7a19c51522
Layer C close-out for SP11 B1b smoke-recovery work. Two atomic commits land the fix: -b3b4d0278: z-score normalization for mag-ratio canary (Bug 5) -fd24b5383: launch-order — reward_component_ema before canary (Bug 6) Audit doc (docs/dqn-wire-up-audit.md) adds the close-out entry covering all 6 bugs (slot 63 overload, stale rc[] init, cf_flip ordering, cf-component feedback loop, magnitude-asymmetric ratios, launch-order), the 6 new ISV variance EMA slots [361..367) producer/consumer wiring, and validation evidence from the 3 smoke-test workflows (smoke-test-6wd2c killed → smoke-test-4rbv9 killed → smoke-test-gwfn8 PASSED 15m11s on commitfd24b5383). 3 memory pearls written to user memory (not in repo): - pearl_zscore_normalization_for_magnitude_asymmetric_signals - pearl_canary_input_freshness_launch_order - pearl_controller_amplifies_dominant_magnitude_trap MEMORY.md index updated under Controllers and signals. Open follow-up flagged: residual within-fold sharpe degradation across epochs is a separate triage scoped to post-T10 validation, not part of B1b smoke-recovery. 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.