Question 1

How to install FinTA and handle dependencies?

Accepted Answer

Install via pip with 'pip install finta' or from GitHub for the latest version. It requires Python 3.6+ and Pandas 1.0.0+, so ensure your environment meets these requirements.

Question 2

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

Accepted Answer

FinTA is pure Python with Pandas integration, ideal for data analysis workflows, while TA-Lib is a C library with Python bindings, often faster but harder to install. Choose FinTA for ease of use and TA-Lib for performance-critical applications.

Question 3

How to calculate RSI with FinTA?

Accepted Answer

Use TA.RSI(ohlc) where 'ohlc' is a DataFrame with 'open', 'high', 'low', 'close' columns. It returns a Pandas Series with RSI values, as shown in the README examples.

Question 4

Does FinTA work with cryptocurrency data?

Accepted Answer

Yes, FinTA works with any properly formatted OHLC data, including cryptocurrencies. The README includes an example loading data from a CSV file, which can be adapted for crypto datasets.

Question 5

Can I add custom indicators to FinTA?

Accepted Answer

Yes, the project welcomes pull requests for new indicators, provided they are public domain and royalty-free. Contributors should study the implementation and follow the contributing guidelines.

Question 6

Is FinTA suitable for real-time trading?

Accepted Answer

Not ideally, as it relies on Pandas DataFrames which may not be optimized for low-latency, real-time processing. It's better suited for historical data analysis and backtesting.

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