Volatility Trading Analysis with Python
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Volatility Trading Analysis with Python

Learn volatility trading analysis from advanced to expert level with practical course using Python programming language.
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0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
11 students enrolled
Created by Diego Fernandez
Last updated 9/2017
English
Current price: $10 Original price: $50 Discount: 80% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 6 hours on-demand video
  • 6 Articles
  • 9 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Read or download CBOE®, S&P 500®, VelocityShares® volatility strategies benchmark indexes and replicating funds data to perform historical volatility trading analysis by installing related packages and running code on Python IDE.
  • Estimate historical or realized volatility through close to close, Parkinson, Garman-Klass, Rogers-Satchell, and Garman-Klass-Yang-Zhang metrics.
  • Calculate forecasted volatility through random walk, historical mean, simple, exponentially weighted, autoregressive integrated moving averages and general autoregressive conditional heteroscedasticity models.
  • Measure market participants implied volatility through related volatility index.
  • Estimate futures prices and explore volatility and asset returns correlation, volatility risk premium, volatility term structure and volatility skew patterns.
  • Assess volatility hedge and volatility tail hedge futures trading strategies historical risk adjusted performance using related hedged equity volatility futures strategy benchmark indexes replicating ETFs.
  • Approximate options call and put prices through Black and Scholes model together with related option Greeks.
  • Evaluate buy write, put write and volatility tail hedge options trading strategies historical risk adjusted performance using related buy write, put write and volatility options strategy benchmark indexes and replicating ETFs or ETNs.
View Curriculum
Requirements
  • Python programming language is required. Downloading instructions included.
  • Python Distribution (PD) and Integrated Development Environment (IDE) are recommended. Downloading instructions included.
  • Practical example data and Python code files provided with the course.
  • Prior basic Python programming language knowledge is useful but not required.
Description

Learn volatility trading analysis through a practical course with Python programming language using CBOE®, S&P 500®,VelocityShares® volatility strategies benchmark indexes and replicating ETFs or ETNs historical data for risk adjusted performance back-testing. It explores main concepts from advanced to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced sophisticated investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field.

Become a Volatility Trading Analysis Expert in this Practical Course with Python

  • Read or download CBOE®, S&P 500®, VelocityShares® volatility strategies benchmark indexes and replicating funds data to perform historical volatility trading analysis by installing related packages and running code on Python IDE.
  • Estimate historical or realized volatility through close to close, Parkinson, Garman-Klass, Rogers-Satchell, and Garman-Klass-Yang-Zhang metrics.
  • Calculate forecasted volatility through random walk, historical mean, simple, exponentially weighted, autoregressive integrated moving averages and general autoregressive conditional heteroscedasticity models.
  • Measure market participants implied volatility through related volatility index.
  • Estimate futures prices and explore volatility and asset returns correlation, volatility risk premium, volatility term structure and volatility skew patterns. 
  • Assess volatility hedge and volatility tail hedge futures trading strategies historical risk adjusted performance using related hedged equity volatility futures strategy benchmark indexes replicating ETFs. 
  • Approximate options call and put prices through Black and Scholes model together with related option Greeks. 
  • Evaluate buy write, put write and volatility tail hedge options trading strategies historical risk adjusted performance using related buy write, put write and volatility options strategy benchmark indexes and replicating ETFs or ETNs.

Become a Volatility Trading Analysis Expert and Put Your Knowledge in Practice

Learning volatility trading analysis is indispensable for finance careers in areas such as derivatives research, derivatives development, and derivatives trading mainly within investment banks and hedge funds. It is also essential for academic careers in derivatives finance. And it is necessary for experienced sophisticated investors’ volatility trading strategies research.

But as learning curve can become steep as complexity grows, this course helps by leading you step by step using CBOE®, S&P 500®, VelocityShares®  volatility strategies benchmark indexes and replicating ETFs or ETNs historical data for risk adjusted performance back-testing to achieve greater effectiveness. 

Content and Overview

This practical course contains 45 lectures and 6 hours of content. It’s designed for advanced volatility trading analysis knowledge level and a basic understanding of Python programming language is useful but not required.

