d9a4d98a3d
Smoke smoke-test-fxvkk (commit 48c25d999, Mech 6 anchored clip) F1-NaN'd
at step 3720 with slots 36-38, 40-42 still firing — Adam EMAs saturated
despite Mech 6 capping the global clip threshold.
Diagnosis: Mech 6 bounds the AGGREGATE L2 gradient norm, but Adam m/v
EMAs are PER-ELEMENT. A gradient with one large element (e.g.,
element_X = 100, rest small) has L2 norm ≈ 100, passing a clip
threshold of 1000 untouched. Adam m_X EMA accumulates the large
element; β1=0.9 steady-state gives m_X ≈ 1000, exceeding slot 36
threshold (100 × isv).
Fix: in dqn_adam_update_kernel, after the global L2 clip is applied,
clip EACH gradient element to ±per_element_cap where:
per_element_cap = 10 × sqrt(adaptive_clip / total_params)
Rationale:
- sqrt(adaptive_clip / N) is the average per-element contribution to
the L2 norm budget
- 10× allows legitimate per-element deviations up to 10× average
- Scales with adaptive_clip — when global clip is doing its job
(Mech 6), per_element_cap is tight enough to prevent saturation
- When global clip is loose (post-fold warmup), per_element_cap
scales with it — never tighter than the global clip's intent
Together with Mech 6, prevents Adam saturation at both the aggregate
(L2 norm) and per-element levels. Closes the residual pathology after
Mech 6's partial 3060→3720 improvement.
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.