cdb32cd8bf
DQN correctness: - N-step Bellman target uses gamma^n (was gamma^1), fixing ~2% Q-value bias - Cash reserve enforcement actually reduces position (was warn-only) - CVaR sort_last_dim(true) for IQN random tau ordering - Marsaglia-Tsang gamma sampling uses normal(0,1) not uniform(0,1) - DQNConfig::default state_dim 51→54 (51 market + 3 portfolio) PPO correctness: - set_learning_rate preserves trained weights (was recreating entire model) - update_value_only() for critic pretraining (was training both networks) - PolicyNetwork::entropy() single forward pass (was 2x GPU compute) - LSTM entropy uses proper H=-sum(p*log(p)) (was -mean(log_probs)) - grad_norm metric set to None (was reporting policy loss as gradient norm) Config parity (train/eval/hyperopt/enhanced_ml): - PPO hyperopt state_dim 51→54, value_hidden_dims 3→5 layer - enhanced_ml feature_count 16→54, PPO policy_hidden_dims [128,64,32]→[128,64] - Eval: tensor core alignment, Rainbow from hyperopt params, warmup alignment - GPU batch model_state_dim 51→54 (matches kernel output) Infrastructure: - QNetwork dropout training mode (AtomicBool toggle, was always disabled) - reward_history Vec→VecDeque (O(1) front removal, was O(n)) - Plateau detection distinguishes worsening from plateau in log messages 2732 tests pass, 0 clippy warnings. 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.