Learn how to use statsmodels for Time Series Analysis
Calculate Financial Statistics, such as Daily Returns, Cumulative Returns, Volatility, etc..
Use Exponentially Weighted Moving Averages
Use ARIMA models on Time Series Data
Calculate the Sharpe Ratio
Optimize Portfolio Allocations
Understand the Capital Asset Pricing Model
Some knowledge of programming (preferably Python)
Ability to Download Anaconda (Python) to your computer
Basic Statistics and Linear Algebra will be helpful
Welcome to Python for Financial Analysis and Algorithmic Trading! Are you interested in how people use Python to conduct rigorous financial analysis and pursue algorithmic trading, then this is the right course for you!
This course will guide you through everything you need to know to use Python for Finance and Algorithmic Trading! We’ll start off by learning the fundamentals of Python, and then proceed to learn about the various core libraries used in the Py-Finance Ecosystem, including jupyter, numpy, pandas, matplotlib, statsmodels, zipline, Quantopian, and much more!
We’ll cover the following topics used by financial professionals:
NumPy for High Speed Numerical Processing
Pandas for Efficient Data Analysis
Matplotlib for Data Visualization
Using pandas-datareader and Quandl for data ingestion