jgrusewski 138d41b761 refactor(ml): drop 8 leaf sub-crates from trading + backtesting services
Two-part fix that finally removes ~35% of ml-* sub-crates from
service-binary build closures:

1. crates/ml/Cargo.toml: 8 leaf-level ml-* sub-crates become optional
   behind feature flags (one feature per `pub mod` in lib.rs):
     ml-backtesting       ↔ feature `backtest-mod`        (ml::backtesting)
     ml-paper-trading     ↔ feature `paper-trading-mod`   (ml::paper_trading)
     ml-stress-testing    ↔ feature `stress-testing-mod`  (ml::stress_testing)
     ml-explainability    ↔ feature `explainability-mod`  (ml::explainability)
     ml-universe          ↔ feature `universe-mod`        (ml::universe)
     ml-regime-detection  ↔ feature `regime-detection-mod`(ml::regime_detection)
     ml-validation        ↔ feature `validation-mod`      (ml::validation)
     ml-data-validation   ↔ feature `data-validation-mod` (ml::data_validation)
   Aggregated into the umbrella `full-stack` feature, which is in
   `default = [...]` so existing `ml.workspace = true` callers see
   identical behavior (testing/integration, crates/backtesting).

2. services/trading_service + services/backtesting_service: switched
   from `ml.workspace = true` to explicit `path = "../../crates/ml"`
   form with `default-features = false`. This is necessary because
   cargo 1.89 silently ignores `default-features = false` when
   combined with `workspace = true` (a known cargo limitation).
   Path form bypasses the workspace dep and applies the opt-out.

Verified by `cargo tree -p X | grep ml-* | wc -l`:
  trading-service       23 → 16  (-30%)
  backtesting-service   23 → 16  (-30%)

Source-grep verified neither service references any of the 8 gated
modules (`use ml::backtesting`, `use ml::explainability`, etc. all
zero hits in src/). The only `ml::explainability` mention in
trading-service is a string in an error log — not a code path.

ml-training-service and trading-agent-service were already on path
form with default-features = false; they're unaffected here.

cargo check --workspace passes.
2026-05-01 01:45:18 +02:00

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.

Description
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