Practical Quantitative Finance with R - by Jack Xu (Paperback)
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<p/><br></br><p><b> About the Book </b></p></br></br>The book provides a complete explanation of R programming in quantitative finance. It demonstrates how to prototype quant models and backtest trading strategies. It pays special attention to creating business applications and reusable R libraries that can be directly used to solve real-world problems in quantitative finance.<p/><br></br><p><b> Book Synopsis </b></p></br></br><p>The book "Practical Quantitative Finance with R - Solving Real-World Problems with R for Quant Analysts and Individual Traders" provides a complete explanation of R programming in quantitative finance. It demonstrates how to prototype quant models and backtest trading strategies. It pays special attention to creating business applications and reusable R libraries that can be directly used to solve real-world problems in quantitative finance. The book contains: </p> <p>a) Overview of R programming, user-defined functions, and R packages, which is necessary to create finance applications.</p> <p>b) Step-by-step approaches to create a variety of 2D/3D charts, stock charts, and technical indicators with the basic R and custom R packages.</p> <p>c) Introduction to free market data retrieval from online data sources using R. These market data include EOD, real-time intraday, interest rate, foreign exchange rate, and option-chain data.</p> <p>d) Detailed procedures to price equity options and fixed-income instruments, including European/American/Barrier options, bonds, and CDS, as well as related topics such as cash flows, term structures, yield curves, discount factors, and zero-coupon bonds.</p> <p>e) Introduction to linear analysis, time series analysis, and machine learning in finance, which covers linear regression, PCA, ARIMA, GARCH, KNN, random forest, SVM, and neural networks.</p> <p>f) In-depth descriptions of trading strategy development and backtesting, including strategies for single stock trading, stock pairs trading, and trading for multi-asset portfolios.</p> <p>g) Introduction to portfolio optimization based on the mean-variance and mean-CVaR methods</p>
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