bbf9de3ea1
ROOT CAUSE: grad_norm kernel was inside graph_adam, but grad_buf was
zeroed by the gradient vaccine between graph_forward and graph_adam.
The vaccine zeros grad_buf to compute g_val, fails with INVALID_VALUE,
and the non-fatal error handler skips the restore — leaving grad_buf
zeroed when graph_adam's grad_norm kernel reads it.
TWO FIXES:
1. Move grad_norm computation OUT of graph_adam entirely:
- New compute_grad_norm_outside_graph() method runs between graphs
- Zeros grad_norm_f32_buf, launches grad_norm kernel, launches finalize
- Called AFTER all gradient injections, BEFORE replay_adam()
- Reads the ACTUAL modified grad_buf (C51 + CQL + IQN + ensemble)
- All 3 replay paths updated (train_step_gpu, execute_train_scalars_only,
replay_adam_and_readback)
2. Vaccine grad_buf restore on failure:
- New restore_grad_from_vaccine_save() method
- When vaccine fails after zeroing grad_buf, copies saved g_train back
- Prevents grad_buf from staying zeroed after vaccine error
RESULT: grad_norm is now stable and nonzero across all epochs:
Epoch 1: 0.6969
Epoch 2: 0.6982
Epoch 3: 0.6998
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.