Package 'portfolio'

Title:	Analysing Equity Portfolios
Description:	Classes for analysing and implementing equity portfolios, including routines for generating tradelists and calculating exposures to user-specified risk factors.
Authors:	Jeff Enos [aut], David Kane [aut], Daniel Gerlanc [aut, cre], Kyle Campbell [ctb]
Maintainer:	Daniel Gerlanc <[email protected]>
License:	GPL (>= 2)
Version:	0.5-2
Built:	2025-02-15 05:24:58 UTC
Source:	https://github.com/dgerlanc/portfolio

Help Index

Analysing equity portfolios
Assay Research rankings as of 2004-12-31
Class "contribution"
DJIA for January, 2005
Class "exposure"
Security data of large global companies for 2004
Create a Map of the Market
Class "matchedPortfolio"
Class "matchedPortfolioCollection"
Class "performance"
Class "portfolio"
Class "portfolioBasic"
Class "tradelist"
Class "trades"
Calculate Position Weights

Analysing equity portfolios

Description

Classes for analysing and implementing equity portfolios.

Details

Package:	portfolio
Version:	0.4-5
Date:	2010-02-18
Depends:	R (>= 2.4.0), methods, graphics, grid, lattice, nlme
License:	GPL (>= 2)
LazyLoad:	yes

Index:

assay                   Assay Research rankings as of 2004-12-31
contribution-class      Class "contribution"
dow.jan.2005            DJIA for January, 2005
exposure-class          Class "exposure"
global.2004             Security data of large global companies for
                        2004
map.market              Create a Map of the Market
matchedPortfolio-class
                        Class "matchedPortfolio"
matchedPortfolioCollection-class
                        Class "matchedPortfolioCollection"
performance-class       Class "performance"
portfolio-class         Class "portfolio"
portfolio-package       Analysing equity portfolios
portfolioBasic-class    Class "portfolioBasic"
tradelist-class         Class "tradelist"
trades-class            Class "trades"
weight                  Calculate Position Weights

Further information is available in the following vignettes:

`matching_portfolio`	Matching Portfolios (source, pdf)
`portfolio`	Using the portfolio package (source, pdf)
`tradelist`	Using the tradelist class (source, pdf)

Author(s)

Jeff Enos <[email protected]> and David Kane <[email protected]>, with contributions from Daniel Gerlanc <[email protected]> and Kyle Campbell <[email protected]>

Maintainer: Jeff Enos <[email protected]>

Assay Research rankings as of 2004-12-31

Description

A universe of the 5000 largest global stocks as of 2004-12-31, and a flag indicating whether a security was ranked by Assay Research at that time.

Special thanks to Assay Research for granting us permission to release this data.

Usage

data(assay)data(assay)

Format

A data frame with 5000 observations on the following 11 variables.

