Question 1

How to calculate MACD with stockstats?

Accepted Answer

Access `df['macd']` to compute the MACD lines; this generates three columns at once ('macd', 'macds', 'macdh') for the indicator, signal, and histogram. Ensure your data is wrapped with `stockstats.wrap()` first, and use `df.get('macd')` to initialize all related columns.

Question 2

Stockstats vs TA-Lib: which is better for Python?

Accepted Answer

Stockstats is ideal for pandas-centric workflows due to its seamless DataFrame integration and simpler API, while TA-Lib is faster and more established in quantitative finance but has a steeper learning curve. Choose Stockstats for ease of use with pandas, TA-Lib for performance-critical applications.

Question 3

Can stockstats handle real-time data updates?

Accepted Answer

No, Stockstats is designed for batch processing with pandas DataFrames and does not support streaming or real-time data natively. You'd need to manually append new data and recalculate indicators, which can be inefficient for high-frequency updates.

Question 4

How to avoid memory issues with many indicators?

Accepted Answer

Calculate only necessary indicators on-demand instead of using `init_all()`, delete unused columns with `del df['column']` to force re-evaluation, and ensure your data is downsampled or chunked for large datasets to mitigate pandas' memory overhead.

Question 5

What data sources work with stockstats?

Accepted Answer

Any data source providing OHLCV columns in a pandas DataFrame, such as CSV files, yfinance downloads (with `multi_level_index=False`), or APIs. The library is format-agnostic as long as columns like 'close' and 'volume' are present.

Question 6

How to add a custom indicator to stockstats?

Accepted Answer

You must extend the `StockDataFrame` class by modifying the source code, as there's no built-in plugin system. This involves adding new methods and registering them in the indicator mapping, which requires familiarity with the library's internals.

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