d6bfad7033
Replaces the per-commit audit-doc entries from commits 1-3 with a single
consolidated Phase 5 close-out entry. The consolidated entry covers:
- The full design rationale: gate the REWARD (not the target); avoids
needing q_mean_a entirely. At low aux confidence, r_used → 0 ⇒
Bellman target collapses to gamma * Q(s', a').
- All 5 components: trainer buffer, FusedTrainerCtx accessor, training
loop wire-up, kernel signature + gate, launcher arg.
- NULL-tolerance contract: aux_conf_at_state == NULL OR isv_signals
== NULL ⇒ gate = 1.0 (identity).
- Default-state semantics: alloc_zeros 0.0 sentinel → gate ≈ 0.12 →
reward mostly suppressed pre-population (graceful degradation).
- reward_bias interaction (composes cleanly — gate damps reward
pre-projection, reward_bias lifts target Q-mean per-branch).
- Plan accuracy errata: the user spec's "add aux_conf_at_state_buf
field to GpuBatch struct" was unnecessary — GpuBatch doesn't
carry the SP13 B1.1b aux_sign_labels_ptr either; both follow the
"trainer-only buffer + direct-gather" pattern.
- Test coverage: 3 CPU math tests + 1 GPU behavioral integration test.
- Confidence: medium-high that the gate fires correctly on real data;
end-to-end smoke validation deferred (no smokes dispatched per
controller instruction).
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.