Python for Financial Analysis and Algorithmic Trading

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

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!

What you’ll learn

  • Use Matplotlib to create custom plots.
  • Use NumPy to quickly work with Numerical Data.
  • Calculate Financial Statistics, such as Daily Returns, Cumulative Returns, Volatility, etc…
  • Use ARIMA models on Time Series Data.
  • Optimize Portfolio Allocations.
  • Learn about the Efficient Market Hypothesis.
  • Use Pandas for Analyze and Visualize Data.
  • Learn how to use statsmodels for Time Series Analysis.
  • Use Exponentially Weighted Moving Averages.
  • Calculate the Sharpe Ratio.

Course Content

  • Python Programming Language – A Step by Step Guide –> 5 lectures • 35min.
  • Python For Finance: Algorithmic Trading –> 5 lectures • 34min.
  • Python for financial analysis and algo trading –> 6 lectures • 18min.
  • Become a More Efficient Python Programmer –> 3 lectures • 15min.
  • Python for financial analysis –> 7 lectures • 1hr 19min.

Python for Financial Analysis and Algorithmic Trading

Requirements

  • Basic Statistics and Linear Algebra will be helpful.
  • Some knowledge of programming (preferably Python).

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:

  • 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)
  • Statsmodels
  • 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

 

Got Python? If you’re serious about financial markets and algorithmic trading, then you’re going to need it. Python is a computer programming language that is used by institutions and investors alike every day for a range of purposes, including quantitative research, i.e. data exploration and analysis, and for prototyping, testing, and executing trading algorithms. In the recent past, however, only the big institutional players had the money and tech know-how to harness the benefits of algorithmic trading, but the times they are a-changin’. Before we dig deeper into the finer points of Python and how to get started in algorithmic trading with Trality, let’s take a brief trip back to the future.