date: A vector containing a single Date: 2004-12-31.
id: A character vector of SEDOLs and CUSIPs.
symbol: A character vector of symbols.
name: A character vector of the names of the companies.
country: A factor with levels AUS AUT BEL CHE DEU DNK ESP FIN FRA GBR HKG ITA JPN NLD NOR NZL SGP SWE USA.
currency: A factor with levels AUD CHF DKK EUR GBP HKD JPY NOK NZD SEK SGD USD.
price: A numeric vector of prices.
sector: A factor with levels Communications Conglomerates Cyclicals Energy Financials Industrials Materials Staples Technology Utilities
sec: An alternative sector specification. This factor has levels CND, CNS, COM, ENE, FIN, HTH, IND, MAT, TEC and UTL.
ind: Industry specification. This factor has levels AERDF, AIRLN, AUTOP, AUTOS, BANKS, BEVGS, BIOTC, BUILD, CHEMS, CNENG, CNFIN, CNMAT, COMEQ, COMPT, COMSS, CONGL, CPMKT, DICNS, DISTR, DVFIN, DVTEL, ELEQI, ELEQT, ELUTL, ENEQS, FDPRD, FDRET, GSUTL, HEPSV, HEQSP, HETEC, HOTEL, HSDUR, HSPRD, INSUR, INTSS, IPPET, ITCAT, ITCON, LEISR, LFSCI, LOGIS, MACHN, MEDIA, METAL, MGFIN, MLRET, MLUTL, OFFIC, OILGS, PACKG, PAPER, PHARM, PRPRD, REALE, REDEV, REITS, RRAIL, SEMIP, SEMIS, SHIPS, SMOKE, SOFTW, SPRET, TEXAP, TRADE, TRINF, WIREL and WTUTL
liq: A numeric vector of liquidities.
on.fl: A logical vector indicating presence on the Assay Focus List as of 2004-12-31.
ret.0.1.m: A numeric vector of one-month forward returns
ret.0.3.m: A numeric vector of three-month forward returns
ret.0.6.m: A numeric vector of one-month forward returns
ret.1.0.m: A numeric vector of one-month prior returns
ret.6.0.m: A numeric vector of six-month prior returns
ret.12.0.m: A numeric vector of twelve-month prior returns
mn.dollar.volume.20.d: A numeric vector of mean dollar volumes of the past 20 days
md.dollar.volume.120.d: A numeric vector of median dollar volumes of the past 120 days
cap.usd: A numeric vector of market capitalisation in USD.
cap: A numeric vector of market capitalisation in local currency.
sales: Annual gross sales of the company.
net.income: Annual net income of the company.
common.equity: Annual common equity of the company.

Examples

data(assay)
data(assay)

Class "contribution"

Description

Portfolio contribution of numeric measures (intervals) and categories.

Objects from the Class

Objects can be created by calls of the form new("contribution", ...).

Slots

data:: Object of class "list" containing contributions, as data.frame objects. The names of this list correspond to the category variable names.

Methods

plot: signature(x = "contribution", y = "missing"): Plot this object.
show: signature(object = "contribution"): show this object, briefly.
summary: signature(object = "contribution"): display a summary of this object.

Author(s)

Jeff Enos [email protected]

DJIA for January, 2005

Description

Basic descriptive and market data for those securities in the DJIA as of the end of January, 2005.

Usage

data(dow.jan.2005)data(dow.jan.2005)

Format

A data frame with 500 observations on the following 15 variables.

symbol: a character vector
name: a character vector
cap.bil: a numeric vector
price: a numeric vector
sector: a factor with levels Communications Conglomerates Cyclicals Energy Financials Industrials Materials Staples Technology Utilities
month.ret: a numeric vector

Examples

data(dow.jan.2005)
data(dow.jan.2005)

Class "exposure"

Description

Portfolio exposures to numeric measures and categories.

Objects from the Class

Objects can be created by calls of the form new("exposure", ...).

Slots

data:: Object of class "list" containing exposures, as data.frame objects. The names of this list correspond to the exposure variable names. The special exposure "numeric" contains exposures to all numeric variables.

Methods

plot: signature(x = "exposure", y = "missing"): Plot this object.
show: signature(object = "exposure"): show the object, briefly.
summary: signature(object = "exposure"): display a summary of this object.

Author(s)

Jeff Enos [email protected]

Security data of large global companies for 2004

Description

Contains basic security, category, and return information for a selection of large companies for each month of 2004. While 500 companies are included each month, the set of companies changes each month.

