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Learn how to efficiently calculate the Low Of Day (LOD) for multiple stocks in an incomplete daily bar data using Python Pandas without triggering errors.
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How to Calculate Low Of Day (LOD) for Stock Data Using Python Pandas

Working with financial data can be complex, particularly when you're trying to derive insights from high-frequency trading information like minute-by-minute stock prices. A common scenario is needing to calculate the Low Of Day (LOD) price for stocks where you have minute data but not yet completed daily bars.

In this post, we'll explore how to calculate the Low Of Day for multiple stocks across various sessions in a pandas DataFrame, and address a common issue related to SettingWithCopyWarning.

Problem Statement

Imagine you have a DataFrame containing minute-level stock data for multiple stocks. Each stock has multiple trading sessions throughout a day, and you want to populate the LOD column, representing the lowest price of the day up to each minute in the DataFrame. Here’s a small excerpt of what the data looks like:

[[See Video to Reveal this Text or Code Snippet]]

Your goal is to compute the LOD for every minute and fill the corresponding LOD column as demonstrated below:

[[See Video to Reveal this Text or Code Snippet]]

Understanding the Current Approach

The initially provided approach uses a loop to manually track the session and compute the LOD. While this gets the job done, it can lead to the dreaded SettingWithCopyWarning. This happens when you're trying to modify a subset of a DataFrame without being explicit about your intentions, potentially leading to unintended consequences.

Here’s the current loop-based solution:

[[See Video to Reveal this Text or Code Snippet]]

However, we have a better option!

A More Efficient Solution With Pandas

We can utilize the power of pandas' built-in functions to streamline our LOD calculation. With groupby and expanding, we can efficiently calculate the LOD without looping through each row, which reduces complexity and avoids warnings.

Implementing the Solution

Here’s how to compute the LOD using pandas in a more efficient way:

Ensure your time data is in the correct datetime format and sorted.

Use the groupby method combined with expanding().min() to calculate the LOD.

Here’s the code:

[[See Video to Reveal this Text or Code Snippet]]

Explanation of the Code

Data Conversion: Ensure the Time column is in datetime format and sorted by Symbol and Time.

Group By Symbol: The groupby function groups the data by the Symbol column.

Expanding Minimum: The expanding().min() function calculates the minimum of the Low prices for all previous rows, effectively capturing the Low Of Day up to that point.

Values Assignment: The .values ensures you directly assign the calculated minimums back to the new column LOD.

Result

After implementing this code, your DataFrame should now accurately display the LOD for each stock's session:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

In conclusion, calculating the Low Of Day (LOD) for stock data can be achieved efficiently using Pandas' built-in capabilities. The use of groupby() and expanding() methods not only simplifies the calculation but also eliminates common pitfalls like the SettingWithCopyWarning. Implement this solution to enhance your data handling and analysis for high-frequency trading data!