ff70734802
Adds the aux heads that map AuxTrunk's output (64-dim) to per-(direction, horizon) predicted outcomes, plus the Huber loss kernel that supervises them against the D-style labels generated in B1. Files (1212 LOC): - cuda/aux_heads.cu (197 LOC): fused fwd+bwd for linear projection h_aux[B, 64] → y_long_hat[B, N_HORIZONS] + y_short_hat[B, N_HORIZONS]. Single launch for both directions, per-batch grad scratch + caller-side reduce_axis0, cooperative h_aux staging in shmem. - cuda/aux_loss.cu (143 LOC): Huber loss fwd+bwd with NaN-masking. Per direction call; returns Σ Huber + valid_count separately so caller picks reduction policy. - src/aux_heads.rs (412 LOC): AuxHeads + AuxHuberLoss wrappers, weight structs, Xavier init under scoped_init_seed. - tests/aux_heads.rs (460 LOC): 4 #[ignore]'d GPU oracle tests (fwd_matches_naive, bwd_finite_diff_matches_bias, huber_loss_matches_ naive, huber_loss_nan_mask_does_not_propagate). 4/4 PASS on RTX 3050. - build.rs: KERNELS += aux_heads, aux_loss; cache-bust bumped to v17. - src/lib.rs: pub mod aux_heads. Design decisions (full notes in subagent report): - Linear heads (no GRN) — D-labels already encode asymmetric loss-aversion - Per-direction Huber launches (cleaner per-direction telemetry) - NaN-masking in loss kernel (single NaN label can't poison batch grad) - Unreduced sum + valid_count separately (caller picks mean policy) Not yet wired into trainer (B5) or used in policy (B7). 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.