Files

jgrusewski b70c6a0bd7 refactor: rewire all service build.rs to proto/ root directory

Delete per-service proto/ directories. All 7 services now compile
from the single canonical proto/ at workspace root.

- trading_service: 5 protos + ml_training client -> ../../proto/
- ml_training_service: ml_training server + fxt_trading client -> ../../proto/
- broker_gateway_service: broker_gateway -> ../../proto/
- trading_agent_service: trading_agent + ml client -> ../../proto/
- data_acquisition_service: data_acquisition -> ../../proto/
- api_gateway: 8 proto compilations -> ../../proto/
- monitoring_service: keeps local proto (3 training RPCs only, deleted in Task 5)

Added proto/fxt_trading.proto (fat-client proto, package foxhunt.tli)
separate from proto/trading.proto (backend, package trading) since the
API gateway needs both for protocol translation.

Added unimplemented stubs for merged monitoring.proto RPCs:
- trading_service: 3 training RPCs (served by monitoring_service)
- api_gateway monitoring_proxy: 13 system health RPCs (Task 4)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-03-03 20:34:09 +01:00

ml/config

🚀 Wave 9: TFT INT8 Quantization Complete (20 Agents, TDD)

2025-10-15 21:38:04 +02:00

src

fix(fxt,services): remove mock fallbacks and add gRPC health checks

2026-03-03 14:14:51 +01:00

tests

cleanup: delete 92 scattered .md files and .serena artifacts

2026-03-01 22:42:36 +01:00

build.rs

refactor: rewire all service build.rs to proto/ root directory

2026-03-03 20:34:09 +01:00

Cargo.toml

fix(fxt,services): remove mock fallbacks and add gRPC health checks

2026-03-03 14:14:51 +01:00

Dockerfile

fix: comprehensive audit — real brokers, deployment fixes, production safety

2026-02-25 00:32:10 +01:00

README.md

docs: create/update README.md for all services, CLI, and testing crates

2026-03-01 22:50:38 +01:00

requirements-tuner.txt

🎯 Wave 153: ML Hyperparameter Tuning - Production Ready & Validated

2025-10-13 16:10:55 +02:00

tuning_config_optimized.yaml

🚀 Wave 160 Phase 2: ML Training Infrastructure + TLOB Investigation

2025-10-14 10:42:56 +02:00

tuning_config.yaml

🚀 Wave 160 Phase 5: Complete ML Ensemble + Production Deployment (27 Agents)

2025-10-14 18:41:48 +02:00

README.md

ml_training_service

Model training orchestration and lifecycle management for DQN, PPO, TFT, Mamba2, TLOB, and Liquid models with progress tracking and artifact storage.

Key Types

MlTrainingServiceImpl -- main gRPC service
JobTracker -- training job state machine
CheckpointManager -- model artifact persistence

Features

minimal (default) -- minimal ML feature set for financial models
gpu -- SIMD GPU acceleration (requires CUDA)
mock-data -- mock training data (testing, bypasses database)

Configuration

GRPC_PORT -- gRPC listen port
DATABASE_URL -- PostgreSQL for job metadata and training history
Prometheus metrics on port 9094

Testing

SQLX_OFFLINE=true cargo test -p ml_training_service --lib