What you'll learn
Use MatDescriptionlib to create custom Descriptions
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
Description
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, matDescriptionlib, 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
MatDescriptionlib 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 Descriptions and Partial Auto Correlation Descriptions
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.

Homepage

https://www.udemy.com/course/python-for-financial-analysis-and-algorithmic-trading/