Backtesting Momentum Investing Strategies Using Google Sheets

Hello LinkedIn community!

I'm excited to share my journey into the world of quantitative trading and the insights I've gained from backtesting momentum investing strategies using Google Sheets and Python. As a self-taught quant trader with over 12 years of experience in the product management space, transitioning to quant trading has been a thrilling and rewarding experience.

My Background

With a strong foundation in strategic planning, data-driven decision-making, and problem-solving, I’ve always been passionate about numbers, patterns, and the financial markets. My journey into quantitative trading began out of this passion, and I've dedicated countless hours to mastering trading strategies, data analysis, and backtesting techniques.

What is Momentum Investing?

Momentum investing capitalizes on the continuation of existing market trends. In absolute momentum, you compare an asset's price to its historical performance. If the current price is higher than the past price (based on certain parameters), it signals a buy; otherwise, it signals a hold or sell.

Backtesting Setup

For this analysis, I focused on the NIFTYBEES ETF, tracking the Nifty 50 index, with a simple strategy:

• Go Long: When NIFTYBEES is above the 200-day Exponential Moving Average (EMA).
• Hold: When NIFTYBEES is at or below the 200-day EMA.

I used 10 years of historical data to backtest this strategy using both Google Sheets for its accessibility and Python for its automation capabilities.

Google Sheets Backtesting

Google Sheets offers an accessible way to handle data and perform basic calculations:

1. Data Import: Load historical price data for NIFTYBEES.
2. Calculate 200-day EMA: Use built-in functions to compute the EMA.
3. Apply Trading Rules: Determine buy or hold signals based on the EMA.
4. Performance Analysis: Track strategy performance over time.

Python Backtesting

For a more advanced approach, Python provides powerful libraries like Pandas and NumPy:

1. Data Import: Read historical price data into a DataFrame.
2. Calculate 200-day EMA: Use rolling and exponential functions.
3. Apply Trading Rules: Vectorize buy and hold conditions.
4. Performance Analysis: Evaluate strategy performance using statistical functions.

Results Summary

Here are the key findings from my backtest comparing the absolute momentum strategy with a simple buy-and-hold approach:

Key Observations

• Absolute Return & CAGR: The buy-and-hold strategy yielded higher absolute returns and CAGR, but the absolute momentum strategy provided substantial gains with less volatility.
• Max Drawdown: The absolute momentum strategy had a significantly lower maximum drawdown (-20.19% vs. -36.34%), indicating better risk management.
• Rolling Returns: The differences in rolling returns were marginal, showing that the momentum strategy held up well over various time frames.
• RoMAD: The higher RoMAD for the absolute momentum strategy (49% vs. 33%) highlights its superior risk-adjusted performance.

Performance Visualization

The chart compares the NAV (Net Asset Value) of the Absolute Momentum Strategy (in blue) and the Buy-and-Hold Strategy (in red) over 10 years.

Key Insights

• Overall Performance: The buy-and-hold strategy shows higher growth but with more significant drawdowns.
• Drawdowns: The momentum strategy manages drawdowns better, particularly during downturns.
• Smoother Growth: The momentum strategy provides a smoother performance curve, indicating more stable growth.

Conclusion

Backtesting the absolute momentum strategy demonstrated that while it might not outperform a buy-and-hold strategy in terms of absolute returns, it offers better risk management with lower drawdowns and a higher risk-adjusted return. This makes it an attractive strategy for more conservative investors seeking to minimize risk while still achieving respectable returns.

Whether you're a novice investor or a seasoned trader, backtesting your strategies is crucial for understanding their potential performance. Using accessible tools like Google Sheets and powerful languages like Python, you can rigorously test and refine your strategies before applying them in live markets.

Connect with Me

Feel free to reach out if you have any questions, thoughts, or if you'd like to discuss trading strategies further. Let's connect and explore the fascinating world of quant trading together!