d6eca73e52
Adds intent_dist_q/h/f alongside eval_dist_q/h/f to separate policy-
learning quality from Kelly-enforcement reality. Per memory pearl
project_magnitude_eval_collapse_kelly_capped.md: EVAL_DIST Quarter
dominance is a downstream artefact of Kelly cap × warmup_floor ×
safety_multiplier math, NOT a policy-learning failure. The intent
metric exposes the policy's pre-Kelly-cap chosen mag bucket so
operators can distinguish "policy isn't learning Full" from "Kelly
cap is suppressing Full" — the latter being an operationally-correct
state during cold-start positions.
Pure observability: no Kelly-math tweaks, no new tuned constants, no
reward-bias mechanisms (all explicitly forbidden by the memory pearl).
The intent_mag bucket comes straight from the factored action's
mag_idx (0/1/2) which already maps 1:1 to Quarter/Half/Full buckets.
The pre-cap mag_idx is already captured by the action-select kernel
into intent_mag_buf and exposed via the trainer's pre-existing
last_eval_intent_magnitude_dist host field — this commit only wires
that signal into HEALTH_DIAG.
Wired through (one coordinated commit per feedback_no_partial_refactor):
- health_diag.rs: HealthDiagSnapshot gains intent_dist_q/h/f fields
adjacent to eval_dist_*; size assertion bumped 147 → 150 fields.
- health_diag_kernel.cu: 3 new WORD_INTENT_DIST_* slots at [77..80);
every downstream WORD_* shifted by +3 in lockstep; WORD_TOTAL +
static_assert bumped 147 → 150. (The slots are reserved identically
to WORD_EVAL_DIST_* — both currently inert; HEALTH_DIAG GPU port
Phases 2/3 will populate them in lockstep with their sibling slots.)
- training_loop.rs: new adjacent tracing::info!() emitting
"intent_dist [iq=... ih=... if=...]" from the existing host-side
last_eval_intent_magnitude_dist field, immediately after the big
HEALTH_DIAG line containing eval_dist [eq=... eh=... ef=...].
- docs/dqn-wire-up-audit.md: new top-of-file entry per Invariant 7.
Behavior: zero change. Only adds observability.
Verification:
- cargo check -p ml --offline: clean.
- cargo test -p ml --lib --offline -- health_diag: 3/3 pass.
- cargo test -p ml --test sp4_producer_unit_tests --release
--ignored: 16/16 GPU tests pass.
Closes #212.
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;