cc96dd7fd3
Root cause of L40S segfault (train-tgdlq workflow, commit0d639dfe9): 1. F4 (IB) and F5 (barrier) gradient kernels indexed cql_d_adv_logits and on_b_logits_buf with stride b0_size (4) instead of total_actions (13). The buffer is sized [B, total_actions, num_atoms]. Writes for batch i>0 landed in other actions'/batches' gradient slots — corrupting CQL gradient for every batch beyond the first. After cuBLAS backward, weights diverged in undefined ways; validation forward then segfaulted reading NaN-laced parameters in GpuBacktestEvaluator init. Fix: add `total_actions` kernel parameter (int), use it as the full stride for adv_row and d_adv_a pointer arithmetic; keep b0_size as the loop bound (direction-branch-only update). All three launch sites updated: inlined F5 launch in apply_cql_gradient, inlined F4 launch alongside, and the standalone inject_barrier_into_cql_d_logits method. 2. D6 ensemble oracle fired at epoch 0 because per-branch Q-gaps are all 0 at random init (range = 0 → score = 1.0 → plasticity trigger). Shrink-and-perturb ran immediately, then again next epoch, etc. Gate behind `learning_health.epoch > 5` (3 warmup + 2 buffer epochs for Q to move) so the oracle only fires on post-warmup real collapse, not untrained networks. Both bugs are regressions from today's work — F4/F5 introduced yesterday, D6 became live after the ens_disagreement real-signal fix ind9d35b6fa. 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.