932ac2bda8
Two bugs caught by the L40S smoke (train-qhgj6) that couldn't surface on
local RTX-3050 single-fold runs:
1. PER dtoh inside CUDA Graph capture (Fold 1 crash)
Failure: CUDA_ERROR_STREAM_CAPTURE_INVALIDATED at per_prefix_scan on
Fold 1 re-capture. Chain: fused_training parent graph captures →
memcpy_dtoh + cuStreamSynchronize in gpu_replay_buffer::update_priorities_gpu
(health<0.8 diversity path) poisons the stream → subsequent per_sample
kernel on the same stream sees an invalidated capture context.
The prior comment claimed "runs once per epoch, DtoH cost acceptable"
— wrong, it runs every priority update when health<0.8 (common during
Fold handoff when health_cache is re-seeded low). Any dtoh inside
capture invalidates regardless of latency.
Proper fix (no shortcut):
* New kernel actions_sum_scale_reduce_u32 — single-block deterministic
tree reduction over sample_actions (u32) → writes (sum*1000)/n as i32
to a device-accessible slot. No atomics (consistent with the 1/N
determinism policy from commit c82386500).
* mean_action_scaled storage is pinned + device-mapped (cuMemAllocHost
+ cuMemHostGetDevicePointer — same pattern as rng_step_dev_ptr and
size_dev_ptr elsewhere in the file). Zero-copy between host and
device, graph-safe, no explicit free needed (process-exit cleanup,
matches existing pattern).
* pow_alpha_diverse_f32 now takes const int* mean_action_scaled_ptr
and does a plain global load — NOT __ldg. The read-only cache used
by __ldg is not guaranteed coherent with device-mapped host memory;
multi-trial smoke regression caught it (median q_gap collapsed
from 2.0 → 0.15 with __ldg, recovered to 2.8 with plain load).
Verified: multi-trial smoke 5/5 pass, median_q_gap=2.80 (beats 2.00
baseline), Best Sharpe peaks 19-30 per trial. No stream capture
invalidation.
2. evaluate step CLI drift in Argo template
evaluate_baseline's Args struct uses --models-dir and --output (single
file path). Template was passing --checkpoint-dir and --output-dir,
causing clap to reject the invocation. Fixed argument names + added
mkdir for the eval subdir + updated the comment to pin the source of
truth for future drift catches.
Both fixes are graph-capture-clean and match the "wire properly or delete"
discipline. No masking, no feature flags, no dead params.
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.