2606506cd8
- argo-train.sh: --profile flag forces multi-seed render path so the nsys wrapper + foxhunt-training-artifacts upload step are visible in --dry-run YAML without cluster contact (test surface). - train-multi-seed-template.yaml: new `profile` parameter (default "false") gates the per-(seed, fold) `nsys profile --capture-range=cudaProfilerApi` wrapper and the `mc cp` upload to foxhunt-training-artifacts/profiles/<sha>/. mc binary fetched on-demand (ci-builder image lacks it). MinIO creds optional — upload warn-skips if absent. - Dockerfile.foxhunt-training-runtime: install nsight-systems-cli unpinned (pinning the stale 2024.4.1.61-1 from earlier plans breaks builds when apt index advances). - minio.yaml: add foxhunt-training-artifacts bucket to minio-init. - compare-nsys-profiles.py: V0 regression detector — compares cuda_gpu_kern_sum total_ns / epoch_count between two profiles; exits 1 on >20% slowdown. NVTX per-epoch ranges deferred to T5. - tests/test_nsys_harness.sh: dry-run grep test — verifies both required strings appear when --profile is set, and that the default (no --profile) path keeps profile=false in the rendered template. - dqn-wire-up-audit.md: Plan 5 Task 3 row added documenting the harness + the baseline-capture deferral to T5. Backward compat: test_multi_seed_harness.sh from P5T1 still PASS.
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.