875f263ec9
Backward pass dX computation now uses f32 scratch buffers with bf16 staging for the backward chain. Pattern: f32 GemmEx → relu_mask (f32) → f32→bf16 cast → next layer reads bf16 dY. Changes: - 6 bw_d_h_* scratch buffers: CudaSlice<half::bf16> → CudaSlice<f32> - New bw_dy_bf16_staging: shared bf16 buffer for layer transitions - backward_fc_layer dX: gemmex_bf16 → gemmex_bf16_acc_f32 (f32 output) - launch_dx_only: gemmex_bf16 → gemmex_bf16_acc_f32 (f32 with beta) - relu_mask_kernel: reads/writes f32 (no bf16 clamp needed) - f32_to_bf16_cast_kernel: ±500 clamp at type boundary (in backward_kernels.cu) - cast_dx_to_staging: f32 scratch → bf16 staging per layer - IQN/ensemble backward: bf16→f32 cast for dX input, f32→bf16 for dY output - bw_d_h_s2_as_bf16(): attention backward receives bf16 via staging Hyperparameters updated for f32 Adam: - learning_rate: 1e-5 → 1e-4 (updates must exceed bf16 shadow step ~1e-3) - adam_epsilon: 1e-3 → 1e-8 (standard Adam, bf16 workaround no longer needed) - grad_norm NaN skip kept as defense-in-depth (source still under investigation) 895/895 unit + 359/359 ml-dqn tests pass. 7-11/11 smoke tests (intermittent NaN from unknown source — NOT backward dX). 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.