4802879494
Migrates the magnitude-branch's direction-Q conditioning input to the
SP17 mean-zero contract. mag_concat_qdir builds a per-(sample, action)
softmax over V[z] + A_dir[a, z] across THREE inner passes (max,
sum_exp, E[Q]); all three now read `dir_logits_b[..] - a_mean_per_atom[z]`.
Without this migration the magnitude trunk-input would see raw E[Q_dir]
while c51_loss/c51_grad backward consumes centered A — mixed contract
ruled out by `feedback_no_partial_refactor`.
The Plan-4 q_rms adaptive scale (ISV[96]) is preserved unchanged;
centering changes the per-action E[Q_dir] values that feed it but the
scale formula itself is structurally orthogonal. Backward into the
direction logits flows through c51_grad (already mean-zero) so no bw
change is needed in this kernel.
GPU oracle test: B=1, SH2=2, NA=3, B0=4 with the same synthetic where
mean_a = [0.75, 0, 0.75] is non-zero. Asserts:
(a) h_s2[0..SH2] copied verbatim into concat prefix
(b) tail slots = centered_E[Q_dir] × (1.0 / q_rms) to ε=1e-5
(c) tail[0] < 0 / tail[3] > 0 sign asymmetry (regression detector)
Verification (RTX 3050 Ti):
cargo check --workspace → clean
cargo test sp17_dueling_oracle_tests --features cuda
-- --ignored → 4/4 PASS
⚠ INTERIM STATE: Thompson direction-select + aux-CQL barrier/ib still
read raw advantage. Commits C-D close them; Commit E annotates the two
already-centered c51_loss/c51_grad pre-SP17 sites.
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.