68197c2c25
Plan 4 Task 2c.1. Additive module — kernels compile and load via the existing kernel-loading infrastructure but have NO production callers in this commit. Task 2c.3+4 wires them into the trunk encoder. Eight kernels in grn_kernel.cu (Linear_a/Linear_b/Linear_residual are cuBLAS GEMMs, not new kernels): - grn_elu_inplace: element-wise ELU (canonical α=1.0) - grn_glu_forward: GLU split + sigmoid (saves sigmoid for backward) - grn_residual_layernorm_forward: residual add + LN, saves mean/rstd/normed - grn_layernorm_backward_dx: FULL JACOBIAN (not the simplified attn_layer_norm_bwd_dx which would silently propagate approximation into trunk gradients) - grn_layernorm_backward_dgamma_dbeta_p1: per-block partial reduction (no atomicAdd per feedback_no_atomicadd.md) - grn_layernorm_backward_dgamma_dbeta_p2: final reduce across blocks - grn_glu_backward - grn_elu_backward LN backward formula (full Jacobian per pearl_cold_path): d_out_g = d_out * gamma s1 = sum_d(d_out_g) s2 = sum_d(d_out_g * normed) d_x = (1/H) * rstd * (H * d_out_g - s1 - normed * s2) Layout convention: row-major [B, H] (sample-major, matches spec §4.E.2 and dt_layernorm_kernel) — intentionally different from attention_kernel.cu's [D, B] col-major; the trunk encoder downstream of GRN uses row-major buffers, so per-row LN avoids a transpose. build.rs registers grn_kernel.cu (kernel count: 57 → 58). Verified nvcc compiles cleanly via `cargo build -p ml --lib`. Module is dead code until 2c.3+4. Wire-up audit updated with the additive entry per Invariant 7. Co-Authored-By: Claude Opus 4.7 (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.