Advanced Trading Analysis with R

Requirements

• • R statistics software must be installed. Downloading directions are provided.
  • RStudio Integrated Development Environment (IDE) is highly recommended. Downloading instructions are provided.
  • Practical examples of data and script code file included with the course.
  • Basic R statistics software experience is beneficial but not essential.

Description

• Learn advanced trading analysis with an interactive course that uses R statistical software to back-test the S&P 500(r) Index ETF prices. The course covers the fundamentals from beginner to expert, which will help you earn higher grades, build your academic skills, use your knowledge in your job or conduct your research as an expert investor. All this while studying knowledge of Nobel Prize winners and best practitioners in the area.
• Learn to become an advanced Trading Analysis Expert in this practical course with R
  • Download or read S&P 500(r) Index ETF’s prices and data and conduct advanced analysis of trading through the installation of related programs and running scripts on RStudio IDE.
  • Implement trading strategies through trend-following indicators such as simple moving averages, moving averages convergence-divergence and mean-reversion indicators such as Bollinger bands(r), relative strength index, statistical arbitrage through z-score.
  • Increase the historical risk-adjusted performance by optimizing the strategy parameters using an extensive grid search of all indicator combinations of parameters.
  • Examine simulated trials to optimize strategies past performance in risk-adjusted terms using annualized return and annually adjusted Sharpe ratio metrics.
  • Optimize the parameters of your strategy to reduce overfitting or data snooping multiple hypothesis testing adjustments.
  • Approximate means of the population for statistical inference tests two tails multiple probabilities.
  • Adjust the mean of population multiple probabilities using family-wise error rate, Bonferroni procedure, and false discovery rate or Benjamini-Hochberg method.
  • Reduce the strategy parameters for optimization overfitting or data snooping into each bootstrap time series test for multiple adjustments.
  • Simulate the mean probability distribution by random fixed block re-sampling with replacement.
  • Estimate the mean of bootstrap population percentile of the statistical inference confidence interval and two tails to determine percentile probabilities value.
  • Correctly individual bootstrap population means multiple value probability comparison using family-wise adjustment of error rates.
• Become an Advanced Trading Analysis Expert and Put Your Knowledge in Practice
• The ability to master sophisticated trading techniques is crucial for careers in finance, like sophisticated quantitative research, quantitative development and quantitative trading, mostly within hedge funds and investment banks. It is also crucial for academic career opportunities in advanced quantitative finance. It is also essential for investors with experience to perform advanced research and development of quantitative trading.
• However, as the learning curve may be steeper as complexity increases, This course assists in guiding you step-by-step using S&P 500(r) Index ETF pricing historical data to improve your effectiveness.
• Content and Overview
• The course includes 38 classes along with 5.5 hours of material. This course is intended to help students master advanced trading analysis levels and know the basics of R statistical software. But it is not necessary.
• The first step is to learn how to download and read S&P 500(r) Index ETF historical prices to carry out advanced trading analysis by installing related software and running script code in RStudio IDE.
• After that, you’ll apply a trading strategy based on the category it falls under. You’ll then explore major strategies such as mean-reversion and trend-following. For the trend-following strategy category, you’ll use simple moving averages and moving averages convergence-divergence indicators in the case of mean-reversion strategies using indicators like Bollinger bands(r), the relative strength index, and statistical arbitrage using the z-score. Then you’ll optimize the parameters of your strategy by maximizing risk-adjusted historical performance using an extensive grid search of all indicator parameter combinations. Then, you’ll look at the principal strategy parameters for optimization, including net trading profits as well as loss. You’ll also explore maximum drawdown and profits to drawdowns that exceed. In the next step, you’ll conduct a report on strategy performance by evaluating the results of optimization trials that simulate risk-adjusted performance using historical data. The next step is to explore the main areas of strategy reporting, including performance metrics. To measure performance, you’ll need annualized return along with annualized standard deviation and Sharpe ratio that is annualized.
• Then, you’ll perform the adjustment for multiple hypothesis testing to limit historical parameters optimizing overfitting or spying. Then you’ll be defining multiple hypothesis testing and statistical inference. In the next step, you’ll determine probabilities value estimation. To determine probability value, you’ll conduct multiple tests of the population mean with two tails. Next, you’ll define multiple probability values estimation adjustment. To make multiple probability value estimation, it is necessary to use family-wise error rate, Bonferroni procedure, and Benjamini-Hochberg procedure for multiple probability value estimation adjustments.
• Then, you’ll conduct individual time series bootstraps testing multiple comparison adjustments to limit historical parameters optimizing overfitting, or spying. In the next step, you’ll perform individual time series bootstraps to aid in simulating mean probability distributions for the population using random fixed block re-samples with a substitute. Next, you’ll define bootstrap parameters estimation statistical inference. Then, you’ll define point estimation. For point estimation, you’ll do population mean point estimation. Later, you’ll define bootstrap confidence intervals estimation. To estimate bootstrap confidence intervals, it is necessary to do a bootstrap population mean percentile estimates of the confidence interval. After that, you’ll establish tests for bootstrap hypotheses. Next, you’ll define bootstrap probability value estimation. To estimate bootstrap probability value, you’ll conduct the bootstrap population mean percentile test, which is a two tails test. Then, you’ll determine an individual bootstrap probability estimation with multiple adjustments for comparison. For individual bootstrap probability value estimation multiple comparison adjustment, you’ll make family-wise error rate individual probability value estimation multiple comparison adjustment.

Who is this course intended for:

• • Students in postgraduate or undergrad wish to understand advanced trading analysis with R stats software.
  • A financial professional or academic researcher looking to increase your understanding of quantitative finance.
  • A seasoned investor who wants to study the latest techniques for trading quantitatively.
  • This course is not all about “get rich quick” trading methods or formulas that promise to make you rich.