1764cc394b
Step 2 of aux-diagnosis-deeper plan (NumPy on local ES data) falsified the F2 hypothesis: 1. Local data has 0% zero-bid/zero-ask records. F2's "one-sided book contamination" was incorrect — the actual databento sentinel for missing price is INT64_MAX × 1e-9 ≈ 9.22e9 (a HUGE POSITIVE number that passes the `> 0` check). F2 was a no-op on real data. 2. Sentinel rate on local Q1 is 0.006% — negligible. 3. Local pos_fraction at K=10 is 19.59%, cluster reports 35.07%. The ~75% gap is plausibly explained by overnight session gaps in the full 5M-record quarter file (which I didn't see in my 500k local sample). These are real price discontinuities, not "contamination". 4. F2 introduced an epoch-4 NaN explosion (alpha-perception-7shgw) because NaN cascaded through the σ_K Welford rolling computation. Reverting: - crates/ml-alpha/src/data/loader.rs::mid_price_f32 → blind average - crates/ml-alpha/src/multi_horizon_labels.rs:218 → is_finite only KEEPING: - F1 dir_acc fix in perception.rs:3647 — empirically working (metric hovers ~0.5 instead of pinned sub-chance) cargo check --workspace --all-targets 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.