25eba79ad5
reward_subsystem_controller_kernel: 5 canaries → 10 outputs, true Z-score (delta_ema/sqrt(var_ema)), sigmoid blending, weight renormalization to Σ=1, saboteur post-clamp, curiosity permanent floor (0.2 × bound). Pearls A+D chained on outputs per spec §3.4.1. novelty_simhash_kernel: 42×16 random projection → 16-bit SimHash code, 1M-slot bucket count table for novelty signal `1/sqrt(1+count)`. Race- tolerated update per feedback_no_atomicadd (under-counts bias novelty UPWARD — safe direction). novelty_simhash_proj_init_kernel: Philox-seeded GPU init for the projection matrix (CPU is read-only per feedback_no_cpu_forwards). HEALTH_DIAG `sp11_reward` line emits 10 outputs + improvement_z each epoch. Reset registry: novelty hash table reset arm wired (closes the A0 deferral); projection matrix is frozen at trainer init for run lifetime, not reset. All 20 SP11 slots populate every step. No consumer reads them yet — training behavior unchanged from A1. 3 new GPU oracle tests pass on RTX 3050 Ti (controller midpoint, weight renorm, saboteur clamp). Spec: docs/superpowers/specs/2026-05-04-sp11-reward-as-controlled-subsystem.md §3.4 §3.5.2 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.