Depends: R (>= 2.4.0), methods, graphics, grid, lattice, nlme
Packaged: Wed Oct  4 10:18:57 2006; enos

Packaged: Mon Jan  8 17:26:44 2007; enos

Packaged: Tue Jan 16 17:56:02 2007; enos

Packaged: Sun Apr 22 19:26:06 2007; enos

Packaged: Tue Oct  2 08:56:31 2007; enos

Packaged: Mon Apr 21 22:09:40 2008; enos

Packaged: Fri Oct 31 15:25:56 2008; enos

with contributions from Daniel Gerlanc <daniel@gerlanc.com> and
Depends: R (>= 2.10), methods, graphics, grid, lattice, nlme

with contributions from Daniel Gerlanc <dgerlanc@gmail.com> and
Depends: R (>= 3.0), methods, graphics, grid, lattice

Description: Classes for analysing and implementing equity portfolios,
including routines for generating tradelists and calculating
exposures to user-specified risk factors.
Depends: R (>= 3.0), graphics, grid, lattice, methods

Description: Simplify your portfolio optimization process by applying a contemporary modeling way to model and solve your portfolio problems. While most approaches and packages are rather complicated this one tries to simplify things and is agnostic regarding risk measures as well as optimization solvers. Some of the methods implemented are described by Konno and Yamazaki (1991) <doi:10.1287/mnsc.37.5.519>, Rockafellar and Uryasev (2001) <doi:10.21314/JOR.2000.038> and Markowitz (1952) <doi:10.1111/j.1540-6261.1952.tb01525.x>.
Depends: R (>= 3.5), xts, MASS, magrittr, modopt.matlab

Title: Automated Backtesting of Portfolios over Multiple Datasets
Description: Automated backtesting of multiple portfolios over multiple
datasets of stock prices in a rolling-window fashion. Intended for
researchers and practitioners to backtest a set of different portfolios,
as well as by a course instructor to assess the students in their portfolio
design in a fully automated and convenient manner, with results conveniently
formatted in tables and plots. Each portfolio design is easily defined as a
function that takes as input a window of the stock prices and outputs the
portfolio weights. Multiple portfolios can be easily specified as a list
of functions or as files in a folder. Multiple datasets can be conveniently
extracted randomly from different markets, different time periods, and
different subsets of the stock universe. The results can be later assessed
and ranked with tables based on a number of performance criteria (e.g.,
expected return, volatility, Sharpe ratio, drawdown, turnover rate, return
on investment, computational time, etc.), as well as plotted in a number of
Imports: doSNOW, evaluate, foreach, PerformanceAnalytics, quantmod,
Suggests: CVXR, DT, ggplot2, gridExtra, knitr, prettydoc, readtext,