6dcaf1a1c9
Foundation commit for SP5 per-branch + per-group adaptation layer.
Allocates slot ranges 174..286 with 6 cross-fold-persistent Kelly
slots at 280..286 (NOT in fold-reset registry per spec). Intentional
2-slot gap at 278..280 makes the cross-fold carve-out structurally
visible.
Slot layout:
174..226 Per-branch (Pearls 1, 2, 3 + Q-var shared signal)
226..250 Per-group Adam β1/β2/ε (Pearl 4)
250..270 Per-branch IQN τ schedule (Pearl 5)
270..274 Per-direction trail distance (Pearl 8)
274..278 Per-branch num_atoms (Pearl 1-ext)
280..286 Cross-fold-persistent Kelly (Pearl 6)
LAYOUT_FINGERPRINT_SEED bumped — existing checkpoints fail-fast.
ISV_TOTAL_DIM 173 → 286.
No producer/consumer wired yet. Layer A commits A1-A8 populate slots
in per-pearl commits in dependency order:
Pearl 1 → Pearl 3 → Pearl 2 → Pearl 4 → Pearl 5 → Pearl 6 → Pearl 8 → Pearl 1-ext
Refs: docs/superpowers/specs/2026-05-01-sp5-magnitude-differentiation-and-eval-collapse-design.md (HEAD 6e6e0fa11)
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.