617eb61aab
First smoke at ff98edc77 produced NaN in:
- q_by_action (dir Qs from q_out_buf)
- a_mag (mag SGEMM forward output)
- v_share_traj + grad_decomp_pinned (all-branch backward)
- WR = 43.40% (below ~46% baseline)
Key clue: a_dir = -0.0029 finite but q_by_action NaN. Raw dir logits
fine, expected Q (with atom-shift) NaN. So atom-shift produces NaN
from finite inputs. Either W or state_121 contains NaN.
This diagnostic zeroes both:
- aux_w_prior_init: W [-0.5, 0, +0.5, 0] -> [0, 0, 0, 0]
- beta scale prior: 0.5 -> 0.0
With W=0 + beta=0, atom-shift and beta are runtime no-ops. All wiring
remains intact (kernels loaded, scratch populated, dW/Adam launched).
Diagnostic outcomes:
- Clean smoke -> bug was W magnitude x downstream interaction. Restore
W at smaller magnitude.
- Still NaN -> bug is in Step 8/11 backward logic. Investigate
c51_aux_dw_kernel + adam_w_aux interaction.
Cargo check clean. Ready for diagnostic smoke.
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.