):
Run 1 (high run-variance draw): EVAL_DIST Q=0.911 H=0.057 F=0.032
— still fails H10 eh+ef≥0.30
Run 2: EVAL_DIST Q=0.350 H=0.121 F=0.529
— PASSES all 5 assertions
— FIRST FULL SMOKE PASS SINCE 4-BRANCH
Previous best (before this commit):
(pre-safety-A, v5+adaptive-Kelly only): Q=0.325 H=0.675 F=0.000
— passed H10 at line 134 but failed Task 2.X line 153 (ef < 0.05)
The commit trades the reliable Half-dominance regime for a bi-modal
distribution that includes Full on many runs. Run-to-run variance
on a 20-epoch smoke is expected per session memory; intent tracking
confirms the policy consistently wants Full at eval (0.73-0.85 across
runs), so the gap is purely in realised cap, not policy learning.
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;