814bc1fe4e
Branch worktree-agent-a510b4c9, commit 6cb2257163. Caps any branch's
weight-gradient L2 norm at `num_branches × median(branch_norms)` via a
new single-pass reduce+rescale CUDA kernel, wired into both the
ungraphed fallback and the `adam_grad_child` graph capture path.
Motivation: L40S train-mdh86 (terminated at epoch 20 after Sharpe
regression) showed grad_ratio_mag_dir ∈ {14793, 11934, 12858, 16406,
15141} for the first five epochs, then collapsed to 111× in a single
step at epoch 7 and destabilised learning for the remaining epochs.
HEALTH_DIAG forensics at /tmp/l40s_diag/health.log confirmed the
imbalance as the probable trigger.
Post-fix grad_ratio_mag_dir on the equivalent local smoke: 55, 78, 35,
11, 10 — a 268–1503× reduction in ratio magnitude.
Architectural rule (no tuned knobs, per feedback_adaptive_not_tuned.md):
cap = num_branches × median(branch_norms)
num_branches=4 is the factored-action axis count (architectural)
median is a per-step statistical reference (adaptive)
product is fully signal-driven
Smokes (local RTX 3050 Ti):
magnitude_distribution: PASS — EVAL_DIST Q=0.153 H=0.255 F=0.592,
3 internal folds Sharpe +16.7 / +38.8 / +49.1
multi_fold_convergence: PASS — 3/3 folds Sharpe +57.8 / +55.6 / +119.3
Expected to resolve the L40S regression when validated with the next
argo-train.sh run.
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.