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QuantX Project Roadmap

This document outlines the current completion status and future development milestones for the QuantX Quantitative Trading and Portfolio Optimization System.

🗺️ High-Level Development Status

gantt
    title QuantX Project Development Timeline
    dateFormat  YYYY-MM-DD
    section Phase 1: Core Architecture
    Microservices & Database Setup :done, 2026-06-01, 14d
    Kafka & Redis Pipelines        :done, 2026-06-15, 7d
    section Phase 2: Quantitative Execution
    Forecasting & RL Models        :done, 2026-06-22, 10d
    MVO & Risk Parity Engines       :done, 2026-06-25, 7d
    Risk Management & VaR Checks    :done, 2026-06-28, 4d
    section Phase 3: Dashboard & Developer Experience
    Next.js UI Terminal            :done, 2026-06-29, 3d
    Docker Integration & Tests     :done, 2026-07-01, 2d
    section Phase 4: MLOps & Drift
    Model Registry & Lineage       :active, 2026-07-02, 10d
    Automated Retrain Pipelines    : 14d
    section Phase 5: Production & Scale
    PostgreSQL & Kafka Clusters    : 10d
    Kubernetes & Orchestration     : 7d
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✅ Completed Tasks

Phase 1: Core Service Architecture & Messaging

  • Multi-Service Framework: Created modular service boundaries for api-gateway, market-data-service, feature-service, signal-service, and portfolio-service.
  • Event Streaming: Established Kafka topics (market.raw.ohlcv, market.features, market.signals, and portfolio.trades) for real-time async communication.
  • Feature Store: Set up Redis caches to serve as a low-latency feature store.
  • Database Schema: Designed a robust schema supporting portfolios, positions, trades, prices, technical features, predictions, and historical risk metrics.

Phase 2: Quantitative Trading & Risk Management

  • Statistical Forecasting: Implemented PyTorch models including LSTM, GRU, and Transformer sequence forecasters with temporal splits.
  • Reinforcement Learning: Configured a Gymnasium-compatible TradingEnvironment supporting PPO, DQN, and A2C training via stable-baselines3.
  • Portfolio Optimization: Built mathematical models for Mean-Variance Optimization (MVO), Risk Parity (Marlim), and Black-Litterman portfolio rebalancing.
  • Risk Shielding: Created a RiskManager module to compute portfolio-wide 95% VaR / CVaR and reject trades exceeding exposure/leverage limits.
  • Backtest Simulator: Developed an event-driven backtesting engine to simulate historical strategy performance, CAGR, Sharpe/Sortino ratios, and drawdowns.

Phase 3: Dashboard Terminal & Developer Experience

  • Next.js Web Console: Created a responsive financial terminal displaying real-time price tick charts, signal feeds, active holdings, and optimization simulations.
  • Bug Fixes:
    • Resolved Next.js compile errors caused by a syntax mismatch in handleRunBacktest.
    • Integrated portfolio-service into docker-compose.yml to resolve internal container routing failures.
  • DB Populator: Built and ran populate_db.py to seed SQLite fallbacks with mock assets, 120-day historical prices, active trades, and positions.
  • Developer Guide: Completed local_dev_guide.md specifying step-by-step instructions for launching services in offline SQLite mode.
  • Testing: Wrote unit tests for risk management, rebalancing, indicators, and feature drift, achieving 100% passing tests (27/27).

📋 Remaining & Future Tasks

Phase 4: MLOps Pipelines & Model Drift (Target: Q3 2026)

  • Model Registry Setup: Deploy MLflow or a similar registry to track model metrics, hyperparameter training runs, and version binary model weights.
  • Automated Retraining Orchestrator: Implement Airflow/Composer DAGs to trigger model retraining automatically when data drift is detected.
  • Real-time Drift Alerter: Enhance the feature-monitor service to send Slack/PagerDuty webhooks when feature values diverge significantly from baseline z-scores.

Phase 5: Production Deployment & Scale Hardening (Target: Q4 2026)

  • PostgreSQL Migration: Migrate from SQLite to PostgreSQL for production execution using the compiled schema.sql.
  • Kubernetes Orchestration: Write Helm charts and deployment manifests to deploy microservices on Google Kubernetes Engine (GKE) or AWS EKS.
  • Service Mesh & Security: Configure Istio or Linkerd to handle TLS encryption, mTLS between services, and request rate limiting.
  • Monitoring & Alerting Dashboards: Finalize Prometheus metric scraping endpoints and build detailed Grafana templates to monitor service health, memory usage, and API latency.

Phase 6: Live Trading & Broker Integrations (Target: Q1 2027)

  • Alpaca Live Account Integration: Complete integration tests for live account endpoints, including support for real-time WebSocket stream connections for order updates.
  • Options and Forex Support: Expand the market-data-service to parse Options chains and Forex feeds.
  • Distributed Backtesting: Utilize Ray or Celery tasks to parallelize backtests across multiple CPU cores for faster hyperparameter sweeps.

Tip

To launch the application locally in offline/SQLite fallback mode, run populate_db.py and then refer to the startup instructions in local_dev_guide.md.