Cryptocurrency Investment Strategy with Factor Investing and Portfolio Insurance

By GrapeInvestmentGroup (team project)

This repository contains an implementation of a cryptocurrency-focused investment strategy that combines Factor Investing with a Portfolio Insurance strategy. The goal is to optimize returns while managing downside risk dynamically.

Strategy Overview

The strategy employs Factor Investing principles with a dynamic Synthetic Put (SP) portfolio insurance approach. The strategy is divided into two main components:

1. Factor-Based Cryptocurrency Selection:

   • Momentum: Selects cryptocurrencies based on past price performance.
   • Size: Uses market capitalization to target smaller-cap cryptocurrencies, which historically show higher growth potential.
   • Value: Employs the Network Value to Transactions (NVT) ratio to identify undervalued cryptocurrencies.

2. Portfolio Insurance using Synthetic Put (SP):

   • The SP strategy dynamically adjusts the portfolio’s hedge ratio based on the relative performance of long and short positions.
   • This helps mitigate potential losses during market downturns while allowing for upside potential.

Key Components of the Code

>> strategy.ipynb

1. Setup & Data Loading

• The strategy loads historical price and market cap data for the top 10 cryptocurrencies and traditional assets (ETFs, gold) using CSV files and yfinance.
• Data is cleaned and prepared for backtesting.

2. Factor Calculation

• Momentum is calculated as the weekly percentage change.
• Size is based on market capitalization.
• Value is computed as the inverse of the NVT ratio.

3. Backtesting

• The backtest function simulates portfolio performance over time, adjusting the portfolio based on factor signals and rebalancing every three months.
• Portfolio Insurance (SP) is applied by adjusting the hedge ratio when long positions underperform relative to short positions.

4. Performance Metrics

• The strategy calculates key metrics such as CAGR, Sharpe Ratio, Sortino Ratio, Max Drawdown, and Calmar Ratio to assess the strategy’s performance.

5. Visualizations

• Several plots are generated to visualize:
  • Portfolio value over time.
  • Equity drawdowns.
  • Daily returns on assets.
  • Portfolio holdings breakdown.

How to Run the Strategy

1. Clone this repository.
2. Ensure all dependencies are installed (see requirements.txt).

pip install -r requirements.txt

3. Load data files or ensure access to yfinance API for traditional assets.
4. Run the Jupyter notebook or Python script to execute the strategy, backtest, and visualize results.

Improvements & Future Work

• Additional Factors: Incorporate volatility, liquidity, or sentiment data to enhance factor-based selection.
• Risk Management: Explore advanced techniques like Value at Risk (VaR) or Conditional Value at Risk (CVaR).
• Derivatives: Consider adding crypto derivatives (options, futures) to hedge or leverage positions.
• Broader Asset Selection: Expand the strategy to include more cryptocurrencies and traditional assets for better diversification.