68804a1a51
Three root causes of sporadic NaN during training: 1. --use_fast_math (nvcc) breaks IEEE 754 NaN semantics: fmaxf(NaN,x) returns NaN instead of x, isnan()/isinf() compile to false. Replaced with --ftz=true --fmad=true --prec-div=true --prec-sqrt=true across all 4 build.rs (ml, ml-dqn, ml-ppo, ml-core). 2. Cross-stream race: replay buffer wrote batch data on the device's original stream while the trainer read it on a forked stream. Fixed by passing the forked stream to the DQN agent via agent_device, so all GPU components share a single CUDA stream (zero sync overhead). 3. Rewards/dones stored as bf16 in replay buffer caused done=0xFFFF NaN. Converted entire rewards/dones pipeline to f32: experience collector, replay buffer storage, nstep kernel, loss/grad kernels. Also: - Removed fast_isnan/fast_isinf/fast_isfinite wrappers — standard isnan/isinf/isfinite work correctly without --use_fast_math - Updated dqn-smoketest.toml: lr=1e-4, epsilon=1e-8 (f32 Adam values) - Removed debug printfs from gather kernels - Added curiosity_weight to training profile system - Cleaned up smoke_params() inline overrides 11/11 smoke tests pass, 5/5 stress runs of 50-epoch test pass, 359/359 ml-dqn + 895/895 ml unit tests pass. 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.