2d0e6b6bdc
Replace static moe_lambda=0.01 with a GPU-driven controller that scales
λ inversely with observed gate-entropy: λ_eff = λ_floor + λ_max_extra ×
max(0, ent_target − ent_ema)/ent_target, where ent_ema is the existing
ISV[126] producer (Phase 2 task 2.4 wire-up).
Motivation: the L40S validation run train-multi-seed-fgg9x exposed
single-expert specialization in fold 1 (expert 4 → 81% util, 7/8
experts < 5%) under static λ=0.01. The load-balance push was too weak
to prevent winner-take-all once the gate had a strong preference.
Adaptive λ rises proportional to the entropy deficit, restoring
diversity pressure when the gate concentrates.
Per pearl_blend_formulas_must_have_permanent_floor: λ_floor remains a
permanent minimum (= old static 0.01) so the controller never weakens
below baseline. Per feedback_isv_for_adaptive_bounds: signal flows
through ISV[128] (new MOE_LAMBDA_EFF_INDEX); consumer kernel reads
the slot at runtime; no DtoH on hot path.
Wiring (feedback_no_partial_refactor):
• new kernel moe_lambda_eff_update (single-block cold-path cadence,
matches kelly_cap_update / cql_alpha_seed_update precedent)
• new ISV slot 128, ISV_TOTAL_DIM 127 → 129
• layout_fingerprint_seed updated (schema_hash bumps; PVC fxcache will
auto-regen on next deploy)
• moe_load_balance_loss kernel takes (isv_signals, isv_lambda_eff_idx)
instead of float lambda — matches mag_concat_qdir's ISV-read pattern
• config: pub moe_lambda field replaced with moe_lambda_floor (0.01),
moe_lambda_max_extra (0.09), moe_entropy_target_frac (0.7)
• state-reset registry entry — ISV[128] resets to floor at fold boundary
• HEALTH_DIAG aux_moe line gains λ_eff field for observability
• hyperopt adapter (DQNHyperparameters) migrated to new knobs
• constructor bootstraps ISV[128]=floor + ISV[118..127)=uniform 1/K +
ISV[126]=ln(K) so cold-start matches legacy byte-for-byte
Smoke validates: magnitude_distribution still passes/fails identically
to HEAD (eq=0.586 same as pre-change eq=0.592 — unrelated Kelly cap
behaviour per project_magnitude_eval_collapse_kelly_capped.md).
HEALTH_DIAG fold 3 confirms controller live: ent decays 1.381 → 0.746
across epochs 7-19, λ_eff rises 0.0146 → 0.0539 monotonically.
Unit test moe_lambda_eff_update_writes_correct_values exercises 5
regimes (above-target / at-target / half-collapse / full-collapse /
deeply-above-target) on the GPU kernel — all pass within 1e-5.
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.