129b7fd3d0
Augments the existing DIAG_BUG2 one-shot block with two narrower scans
that disambiguate where state[0] heavy-tail (mean=6e-3, std=570,
mean_abs=20 over 3200 samples on smoke-test-xb78r) actually originates.
H1 (data-source corruption): scan features_buf.host_ptr for bars where
|feat[bar*42 + 0]| > 100. host_ptr aliases the same physical memory
the kernel reads (mapped-pinned) — direct read of the data
state_gather sees. Reports up to 10 outlier bar indices + mean/std/
max_abs over up to 2M bars.
H2 (kernel/gather bug): scan gpu_batch.states/next_states (already
DtoH-downloaded) for sample indices where |state[i*sd + 0]| > 100.
Reports up to 10 outlier sample indices.
Verdict line logged: feat-outliers nonempty → H1; state-outliers
nonempty AND feat-outliers empty → H2.
Context: smoke-test-xb78r at HEAD cba9f25ed went through DBN fallback
(train_baseline_rl.rs:611, "Loaded N bars (ts to ts)" message) — no
fxcache file exists on training-data PVC. DBN-fallback applies z-score
normalization (line 633) which has small stddev for log-return columns;
a single bar with bar.open ≈ 0 produces ln(ratio) = -30 → normalized
= -30000. Few bars in 700K could carry this signature.
Triggers once via static AtomicBool. Pre-graph-capture, no perf impact.
Cleanup gate: remove DIAG_BUG2 block once Bug 2 root cause is fixed
(likely a tighter clamp inside safe_log_return for log-return columns).
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.