Moving Averages on S&P 500

This project analyzes S&P 500 stock data from 1927 to 2020 to evaluate the effectiveness of two widely used technical indicators: the Simple Moving Average (SMA) and the Exponential Moving Average (EMA). The analysis focuses on generating buy/sell signals, comparing SMA vs. EMA performance, and identifying which may serve better for long- vs. short-term investment strategies.

Objectives

• Analyze daily returns of the S&P 500 index
• Calculate and visualize 20-day SMA and EMA
• Generate and evaluate buy/sell signals based on trend crossovers
• Explore indicator strengths and limitations in predicting market behavior

Technologies Used

• Python 3.9
• Pandas (for data manipulation)
• Matplotlib (for plotting)
• Jupyter Notebook
• Excel (for initial data handling)

Dataset

S&P 500 historical data sourced from Nasdaq:
https://www.nasdaq.com/market-activity/index/spx/historical

Full Report

See the detailed analysis and insights in the Final Report (PDF)

Files in this Repo

• jupyter_notebook_analysis.ipynb: Python code and visualizations
• report.pdf: Full project report and conclusions
• .gitignore: Tracks excluded files (e.g., .ipynb_checkpoints/)

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Summary of Findings

• SMA performs better for long-term investment strategies
• EMA is more responsive and suitable for short-term trading
• Both indicators are lagging and work best when used with other tools (e.g., RSI, MACD)
• Seasonal trends and trend reversals can provide added context when interpreting moving averages

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Contact

For questions or feedback, feel free to reach out via GitHub or email.