ed3fa066b9
Replace ExperienceCollectorConfig.noise_sigma: f32 with noise_sigma_per_branch: [f32; 4] (branch order: dir/mag/ord/urg). add_advantage_noise kernel (experience_kernels.cu) now takes const float* noise_sigma[4] + b0/b1/b2/b3 branch-size params. Each thread derives its branch_idx from action offset using cumulative branch size offsets; applies that branch's sigma. Sigma=0 fast-exits with no PRNG work. GpuExperienceCollector gains noise_sigma_dev: MappedF32Buffer[4] (mapped-pinned, zero HtoD copy per feedback_no_htod). CPU writes the 4 sigma values via write_from_slice before each kernel launch; kernel reads via dev_ptr. training_loop.rs reads ISV[NOISY_SIGMA_BASE..+4] = ISV[210..214) directly — one slot per branch — instead of averaging all 4 into a scalar. Cold-start floor 0.01 is Invariant 1 (numerical stability). Falls back to [hyperparams.noise_sigma; 4] when fused_ctx unavailable. Default::default() supplies [0.1; 4]. mod.rs test (line 895) uses Default::default() unchanged — no explicit field to update. docs/isv-slots.md updated to reflect SP6 Pearl 3 consumer wired. Files changed: 4 (experience_kernels.cu, gpu_experience_collector.rs, training_loop.rs, docs/isv-slots.md). No Pearl 2 or Pearl 5 files touched. Co-Authored-By: Claude Sonnet 4.6 <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.