a385c1d2be
`compute_adaptive_tau` (gpu_dqn_trainer.rs:7045) had zero production
callers — its q_div_ema field's doc-comment explicitly flagged it
"NOTE: only consumed by the orphan helper compute_adaptive_tau (zero..)".
Pre-SP3 attempt at adaptive-tau control, superseded by the SP3/SP4 ISV-
driven tau controller (`tau_kernel.cu` + ISV[TAU_INDEX]).
Removed:
- compute_adaptive_tau method (CPU EMA + ratio-scaled tau formula)
- q_divergence_readback method (only caller was compute_adaptive_tau)
- q_div_ema field + initialiser + fold-reset assignment
- q_divergence_pinned + q_divergence_dev_ptr struct fields, alloc, free
- q_divergence_dev_ptr memsets at C51 launch sites (2)
- q_divergence_dev_ptr arg in launch_c51_loss kernel call
- c51_loss_batched kernel parameter `q_divergence` (never written by
kernel body — comment "removed from hot path — zero atomicAdd"
confirmed buffer was inert; deleting parameter keeps kernel ABI clean
per feedback_no_partial_refactor)
- Stale doc-comments referencing q_divergence in c51 reduction kernel
- readback_pinned [12]=q_divergence layout doc (slot was never read)
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 —
dead code only.
Refs: feedback_no_cpu_compute_strict sweep audit (commit 6a6b58aec)
flagged this as orphan; feedback_wire_everything_up mandates deletion.
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.