a52d996135
Phase 3 of the MoE regime redesign per
docs/superpowers/specs/2026-04-27-moe-regime-redesign-design.md.
Atomic forward+backward wire-up — replaces the existing h_s2 producer
in fused_training.rs with the gated expert mixture:
state[B, 128] -> shared GRN trunk -> h_s1[B, 256]
|
+-> 8 expert MLPs (256->64->256) -> expert_outputs[8, B, 256]
+-> gate (128->64->8 softmax) ----> gate[B, 8]
|
moe_mixture_forward -> h_s2[B, 256] -> branching heads + C51 + IQN
Backward: moe_mixture_backward (de_k = g · dh_s2) + moe_dgate_reduce
(dg = Σ_c e_k · dh_s2) + load-balance aux gradient + cuBLAS SGEMM
backward through gate + each expert's 2 linear layers. Adam optimizer
step now updates gate + 8 experts via params_buf.
Loss: λ · K · Σ_k (mean_b g[b,k])² added to total loss with λ from
hyperparams.moe_lambda (default 0.01).
Per-step ISV producer launch (moe_expert_util_ema_update) writes 8
utilization EMA + 1 gate-entropy EMA into ISV[118..127). Per-epoch
HEALTH_DIAG aux_moe line emits utilization vector + entropy live so
operators can see whether experts are differentiating or collapsing.
Smoke test: DONE. 3/3 folds, all checkpoints saved, 728s (12.1 min,
within 25-min budget). Gate differentiated by epoch 1: expert 2 rose
from 0.119 → 0.286 → 0.323 over fold 1-2 while others remained at
0.097-0.113. Gate entropy 1.611 at fold 2 epoch 4 < ln(8)=2.079.
val_loss finite across all 3 folds; average fold metric 22.4.
Per feedback_no_partial_refactor.md: all consumers of the h_s2 contract
(branching heads, IQN aux, attention focus, backward chain) migrate in
this single commit.
Per feedback_no_htod_htoh_only_mapped_pinned.md: no new HtoD/HtoH
introduced; gate softmax + expert outputs + mixture all GPU-resident,
ISV producer GPU-driven. Load-balance scalar uses cuMemAllocHost +
cuMemHostGetDevicePointer_v2 (mapped pinned), matching existing pattern.
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.