Project3_​Team9

Step 1: Combine Quarterly Data

Use Concatenate to merge Q1–Q4 datasets into a single annual dataset.

This step ensures all reviews or metrics are combined for Part 2 analysis.

Step 2: Remove Duplicate Records

Prevent repeated entries when merging quarterly files.

Visualization 1: Annual Daily Trading Trend

The line chart shows the daily trading volume changes in 2024, which is used to observe the overall upward/downward trend and fluctuations throughout the year.

Step3: String to Date

Convert the string type of "day" into a real date and time to facilitate subsequent aggregation by year, quarter, month, etc.

Step4:Extract Date and Time

Extract the year, quarter, month and day of the week from the date, and prepare dimensions for analyzing the quarterly/monthly/daily and weekend/sunday patterns.

Step8:Grouped by quarter, the total transaction volume and average transaction volume

Use GroupBy to summarize the transaction volume by quarter. Compare which quarter has the highest total volume and average daily volume, and use this to answer the question 'Which quarter is the most active?'.

Visualization 2: Bar Chart of Quarterly Total Transactions

The bar chart presents the total transactions for each quarter, allowing for an intuitive comparison of the activity levels in different quarters.

Step5: Grouped By Month

Summarize the total transaction volume by month to identify the month with the highest transaction volume throughout the year.

Step6: Sort by total monthly volume from highest to lowest

Use the Sorter to sort by the total monthly transaction volume from highest to lowest, preparing for the selection of the Top months.

Step7: Select the months with the top 3 trading volumes

Using the Top k Selector, identify the 3 months with the highest trading volumes throughout the year, and use them as the key months in the story.

Visualization 3: Top 3 Monthly Bar Chart

The bar chart shows the three most active months of the year, clearly answering the question 'Which months were the most active?'

Step9:Grouped the average transaction volume by day of the week

Calculate the average daily transaction volume by day of the week and observe which day is the most active within the week

Step10: Only consider weekends / Only consider weekdays

Use Row Filter to separate the average transaction volumes for weekends and weekdays, providing data for comparing whether 'weekends are more active'

Visualization 4: Average Trading Volume per Week (7 Days)

The bar chart presents the average trading volume for each of the 7 days of the week, allowing for the identification of patterns within the week (working days vs weekends)

Step11: Identify the 10 "most exaggerated" days of the year (peak days)

Sort by daily trading volume and select the top 10, which will be used in the story to name the busiest trading dates (possibly corresponding to market events or policy changes)

Main Question:

In 2024, how did the trading activities of USCD change across different quarters and months? Which months were the most active, and were there any differences between weekdays and weekends?