5e23005dea
Per-horizon GRN structure (Lim et al. 2021 §3.3 adapted to scalar output): eta_2[k, m] = GELU(W1[k, m, :] @ h + b1[k, m]) # [HIDDEN] → [HEAD_MID] eta_1[k, m] = W2[k, m, :] @ eta_2[k, :] + b2[k, m] # [HEAD_MID] → [HEAD_MID] gate_lin[k] = W_gate[k, :] @ eta_1[k, :] + b_gate[k] # → scalar main[k] = W_main[k, :] @ eta_1[k, :] + b_main[k] # → scalar skip[k] = W_skip[k, :] @ h + b_skip[k] # [HIDDEN] → scalar logit[k] = skip[k] + sigmoid(gate_lin[k]) * main[k] p[k] = sigmoid(logit[k]) Gated residual lets each per-horizon head learn "linear vs deeper-transform" gating, matching the regime-conditional alpha pattern from pearl_snapshot_alpha_is_regime_conditional (~20% of book states carry the edge; spread-Q4 hits 75% acc, middle quintiles below chance). Backward chain rule covers all 10 parameter tensors + the trunk gradient (skip-path direct + main-path through W2→GELU→W1, lambda-scaled). Single-writer discipline (no atomicAdd per feedback_no_atomicadd.md): - Thread m owns row m of grad_w1 (col i in 0..HIDDEN), row m of grad_w2 (col m_in in 0..HEAD_MID), and column m of d_eta_2. - Threads 0..4 own per-horizon scalar grads (skip/gate/main biases). - Trunk grad_h tiles i over 2 strides of HEAD_MID for HIDDEN=128 coverage. Shared mem: ~6.5KB (s_a1 + s_z2 + s_d_eta1 + s_d_eta2 + s_d_z1 + scalars), well within 48KB limit. Existing 2-layer MLP kernels (Tasks 1.3/1.4) stay in the cubin as ablation baseline; the wired path becomes GRN once perception.rs lands. build.rs cache-bust → v7. 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.