Python for Financial Analysis and Algorithmic Trading
Download Python for Financial Analysis and Algorithmic Trading course. Learn numpy , pandas , matplotlib , quantopian , finance , and more for algorithmic trading with Python!
Description of Python for Financial Analysis and Algorithmic Trading
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 Python for Financial Analysis and Algorithmic Trading 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:
- Python Fundamentals
- NumPy for High Speed Numerical Processing
- Pandas for Efficient Data Analysis
- Matplotlib for Data Visualization
- Using pandas-datareader and Quandl for data ingestion
- Pandas Time Series Analysis Techniques
- Stock Returns Analysis
- Cumulative Daily Returns
- Volatility and Securities Risk
- EWMA (Exponentially Weighted Moving Average)
- ETS (Error-Trend-Seasonality)
- ARIMA (Auto-regressive Integrated Moving Averages)
- Auto Correlation Plots and Partial Auto Correlation Plots
- Sharpe Ratio
- Portfolio Allocation Optimization
- Efficient Frontier and Markowitz Optimization
- Types of Funds
- Order Books
- Short Selling
- Capital Asset Pricing Model
- Stock Splits and Dividends
- Efficient Market Hypothesis
- Algorithmic Trading with Quantopian
- Futures Trading
Who this course is for:
- Someone familiar with Python who wants to learn about Financial Analysis!
What you’ll 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
Very good stuff! Good presentation, good resources and well explained. Some notes: as of May 2020 some of the Quantopian features used in these series of lectures are no longer available (for example future contracts, sentimental analysis) also Quantopian no longer allows to trade real money and so is essentially just a training ground for trading algorithms.
Overall very good stuff, well prepared and very comprehensive.
I learned a tremendous amount about Pandas and Quantopian. This an excellent Python for Financial Analysis and Algorithmic Trading course. Wasn’t easy but if you hang in there and put in the effort to learn, you’ll be well rewarded with some great knowledge!
Very good course, on the whole, I certainly learned a lot! However, I believe the last part could be brought more up to date as certain code and features are now out of date.
It is a very good Python for Financial Analysis and Algorithmic Trading course with very interesting examples. In particular, it provides Python codes that can be used not only for trading but they can be adapted for developing other data analysis algorithms. The only part I didn’t capture very well is the trading part with stocks and futures after an algorithm is developed and tested, how it can be applied, and run using real stock data in a given account (e.g., Fidelity, Charles Schwab, etc)?
I really like this course going from Python basic/crash course all the way to advance topics in financial analysis.
Now I am wondering if there’s a part 2 to learn more and apply what I’ve learn here.
Thank you so much for this Python for Financial Analysis and Algorithmic Trading course!
I really enjoyed this course. It filled in the gaps in my knowledge and gave me a cohesive overview of python and equity trading and how they fit together.