Project Roadmap: Multi-Asset VWAP Trend-Following Strategy with Portfolio Optimization

1. Project Scope of Work

1.1 Project Overview

This project aims to develop a multi-asset trend-following strategy using VWAP (Volume Weighted Average Price) as a core indicator. The strategy will incorporate Supertrend for signal confirmation and leverage machine learning for portfolio optimization. The final model will be backtested and evaluated based on performance metrics such as Sharpe Ratio, Sortino Ratio, and Maximum Drawdown.

1.2 Objectives

• Develop a VWAP-based trend-following strategy for multiple crypto assets.
• Generate trade signals using VWAP crossovers and confirm with Supertrend.
• Optimize portfolio allocation using machine learning models (e.g., Random Forest, Gradient Boosting).
• Perform backtesting to assess the strategy's effectiveness compared to individual asset strategies.
• Evaluate performance metrics to ensure risk-adjusted returns.

2. Technical Requirements

2.1 Tools & Libraries

• Data Collection & Processing: Alpaca API, pandas, NumPy
• Technical Indicators: ta (Technical Analysis Library), finta
• Machine Learning: scikit-learn, XGBoost, LightGBM
• Backtesting & Evaluation: Backtrader, zipline, QuantConnect
• Visualization: Matplotlib, seaborn, Plotly
• Environment: Jupyter Notebook / VS Code / Google Colab

2.2 Environment

• Development: Python 3.10+
• Trading Data Source: Alpaca API (Paper Trading)
• Computational Resources: Local machine or Google Colab
• Version Control: GitHub repository

3. Workflow & Timeline

The project will be divided into 5 phases, with each phase spanning one week.

Phase 1: Data Collection & Preparation (Week 1)

• Collect historical price and volume data for selected crypto assets using Alpaca API.
• Compute VWAP for each asset.
• Preprocess data: handling missing values, normalization, and feature engineering.
• Define the data structure for modeling.

Phase 2: Signal Generation (Week 2)

• Develop VWAP-based trade signals (buy/sell signals based on VWAP crossovers).
• Integrate Supertrend for signal confirmation.
• Validate signals using exploratory data analysis (EDA).
• Visualize trade signals for different assets.

Phase 3: Portfolio Optimization with Machine Learning (Week 3)

• Define risk metrics (volatility, drawdown, Sharpe Ratio, correlation matrix).
• Train ML models (Random Forest, XGBoost, LightGBM) to allocate portfolio weights based on:
  • Signal strength
  • Risk-adjusted return
  • Asset correlations
• Optimize portfolio allocation and compare different models.

Phase 4: Backtesting & Performance Evaluation (Week 4)

• Implement backtesting framework using Backtrader or QuantConnect.
• Compare the multi-asset strategy vs. individual asset strategies.
• Evaluate performance using:
  • Sharpe Ratio
  • Sortino Ratio
  • Max Drawdown
  • Profit Factor
• Adjust parameters based on results.

Phase 5: Documentation & Finalization (Week 5)

• Document the entire workflow:
  • VWAP-based signal generation
  • Portfolio optimization methodology
  • Backtesting results
• Write an analysis report on the benefits of using VWAP in a multi-asset strategy.
• Prepare a GitHub repository with notebooks and scripts.

4. Additional Enhancements for Efficiency

• Automate Data Fetching: Use scheduled scripts to fetch real-time data.
• Hyperparameter Optimization: Utilize GridSearchCV or Optuna for tuning ML models.
• Parallel Processing: Use multiprocessing for backtesting across multiple assets.
• Deployment Plan: If successful, consider running the strategy in a paper trading environment for real-world validation.

5. Final Deliverables

• Codebase: Jupyter notebooks & scripts for data collection, signal generation, portfolio optimization, and backtesting.
• Performance Report: Analysis of strategy effectiveness with key insights.
• Backtesting Results: Visualizations and performance comparisons.
• GitHub Repository: Organized code and documentation.

Timeline

Week	Phase	Deliverables
Week 1	Data Preparation	Clean dataset with VWAP calculated for each asset.
Week 2	Signal Generation	Dataset with buy/sell signals and visualizations.
Week 3	Portfolio Optimization	Trained ML model and optimized portfolio weights.
Week 4	Backtesting	Backtest results, performance metrics, and visualizations.
Week 5	Documentation and Finalization	Final report, presentation, and GitHub repository.

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This roadmap provides a structured approach to executing the project efficiently while ensuring high-quality results.