6f53d676fb
Audit-only commit per user design call DD9. The plan's Task 1.4
incorrectly claimed c51_loss_batched + c51_grad_kernel needed migration;
both kernels have been mean-zero centered since commit 56373f094.
Audit findings:
c51_loss_kernel.cu::c51_loss_batched (line 760-806):
- Forward dueling reduction at lines 765-779 already computes
`a_mean = (1/n_d) Σ shmem_adv[a, j]` and applies
`centered = shmem_adv[a_d, j] - a_mean` — IS the SP17 mean-zero
projection. Annotated `/* SP17 mean-zero */`.
- Counterfactual magnitude path at lines 788-805: same pattern.
- Spectral decoupling at lines 812-827: same pattern.
- Bellman target argmax at lines 848-855: shmem_proj[j] IS a_mean_per_atom.
- Per-d=1 magnitude std normalization (line 770-778) is ORTHOGONAL to
SP17 mean-zero — a separate variance-control concern for the magnitude
branch's tighter Q distribution. Annotated
`/* SP17: per-d=1 mag std normalization (orthogonal) */`.
c51_grad_kernel.cu::c51_grad_kernel (line 287-291):
- `dueling_grad = (a == a_d) ? (1.0f - inv_A) : (-inv_A)` at line 288 IS
the SP17 mean-zero Jacobian: J[a, a_d] = δ(a, a_d) − 1/n_d. The chain
rule for centered logit `A_taken − mean_a A` w.r.t. A[a'] produces
exactly this Kronecker-delta-minus-uniform pattern.
- Per-d=1 magnitude std grad multiplier (line 290) — same orthogonal
concern as c51_loss.
Zero code-path change. Both kernels behave bit-identically to pre-E.
Wire-up status (FINAL — every consumer SP17-compliant):
compute_expected_q (Task 1.2) MIGRATED
quantile_q_select (Commit A) MIGRATED
mag_concat_qdir (Commit B) MIGRATED
Thompson direction-select (Commit C) MIGRATED + V wired in
barrier_gradient_direction (Commit D) MIGRATED
ib_gradient_direction (Commit D) MIGRATED
c51_loss_batched (this commit) ALREADY-COMPLIANT, ANNOTATED
c51_grad_kernel (this commit) ALREADY-COMPLIANT, ANNOTATED
After this commit, EVERY advantage-logit read in cuda_pipeline goes
through the SP17 mean-zero contract. No un-centered raw-advantage reads
remain in production kernels.
Verification (RTX 3050 Ti):
cargo check --workspace → clean
cargo test sp17_dueling_oracle_tests --features cuda -- --ignored → 6/6 PASS
grep adv_a[ kernels (excluding centered/comments) → empty
grep dir_logits_b[ kernels (excluding centered/comments) → empty
Plan: docs/superpowers/plans/2026-05-08-sp17-dueling-q-network.md
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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.