d63cb7992e
Allocates ISV slots [383..396) for the Aux→Q Wire + Earned Gradient Flow pearl (Layer B of SP14). Mirrors sp13_isv_slots.rs pattern. The plan originally documented [381..394), but Phase 0 verification found SP13 closeout added HOLD_RATE_TARGET_INDEX=381 and HOLD_RATE_OBSERVED_EMA_INDEX=382 after the plan was written, so the range shifts by +2. Slots fall into 4 functional groups: - Q-disagreement EMAs (short, long; K=4↔K=2 mapping with Hold/Flat masked) - Adaptive controllers (k_aux, k_q, β; variance-driven) - Welford variance EMAs (3, one per adaptive scalar) - Schmitt state + α_grad outputs + circuit breaker Plus 14 structural constants for numerical-stability anchors: K_BASE_*, K_MIN, VARIANCE_REF_*, BETA_BASE, BETA_MAX, SCHMITT_BAND, WARMUP_STEPS_FALLBACK, LOCKOUT_*, Q_DISAGREEMENT_BASELINE. Per feedback_isv_for_adaptive_bounds: adaptive bounds (k_*, β, post_open_min, lockout) live in ISV; numerical anchors live as structural constants. Per pearl_first_observation_bootstrap: all EMAs reset to sentinels and Pearl-A bootstraps on first observation. Producer + consumer wiring lands in subsequent tasks (B.2-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.