5f26c57514
nsys profile of multi_fold_convergence on L40S identified compute_expected_q as the #3 GPU consumer at 12.9% (207ms / 1382 calls — ~150 µs per call, compute-bound at typical batch=8192 × num_atoms=51). The kernel computed each per-action softmax over atoms in 3 passes (max → sum_exp → normalize+expected +var+entropy+util), re-reading v_row[z]+adv_a[z] 3 times per atom for each of the 4 × ~3.25 = 13 actions per sample. Replaced with online softmax (running-max + running-sum) so expected_q, sum_z_sq, and entropy all accumulate in a single forward pass over atoms. Atom utilisation still needs a 2nd pass because it depends on the final S = sum exp(logit-M) being known to compute prob_z = exp(logit_z-M)/S for the threshold test. Inner loop reduction: 3 → 2 passes, ~33% fewer global loads of branch advantage logits (which dominate because v_row[z] is reused across 13 (action, branch) pairs and may stay in L1, while adv_a[z] flips per action and is pure global). Online accumulation pattern (Page-Olshen): M, S, TZ, TZ², TLM = -inf, 0, 0, 0, 0 for z in 0..num_atoms: logit = v_row[z] + adv_a[z] if logit > M: scale = exp(M - logit) # ≤ 1, no overflow S, TZ, TZ², TLM *= scale M = logit w = exp(logit - M) S += w TZ += w * z_val TZ² += w * z_val² TLM += w * (logit - M) expected_q = TZ / S sum_z_sq = TZ² / S entropy = log(S) - TLM / S # analytical: -sum(p log p) Correctness: bit-stable when atoms processed in fixed order (z = 0..num_atoms-1). The analytical entropy form is mathematically identical to -sum(p log p): -sum(p log p) = log(S) - (1/S) * sum(exp(logit-M) * (logit-M)) = log(S) - TLM / S The previous code's `if (prob > 1e-10f)` underflow guard is no longer needed: exp(logit - M) underflows cleanly to 0 when logit << M, and the analytical form does not multiply tiny probs by very negative logs. Bit-stable per feedback_stop_on_anomaly.md — no fuzzy tolerance change. Atom-stat block-sum reduction unchanged (warp shfl + shared-mem bank, deterministic). Expected wall-clock saving: ~33% of 207ms = ~70ms across the smoke run; on production batch=8192 × 1382 calls per fold, roughly proportional. Lower-bound estimate — at low batch (smoke) the kernel may be launch-bound rather than load-bound. nsys re-profile after deployment will quantify. ABI unchanged; no Rust caller changes required. Per Invariant 7 the audit doc dqn-gpu-hot-path-audit.md is updated with Fix 19 entry. Build: SQLX_OFFLINE=true cargo check -p ml --lib clean (12 warnings, baseline). Tests: cargo test -p ml --lib --no-run clean. 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.