Stock Technical Analysis with Python – Diego Fernandez

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$197   $34 – Stock Technical Analysis with Python – Diego Fernandez

What you’ll learn

• Read or download S&P 500® Index ETF prices data and perform technical analysis operations by installing related packages and running script code on RStudio IDE.
• Compute lagging stock technical indicators or overlays such as moving averages, Bollinger bands, parabolic stop and reverse.
• Calculate leading stock technical indicators or oscillators such as average directional movement index, commodity channel index, moving averages convergence/divergence, rate of change, relative strength index, stochastic momentum index and Williams %R.
• Determine single technical indicator based stock trading opportunities through price, double, bands, centerline and signal crossovers.
• Define multiple technical indicators based stock trading occasions through price crossovers confirmed by bands crossovers.
• Outline long-only stock trading strategies based on single or multiple technical indicators trading openings.
• Assess stock trading strategies performance by comparing their annualized return, standard deviation and Sharpe ratio against buy and hold benchmark.

Requirements

• R statistical software is required. Downloading instructions included.
• RStudio Integrated Development Environment (IDE) is recommended. Downloading instructions included.
• Practical example data and R script code files are provided with course.
• Prior basic R statistical software knowledge is useful but not required.

Description

Full Course Content Last Update 12/2017

Learn stock technical analysis through a practical course with R statistical software using S&P 500® Index ETF historical data for back-testing. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do research as experienced investor. All of this while referencing best practitioners in the field.

Become a Stock Technical Analysis Expert in this Practical Course with R

• Read or download S&P 500® Index ETF prices data and perform technical analysis operations by installing related packages and running script code on RStudio IDE.
• Compute lagging stock technical indicators or overlays such as moving averages, Bollinger bands, parabolic stop and reverse.
• Calculate leading stock technical indicators or oscillators such as average directional movement index, commodity channel index, moving averages convergence/divergence, rate of change, relative strength index, stochastic momentum index and Williams %R. 
• Determine single technical indicator based stock trading opportunities through price, double, bands, centerline and signal crossovers.
• Define multiple technical indicators based stock trading occasions through price crossovers confirmed by bands crossovers.
• Outline long-only stock trading strategies based on single or multiple technical indicators trading openings.
• Assess stock trading strategies performance by comparing their annualized return, standard deviation and Sharpe ratio against buy and hold benchmark.

Become a Stock Technical Analysis Expert and Put Your Knowledge in Practice

Learning stock technical analysis is indispensable for finance careers in areas such as equity research and equity trading. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors stock technical trading research and development.

But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500® Index ETF prices historical data for back-testing to achieve greater effectiveness.

Content and Overview

This practical course contains 46 lectures and 7 hours of content. It’s designed for all stock technical analysis knowledge levels and a basic understanding of R statistical software is useful but not required.

At first, you’ll learn how to read or download S&P 500® Index ETF prices historical data to perform technical analysis operations by installing related packages and running script code on RStudio IDE. 

Next, you’ll calculate lagging stock technical indicators such as simple moving averages (SMA), exponential moving averages (EMA), Bollinger bands (BB), parabolic stop and reverse (SAR). After that, you’ll compute leading stock technical indicators such as average directional movement index (ADX), commodity channel index (CCI), moving averages convergence/divergence (MACD), rate of change (ROC), relative strength index (RSI), stochastic momentum index (SMI) and Williams %R.

Then, you’ll define single technical indicator based stock trading openings through price, double, bands, centerline and signal crossovers. Next, you’ll determine multiple technical indicators based trading opportunities through price crossovers which need to be confirmed by second technical indicator band crossover. Later, you’ll give shape to long-only stock trading strategies using single or multiple technical indicators trading occasions.

Finally, you’ll evaluate stock trading strategies performance with buy and hold as initial benchmark and comparing their annualized return for performance, annualized standard deviation for volatility or risk and annualized Sharpe ratio for risk adjusted return.

Who this course is for:

• Undergraduates or postgraduates at any knowledge level who want to learn about stock technical analysis using R statistical software.
• Finance professionals or academic researchers who wish to deepen their knowledge in quantitative finance.
• Experienced investors who desire to research stock technical trading strategies.
• This course is NOT about “get rich quick” trading systems or magic formulas.

$197   $34 – Stock Technical Analysis with Python – Diego Fernandez

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