86f7f1fa76
Codebase audit identified 23 findings across 4 dimensions (production safety, code health, deployment readiness, test quality). This commit fixes all of them. Broker execution layer (was entirely stubbed): - Real IBKR TWS client via ibapi crate (950+ lines, feature-gated) - ICMarkets ctrader-openapi now always-on (removed feature flag) - Real broker routing with health monitoring and exponential backoff reconnect - Validated against live IB Gateway Docker (6/6 connectivity tests pass) Deployment blockers: - Fixed 6 broken Dockerfiles (removed COPY foxhunt-deploy) - Created foxhunt K8s namespace, secret templates, migration job - Added liveness probes to all 7 K8s services - IB Gateway manifest (ghcr.io/gnzsnz/ib-gateway:stable) - IBKR credentials in Scaleway Secret Manager via Terragrunt - Fixed port collisions and mismatches across services Production safety (9 critical + 6 high/medium fixes): - Asset-class-specific VaR volatility (not flat 2%) - Real parametric VaR with z-score 95th percentile - Kyle's lambda regression (100-bar rolling window) - Per-feature running statistics from historical data - VWAP-based slippage reference, regime duration tracking - Real Databento JSON parsing for OHLCV/Trade/Quote Code health: - Removed #![allow(dead_code)] from ml, data, config - Fixed log:: → tracing:: in 4 production files - Removed dead workspace deps (ratatui, crossterm) Verified: cargo check --workspace (0 errors), trading_engine 330 tests pass. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
ml_training_service
Model training orchestration and lifecycle management for the Foxhunt HFT trading system. Manages training jobs for DQN, PPO, TFT, Mamba2, TLOB, and Liquid models with progress tracking, resource allocation, and model artifact storage.
Building
# Default (minimal features)
cargo build --release -p ml_training_service
# With GPU acceleration (requires CUDA)
cargo build --release -p ml_training_service --features gpu
# With mock training data (testing only, bypasses database)
cargo build --release -p ml_training_service --features mock-data
Features
| Feature | Default | Description |
|---|---|---|
minimal |
Yes | Minimal ML feature set for financial models |
gpu |
No | SIMD GPU acceleration (requires CUDA) |
debug |
No | Additional debug logging |
mock-data |
No | Use mock training data instead of PostgreSQL |
Configuration
The gRPC listen port is set via the GRPC_PORT environment variable. Prometheus metrics are exposed on port 9094.
PostgreSQL (via sqlx) is used for job metadata, training history, and state management. Set the connection string with DATABASE_URL.
Running
GRPC_PORT=50053 DATABASE_URL="postgresql://user:pass@localhost:5432/foxhunt_training" \
./target/release/ml_training_service serve
Testing
# Unit tests (offline, no database required)
SQLX_OFFLINE=true cargo test -p ml_training_service --lib
# Integration tests (requires running PostgreSQL)
cargo test -p ml_training_service