video1.md

VIDEO 1: https://drive.google.com/drive/folders/14V4sITqByyge5T4iGgKPcuQuKOs4fBq3?usp=sharing

Based on the conversation in the provided transcript, here is a list of detailed Power BI tasks with their corresponding begin timecodes:

1. [00:05:28] Create a query to retrieve data for the client portfolio report, including the following fields:

   • Client identification
   • Unique identifier
   • Client name
   • Product name (calculated based on the source)
   • Balance amount
   • Participation percentage

2. [00:20:48] Implement logic in the query to populate the product name field correctly based on the source:

   • For FIC funds and Voluntary Pension Funds, use the fund name
   • For titles, use the name and name fields
   • For current accounts, directly input "Current Accounts"

3. [00:27:51] Create a query to retrieve data for the economic group portfolio report, similar to the client portfolio report query, but replace client fields with the economic group field.

4. [00:28:57] In the economic group portfolio report query, show only the economic group field instead of client identification, unique identifier, and client name.

5. [00:36:47] Create a second query for the client portfolio report to group data by active type name, including the following fields:

   • Client identification
   • Unique identifier
   • Client name
   • Active type name
   • Balance amount (summarized by active type name)
   • Participation percentage (calculated based on the active type's participation in the client's portfolio)

6. [00:39:03] Create a third query for the client portfolio report to retrieve profitability and volatility data, using the following fields from the profitability-volatility-client table:

   • Unique identifier
   • Profitability
   • Volatility

7. [00:40:47] Create queries similar to tasks 5 and 6 for the economic group portfolio report, using the corresponding economic group tables and fields.

8. [00:23:24] Consider optimizing query performance by building queries to directly retrieve the required data instead of performing calculations within Power BI, as it may be faster when working with the data in the workspace.