At first, you’ll learn how to read or download CBOE®, S&P 500®,VelocityShares® volatility strategies benchmark indexes and replicating ETFs or ETNs data to perform historical volatility trading analysis by installing related packages and running code on Python IDE. 

Then, you’ll do volatility analysis by estimating historical or realized volatility through close to close, Parkinson, Garman-Klass, Rogers-Satchell and Garman-Klass-Yang-Zhang metrics. After that, you’ll use these estimations to forecast volatility through random walk, historical mean, simple moving average, exponentially weighted moving average, autoregressive integrated moving average and general autoregressive conditional heteroscedasticity models. Next, you’ll measure market participants implied volatility through related volatility index.

Later, you’ll estimate futures prices and compare them with actual historical data. Then, you’ll explore volatility and asset returns correlation, volatility risk premium, volatility term structure and volatility skew patterns. After that, you’ll assess volatility risk through stocks realized or historical volatility monthly differences probability distribution non-normality. Next, you’ll evaluate volatility hedge and volatility tail hedge futures trading strategies historical risk adjusted performance using related hedged equity volatility futures strategy benchmark indexes replicating ETFs.

After that, you’ll estimate option call and put prices through Black and Scholes model together with related option Greeks. Next, you’ll assess returns risk through historical stock prices monthly changes probability distribution non-normality. Finally, you’ll evaluate covered call or buy write, cash secured short put or put write and volatility tail hedge options trading strategies historical risk adjusted performance using related buy write, put write and volatility tail hedge options strategy benchmark indexes and replicating ETFs or ETNs

Who is the target audience?
  • Students who want to learn about volatility trading analysis using Python programming language.
  • Finance professionals or academic researchers who wish to deepen their knowledge in derivatives finance.
  • Sophisticated investors with experience in financial derivatives who desire to research volatility trading strategies.
  • This course is NOT about “get rich quick” trading strategies or magic formulas.
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Curriculum For This Course
45 Lectures
05:57:15
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Course Overview
7 Lectures 33:14

In this lecture you will view course disclaimer and learn which are its objectives, how you will benefit from it, its previous requirements and my profile as instructor.

Preview 06:01

In this lecture you will learn that it is recommended to view course in an ascendant manner as each section builds on last one and also does its complexity. You will also study course structure and main sections (course overview, volatility analysis, futures trading strategies and options trading strategies).

Preview 02:37

In this lecture you will learn volatility trading analysis definition, Miniconda Distribution for Python 3.6 64-bit (PD) and Python PyCharm Integrated Development Environment (IDE) downloading websites.

Volatility Trading Analysis
05:14

In this lecture you will learn volatility trading analysis data reading or downloading into Python PyCharm Integrated Development Environment (IDE), data sources, code files originally in .TXT format that need to be converted in .PY format, Python packages Miniconda Distribution for Python 3.6 64-bit (PD) installation (numpy, pandas, scipy, pandas-datareader, quandl, matplotlib, statsmodels, arch and py_vollib) and related code (import <package> as <name>, read_csv(), DataReader(), join(), columns(), dropna()  functions).

Volatility Trading Analysis Data
19:13

Before starting course please download .TXT data file in .CSV format as additional resources.

Course Data File
00:03

Before starting course please download .TXT Python code files as additional resources.

Course Code Files
00:03

You can download .PDF section slides file as additional resources.

Course Overview Slides
00:02
+
Volatility Analysis
15 Lectures 01:38:09

You can download .PDF section slides file as additional resources.

Volatility Analysis Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to volatility analysis (historical or realized volatility, forecasted volatility and implied volatility).

Volatility Analysis Overview
07:56

In this lecture you will learn Close to Close volatility estimator definition and main calculations (sqrt(), DataFrame.rolling().std(), plot(), legend(), title(), show() functions).

Preview 05:37

In this lecture you will learn Parkinson volatility estimator definition and main calculations (sqrt(), DataFrame.rolling().sum(), format(), plot(), legend(), title(), show() functions).

