b791bc8f7f
Phase 1.1 landed sharpe_per_bar_kernel.cu + launch_sp15_sharpe_per_bar
as orphan scaffolding because the val-side sharpe was inline in
backtest_metrics_kernel's 8-metric fusion (lines 208-211, 277), not
a host-side loop the spec sketch had assumed.
This refactor splits sharpe out:
- backtest_metrics_kernel computes 7 metrics now (sortino, win_rate,
max_dd, calmar, omega, VaR, CVaR; remaining counters unchanged).
Output stride drops 14 -> 13; shmem 6 -> 5 reduction arrays.
- gpu_backtest_evaluator calls launch_sp15_sharpe_per_bar against
the same GPU-resident per-bar returns buffer, once per window
(kernel is single-block by design; n_windows is small).
- Annualization moves host-side: WindowMetrics.sharpe =
raw_sharpe * annualization_factor.
Atomic per feedback_no_partial_refactor: kernel split + offset
rebase (every metric below sharpe shifted down by 1) + the lone
WindowMetrics.sharpe consumer (consume_metrics_after_event)
migrated in one commit. No parallel paths.
Output value of WindowMetrics.sharpe is preserved (verified to
1e-5 relative error against f64 closed-form via new oracle test
unified_sharpe_kernel_equivalence_under_annualization). All
existing Phase 1.1 oracle tests still pass; ml lib test suite
holds at the 945/13 baseline (no new regressions).
Eliminates the Phase 1.1 orphan launcher per feedback_wire_everything_up.
Sets up Phase 1.2.b cost-net sharpe to also use launch_sp15_cost_net_sharpe
on the cost-net returns buffer (separate task, separate commit).
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.