f40ccc16a7
Combined spec/quality review caught two minor issues in the Pearl 6 commit. Both are mechanical fixes; no behavior change. 1. Test 12 (pearl_6_kelly_within_fold_ewma_blend) was missing an explicit assertion for slot 282 (TRADE_VAR_SMOOTH_IDX). The test setup initialized tvar_i32 and the launcher passed it through the kernel's parameter slot, but no assert! ever fired against the resulting ISV value. With n_envs=1, the kernel's `(kelly_count > 1) ? variance : 0.0f` branch returns 0 (no cross-env variance possible with 1 env), so EWMA blend yields 0.99 × 0.5 + 0.01 × 0.0 = 0.495. Added the missing assertion to close the within-fold coverage gap for s==2. 2. pearl_6_kelly_kernel.cu:136 doc comment said the slot computes "standard deviation of per-env Kelly fractions" but the code actually computes `ksum_sq / kelly_count` — i.e. the variance (second moment), not the standard deviation. The slot name TRADE_VAR_SMOOTH_INDEX correctly indicates variance; the comment was wrong. Updated comment to match: 'variance of per-env Kelly fractions' with explicit note that this is the second moment, NOT std-dev (no sqrtf applied). 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.