Parkinson Volatility Estimator
07:32

In this lecture you will learn Garman-Klass volatility estimator definition and main calculations (sqrt(), DataFrame.rolling().sum(), format(), plot(), legend(), title(), show() functions).

Garman-Klass Volatility Estimator
07:46

In this lecture you will learn Rogers-Satchell volatility estimator definition and main calculations (sqrt(), DataFrame.rolling().sum(), format(), plot(), legend(), title(), show() functions).

Rogers-Satchell Volatility Estimator
08:02

In this lecture you will learn Garman-Klass-Yang-Zhang volatility estimator definition and main calculations (sqrt(), DataFrame.rolling().sum(), format(), plot(), legend(), title(), show() functions).

Garman-Klass-Yang-Zhang Volatility Estimator
08:53

In this lecture you will learn random walk volatility forecast definition and main calculations (shift(), dropna(), plot(), legend(), title(), show() functions).

Random Walk Volatility Forecast
05:17

In this lecture you will learn historical mean volatility forecast definition and main calculations (mean(), plot(), axhline(), legend(), title(), show() functions).

Historical Mean Volatility Forecast
03:49

In this lecture you will learn simple moving average volatility forecast definition and main calculations (DataFrame.rolling().mean(), plot(), legend(), title(), show() functions).

Simple Moving Average Volatility Forecast
03:55

In this lecture you will learn exponentially weighted moving average volatility forecast definition and main calculations (DataFrame.ewm().mean(), plot(), legend(), title(), show() functions).

Exponentially Weighted Moving Average Volatility Forecast
04:27

In this lecture you will learn autoregressive integrated moving average volatility forecast definition and main calculations (ARIMA().fit(), predict(), shift(), plot(), legend(), title(), show() functions).

Autoregressive Integrated Moving Average Volatility Forecast
06:38

In this lecture you will learn general autoregressive conditional heteroscedasticity volatility forecast definition and main calculations (arch_model().fit(), conditional_volatility, params[], plot(), legend(), title(), show() functions).

General Autoregressive Conditional Heteroscedasticity Volatility Forecast
06:45

In this lecture you will learn implied volatility definition and main calculations (shift(), dropna(), plot(), legend(), title(), show() functions).

Implied Volatility
05:45

In this lecture you will learn volatility forecasting accuracy definition and main calculations (Series.to_frame(), columns, join(), dropna(), format(), meanabs(), mse(), rmse() functions).

Volatility Forecasting Accuracy
15:45
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Futures Trading Strategies
12 Lectures 01:49:19

You can download .PDF section slides file as additional resources.

Futures Trading Strategies Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to futures trading strategies (futures analysis, volatility and asset returns correlation, volatility risk premium, volatility term structure, volatility skew patterns, futures payoff, volatility risk assessment, volatility hedge futures trading strategy and volatility tail risk futures trading strategy). 

Futures Trading Strategies Overview
13:03

In this lecture you will futures definition and main calculations (exp(), format(), DataFrame() functions). 

Futures
06:28

In this lecture you will learn volatility and assets returns correlation definition and main calculations (loc[], dropna(), subplots(), plot(), legend(), suptitle(), show(), log(), shift(), isnan(), corr() functions).

Volatility and Assets Returns Correlation
07:29

In this lecture you will learn volatility risk premium definition and main calculations (loc[], dropna(), sqrt(), DataFrame.rolling().std(), shift(), Series.rolling().mean(), plot(), legend(), title(), show(), axhline() functions).

Volatility Risk Premium
07:03

In this lecture you will learn volatility term structure definition and main calculations (loc[], dropna(), Series.rolling().mean(), plot(), legend(), title(), show(), axhline() functions).

Volatility Term Structure
05:52

In this lecture you will learn volatility skew definition and main calculations (loc[], dropna(), Series.rolling().mean(), plot(), legend(), title(), show()functions).

Volatility Skew
05:35

In this lecture you will learn futures payoff definition and main calculations (format() function).

Futures Payoff
17:35

In this lecture you will learn volatility risk assessment definition and main calculations (loc[], dropna(), sqrt(),  DataFrame.rolling().std(), plot(), legend(), title(), show(), shift(), dropna(), axhline(), figure(), add_subplot(), probplot(), set_title() functions).

