Python for Financial Analysis and Algorithmic Trading

Learn numpy , pandas , matplotlib , quantopian , finance , and more for algorithmic trading with Python!

4.48 (17812 reviews)

Udemy

platform

English

language

Programming Languages

What you will learn

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

Course Gallery

Python for Financial Analysis and Algorithmic Trading – Screenshot 1 — Screenshot 1 – Python for Financial Analysis and Algorithmic Trading

Python for Financial Analysis and Algorithmic Trading – Screenshot 2 — Screenshot 2 – Python for Financial Analysis and Algorithmic Trading

Python for Financial Analysis and Algorithmic Trading – Screenshot 3 — Screenshot 3 – Python for Financial Analysis and Algorithmic Trading

Python for Financial Analysis and Algorithmic Trading – Screenshot 4 — Screenshot 4 – Python for Financial Analysis and Algorithmic Trading

Charts

Students

Price

Rating & Reviews

Enrollment Distribution

Comidoc Review

Our Verdict

This course offers valuable insights into Python libraries critical for financial analysis. However, the reliance on the now-defunct Quantopian platform hinders the overall experience. With a more user-friendly update utilizing Blueshift, this course could better serve aspiring traders and analysts.

What We Liked

Comprehensive coverage of essential Python libraries for financial analysis and algorithmic trading, including NumPy, Pandas, Matplotlib, and statsmodels
Provides a strong foundation in time series analysis, financial statistics calculation, and portfolio optimization techniques
High-quality instruction with clear explanations and real-world examples
Recent update addresses the decommissioning of Quantopian to some extent by introducing Blueshift, though it is not as polished or user-friendly

Potential Drawbacks

Algorithmic trading section relies on now-defunct Quantopian platform, causing numerous issues and preventing learners from completing the course as intended
Limited practical exercises throughout the course resulting in a lack of hands-on experience for learners
No clear guidance on setting up the environment or dealing with installation problems for different operating systems
Recent update introducing Blueshift is not as polished, causing some frustration and confusion for learners