Description: R interface to PortfolioEffect cloud service for estimating

See <https://www.portfolioeffect.com/> for more information on the

Title: High Frequency Portfolio Analytics by PortfolioEffect
Depends: R (>= 2.13.2), methods, grid, ggplot2, rJava
Authors@R: c(person("Aleksey", "Zemnitskiy", role = c("aut", "cre"),
person("Andrey", "Kostin", role = c("aut")),
person("Oleg", "Nechaev", role = c("aut")),
person("Craig Otis and others", role = c("ctb", "cph"),
person("Daniel Lemire, Muraoka Taro and others",
person("Joe Walnes, Jorg Schaible and others",
comment = "Apache Log4j and Commons Lang libraries"),
comment = "GNU Trove and GNU Crypto libraries")
Description: R interface to PortfolioEffect Quant service for backtesting
high frequency trading (HFT) strategies, intraday portfolio analysis
and optimization. Includes auto-calibrating model pipeline for market
microstructure noise, risk factors, price jumps/outliers, tail risk
(high-order moments) and price fractality (long memory). Constructed
portfolios could use client-side market data or access HF intraday price
history for all major US Equities. See https://www.portfolioeffect.com/
for more information on the PortfolioEffect high frequency portfolio
Craig Otis and others [ctb, cph] (OpenFAST library),
Daniel Lemire, Muraoka Taro and others [ctb, cph] (JavaFastPFOR
Joe Walnes, Jorg Schaible and others [ctb, cph] (XStream library),
Dain Sundstrom [ctb, cph] (Snappy library),
Extreme! Lab, Indiana University [ctb, cph] (XPP3 library),
The Apache Software Foundation [ctb, cph] (Apache Log4j and Commons
Free Software Foundation [ctb, cph] (GNU Trove and GNU Crypto

Description: R interface to PortfolioEffect cloud service for backtesting

Authors@R: c(person("Andrey", "Kostin", role = c("aut", "cre"), email = "andrey.kostin@portfolioeffect.com"),
history for all major US Equities. See <https://www.portfolioeffect.com/>

Title: Small/Large Sample Portfolio Optimization
Description: Two functions for financial portfolio optimization by linear programming are provided. One function implements Benders decomposition algorithm and can be used for very large data sets. The other, applicable for moderate sample sizes, finds optimal portfolio which has the smallest distance to a given benchmark portfolio.