c363a7e94c
Per-position softmax-normalised feature gating for the trunk entry. Per (b, k) sample: gate_logit[i] = sum_j W_vsn[i, j] * x[j] + b_vsn[i] gates = softmax(gate_logit) # [FEATURE_DIM] y[i] = x[i] * gates[i] Backward chain rule (cleanly factored from the softmax Jacobian): d_gates[i] = grad_y[i] * x[i] d_logit[i] = gates[i] * (d_gates[i] - sum_j gates[j] * d_gates[j]) grad_W[i,j] += d_logit[i] * x[j] grad_b[i] += d_logit[i] grad_x[j] = grad_y[j] * gates[j] + sum_i d_logit[i] * W[i,j] Single-writer (no atomicAdd): thread tid owns row tid of grad_W and column tid of d_x_via_W. ONE block per launch (loops n_rows internally), same pattern as 2-layer / GRN bwd kernels. Softmax uses standard max-subtract + sum trick for numerical stability. Block dim = 64 (one warp + 24 idle threads at FEATURE_DIM=40). Wiring blocked on: Mamba2 backward needs to emit `d_input` (currently dropped at line 1413 of mamba2_block.rs via `_d_input`). Next commit exposes that so VSN bwd has the right grad_y signal — and the same refactor unblocks Phase 2B (2-stack Mamba2 needs the inter-stack LN to backprop through the 2nd stack's d_input). build.rs: - "variable_selection" added to KERNELS - Cache bust → v9 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.