010445b5df
User directive 2026-05-17: borrow TFT GRN over the planned 2-layer MLP heads. GRN structure: 2-layer GELU MLP body (eta_2 → eta_1) + GLU gate + main + skip-projection from trunk → final sigmoid. Gives per-horizon "linear vs deeper-transform" gating, matches the regime-conditional alpha pattern (pearl_snapshot_alpha_is_regime_conditional). 5x parameter count vs the 2-layer MLP but the gated residual is exactly what TFT empirically wins on. Phase 2C (TGN Δt Fourier features): 8 sin/cos features of Δt at log-spaced periods [60s, 6s, 600ms, 60ms] appended to snap_features. Critical with decision-stride>1 where Δt varies across positions. Bumps FEATURE_DIM 32→40. Phase 2D (TFT VSN): per-feature softmax-normalised gates at the trunk entry, replacing raw concat of snap_features. Learns to down-weight noisy features per regime (canonical: trade-flow in low-volume, OFI in spread-Q4). 2 new param tensors, 1 new cuda kernel (fwd+bwd). Existing 2-layer MLP kernels from Tasks 1.3/1.4 stay in the cubin as ablation baseline; wired path becomes GRN. Phase 1.7 plan now spells out the full GRN forward + backward chain rule (skip + sigmoid(gate) * main → outer sigmoid), kernel signatures, parameter Xavier init, AdamW × 10 setup, and an extended numerical-grad check covering all 10 new param tensors. Co-Authored-By: Claude Opus 4.7 <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.