d6bd7e1e00
Smoketest TOML: added [generalization] section with toned-down params for tiny [64,64] network: - anti_lr mults 1.1x/0.9x (vs production 3x/0.3x) - stochastic_depth 10% (vs 20%) - feature_mask 15% (vs 30%) - pruning_epoch 100 (beyond 50-epoch test) - saboteur warmup 100 (beyond test) - bottleneck disabled (tiny network) 50-epoch tests: set gradient_collapse_patience=50000 to prevent false positive from deferred grad_norm readback (per-step counter sees 0 because grad_norm_finalize runs outside graph). CUDA context leak fix: experience collector loads EXPERIENCE_KERNELS_CUBIN once for all domain_rand + saboteur kernels (was 4 separate loads). Configurable pruning: pruning_epoch and pruning_fraction moved from hardcoded to DQNHyperparameters + GpuDqnTrainConfig + TOML. Test results: - ml unit: 893 passed, 2 failed (DB only) - ml-dqn unit: 359 passed, 0 failed - Smoke tests: 11/11 PASS - 3-epoch: Sharpe 9.34 - 50-epoch convergence: Sharpe 0.95, MaxDD 11.1%, +63% return - 50-epoch walk-forward: best Sharpe 8.13 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.