04fcbd27cf1db9345ecc0274621485548dc00549
Replaces the kernel's hardcoded `alpha` with the shared `pearls_ad_update`
host-side helper. 9 ISV slots retrofit: 8 per-expert util + 1 gate entropy.
- Kernel `moe_expert_util_ema_update` signature: drops `(isv,
isv_util_base, isv_entropy_index, alpha)` for `(scratch_buf,
scratch_idx_util_base=44, scratch_idx_entropy=52)`.
- Single-block single-thread; computes per-expert col_mean in a single
forward pass (collapses prior dual-pass), writes each to scratch slots
[44..52) and Shannon entropy to slot 52.
- 9 ISV slots wired with Pearls A+D: ISV[118..126) (per-expert util,
Wiener offsets 132..156) + ISV[126] (gate entropy, Wiener offset 156).
- Launcher `gpu_moe_head.rs::launch_expert_util_ema`: drops alpha + isv
args; takes scratch_dev_ptr + 2 scratch_idx args.
- Wrapper `GpuDqnTrainer::launch_moe_expert_util_ema(_ema_alpha_unused)`:
sync + Pearls A+D loop over 9 slots via `apply_pearls(scratch_idx,
isv_idx)` closure.
- `debug_assert_eq!(MOE_NUM_EXPERTS, 8)` guards against K changes
silently breaking the contiguous scratch layout.
Behavior: stationary signals converge to the same value at adaptive rate.
The 9 slots stay semantically identical (per-expert col_mean, gate
entropy); HEALTH_DIAG mirror + the adaptive λ controller
`moe_lambda_eff_update` (which reads ISV[MOE_GATE_ENTROPY_EMA_INDEX])
continue to consume them unchanged.
Tests: `sp4_moe_expert_util_ema_writes_step_obs_via_pearl_a_then_converges_pearl_d`
drives kernel with B=128 K=8 perfect-uniform gate → analytical col_mean=1/8,
entropy=ln(8)≈2.0794. Asserts slot values ∈ ±1e-6/1e-5, non-target slots
remain 0; verifies Pearl A bootstrap + Pearl D convergence.
Per `feedback_no_atomicadd.md`,
`feedback_no_htod_htoh_only_mapped_pinned.md`. Build: `cargo check -p ml
--lib --tests --offline` clean (11 pre-existing warnings, no new warnings).
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.
Description
Languages
Rust
88.2%
Cuda
7.7%
Python
1.3%
Shell
1.1%
PLpgSQL
0.8%
Other
0.8%