This notebook series is a personal note to self written in March 2020, updated in February 2021. It contains code along exercises of the Udemy course of Jose Portilla. It may contain errors, outdated or missing information. Use it at your own risk for educational purposes only. Chapter 10 - 11 are outdated as the Quantopian Platform, required for the exercises - is no longer available.
- Use NumPy to quickly work with Numerical Data
- Use Pandas for Analyze and Visualize Data
- Use Matplotlib to create custom plots
- 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
- Learn about the Efficient Market Hypothesis
- Conduct algorithmic Trading on Quantopian
1 - Python Crash Course
2 - NumPy
3 - General Pandas Overview
4 - Visualization with Matplotlib and Pandas
5 - Data Sources
6 - Pandas with Time Series Data
7 - Capstone Stock Market Analysis Project
8 - Time Series Analysis
9 - Python Finance Fundamentals
10 - Basics of Algorithmic Trading with Quantopian
11 - Advanced Quantopian and Trading Algorithms