$SPY Seasonality Charts and Data Table

Annual Summary Table of Trading Activities

This table provides a comprehensive yearly overview of trading activity, highlighting key metrics such as trading days, price fluctuations, and market trends. It includes the first and last trading days of each year, total trading days, and price movements, including the highest and lowest prices, opening and closing values. By analyzing this data, traders and analysts can track historical price patterns, assess market volatility, and gain insights into yearly stock performance to support strategic investment decisions.

Historical Daily Percent Change Chart โ€“ Stock Market Volatility Analysis

This Historical Percent Change Chart tracks the daily percentage fluctuations in stock prices, including Open, High, Low, Close, and Trading Volume. The chart provides a comprehensive view of market volatility, helping traders and analysts identify significant price movements, trend shifts, and market anomalies over time.

Key Features:

  • Long-Term Data Analysis โ€“ Covers multiple years to provide historical context.
  • Market Volatility Insights โ€“ Highlights sudden price swings and market trends.
  • Comprehensive Price Tracking โ€“ Displays percent changes in Open, High, Low, Close, and Volume.
  • Custom Time Frames โ€“ Allows zooming into specific periods for short-term and long-term trend analysis.
This data visualization is essential for market researchers, technical analysts, and investors looking to make informed decisions based on historical price behavior.
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Trading Days in Each Month Across The Years

This table provides a historical record of trading days per month for each year, offering a clear view of market activity over time. It helps identify seasonal fluctuations, market closures, and variations in trading schedules that may impact investment decisions. By analyzing this data, traders and analysts can spot trends such as months with fewer trading days due to holidays or regulatory changes and assess how these variations might influence liquidity, volatility, and overall market behavior throughout the years. This information is particularly useful for traders planning investment strategies, financial professionals analyzing market cycles, and researchers studying long-term trading patterns.

Seasonality: Same Date Across the Years > Cumulative Percent Change

This table tracks the daily percentage change in closing prices for each day of the year across multiple years, helping traders analyze historical market performance and identify seasonal trends. Each row represents a specific date, with columns showing the corresponding percentage change for different years. The Seasonal Average column calculates the average percent change for each day, highlighting recurring patterns and potential trading opportunities. By comparing daily movements over time, traders and analysts can spot trends, assess market volatility, and refine investment strategies based on historical price behavior.

Seasonal Daily Percent Change – Based on Day of the Year

This seasonality chart displays the daily percent change for each day of the year across multiple years, allowing traders and analysts to identify recurring patterns and market trends. Each colored line represents a different year, showing how price movements fluctuate on a given calendar day. The average value line provides a smoothed view of typical daily changes, helping to highlight consistent trends and deviations from historical norms. By analyzing this data, traders can better understand seasonal volatility, pinpoint potential trading opportunities, and refine strategies based on historical price behavior. wpDataChart with provided ID not found!

Cumulative Seasonal Percent Change – Based on Day of the Week

In this data set, we simply match the trading days of the year – based on the day of the week (Mon, Tue, Wed, etc), and then across the weeks of the year.

Weekly Seasonality View by Day of Week and Week of Year

This chart visualizes the historical performance of a selected metric across different years, grouped by the day of the week and week of the year. Each line represents a yearโ€™s weekly trend, allowing users to identify consistent weekday-driven seasonality patterns (e.g., recurring Monday or Friday trends). The dataset includes weekends and holidays where available (e.g., futures data), giving a complete view of weekly market behavior.