1112abc2a4
Layer C close-out C4 — the most architecturally substantive site of the feedback_no_cpu_compute_strict sweep. GpuDqnTrainer::update_adaptive_clip was running a 6-step host-side compute chain on GPU-produced inputs: winsor (1) + cold-start sentinel + EMA (3) + scalar reduction (4) + ISV upper-bound clamp (5) + mapped-pinned write (6). Per feedback_no_partial_refactor the chain must migrate coherently — splitting EMA-only into a kernel and leaving the surrounding scalar reductions on host would be a partial migration violating the rule. Migration: new update_adaptive_clip_kernel.cu (single-thread, single-block). Takes host-passed observed_grad_norm (already a mapped-pinned readback) + 6 fixed structural constants (legacy values preserved per feedback_no_quickfixes) + 4 mapped-pinned dev_ptrs + ISV[GRAD_CLIP_BOUND_INDEX]. Writes the full output chain (adaptive_clip_pinned, grad_norm_ema_pinned, outlier_diag_pinned). Storage migration: - grad_norm_ema migrated from host-resident f32 to mapped-pinned scalar (matches C1/C2/C3 pattern). - New outlier_diag_pinned mapped-pinned slot for the GRAD_CLIP_OUTLIER warn diagnostic. The kernel writes `delta = observed - clamped` and the host reads it post-launch to format the warn log without running scalar arithmetic on the host. All structural constants preserved: EMA_BETA=0.95, CLIP_MULTIPLIER=2.0, MIN_CLIP=1.0, GRAD_CLIP_OUTLIER_K=100, EPS_CLAMP_FLOOR (SP4). Same cold-start sentinel `prev_ema <= 0.0 ⇒ assign clamped directly`; same Mech 6 (SP3) + Layer B (SP4) bound design. Same outlier-warn log format reconstructed from the mapped-pinned diagnostic slot. Host-side early-return guard preserved (the pre-existing pattern from C1 redesigned). grad_norm_emas_step_count counter unchanged (scalar control-flow metadata, not compute, per the rule's explicit carve-out). Verification: SP4 lib tests + 16 SP4 GPU producer unit tests pass on RTX 3050 Ti. 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.