6a6b58aec5
Final commit of the SP4 Layer C close-out sweep (commits24accea77,605a8f526,ca6315860,1112abc2a). Adds the comprehensive audit grid to dqn-wire-up-audit.md covering all 10 host-side EMA/compute sites identified during the sweep: - 4 migrations completed (C1 redesigned + C2 iqn_loss_ema + C3 utilization_ema + C4 adaptive_clip full chain). - 5 documented exceptions: host-aggregated PnL/eval-result inputs (training_sharpe_ema, max_dd_ema, gamma blend) and multi-step host-normalised composition (HealthEmaTrackers + LearningHealth) where migration crosses the brief's "architectural redesign" stop-condition (would require porting whole compute_epoch_financials or learning_health composition to GPU). - 1 orphan helper flagged for feedback_wire_everything_up (compute_adaptive_tau q_div_ema — zero production callers). - 1 NEW VIOLATION discovered during the audit (calibrate_homeostatic_targets per-step host EMA loop over 6 mapped-pinned slots) deferred for separate Layer C task. - 1 misclassification noted (collector::set_utilization_ema is a setter, not EMA arithmetic). Sweep verification post-grep: git grep -nE "= EMA_BETA \*|= \(1\.0 - EMA_BETA\)|= \(1\.0 - \w*ALPHA\) \* self\.\w*_ema|= \w*_BETA \* self\.\w*_ema" crates/ml/src/cuda_pipeline/ crates/ml/src/trainers/dqn/ → 1 match remaining (orphan compute_adaptive_tau q_div_ema, documented). Going forward: zero-tolerance for new host-side EMA/reduction/mean in SP4-touched code paths per feedback_no_cpu_compute_strict.md (saved 2026-05-01, sweep date appended to memory entry). 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.