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PYTHON FOR FINANCIAL ANALYSIS AND ALGORITHMIC TRADING 2020

 

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PYTHON FOR FINANCIAL ANALYSIS AND ALGORITHMIC TRADING 2020

What you’ll learn

  1. Use NumPy to quickly work with Numerical Data
  2. Use Pandas for Analyze and Visualize Data
  3. Use Matplotlib to create custom plots
  4. Learn how to use statsmodels for Time Series Analysis
  5. Calculate Financial Statistics, such as Daily Returns, Cumulative Returns, Volatility, etc..
  6. Use Exponentially Weighted Moving Averages
  7. Use ARIMA models on Time Series Data
  8. Calculate the Sharpe Ratio
  9. Optimize Portfolio Allocations
  10. Understand the Capital Asset Pricing Model
  11. Learn about the Efficient Market Hypothesis
  12. 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! 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:

  1. Python Fundamentals
  2. NumPy for High Speed Numerical Processing
  3. Pandas for Efficient Data Analysis
  4. Matplotlib for Data Visualization
  5. Using pandas-datareader and Quandl for data ingestion
  6. Pandas Time Series Analysis Techniques
  7. Stock Returns Analysis
  8. Cumulative Daily Returns
  9. Volatility and Securities Risk
  10. EWMA (Exponentially Weighted Moving Average)
  11. Statsmodels
  12. ETS (Error-Trend-Seasonality)
  13. ARIMA (Auto-regressive Integrated Moving Averages)
  14. Auto Correlation Plots and Partial Auto Correlation Plots
  15. Sharpe Ratio
  16. Portfolio Allocation Optimization
  17. Efficient Frontier and Markowitz Optimization
  18. Types of Funds
  19. Order Books
  20. Short Selling
  21. Capital Asset Pricing Model
  22. Stock Splits and Dividends
  23. Efficient Market Hypothesis
  24. Algorithmic Trading with Quantopian
  25. Futures Trading

Size: 2.44 GB



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