2406ebf2f7
Three review-driven fixes: 1. Slot 24/25 cite the actual production buffers d_value_logits_buf (gpu_dqn_trainer.rs:3123) and d_adv_logits_buf (:3125) — the f32 atomicAdd buffers that launch_c51_grad writes — not the staging buffers at :3127/:3129. Pointer expressions exist at the launch site (:17707/:17708), making new accessors optional. Task 3 summary and priority-list entries updated to match. 2. Explicit plan-supersession note inserted immediately above the per-slot table: the audit's per-slot allocation supersedes the plan Task 2 step 6 name table. The plan's allocation was placeholder; the audit's is grounded in per-kernel inspection. Task 2 should use the audit's names (d_value_logits_buf, d_adv_logits_buf, iqn_trunk_m, iqn_d_h_s2_ptr, d_branch_logits_buf, etc.). 3. Summary suspicion-ranking row #3 reconciled with slot-28 dormancy: iqn_backward_per_sample has no Rust caller (verified via grep crates/ml/src/), so row #3 is re-pointed at the production kernel iqn_quantile_huber_loss (iqn_dual_head_kernel.cu:1346-1413, loaded at gpu_iqn_head.rs:2114). Same unsafe-write pattern (no isfinite guard at line 1410's d_q_online[idx] = qw*d_huber/Q), now attributed to the live path. apply_iqn_trunk_gradient at #1 stands — its reasoning (orchestrator consuming iqn_d_h_s2_ptr) is unchanged by which specific kernel writes that buffer. 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.