e96f7fcdd1
Parallel to the 2026-05-08 Class C direction fix but on the **magnitude axis**.
Class C correctly chose REALIZED for direction because env-gated trades (capital
floor / trail / broker cap → Flat) mean no trade happens — recording as Flat is
honest. For magnitude, Kelly cap clamping does NOT gate the trade — the trade
still happens, just at a smaller size. `actual_mag_core` is the bucketed
magnitude derived in trade_physics.cuh:1099-1117 from |position|/max_position;
when the policy intends Full and Kelly clamps to 0.35× max (bucket → Quarter),
recording Quarter loses the policy's intent. Q(s, mag=Full) never receives the
reward gradient from Full-intent trades that actually executed.
One-line swap inside the Class C encoding block at experience_kernels.cu:2689-
2697: `actual_mag_core * b2_size * b3_size` → `mag_idx * b2_size * b3_size`.
`mag_idx` is the local intent magnitude already decoded at line ~2460 from the
original action_idx BEFORE any Kelly/CVaR/Q-gap clamping. Direction axis keeps
Class C semantics (`actual_dir_core` → gated trades record as Flat). Order/
urgency remain intent passthrough.
`actual_mag_core` remains consumed downstream by Task 2.X per-magnitude trade-
lifecycle instrumentation at the seg_mag_bin site below — direction-branch Q
learns from realized; magnitude-branch Q learns from intent. The two axes have
different env-enforcement semantics so they take different actions at the
replay-write site.
Verification: new CPU oracle test crates/ml/tests/sp20_magnitude_intent_record_
test.rs pins the encoding contract with 4 invariants (Full→Quarter scenario,
all-pairs sweep, order/urgency passthrough, direction Class C unchanged). Per
pearl_tests_must_prove_not_lock_observations the test asserts invariants
("recorded mag == intent mag, regardless of realized") not observed values, so
it cannot become a bug-lock if the kernel's compute path changes — only if the
encoding contract itself is broken. All 4 tests pass; cargo check clean.
Audit-doc entry added to docs/dqn-wire-up-audit.md as the SP20 magnitude intent
fix, parallel to the Class C direction fix above with the asymmetry between
the two axes spelled out.
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;