Practical Quantitative Finance with R: Solving Real-World Problems with R for Quant Analysts and Individual Traders
Free Shipping Included! Practical Quantitative Finance with R: Solving Real-World Problems with R for Quant Analysts and Individual Traders by UniCAD, Inc. at Translate This Website. Hurry! Limited time offer. Offer valid only while supplies last. The book ''Practical Quantitative Finance with R – Solving Real-World Problems with R for Quant Analysts and Individual Traders'' provides a complete
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:
a) Overview of R programming, user-defined functions, and R packages, which is necessary to create finance applications.
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.
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.
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.
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.
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.
g) Introduction to portfolio optimization based on the mean-variance and mean-CVaR methods
|Item Size:||0.86 x 9.25 x 9.25 inches|
|Package Weight:||1.46 pounds|
|Package Size:||7.4 x 0.79 x 0.79 inches|