605a8f5268
Layer C close-out C2 — the host-side EMA arithmetic block in GpuDqnTrainer::update_iqn_readiness violated feedback_no_cpu_compute_strict (any compute — EMA, reduction, mean — belongs on GPU regardless of frequency). The cold-start sentinel + adaptive-α EMA + improvement-gauge formula now run GPU-side via update_iqn_readiness_kernel (single-thread, single-block, mirrors update_grad_norm_emas_kernel shape). The kernel takes the host-passed iqn_loss scalar (from gpu_iqn.read_total_loss() mapped-pinned readback) and updates three coupled mapped-pinned scalars (iqn_loss_initial_pinned, iqn_loss_ema_pinned, iqn_readiness_pinned) in-place. __threadfence_system() guarantees PCIe-visibility to mapped-pinned host_ptr accessors and to other-stream kernel reads via dev_ptrs. Storage: iqn_loss_initial and iqn_loss_ema migrated from host-resident f32 fields to mapped-pinned device-mapped scalars (matches grad_norm_fast/slow_ema pattern from C1 redesigned). New accessors iqn_loss_ema_value() and iqn_loss_initial_value() read through the host_ptrs. iqn_readiness pinned slot remains the same — c51_loss_kernel CVaR α consumer dev_ptr unchanged. Cold-start sentinel (prev_initial < 1e-12 ⇒ assign loss directly + readiness=0) preserves the deleted host-side bootstrap branch exactly. Same adaptive-α formula clamp(|err|/(|err|+0.01), 0.01, 0.30) and improvement gauge clamped to [0,1]. state_reset_registry entries updated to reflect the new mapped-pinned storage and producer relocation; reset_iqn_readiness_state writes 0.0 through the pinned slots so the next kernel launch re-enters the bootstrap branch. Verification: SP4 lib tests + 16 SP4 GPU producer unit tests pass on RTX 3050 Ti. 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.