Usage

data(global.2004)data(global.2004)

Format

A data frame with 6000 observations on the following 16 variables.

date: a Date
id: a character vector
symbol: a character vector
name: a character vector
country: a factor with levels AUS AUT BEL CHE DEU DNK ESP FIN FRA GBR HKG ITA JPN NLD NOR SGP SWE USA
currency: a factor with levels AUD CHF DKK EUR GBP HKD JPY NOK SEK SGD USD
cap: a numeric vector
cap.usd: a numeric vector
cap.bil: a numeric vector
price: a numeric vector
price.usd: a numeric vector
round.lot: a numeric vector
sector: a factor with levels Communications Conglomerates Cyclicals Energy Financials Industrials Materials Staples Technology Utilities
liquidity: a numeric vector
liq.w: a numeric vector
volume: a numeric vector
avg.volume: a numeric vector
ret.0.1.m: a numeric vector

Examples

data(global.2004)
data(global.2004)

Create a Map of the Market

Description

Utility function for creating a "map of the market" visualization. Creates a treemap where rectangular regions of different size, color, and groupings visualize the stocks in a portfolio.

Usage

map.market(id, area, group, color,
           scale = NULL,
           lab   = c("group"=TRUE, "id"=FALSE),
           main  = "Map of the Market",
           print = TRUE)
map.market(id, area, group, color,
           scale = NULL,
           lab   = c("group"=TRUE, "id"=FALSE),
           main  = "Map of the Market",
           print = TRUE)

Arguments

`id`	A vector storing the labels to be used for each stock.
`area`	A vector storing the values to be used to calculate the areas of rectangles.
`group`	A vector specifying the group (i.e. country, sector, etc.) to which each stock belongs.
`color`	A vector storing the values to be used to calculate the color of rectangles.
`scale`	An object of class `numeric` indicating the scale to be used in determining colors.
`lab`	A logical vector of length 2 specifying whether group and stock labels should be drawn. If the two values are the same, the second may be omitted.
`main`	A title for the plot.
`print`	An object of class `logical` indicating whether the map should be drawn.

Author(s)

Jeff Enos [email protected]

Examples

data(dow.jan.2005)
map.market(id    = dow.jan.2005$symbol,
           area  = dow.jan.2005$price,
           group = dow.jan.2005$sector,
           color = 100 * dow.jan.2005$month.ret)

data(dow.jan.2005)
map.market(id    = dow.jan.2005$symbol,
           area  = dow.jan.2005$price,
           group = dow.jan.2005$sector,
           color = 100 * dow.jan.2005$month.ret)

Class "matchedPortfolio"

Description

An object of the class "matchedPortfolio" that contains an object of class "portfolioBasic" and a matrix of weights for portfolios that have been matched to the "portfolioBasic" according to variables specified in a formula.

Objects from the Class

Objects can be created by calls of the form new("matchedPortfolio", ...).

Slots

formula:: an object of class formula specifying the treatment variable and the covariates on which to match.
original:: an object of class "portfolioBasic", the attributes of which will be used for matching.
matches:: Object of class "matrix" with a column for each matched portfolio.
method:: Object of class "character" specifying the matching method used to generate the matched portfolio.
omitted.control:: Object of class "numeric" containing the number of observations omitted from the control.
omitted.treatment:: Object of class "numeric" containing the number of observations omitted from the treatment.

Methods

show: signature(object = "matchedPortfolio"): prints basic information about the original portfolio and its matches.
summary: signature(object = "matchedPortfolio"): prints detailed information about the original portfolio and its matches.
performance: signature(object = "matchedPortfolio"): calculates the mean performance across all matched portfolios.
exposure: signature(object = "matchedPortfolio", exp.var = "character"): calculates the exposure across each variable in exp.var.
contribution: signature(object = "matchedPortfolio", contrib.var = "character"): calculates the contribution across each variable in contrib.var.
plot: signature(x = "matchedPortfolio", y = "missing"): graphs exposure and contribution.

Details

The matches matrix contains as many rows as there are stocks in the data slot of original and as many columns as there are matched portfolios. The row labels of the matrix are the values of original@data[["id.var"]] and each column is a matched portfolio. The cell values are the weights of the stock in the portfolio.

Author(s)

Daniel Gerlanc [email protected]

Examples


m.p <- new("matchedPortfolio")

m.p <- new("matchedPortfolio")

Class "matchedPortfolioCollection"

Description

A collection of objects of class matchedPortfolio.

