e140392f86
Adds 6 output slots to compute_backtest_metrics_kernel — per-bucket
(win_rate, trade_count) for the {Trending, Ranging, Volatile} regime
split — so the val backtest surfaces whether the long-running ~46%
aggregate WR hides regime-conditional edge. Trades are bucketed at
trade-OPEN by feature[40] (ADX-norm) per the structural thresholds
(T:ADX>0.4, R:ADX<0.2, V:otherwise), mirroring
gpu_walk_forward.rs::classify_regime_from_features. Block tree-reduce
only per feedback_no_atomicadd. Observability-only emission via new
HEALTH_DIAG[N]: val_regime [wr_T=... n_T=... wr_R=... n_R=... wr_V=...
n_V=...] line; thresholds remain kernel constants per
feedback_isv_for_adaptive_bounds (no controller consumer yet).
Implementation atomic (kernel + launcher + WindowMetrics + HEALTH_DIAG
emit + 3 GPU oracle tests + audit doc):
- backtest_metrics_kernel.cu: per-thread per-regime trade counters,
2-slot boundary buffer extension carrying open-bar regime through
block stitch, output stride 13 → 19, shmem 5 → 11 reduction tiles
- gpu_backtest_evaluator.rs: WindowMetrics +6 fields, metrics_buf
size 13 → 19, launcher passes features_buf + feature_dim, consume
populates per-regime fields
- metrics.rs: val_regime HEALTH_DIAG line in consume_validation_loss
- regime_wr_oracle_tests.rs (NEW): 1 CPU sanity + 3 GPU oracle tests
(bit-exact match ε=1e-5 vs CPU oracle on stratified 30/40/30 batch)
Validation: cargo check --workspace clean; 17/17 gpu_backtest_evaluator
unit tests pass; 1+3/4 regime WR tests pass on local RTX 3050 Ti
(1.89s); audit_sp18_consumers.sh --check exit 0.
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.