Accessing MetaStock End-of-Day Prices from Python

Summary

The document addresses whether Python can retrieve end-of-day stock prices from MetaStock and store them in a SQL database. It explains that MetaStock data uses a binary format, so extraction would require converting the data to text before loading it into a table. The answer also says there is no Python library for direct access to MetaStock’s end-of-day servers.

As an alternative, it suggests a data provider with a Python library that can return pricing and economic data in formats including CSV, JSON, or XML, and can load results into a pandas DataFrame. The discussion is brief and does not compare providers, explain a conversion workflow, or assess coverage and licensing for Asian equities. Treat the recommendation as a starting point for investigating data access rather than a complete solution.

Key ideas

• MetaStock end-of-day data is stored in a binary format that must be converted before SQL import.
• The answer reports that Python cannot directly retrieve data from MetaStock’s end-of-day servers.
• A third-party provider with a Python library is suggested as an alternative source.
• Provider suitability for Asian equity coverage, licensing, and data quality is not evaluated.

Tags

# Metastock end of day data to Python


# Metastock end of day data to Python












I'm thinking of getting End of Day stock prices from Metastock, but was wondering if it would be possible to have Python to automatically extract the stock prices and store it in a SQL.

Would that be possible? To use Python to interact with Metastock and collect data?

Any other recommendations for End of Day stock prices? (Asian Equities)

## Answer by andrew.paul.acosta (score 1, accepted)

https://quant.stackexchange.com/a/23181

Metastock uses a binary format that you would have to convert to text before exporting it to a SQL table. Also, there is no Python library that could extract data directly from Metastock's end-of-day servers.

You may consider quandl which allows Python developers to download pricing data as well as economic indicators. You can download CSV, JSON, or XML. If you use their Python library, you can easily retrieve data into a pandas DataFrame.

https://www.quandl.com/

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