Objects from the Class

Objects can be created by calls of the form new("matchedPortfolioCollection", ...).

Slots

data:: Object of class "list" A list of objects of class matchedPortfolio.

Methods

No methods defined with class "matchedPortfolioCollection" in the signature.

Author(s)

Jeff Enos [email protected]

Class "performance"

Description

Return, per-security return, and exposed portfolio object for one period.

Objects from the Class

Objects can be created by calls of the form new("performance", ...).

Slots

ret:: Object of class "numeric" containing the total return for the period.
profit:: Object of class "numeric" containing the profit for the period, as a monetary amount.
missing.price:: Object of class "numeric" containing the number of missing prices encountered during performance calculation.
missing.return:: Object of class "numeric" containing the number of missing returns encountered during performance calculation.
ret.detail:: Object of class "data.frame" containing the per-security return detail for the period.
t.plus.one:: Object of class "portfolioBasic" containing the portfolio at the end of the period.

Methods

plot: signature(x = "performance", y = "missing"): Plot this object.
show: signature(object = "performance"): show this object, briefly.
summary: signature(object = "performance"): display a summary of this object.

Author(s)

Jeff Enos [email protected]

Class "portfolio"

Description

Class "portfolio" extends class "portfolioBasic" to include price and share information. Price information must be included in the supplementary "data" slot.

Objects from the Class

Objects can be created by calls of the form new("portfolio", ...).

Slots

equity:

Object of class "numeric" containing the equity for this portfolio.e

file:

Object of class "character" containing the file from which this portfolio was loaded, if applicable.

price.var:

Object of class "character" containing the name of the column in the "data" slot to be used in share and weight calculations.

shares:

Object of class "data.frame" containing a data frame of shares for each position. Must have a unique column called "id".

name:

Object of class "character" containing the name for this portfolio.

instant:

Object of class "ANY" containing the instant to which the portfolio pertains.

data:

Object of class "data.frame" containing supplementary information about the positions in this portfolio. Must include a unique "id" column.

id.var:

Object of class "character" containing the name of the column in the data slot to be used as a unique identifier.

symbol.var:

Object of class "character" containing the name of the column in the "data" slot to be used as a descriptive symbol.

in.var:

Object of class "character" containing the name of the column in the "data" slot to be used as a rank vector in calls to create.

weight.var:

Object of class "character" containing the name of the column in the "data" slot to be used as weight overrides in calls to create.

weight.style:

Object of class "character" specifying how to calculate weights in this portfolio. Valid entries are:

"sides.separate": The default. Calculate weight of a position with respect to the total market value of positions on the same side.
"long.tmv": Calculate weight of a position with respect to the total market value of long positions.
"short.tmv": Calculate weight of a position with respect to the (positive) total market value of short positions.
"reference.equity": Calculate weight of a position relative to the reference equity in the equity slot. The equity slot must contain a numeric vector of length 1 for this style.

ret.var:

Object of class "character" containing the name of the column in the "data" slot to be used as the return in calls to performance.

type:

Object of class "character" containing the type of weight formation to use in calls to create. May be one of "relative", "equal", "linear", "sigmoid", "centroid", or "complex". Defaults to equal.

size:

Object of class "characterOrNumeric" containing the size of the portfolio to use in calls to create. May either contain the number of securities per side or one of "decile", "quintile", "quartile", "tercile", or "demile". Defaults to quintile.

weights:

Object of class "data.frame" containing the data frame of weights for this portfolio's positions. Must contain a unique column called "id".

Extends

Class "portfolioBasic", directly.

