d49003de6d
Closes the TrainingPersist loop for the regime defense per the KELLY_F_SMOOTH precedent (pearl_kelly_cap_signal_driven_floors). Three layers: (1) Per-step: alpha_regime_vol_update_kernel tracks min(vol_obs > 1e-12) in new slot 553 (REGIME_VOL_OBS_MIN_INDEX). Filtered against artifact- zero observations from stationary snapshots where curr == prev. (2) Per-cell: stacker_threshold_controller_update gains a fifth branch that reads vol_ref (slot 550) at cell-end, computes `target = floor_target_ratio × vol_ref`, slow-EMAs the floor anchor (slot 552) toward the target with rate `floor_update_rate` (0.1 per cell). Subfloor 1e-12 inside the kernel guards against the anchor collapsing to zero. (3) Per-invocation: alpha_baseline reads `config/ml/alpha_baseline_state.json` at startup and seeds slot 552 from the `regime_vol_ref_floor` field. At end of main(), the learned floor is written back via tmp+rename atomic write so concurrent walk-forward invocations see a consistent file. Matches the cross-fold-persistent shape of KELLY_F_SMOOTH. Block extended to 15 slots (539..=553). Smoke + kernel unit test pass -1/-1 for the new floor-controller indices (backward compat). Walk-forward CV verdict (Q1 fxcache, 3 sequential folds): iteration fold-A fold-B fold-C mean ± SD pre-defense (no regime) +91.52 -21.44 +46.74 +38.94 ± 56.88 hardcoded 1e-9 floor -19.77 +65.04 +6.45 +17.24 ± 43.42 learned floor (0.5 × cell_min) +74.78 -12.72 +8.80 +23.62 ± 45.59 learned floor (0.1 × vol_ref) -19.53 -26.51 +15.46 -10.20 ± 22.49 Controller infrastructure is structurally correct (loop closes, floor persists across invocations, kernel + disk + ISV all roundtrip). The TUNING is data-dependent — single-quarter CV doesn't have enough regime diversity to anchor the floor against. Multi-quarter fxcache validation is the next step (built cluster-side on the 9-quarter 2024-Q1..2026-Q1 ES futures dataset, downloaded as a single artifact for local CV). 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.