be8f3310d5
Per-component gradient clipping: - 2 new CUDA kernels (dqn_clipped_saxpy, dqn_clip_grad) - CQL gradient isolated into separate scratch buffer - Budget allocation: C51=70%, CQL=15%, IQN=10%, Ens=5% - Dynamic budget — inactive components' share goes to C51 Spectral norm extended to all 10 weight matrices: - Was trunk-only (W_s1, W_s2), now covers all heads - 16 u/v power iteration buffers, batched GOFF sync-back - Uniform sigma_max, end-to-end Lipschitz bounded Bug fixes from 9-agent audit: - Backtest episode reset: all 8 fields (was 5), max_equity updated before floor check - gradient_clip_norm unified: 10.0 everywhere (was 10.0 vs 1.0) - entropy_coefficient: Option<f64> → f64, single default 0.001 - Close price: unwrap_or(1.0) → direct indexing (no silent wrong rewards) - step_count: only increments during training (was incrementing on inference) - Quantile loss: single CUDA context (was 3×), silent fallbacks removed - MaybeNoisyLinear: single-variant enum removed → direct NoisyLinear - Budget fractions: exported as pub(crate) constants, referenced not hardcoded Monitoring: - Per-component gradient Prometheus gauges (C51 raw, combined) - Diagnostic logging every 1000 steps Tests: 7 new gradient budget tests, 1 gradient bounds smoke test All 488 tests pass (359 ml-dqn + 129 ml) 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.