6d0ac7beb3
Per-step host-side EMA loop at gpu_dqn_trainer.rs:4671 over 6 mapped-
pinned homeostatic-target slots was a feedback_no_cpu_compute_strict
violation discovered during the sweep audit (commit 6a6b58aec) but
deferred for scope. Sweep audit grid site #9.
Migrated:
- New calibrate_homeostatic_kernel.cu — single-block, six threads
(one thread per homeostatic slot). Reads host-passed `readiness`
scalar (already-clamped IQN gauge from `iqn_readiness` shadow field,
bit-for-bit match of the deleted host clamp), observations from
`homeostatic_obs_dev_ptr`, applies adaptive α
`0.3 × (1 - readiness) + 0.01 × readiness` to targets[k] for k=1..5;
thread 0 forces the Q-mean invariant `targets[0] = 0.0` exactly as
the deleted host post-loop assignment did. __threadfence_system()
after writes for PCIe-visibility to homeostatic_kernel's dev_ptr reads.
- build.rs cubin registration and trainer-struct wiring (cubin static,
field, struct constructor, cubin load) mirror the C2/C3/C4 pattern
from the prior sweep commits.
- Host-side `for k in 0..HOMEOSTATIC_N_OBS` loop in
`calibrate_homeostatic_targets` replaced with a single
`launch_calibrate_homeostatic` kernel launch; chained on the
trainer's stream so it remains graph-capture-compatible.
Preserved:
- Same α formula, same Q-mean=0 invariant, same call ordering.
- Mapped-pinned target buffer retained — homeostatic_kernel still
reads via homeostatic_targets_dev_ptr unchanged.
- No cold-start sentinel: constructor pre-initialises targets to
`[0.0, 0.85, 0.1, 0.0, 1.0, 0.5]` (gpu_dqn_trainer.rs:14583-14588)
so the first EMA call blends defaults with the first observation,
same algebraic shape the deleted host loop relied on.
- State-reset registry unchanged — deleted host loop had no fold reset
(per-call EMA only); GPU port preserves identical per-call semantics.
cargo check clean. SP4 + state_reset_registry lib tests pass (11/11).
16/16 SP4 producer GPU tests pass on RTX 3050 Ti. No behavior change —
pure architectural fix.
Refs: feedback_no_cpu_compute_strict sweep audit grid site #9.
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.