b8a7ac6f70
User-identified root cause: grad_norm_slow_ema (α=0.001, half-life
~693 steps) persists across fold boundaries while every other
distribution-tracking signal (Adam m/v, target nets, atom positions)
gets reset. Mech 6's upper_bound formula (100 × slow_ema × isv) was
anchored to the WRONG scale during F1 ramp-up, driving the
non-monotonic multiplier-tuning dance across smokes:
- smoke-test-fxvkk (mult=100×): F0=44, F1 NaN @ 3720
- smoke-test-ftdjz (mult=100×, +Mech7): F0=38, F1 collapse @ 2040
- smoke-test-d25vq (mult=5×): F0=21, F1 collapse @ 2820
The multiplier was searching the wrong dimension — the anchor itself
was stale.
Three coordinated changes (per feedback_no_partial_refactor):
1. RESTORE Mech 6 multiplier 5× → 100×. The original 100× was correct
for steady-state; the F1 saturation was driven by anchor staleness,
not multiplier looseness. Tightening it harmed F0 (over-clip during
ramp-up when slow_ema lagged grad_norm_ema).
2. ADD reset_grad_norm_slow_ema method on GpuDqnTrainer. Zeroes the
mapped-pinned scalar. First step of new fold builds up slow_ema
fresh, with MIN_CLIP=1.0 floor active during the brief transient.
3. WIRE Mech 8 call in fused_training.rs::reset_for_fold, alongside
Mech 4's existing Adam resets. Mech 6's anchor now aligns with
the new fold's grad scale from step 1 — same philosophy as Mech 3
(target net hard-sync) and Mech 4 (Adam EMA reset).
Net SP3 design: Mech 6 stays at 100× multiplier (broad, principled
headroom), Mech 8 keeps the anchor honest. The pair is more robust
than either change alone:
- Without Mech 8: anchor is stale, multiplier tuning has no winning
setting (5× hurts F0 ramp, 100× lets F1 saturate at slot 36).
- With Mech 8: anchor is fresh per fold; 100× multiplier provides
legitimate per-step headroom over CURRENT fold's grad norm.
Mech 7 stays reverted (per-element clip was misdiagnosis — over-clipped
legitimate gradient outliers without addressing the saturation root
cause).
F0 risk: low — F0 starts with slow_ema=0 anyway (cold start), so Mech 8
is a no-op on F0. Only changes F1+F2 fold-boundary behavior.
F1+F2 expectation: Mech 6's upper_bound now scales with the CURRENT
fold's grad norm, providing legitimate ~10× headroom per step without
allowing Adam EMA saturation. Slot 36-42 should stay quiet.
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.