2a54edc92d
Three polish items from the plan review: 1. Task 2.4 Step 5 — replaced vague "must not regress" with a numeric tolerance-band table: multi_fold best_val_metric ±15% per fold, Best Sharpe ≥ 20 floor, and hard F_Half/F_Full ≥ 0.05 + cf_flip ≥ 0.1 BLOCKERS. Anchors to the baseline metrics doc values captured at policy-quality-baseline. 2. Task 2.6 — converted the ad-hoc text decision at Step 1 into the same five-row decision table format Task 2.1 uses (DELETE / KEEP / KEEP-as- safety-net / INCONCLUSIVE / DELETE-because-inseparable). Step 2 ablation got its own 4-row outcome table keyed to ent_mag delta, multi-fold Sharpe regression, and NaN appearance. 3. Task 2.7 Step 1 — added "Repeat 3× with different seeds" clause and explicit total wall-clock note: ~75 min (3 × 25 min) for the ablation pass before the Step 2 decision. Aligns with the Cross-cutting concern #6 about sample-noise rejection. No task count change (still 11: 2.0–2.10). Net code-delta estimate unchanged. Standing-rule compliance unchanged (no stubs / no atomics / no quickfixes / no hiding / no feature flags / no push-per-task).
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.