5232a1ae31
Two root causes of intermittent training NaN (1/3000 steps) identified and fixed:
1. BF16 portfolio/market feature overflow in experience_kernels.cu:
- 6 portfolio features (lines 220-226) computed with bf16 divisions that
overflow when equity/position values are large (ES at ~5000)
- 16 multi-timeframe market features computed with bf16 subtraction of
similar close prices → precision loss and overflow
- Fix: ALL portfolio + market feature computation now in float
(read bf16 inputs → float arithmetic → write bf16 output)
- NaN states in replay buffer → NaN GemmEx output → NaN loss (eliminated)
2. PER IS-weight Inf→NaN cascade in replay_buffer_kernels.cu:
- powf(tiny_prob, -beta) produces Inf when priorities are very skewed
- normalize_weights_f32 divides all weights by max_weight
- Inf / Inf = NaN (IEEE 754) → ENTIRE batch has NaN IS-weights
- Fix: clamp IS-weight to 1e6 before normalization (well within f32,
normalized to ≤1.0 by max division)
- prob floor at 1e-12 and total_sum floor at 1e-8 prevent division by zero
NaN guards REMOVED from loss kernels (no longer needed):
- mse_loss_kernel.cu: removed fast_isfinite guard on weighted_loss
- c51_loss_kernel.cu: removed fast_isfinite guard on weighted_loss/clamped_ce
895/895 unit + 9/9 smoke tests pass. Zero NaN guards in the training path.
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.