Volatility Risk Assessment
10:54

In this lecture you will learn strategies performance comparison main defintions.

Strategies Performance Comparison
04:26

In this lecture you will learn volatility hedge futures trading strategy definition and main calculations (loc[], dropna(), log(), shift(), isnan(), cumprod(), len(), plot(), title(), legend(), show(), iloc[], std(), max, min(), skew(), kurt(), format(), DataFrame() functions).

Volatility Hedge Futures Trading Strategy
16:27

In this lecture you will learn volatility tail hedge futures trading strategy definition and main calculations (loc[], dropna(), log(), shift(), isnan(), cumprod(), len(), plot(), title(), legend(), show(), iloc[], std(), max, min(), skew(), kurt(), format(), DataFrame() functions).

Volatility Tail Hedge Futures Trading Strategy
14:25
+
Options Trading Strategies
11 Lectures 01:56:30

You can download .PDF section slides file as additional resources.

Options Trading Strategies Slides
00:02

In this lecture you will learn section lectures’ detail and main themes to be covered related to options trading strategies (options analysis, options Greeks, covered call, cash secured short put and volatility tail hedge options trading strategies).

Options Trading Strategies Overview
15:19

In this lecture you will learn options Black and Scholes pricing model definition and main calculations (black_scholes_merton(), format(), DataFrame() functions).

Options
09:18

In this lecture you will learn options Greeks definition and main calculations (delta(), gamma() vega(), theta(), format(), DataFrame() functions).

Options Greeks
11:26

In this lecture you will learn options payoff definition and main calculations (format() function).

Options Payoff
14:09

In this lecture you will learn returns risk assessment definition and main calculations (loc[], dropna(), log(), shift(), plot(), axhline(), legend(), title(), show(), figure(), add_subplot(), probplot(), set_title() functions).

Returns Risk Assessment
07:39

In this lecture you will learn covered call definition.

Covered Call
02:57

In this lecture you will learn covered call trading strategy definition and main calculations (loc[], dropna(), log(), shift(), isnan(), cumprod(), len(), plot(), title(), legend(), show(), iloc[], std(), max, min(), skew(), kurt(), format(), DataFrame() functions).

Covered Call Trading Strategy
17:31

In this lecture you will learn cash secured short put definition.

Cash Secured Short Put
01:41

In this lecture you will learn cash secured short put trading strategy definition and main calculations (loc[], dropna(), log(), shift(), isnan(), cumprod(), len(), plot(), title(), legend(), show(), iloc[], std(), max, min(), skew(), kurt(), format(), DataFrame() functions).

Cash Secured Short Put Trading Strategy
17:59

In this lecture you will learn volatility tail hedge options trading strategy definition and main calculations (loc[], dropna(), log(), shift(), isnan(), cumprod(), len(), plot(), title(), legend(), show(), iloc[], std(), max, min(), skew(), kurt(), format(), DataFrame() functions).

Volatility Tail Hedge Options Trading Strategy
18:29
About the Instructor
Diego Fernandez
3.9 Average rating
480 Reviews
3,335 Students
22 Courses
Exfinsis

Diego Fernandez is author of high-quality online courses and ebooks at Exfinsis for anyone who wants to become an expert in financial data analysis.

His main areas of expertise are financial analysis and data science. Within financial analysis he has focused on computational finance, quantitative finance and trading strategies analysis. Within data science he has concentrated on machine learning, applied statistics and econometrics. For all of this he has become proficient in Microsoft Excel®, R statistical software® and Python programming language® analysis tools. 

He has important online business development experience at fast-growing startups and blue-chip companies in several European countries. He has always exceeded expected professional objectives by starting with a comprehensive analysis of business environment and then efficiently executing formulated strategy.

He also achieved outstanding performance in his undergraduate and postgraduate degrees at world-class academic institutions. This outperformance allowed him to become teacher assistant for specialized subjects and constant student leader within study groups. 

His motivation is a lifelong passion for financial data analysis which he intends to transmit in all of the courses.