3d15e26b43
Full-stack determinism for reproducible training and valid hyperopt comparisons. CUDA gradient kernels (zero atomicAdd): - c51_grad_kernel: restructured from B×4×NA to B×NA threads, each loops 4 branches d_value accumulates in register, d_adv written directly (unique slot per thread) - mse_grad_kernel: same restructure, zero atomicAdd - bn_bias_grad_kernel: plain write (was unnecessary atomicAdd, one thread per slot) CUDA loss kernels (deterministic reduction): - c51_loss_batched: removed atomicAdd(total_loss), per_sample_loss written directly - mse_loss_batched: same removal - c51_mixup_ce: same removal - New c51_loss_reduce kernel: sequential sum grid=(1,1,1) for deterministic total_loss cuBLAS deterministic GEMM: - CUBLAS_TF32_TENSOR_OP_MATH → CUBLAS_DEFAULT_MATH (both forward and backward) - Forces IEEE FP32 accumulation, eliminates TF32 reduction non-determinism Deterministic RNG seeds (all GPU + CPU): - Experience collector: fastrand → LCG with fixed seed 0xDEAD_BEEF - Backtest evaluator: fastrand → LCG with fixed seed 0xBAC0_7E57 - PPO collector: fastrand → LCG with fixed seed 0xAA0_5EED - Stochastic depth: process ID → fixed seed 0x5D5E_ED00 - CPU RNG: rand::thread_rng() → StdRng::seed_from_u64() in IQN, HER, IQL, action.rs Adaptive tau → cosine-annealed tau: - Disconnected q_divergence atomicAdd from training path - q_divergence is monitoring-only (non-deterministic acceptable) - Cosine schedule provides smooth tau adaptation without stochastic coupling Result: epochs 1-2 are bit-identical across runs. Divergence at epoch 3 from remaining C51 loss kernel atomicAdd on q_divergence (monitoring-only, does not affect gradients). 903/903 tests passing. 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.