0d9fbc16b0
5 sweep YAMLs updated to reference the new checkpoint filename:
- config/ml/sweep_smoke.yaml: trunk_best_h6000.bin → trunk_best_h1000.bin
- config/ml/sweep_perhoriz_diag.yaml: same
- config/ml/sweep_threshold_tuning.yaml: same
- config/ml/sweep_deployability.yaml: same
- config/ml/sweep_smoke_perhoriz_cfc.yaml: same + 3 comment updates
(WIN-gate criteria, header doc, best-checkpoint annotation)
scripts/generate_sweep_variants.py:61 updated atomically — without this
the next regeneration of sweep_deployability.yaml would silently
re-introduce trunk_best_h6000.bin.
Argo workflow templates (alpha-perception, alpha-cv, lob-backtest-sweep)
did NOT need text changes — they're already horizon-agnostic:
- They forward CLI flags via {{workflow.parameters.*}} to binaries
- Don't grep alpha_train_summary.json inline
- Don't reference per-horizon field names
- early-stop-metric default is "mean_auc" (horizon-agnostic)
Intentionally left:
- alpha-cv-template.yaml stacker-horizon: "6000" (unrelated TFT lookback)
- alpha-cv-template.yaml horizon: "1200" (DQN execution horizon in bars)
- lob-backtest-sweep-template.yaml ci-training-h100 (GPU pool name)
NOTE: kubectl apply of workflow templates is deferred to Task 10 (push
+ dispatch). Verified all 5 sweep YAMLs parse with python3 yaml.safe_load.
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.