96b77043e0
Clamps C51 atom positions during all dynamic write paths to ±10 × ISV[Q_ABS_REF=16].max(1.0) via inline fminf(fmaxf(...)): - atoms_update_kernel.cu — active GPU-driven shared atom grid (all 4 branches via grid.x = branch_id) - iql_value_kernel.cu::iql_compute_per_sample_support — per-sample [v_min, v_max, delta_z] tile (delta_z recomputed from clamped span) - experience_kernels.cu::adaptive_atom_positions — legacy single-branch entry kept in lockstep per feedback_no_partial_refactor Same ISV bound as Mech 1 (target_q clip) — atoms and target_q share the magnitude scale. ε on the multiplier (`isv.max(1.0)`) per the SP1 ε-floor pearl; bound ≥ 10 even with cold-start ISV. Closes the second of two Q-target inflation pathways: with Mech 1 capping the projection target and Mech 2 capping the atom support, the C51 expected_q (mean of atom × prob) is bounded by ±10 × ISV[16].max(1.0) regardless of probability mass distribution. Prevents the Adam EMA saturation → weight pathology → cuBLAS overflow chain. Reuses isv_signals already in scope at all three kernels — no new kernel arg, no new launch site, no new ISV slot, no new buffer, no new kernel. Co-Authored-By: Claude Opus 4.7 (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.