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The epoch_vol_ema, epoch_dsr_mean, and epoch_dsr_var variables were loaded from global memory at kernel start but never wired into the actual computation. The EMA normalizer (ema_mean/ema_var) and DSR accumulators (dsr_A/dsr_B) were initialized with hardcoded constants, so epoch state round-tripped unchanged. Changes: - Seed ema_mean/ema_var from epoch_dsr_mean/epoch_dsr_var at kernel start - Seed dsr_A/dsr_B from epoch_dsr_mean/epoch_dsr_var at kernel start - Seed ema_init/dsr_initialized from epoch_step_count > 0 (skip cold start on subsequent epochs) - Add local_vol_ema/local_median_vol seeded from epoch_vol_ema/epoch_median_vol - Update vol EMA each timestep from market feature index 3 (log close return), mirroring CPU DQNTrainer::vol_ema / median_vol logic - At writeback, write actual computed dsr_A/dsr_B or ema_mean/ema_var (conditioned on use_dsr), and computed local_vol_ema/local_median_vol, instead of unmodified loaded epoch values - Applied identically to both dqn_full_experience_kernel (standard) and dqn_full_experience_kernel_warp (warp-cooperative) variants Co-Authored-By: Claude Opus 4.6 <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.