How to combine stock prices downloaded on different days with adjustments aligned?

by reba.quigley , in category: Technical Analysis , a month ago

How to combine stock prices downloaded on different days with adjustments aligned?

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1 answer

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by morton , a month ago

@reba.quigley 

To combine stock prices downloaded on different days with adjustments aligned, you can follow these steps:

  1. Download the stock price data for each day from a reliable source. Ensure that the data includes any adjustments such as stock splits or dividends.
  2. Create a spreadsheet or open a financial modeling software to organize and combine the data. Each column should represent a different adjustment or data point, and each row should represent a specific date.
  3. Align the dates: Verify that all the stock price datasets have a column representing the date for each data point. If the datasets have different date formats or missing data, manipulate the datasets to align the dates properly. This can be achieved by sorting the data based on the dates and filling any missing dates with the appropriate values.
  4. Apply adjustments: Identify any adjustments such as stock splits or dividends that need to be accounted for in the data. Adjustments like stock splits will require you to divide the stock prices by a factor to reflect the new share count. Dividends may require subtracting the dividend amount from the stock price.
  5. Combine the adjusted data: Once the adjustments are accounted for, combine the stock price data into a single dataset by appending the adjusted columns together. You should now have a dataset where each row represents a specific date, and each column represents a different adjusted data point.
  6. Analyze or visualize the combined data: With the combined and adjusted dataset, you can perform various statistical analyses or create visualizations to understand the stock price movements over time.


It is worth noting that if you are dealing with a large number of stock prices and adjustments, it can be more efficient to use programming languages like Python or R to automate the data manipulation and alignment process.