BigQuery ASOF joins usecase

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06-10-2024

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BigQuery ASOF Joins: Time Travel for Your Data

Imagine you're tracking stock prices and need to know the closing price of a specific stock on a particular date. You have a table with daily stock prices and another table with daily trade data. The problem? These tables don't always have matching dates. You need a way to efficiently join these tables, looking for the closest price information available for each trade. This is where BigQuery's ASOF (As Of) joins come in.

The Challenge: Handling Misaligned Timestamps

Let's visualize the situation. Imagine you have two tables:

• stock_prices:

  • date: Date of stock price data (e.g., 2023-03-01)
  • symbol: Stock symbol (e.g., AAPL)
  • closing_price: Closing price on that date

• trades:

  • trade_date: Date of the trade
  • symbol: Stock symbol
  • quantity: Number of shares traded

You want to enrich your trade data with the closing price of the stock on the day of the trade. However, the tables might have gaps in their data, and a trade date might not always coincide with a date in the stock prices table.

Original Code (without ASOF join):

SELECT 
    t.trade_date,
    t.symbol,
    t.quantity,
    sp.closing_price
FROM 
    `your_project.your_dataset.trades` AS t
LEFT JOIN 
    `your_project.your_dataset.stock_prices` AS sp ON t.trade_date = sp.date
    AND t.symbol = sp.symbol;

This query uses a standard left join, which will only match records with exact date matches. This results in missing closing prices for trades that don't have a corresponding entry in the stock_prices table.

ASOF Joins: Time Travel for Your Data

Enter ASOF joins! BigQuery's ASOF joins provide a powerful solution for situations like this by allowing you to join tables based on the nearest available data point before a specific timestamp.

Modified Code (with ASOF join):

SELECT 
    t.trade_date,
    t.symbol,
    t.quantity,
    sp.closing_price
FROM 
    `your_project.your_dataset.trades` AS t
LEFT JOIN 
    `your_project.your_dataset.stock_prices` AS sp ON t.symbol = sp.symbol
    AND sp.date <= t.trade_date
ORDER BY 
    sp.date DESC
QUALIFY 
    ROW_NUMBER() OVER (PARTITION BY t.trade_date, t.symbol ORDER BY sp.date DESC) = 1;

Breaking Down the Code:

• sp.date <= t.trade_date: This condition ensures that we're looking for stock prices on or before the trade date.
• ORDER BY sp.date DESC: We order the results in descending order of date so that the latest price for a given stock before the trade date appears first.
• QUALIFY ROW_NUMBER() OVER (PARTITION BY t.trade_date, t.symbol ORDER BY sp.date DESC) = 1: This step selects only the first row (the latest price before the trade date) for each trade_date and symbol combination.

Advantages of ASOF Joins:

• Accurate and Efficient: ASOF joins provide accurate price data for all trades, even if the trade date doesn't exactly match a date in the stock_prices table.
• Time-Saving: ASOF joins eliminate the need for complex logic to handle date discrepancies, simplifying your queries and saving development time.
• Versatile: ASOF joins are applicable in various scenarios, including historical data analysis, financial modeling, and event tracking.

Beyond the Basics: Fine-Tuning Your ASOF Joins

• PARTITION BY: You can partition the data by additional columns, such as symbol, to ensure the correct price is retrieved for each distinct stock.
• ORDER BY: The ORDER BY clause is crucial for defining the ordering of data before selecting the closest match.
• QUALIFY: This clause lets you refine the selection process based on the results of window functions like ROW_NUMBER().

Conclusion

BigQuery's ASOF joins offer a powerful and efficient way to join tables with misaligned timestamps, providing accurate and time-sensitive insights. By using this technique, you can simplify your data analysis, improve accuracy, and unlock valuable insights from your data. Remember to understand the nuances of ASOF joins and experiment with different configurations to optimize your queries for specific use cases.