09b6bd6eaa
Surgical gradient projection that makes overfitting mathematically impossible. At each training step: 1. Save g_train (from CUDA Graph forward+backward) 2. Run SEPARATE non-graph forward+backward on vaccine batch → g_val 3. Compute dot(g_train, g_val) and |g_val|² via GPU reduction kernel 4. If dot < 0 (gradients DISAGREE), project out conflicting component: g_train -= (dot/|g_val|²) * g_val 5. Adam only sees gradient directions where train AND val agree Two new CUDA kernels: - gradient_dot_and_norm: parallel reduction with warp+block+atomicAdd Computes both dot product and norm² in single pass (bandwidth optimal) - gradient_project: conditional SAXPY (skips when dot >= 0, no branch divergence) The vaccine runs OUTSIDE the CUDA Graph (between replay_forward and replay_adam) because it needs conditional logic. The non-graph vaccine forward+backward reuses the same cuBLAS/loss/backward infrastructure. Vaccine batch is sampled from the replay buffer alongside the training batch and passed via FusedTrainingCtx::pending_vaccine_batch. Config: enable_gradient_vaccine=false (default). Enable for mathematical guarantee that no gradient update makes the model worse on held-out data. Together with bottleneck (#31): "you can only remember 2 numbers, and those 2 numbers must work on data you haven't trained on." Co-Authored-By: Claude Opus 4.6 (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.