250cee124e
Smoke smoke-test-xw4c6 showed F1 ep2 grad_norm=8.4B polluting the
adaptive_clip EMA -> next adaptive_clip threshold became huge ->
subsequent extreme grads passed unclipped -> NaN propagation -> grad
collapse -> fold 1 fails despite K+warmup fixing the first-step problem.
Add Winsorized clamp on raw_grad_norm before the adaptive_clip EMA
update: clamped_sample = min(raw, K * previous_adaptive_clip) where
K = 100 (numerical-stability bound, not a tuned constant — explicit
"single sample can grow EMA at most 100x in one step" semantics).
Companion observability log emits GRAD_CLIP_OUTLIER warning per clamp
event so we can see when this fires in subsequent smokes.
Fast/slow grad_norm EMAs (driving warmup factor) intentionally NOT
winsorized — they're a stability signal that SHOULD respond to
outliers, providing extra warmup damping when the system is unstable.
Predicted impact on Plan C smoke F1 ep2: 8.4B grad -> clamped to
~3000 for EMA -> next adaptive_clip caps at ~1000 -> subsequent F1
batches get bounded gradients passed to Adam -> no NaN propagation.
F1+F2 should now train through, exposing whether the underlying
Q-target inflation requires further structural work or whether
clipping alone suffices.
Composes with K+warmup (4ef1d8ebb), A.2, A.1/A.3, F+H — fourth layer
of the adaptive gradient-stability scaffolding (now five total:
A.2 target-sync drift dampening, K+warmup post-reset transient
dampening, N persistent clip-EMA outlier defense).
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.