Developer shares eight Python packages used to build a personal financial agent system

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A developer describes building a personal financial agent and explains why they selected eight Python libraries from the many available. The article covers pandas for data analysis, noting that large datasets can exhaust memory and that chunked reading and type optimization helped. It also discusses the author's experience with rapid library updates breaking code, along with a unified exchange API and yfinance for fetching BIST stock data. The author stresses that yfinance is unofficial, so a backup data source is kept in case of sudden disruptions. The piece concludes with classical machine learning tools used for price prediction and classification.

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