b0b8c94d77
Split CUDA Graph (forward + adam phases with gradient injection point): - IQN trunk gradient flows through single Adam (no dual optimizer conflict) - Spectral norm runs BEFORE forward (not after Adam — no tug-of-war) - σ_max in 40D hyperopt search space [1.0, 10.0] Attention Phase B backward: - Full gradient flow through 4-head self-attention weights - Separate Adam optimizer for attention params - Backward kernel recomputes forward from saved_input (memory-efficient) Ensemble multi-head: - Real cuBLAS value head forward per ensemble head (was copying head 0 logits) - KL diversity gradient kernel with hierarchical reduction - forward_value_head() on CublasForward for per-head SGEMM Regime PER scaling: - Kernel reads target ADX/CUSUM from states_buf directly (zero CPU readback) - Removed 2x memcpy_dtoh per training step Decision Transformer: - 14 CUDA kernels (embed, causal attention, FFN, CE loss + backward + trajectory building) - GPU-native trajectory builder (return-to-go reverse cumsum, momentum expert actions) - Wired into training loop with dt_pretrain_epochs config HER Future/Final: - episode_ids flow through PER buffer (GpuBatch, GpuReplayBuffer, GpuBatchSlices) - GPU-native donor sampling (binary search on episode boundaries) - Strategy dispatch in fused_training.rs Backtest SEGV fix: - Missing q_gaps_buf argument in action_select kernel launch - Dynamic branch_sizes from agent (not hardcoded) Local test: objective=10.48, Sharpe=0.0419, 175K trades, zero errors 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.