Python for Financial Analysis and Algorithmic Trading Geezwild

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

What you’ll learn

Python for Financial Analysis and Algorithmic Trading Course Site

  • Use NumPy to quickly work with Numerical Data
  • Use Pandas for Analyze and Visualize Data
  • Learn how to 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

Requirements

  • Some knowledge of programming (preferably Python)
  • Ability to Download Anaconda (Python) to your computer
  • Basic Statistics and Linear Algebra will be helpful

Description

Welcome to Python for Financial Analysis and Algorithmic Trading! 

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 Jupiter, NumPy, pandas, matplotlib, statsmodels, zipline, Quantopian, and much more!

RECOMMENDED READ:  The Ultimate Excel Programmer Geezwild

 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

 

Who this course is for:

Python for Financial Analysis and Algorithmic Trading Geezwild

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Content From: https://www.udemy.com/course/python-for-finance-and-trading-algorithms/
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