3c035ce1ae
Two coupled fixes to the vol-EMA regime detector exposed by walk-forward CV after all features were promoted to always-on: (1) Bootstrap window for vol_ref (slot 551 = REGIME_VOL_REF_SAMPLES) - Replace the Pearl-A "first observation replaces directly" bootstrap with a running mean over the first N=100 vol_ema observations, then switch to β-tracking. For IID observations the running-mean estimator has variance σ²/N — a 100-sample mean is 10× less noisy than the single-shot replace. (2) Permanent floor on vol_ref (slot 552 = REGIME_VOL_REF_FLOOR) - The bootstrap alone exposed the asymmetric deadband-deadlock: if vol_ref converged to a tiny value during a calm initial stretch, vol_ref / vol_ema fired the moment any realistic vol resumed and Kelly stayed trapped at regime_scale_floor=0.25 forever. Floor lives in ISV slot (TrainingPersist) with a hardcoded 1e-12 sub-floor inside the kernel as numerical-underflow guard. - Host seeds slot 552 with 1e-9. Future controller kernel will refine this from observed cell-level vol minima with cross-fold persistence. Walk-forward CV on Q1 fxcache (3 folds, window=700K, train_frac=0.6): cost fold-A fold-B fold-C mean ± SD 0.00 -19.77 +65.04 (100%) +6.45 +17.24 ± 43.42 Fold B turnaround is the headline: -47.64 (bootstrap-only) → +65.04 (bootstrap + floor) confirms the floor is the load-bearing fix. Cross-fold std-dev compressed 24% at cost=0; mean dropped from +38.94 (pre-defense) to +17.24 (with defense). Classic mean/variance trade. Block extended to 14 slots (539..=552). Kernel sig: vol_ref_floor moved from f32 scalar to vol_ref_floor_index i32, so the anchor is named/addressable in ISV. Co-Authored-By: Claude Opus 4.7 <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.