69141d6266
Implementation plan for the approved MoE spec at
docs/superpowers/specs/2026-04-27-moe-regime-redesign-design.md.
6 phases:
- Phase 0: use_* flag cleanup + count_bonus refactor (precondition,
partially in stash@{0})
- Phase 1: MoE infrastructure (additive — moe_lambda hyperparameter,
9 ISV slots, MoeGate/MoeExpert skeletons, params_buf layout extension)
- Phase 2: 4 new CUDA kernels (mixture forward/backward,
load_balance_loss, expert_util_ema_update) with TDD unit tests
- Phase 3: wire MoE into the training graph (forward, backward, loss
aggregation, ISV producer launch, HEALTH_DIAG aux_moe line)
- Phase 4: atomic deletion of vestigial RegimeConditionalDQN (per
feedback_no_partial_refactor.md)
- Phase 5: Layer 3/5 tests + L40S 30-epoch validation with explicit
kill criteria
Each task has bite-sized steps (2-5 min each) with exact file paths,
copy-pasteable code, exact commands, and TDD pattern (test fails first,
implement to make pass, commit).
Self-review: spec section coverage verified, no placeholders, type
consistency verified across phases.
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.