84de278dfe
Each EGF pearl EMA / state slot resets to its Pearl-A sentinel at fold
boundary, mirroring sp13_aux_dir_acc_short_ema / long_ema entries.
Atomic refactor (feedback_no_partial_refactor): both halves land
together — registry entry + reset_named_state dispatch arm.
Reset slots (11 total, sentinel in parens):
- Q_DISAGREEMENT_SHORT/LONG_EMA (slots 383, 384) → 0.5
- K_AUX_ADAPTIVE (385) → K_BASE_AUX = 20.0
- K_Q_ADAPTIVE (386) → K_BASE_Q = 15.0
- BETA_RATE_LIMITER_ADAPTIVE (387) → BETA_BASE = 0.5
- AUX_DIR_ACC_VARIANCE_EMA, Q_DISAGREEMENT_VARIANCE_EMA,
ALPHA_GRAD_RAW_VARIANCE_EMA (388, 389, 390) → 0.0
(initial k = k_base, β = β_base via ISV-driven controllers)
- GATE1_OPEN_STATE (391) → 0.0 (closed)
- ALPHA_GRAD_SMOOTHED (393) → 0.0
- AUX_DIR_ACC_POST_OPEN_MIN (394) → 1.0 (no min observed)
ALPHA_GRAD_RAW (slot 392, recomputed every step from variance EMAs)
and GRADIENT_HACK_LOCKOUT_REMAINING (slot 395, decays at epoch
boundary) are NOT in the fold-reset registry; both naturally
re-initialise without explicit reset.
Also corrects the isv-slots.md SP14 table: slots 392 and 395 were
incorrectly marked FoldReset in the B.1 entry; corrected to reflect
their actual reset semantics (NOT reset / epoch-boundary decay).
Producer + consumer wiring lands in subsequent tasks (B.3-B.12);
this commit is additive infrastructure only — no behavior change.
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.