Building a Hong Kong Equity Close-Price Matrix from Daily Data

Summary

This tutorial describes assembling a matrix of daily closing prices for Hong Kong stocks using BigQuant data sources and pandas. It first retrieves unique instrument identifiers, then loops through them to collect date and close fields over a specified historical interval, placing each security’s prices into a separate column indexed by date. The stated purpose is to prepare a panel for subsequent analysis or a visual linear strategy workflow.

The article also describes selecting the Hong Kong daily-bar database in a visual strategy template and removing columns with missing values. It provides no trading rule, predictive analysis, or performance evidence. The sample has implementation limitations: it assumes the date series from the first successfully read instrument can serve as the shared index, and the missing-column cleanup refers to an `empty` list without showing how that list is created. The data periods are examples and would need adjustment for a current research task.

Key ideas

• Retrieve unique Hong Kong stock identifiers before requesting daily bars.
• Collect each instrument’s date and closing price into a wide date-indexed matrix.
• The example queries a defined historical range and uses a dedicated Hong Kong daily-bar source.
• The workflow removes columns identified as containing missing values.
• The document explains data preparation, not a trading signal or tested strategy.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.