28475ff3ec
Adds K independent value/advantage head weight sets sharing a common DQN trunk. Provides uncertainty estimation (Q-value variance across heads) and diversity regularization (KL divergence between head distributions). Architecture: - Head 0 stays inside CUDA Graph (zero overhead for ensemble_count=1 default) - Heads 1..K-1 run outside CUDA Graph using post-graph save_h_s2 activations - DtoD clone of head weights at init with stream-sync; diversity grows over training - Pairwise KL uses symmetrized Jensen–Shannon divergence for numerical stability New files: - ensemble_kernels.cu: two NVRTC kernels — ensemble_aggregate_kernel (mean/var Q-values across K heads) and ensemble_diversity_kernel (hierarchical warp→block reduction matching dqn_grad_norm_kernel pattern, no flat atomicAdd) - compile_ensemble_kernels() function in gpu_dqn_trainer.rs - New GpuDqnTrainer accessors: on_v_logits_buf(), tg_h_v_scratch_ptr() FusedTrainingCtx changes: - ensemble_extra_heads: Vec<(DuelingWeightSet, BranchingWeightSet)> - Pre-allocated GPU buffers (logits, mean_q, var_q, diversity_loss) - run_ensemble_step() method runs after CUDA Graph replay (EventTrackingGuard) - Wired in run_full_step() between IQN PER step and spectral norm step Config: ensemble_count=1 (default, zero overhead), ensemble_diversity_weight=0.01 Co-Authored-By: Claude Opus 4.6 (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.