Methods

+: signature(e1 = "portfolio", e2 = "portfolio")
all.equal: signature(target = "portfolio", current = "portfolio"): Compare two portfolio objects for "near equality". Two portfolio objects are all.equal iff they are all.equal as portfolioBasic objects, their shares slots contain exactly the same set of securities and shares vectors that are all.equal.
calcShares: signature(object = "portfolio"): calculate shares from price and weight information, and store the results in the shares slot.
calcWeights: signature(object = "portfolio"): calculate weights from share and price information, and store the results in the weights slot.
create: signature(object = "portfolio"): create a portfolio object in the same manner as portfolioBasic, but also compute share amounts.
expandData: signature(object = "portfolio"): ...
expose: signature(object = "portfolio", trades = "trades"): ...
getYahooData: signature(object = "portfolio", symbol.var = "character"): ...
performance: signature(object = "portfolio"): ...
securityInfo: signature(object = "portfolio", id = "character"): display information about position id within this portfolio.
getYahooData: signature(object = "portfolio", symbol.var = "character"): Returns data for P/E Ratio, Book Value, Market Cap, Price/Book, and Price/Sales.
updatePrices: signature(object = "portfolio", id = "character", price = "numeric"): ...

Author(s)

Jeff Enos [email protected]

Class "portfolioBasic"

Description

An object of the lightweight class "portfolioBasic" contains a data frame of weights and a data frame of supplementary information.

Objects from the Class

Objects can be created by calls of the form new("portfolioBasic", ...).

Slots

name:: Object of class "character" containing the name of this portfolio.
instant:: Object of class "ANY" containing an instant to which this portfolio pertains.
data:: Object of class "data.frame" containing supplementary information about the positions in this portfolio. Must include a unique column specified in the id.var slot.
id.var:: Object of class "character" containing the name of the column in the data slot to be used as a unique identifier.
symbol.var:: Object of class "character" containing the name of the column in the data slot to be used as a descriptive symbol.
in.var:: Object of class "character" containing the name of the column in the data slot to be used as a rank vector in calls to create.
weight.var:: Object of class "character" containing the name of the column in the data slot to be used as weight overrides in calls to create.
ret.var:: Object of class "character" containing the name of the column in the data slot to be used as the return in calls to performance.
type:: Object of class "character" containing the type of weight formation to use in calls to create. May be one of "relative", "equal", "linear", "sigmoid", "centroid", or "complex". Defaults to equal.
size:: Object of class "characterOrNumeric" containing the size of the portfolio to use in calls to create. May either contain the number of securities per side or one of "decile", "quintile", "quartile", "tercile", or "demile". Defaults to quintile.
weights:: Object of class "data.frame" containing the data frame of weights for this portfolio's positions. Must contain a unique column called "id".

Methods

+: signature(e1 = "portfolioBasic", e2 = "portfolioBasic")
all.equal: signature(target = "portfolioBasic", current = "portfolioBasic"): Compare two portfolioBasic objects for "near equality". Two portfolioBasic objects are all.equal iff their weights slots contain exactly the same set of securities and weight vectors that are all.equal.
balance: signature(object = "portfolioBasic", in.var = "character"): balances the positions in portfolio object to be neutral to the categories specified by column in.var in the data slot.
contribution: signature(object = "portfolioBasic", contrib.var = "character"): returns one data.frame with contribution analysis for each element of contrib.var. All results are returned in a list.
create: signature(object = "portfolioBasic"): use this object's creation parameters (such as in slots size and type) to create and return a new object of class portfolioBasic.
exposure: signature(object = "portfolioBasic", exp.var = "character"): returns one data.frame with exposure analysis for each element of contrib.var. All results are returned in a list.
matching: signature(object = "portfolioBasic", covariates = "character": returns a matchedPortfolio object containing n.matches matched portfolios. object is the portfolioBasic to be matched. covariates is a character vector of the attributes on which to match.
performance: signature(object = "portfolioBasic"): returns a list containing performance results.
plot: signature(x = "portfolioBasic", y = "missing"): Plot this object.
portfolioDiff: signature(object = "portfolioBasic", x = "portfolioBasic"): computes the difference, as a portfolioBasic object, between two portfolios.
scaleWeights: signature(object = "portfolioBasic"): scale weights to the weights supplied in the target parameter. To restrict the set of positions whose weights are scaled, use the condition argument.
show: signature(object = "portfolioBasic"): display this object, briefly.
summary: signature(object = "portfolioBasic"): display descriptive information about this portfolio.
initialize: signature(object = "portfolioBasic"): initialize the portfolio by calling create.
mapMarket: signature(object = "portfolioBasic"): create a map of the market plot of the portfolio.

