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Learn advanced trading analysis from proficient to expert level with practical course using Python programming language
An excellent training about Investing & Trading
Advanced Trading Analysis with Python
Learn advanced trading analysis through a practical course with Python programming language using S & P 500 Index ETF prices for back-testing. It explores main concepts from proficient 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 investor. All of this while exploring the wisdom of Nobel Prize winners and best practitioners in the field. Become an Advanced Trading Analysis Expert in this Practical Course with PythonRead or download S & P 500 Index ETF prices data and perform advanced trading analysis operations by installing related packages and running code on Python PyCharm 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, relative strength index, statistical arbitrage through z-score. Maximize historical risk adjusted performance by optimizing strategy parameters through an exhaustive grid search of all indicators parameters combinations. Evaluate simulated strategy optimization trials historical risk adjusted performance through annualized return, annualized standard deviation and annualized Sharpe ratio metrics. Minimize strategy parameters optimization overfitting or data snooping through multiple hypothesis testing adjustment. Approximate population mean statistical inference two tails tests multiple probability values. Adjust population mean multiple probability values through family-wise error rate or Bonferroni procedure and false discovery rate or Benjamini-Hochberg procedure. Reduce strategy parameters optimization overfitting or data snooping through individual time series bootstrap hypothesis testing multiple comparison adjustment. Simulate population mean probability distribution through random fixed block re-sampling with replacement. Estimate bootstrap population mean statistical inference percentile confidence interval and two tails test percentile probability value. Correct individual bootstrap population mean probability value multiple comparison through family-wise error rate adjustment. Become an Advanced Trading Analysis Expert and Put Your Knowledge in PracticeLearning advanced trading analysis is indispensable for finance careers in areas such as advanced quantitative research, quantitative development, and quantitative trading mainly within investment banks and hedge funds. It is also essential for academic careers in advanced quantitative finance. And it is necessary for experienced investors advanced quantitative 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 OverviewThis practical course contains 43 lectures and 7 hours of content. Its designed for advanced trading analysis knowledge levels and a basic understanding of Python programming language is useful but not required. At first, youll learn how to read or download S & P 500 Index ETF prices historical data to perform advanced trading analysis operations by installing related packages and running code on Python PyCharm IDE. Then, youll implement trading strategy based on its category. Next, youll explore main strategy categories such as trend-following and mean-reversion. For trend-following strategy category, youll use indicators such as simple moving averages and moving averages convergence-divergence. For mean-reversion strategy category, youll use indicators such as Bollinger bands, relative strength index and statistical arbitrage through z-score. After that, youll optimize strategy parameters by maximizing historical risk adjusted performance through an exhaustive grid search of all indicators parameters combinations. Later, youll explore main strategy parameters optimization objectives such as final portfolio equity metric. Then, youll do strategy reporting by evaluating optimization trials simulated risk adjusted performance using historical data. Next, youll explore main strategy reporting areas such as performance metrics. For performance metrics, youll use annualized return, annualized standard deviation and annualized Sharpe ratio. After that, youll do multiple hypothesis testing adjustment to reduce historical parameters optimization over-fitting or data snooping. Later, youll define multiple hypothesis testing statistical inference. Then, youll define probability value estimation. For probability value estimation, youll do multiple population mean two tails tests. Next, youll define multiple probability values estimation adjustment. For multiple probability values estimation adjustment, youll do family-wise error rate or Bonferroni procedure and false discovery rate or Benjamini-Hochberg procedure multiple probability values estimations adjustments. Later, youll do individual time series bootstrap hypothesis testing multiple compariso
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