28c707f6ab
Gate (4 tensors, [127..131)) + 8 experts × 4 tensors (32 tensors, [131..163)) appended to GpuDqnTrainer params_buf layout. NUM_WEIGHT_TENSORS 127 → 163. Layout fingerprint hash recomputes — old checkpoints fail to load with fingerprint-mismatch error per the no-fallback contract in the spec. Gate tensors: gate_w1[STATE_DIM=128,64], gate_b1[64], gate_w2[64,8], gate_b2[8]. Zero-init so g(s) = uniform 1/K at cold start. Expert tensors (per expert k∈[0,8)): w1[SH2,BTN], b1[BTN], w2[BTN,SH2], b2[SH2] where SH2=cfg.shared_h2=256, BTN=MOE_EXPERT_BOTTLENECK=64. Xavier init on w1/w2; zero on biases. Total ~268k new params. Adam state (m_buf/v_buf), gradient scratch, target_params_buf all extend in lockstep — all sized from compute_total_params() which sums over the full 163-tensor layout. No static sizes to update. Tensors allocated but not yet wired into forward/backward — Phase 3 wires gate forward, expert forward, mixture replacement of h_s2, and the corresponding backward chain. No test assert updates required — no test hardcodes NUM_WEIGHT_TENSORS or the layout fingerprint value. Spec: docs/superpowers/specs/2026-04-27-moe-regime-redesign-design.md §6.1. 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.