jgrusewski 3e36f4a0e6 feat(ml-alpha): Phase 2A-C — trainer integration behind FOXHUNT_USE_MULTI_HEAD_POLICY flag
Wires MultiHeadPolicy into the live trainer's π forward + backward
+ Adam path, gated by FOXHUNT_USE_MULTI_HEAD_POLICY (default off).
Mirrors the FOXHUNT_USE_ROLLOUT precedent (commit 779c03b9d) for
load-bearing refactors with emergency rollback.

## Wiring sites

* Forward: 4 call sites — step_synthetic_body (training_graph),
  step_with_lobsim (prefill_graph CPU), step_with_lobsim_gpu_body
  (prefill_graph GPU), eval_policy_probs_per_action (no graph).
* Backward: step_synthetic_body only (line 5764+).
* Adam apply: 4 new Adam optimizers (W_heads, b_heads, W_gate,
  b_gate) when flag on.
* LR plumbing: same lr_pi mirrored onto all 4 MHP Adams.
* ISV bootstrap: slots 761-764 (K=3, gating_entropy_floor=0.549,
  head_entropy_floor=0.959, aux_prior_β=0.05) written in a
  flag-gated block AFTER the main bootstrap loop (preserves
  flag-off bit-equality — see surprise #2).
* regime_h plumbing: new pub accessor PerceptionTrainer::
  step_regime_d_view() mirrors h_t_view; gate kernel reads the
  same buffer the encoder reads (no new memcpy, no shape change).

## CUDA Graph capture treatment

Host-side select. The flag is invariant for trainer lifetime, so
the branch evaluates at graph CAPTURE time — the selected kernel
sequence is baked into the captured graph and replays single-path.
No host branches inside the captured stream. Verified by
determinism preservation in both flag-off and flag-on regimes.

## Verification (all gates passed)

* FOXHUNT_USE_MULTI_HEAD_POLICY=0 ./scripts/determinism-check.sh
  --quick: exit 0.
* Flag-off diag.jsonl byte-equal to e22da61cf baseline (modulo
  elapsed_s timestamps).
* FOXHUNT_USE_MULTI_HEAD_POLICY=1 ./scripts/determinism-check.sh
  --quick: exit 0 (new path is deterministic).
* 11/11 multi_head_policy_invariants tests still pass.
* FOXHUNT_USE_MULTI_HEAD_POLICY=1 ./scripts/local-mid-smoke.sh:
  2500 steps (2000 train + 500 eval) completed without NaN.
  0 NaN/null in 2000-row diag.jsonl and 501-row eval_diag.jsonl.
  eval_summary.total_pnl_usd = -$3.82M (single-seed at random
  init — not load-bearing; behavioral verdict requires 3-seed
  per pearl_local_smoke_noise_floor_and_regime_concentration).
* 68/0/6 lib tests (passed/failed/ignored).
* Pre-commit: 0 atomicAdd, 0 raw memcpy_htod/dtoh, 0 TODO,
  0 host-branches inside captured streams.

## Surprises handled (not silently)

1. step_regime_d was private on PerceptionTrainer — added clean
   pub accessor step_regime_d_view mirroring h_t_view pattern.
2. Unconditional ISV bootstrap broke flag-off bit-equality
   (writing 761-764 from 0.0 sentinel shifted isv_state checksum).
   Caught by verification gate. Resolved by moving the 4 writes
   into a flag-gated block AFTER the main bootstrap loop. Flag-off
   leaves slots 761-764 at 0.0 sentinel, matching e22da61cf.
3. Spectral-norm regularization on legacy policy_head.w_d still
   runs when flag on (cost: one kernel/step, no correctness
   impact). Documented as known follow-up — proper gating happens
   in Phase 2A-E productionization, not C.3 (which must preserve
   flag-off bit-equality).
4. Adam state across flag toggles: 4 MHP Adams are None when
   flag off. Mirrors FOXHUNT_USE_ROLLOUT precedent — flag cached
   at construction, one-shot read.
5. Device-aggregated diag deferred (gate_probs_mean[K],
   gate_argmax_mass[K], gate_entropy_mean, per_head_entropy_mean
   [K]). These require either a new reduction kernel + ISV slots
   or host-readback (forbidden by feedback_no_htod_htoh_only_
   mapped_pinned). Emitted ISV-resident summary leaves only
   (multi_head_policy.{active,k,gating_entropy_floor,head_
   entropy_floor,aux_prior_beta}). Phase 2A-D scope.

## Linked
* Phase 2A-A: 0b3e40150 (MultiHeadPolicy foundation, inert)
* Phase 2A-B: e22da61cf (backward + aux KL prior)
* Plan: docs/superpowers/plans/2026-06-03-multi-head-policy-
  implementation.md
* Spec ADDENDUM: docs/superpowers/specs/2026-06-02-multi-head-
  policy-with-r-multiple.md
* Q-distill pearl: pearl_foxhunt_pi_trained_by_q_distillation_
  not_ppo.md

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-03 11:07:27 +02:00

Foxhunt

Production HFT trading system in Rust.

Architecture

The workspace contains 32 crates organized as follows:

Core Libraries (16)

Crate Purpose
trading_engine Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing
risk VaR, Kelly, circuit breakers, kill switches, compliance
risk-data Risk data types and shared structures
trading-data Trading data types
ml DQN Rainbow, PPO, TFT, Mamba2, ensemble inference
ml-data ML data types and feature definitions
data Market data ingestion and storage
backtesting Replay engine, strategy tester
adaptive-strategy Ensemble execution, microstructure analysis
common Shared types, resilience, error handling
storage S3 and local model storage
model_loader Model serialization and loading
market-data Market data feed handlers
database PostgreSQL access layer (SQLx)
config Configuration management
tli CLI commands and tooling

Services (8)

Service Purpose
backtesting_service gRPC backtesting service
broker_gateway_service FIX routing, broker connectivity
trading_service Core trading operations
ml_training_service Model training orchestration
data_acquisition_service Market data acquisition
trading_agent_service Autonomous trading agents
api_gateway gRPC API gateway with auth
web-gateway Axum REST + WebSocket gateway

Frontend

web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.

Building

# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace

# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib

# Clippy
SQLX_OFFLINE=true cargo clippy --workspace

ML Models

Four production model architectures on Candle v0.9.1 with CUDA:

  • DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
  • PPO -- Proximal Policy Optimization with GAE and LSTM policies
  • TFT -- Temporal Fusion Transformer for multi-horizon forecasting
  • Mamba2 -- State space model for sequence prediction

Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.

Infrastructure

  • Git: Gitea at git.fxhnt.ai (Tailscale-only), Scaleway DEV1-S
  • Observability: OpenTelemetry OTLP (env OTEL_EXPORTER_OTLP_ENDPOINT)
  • Database: PostgreSQL with SQLx offline mode for CI

License

Proprietary. All rights reserved.

Description
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