Description

Full Course Content Last Update 06/2017

Section 1 Content Last Update 04/2020

Learn stock technical analysis through a practical course with Python programming language 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 Python

    Read or download S&P 500® Index ETF prices data and perform technical analysis operations by installing related packages and running code on Python 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 oscillator 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 (buy) or short (sell) stock trading strategies based on single or multiple technical indicators trading openings.
    Evaluate stock trading strategies performances by comparing them 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 45 lectures and 8.5 hours of content. It’s designed for all stock technical analysis knowledge levels and a basic understanding of Python programming language 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 code on Python 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 oscillator (Full STO) and Williams %R.

Then, you’ll define single technical indicator based stock trading openings through price, double, bands 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 stock trading strategies which are long (buying) or short (selling) 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 Python programming language.
    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.

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.

Last Updated 4/2020