jgrusewski 04fcbd27cf feat(sp4): Task A13.2 — retrofit moe_expert_util_ema with Pearls A+D
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>
2026-05-01 02:12:22 +02:00

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
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