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What you’ll learn
Calculate leading stock technical indicators or oscillators such as average directional movement index, commodity channel index, moving averages convergence/divergenc
Define multiple technical indicators based stock trading occasions through price crossovers confirmed by bands crossovers.
Assess stock trading strategies performance by comparing their annualized return, standard deviation and Sharpe ratio against buy and hold
Compute lagging stock technical indicators or overlays such as moving averages, Bollinger bands, parabolic stop and reverse.
No necessary experience needed
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 an experienced investor. All of this while referencing the 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 on 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 the buy and hold benchmark.
Who this course is for:
- Undergraduates or postgraduates at any knowledge level who want to learn about stock market data analysis and visualisation using Python programming language.
- Experienced investors who desire to research stock technical trading strategies.
- Anyone who is interested to learning stock market data analysis
- Finance professionals or academic researchers who wish to deepen their knowledge in quantitative finance.