The 5 absolute must have Python libraries to build an algorithmic trading environment:
pandas
What is it: A fast, powerful, flexible and easy to use data analysis and manipulation tool.
Example usage: Aquire stock market data and apply a rolling regression.
Cost: Free
pandas.pydata.org
What is it: A fast, powerful, flexible and easy to use data analysis and manipulation tool.
Example usage: Aquire stock market data and apply a rolling regression.
Cost: Free
pandas.pydata.org
Jupyter Notebook
What is it: A web application for creating and sharing computational documents.
Example usage: Create, run and share the code for a trading strategy.
Cost: Free
jupyter.org
What is it: A web application for creating and sharing computational documents.
Example usage: Create, run and share the code for a trading strategy.
Cost: Free
jupyter.org
Backtrader
What is it: A backtesting and trading library used to write trading strategies, indicators and analyzers.
Example usage: Code and backtest a trading strategy and execute trades.
Cost: Free
backtrader.com
What is it: A backtesting and trading library used to write trading strategies, indicators and analyzers.
Example usage: Code and backtest a trading strategy and execute trades.
Cost: Free
backtrader.com
scikit-learn
What is it: Simple and efficient tools for predictive data analysis and machine learning.
Example usage: Create a logistic regression to (try to) predict future stock returns.
Cost: Free
scikit-learn.org
What is it: Simple and efficient tools for predictive data analysis and machine learning.
Example usage: Create a logistic regression to (try to) predict future stock returns.
Cost: Free
scikit-learn.org
PyQuant News is the best place for resources for developers using Python for scientific computing and quantitative analysis.
How to build an algorithmic trading environment with Python:
β’ NumPy
β’ pandas
β’ Backtrader
β’Β scikit-learn
β’ Jupyter Notebook
How to build an algorithmic trading environment with Python:
β’ NumPy
β’ pandas
β’ Backtrader
β’Β scikit-learn
β’ Jupyter Notebook
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