Matched portfolios

The matching method allows one to benchmark a portfolio against a similar portfolio formed from other stocks in the universe. The universe consists of all the stocks in the data slot of original.

matching calculates a propensity score for each stock in the universe. covariates determines which attributes are used to calculate the propensity score. covariates must refer to the names of columns in the data slot of original.

Matching accepts an optional argument, method, which sets the algorithm for determining the best match for each stock. There are 2 available algorithms, "greedy" and "sample". "greedy" is the default and generates 1 matched portfolio. "sample" randomly matches each stock in original with one of the stocks in the universe. Although the matching is random, stocks in original are most likely to be matched with stocks having similar propensity scores.

n.matches is another optional argument to matching which determines the number of matched portfolios to generate. Requesting more than 1 matched portfolio. (n.matches > 1) while using greedy is not allowed. When using sample, there is no bound on n.matches.

Author(s)

Jeff Enos [email protected] with contributions from Daniel Gerlanc [email protected]

Examples

data(dow.jan.2005)

p <- new("portfolioBasic",
         id.var = "symbol",
         in.var = "price",
         sides = "long",
         ret.var = "month.ret",
         data = dow.jan.2005)

summary(p)

exposure(p, exp.var = c("price", "sector"))
performance(p)
contribution(p, contrib.var = c("cap.bil", "sector"))

p <- new("portfolioBasic",
         id.var = "symbol",
         in.var = "price",
         type = "linear",
         sides = c("long", "short"),
         ret.var = "month.ret",
         data = dow.jan.2005)

summary(p)

exposure(p, exp.var = c("price", "sector"))
performance(p)
contribution(p, contrib.var = c("cap.bil","sector"))
data(dow.jan.2005)

p <- new("portfolioBasic",
         id.var = "symbol",
         in.var = "price",
         sides = "long",
         ret.var = "month.ret",
         data = dow.jan.2005)

summary(p)

exposure(p, exp.var = c("price", "sector"))
performance(p)
contribution(p, contrib.var = c("cap.bil", "sector"))

p <- new("portfolioBasic",
         id.var = "symbol",
         in.var = "price",
         type = "linear",
         sides = c("long", "short"),
         ret.var = "month.ret",
         data = dow.jan.2005)

summary(p)

exposure(p, exp.var = c("price", "sector"))
performance(p)
contribution(p, contrib.var = c("cap.bil","sector"))

Class "tradelist"

Description

Note: This class is a rough first pass and will change drastically in future releases.

An object of the class "tradelist" containing a data frame of trades and a data frame of supplementary information.

Objects from the Class

Objects can be created by calls of the form new("tradelist", orig, target, ...).

Slots

type:: Object of class "character" specifying the type of the tradelist. Must be "all" or "ranks".
id.var:: Object of class "character" containing the name of the column in the data slot to be used as a unique identifier.
price.var:: Object of class "character" specifying the column in the data slot to be used as the reference price. Defaults to price.usd.
candidates:: Object of class "data.frame" containing one row for each candidate trade.
ranks:: Object of class "data.frame" where candidate trades have been interleaved by trade type (B,S,X,C) and assigned a unique rank, "rank.t".
chunks:: Object of class "data.frame" that contains one row for each chunk, a smaller portion of an order.
swaps:: Object of class "data.frame" where buys and sells have been matched with other shorts and covers of similar market value and desirability.
swaps.actual:: Object of class "data.frame" where the least desirable chunks that would exceed if "turnover" if ordered have been removed.
actual:: Object of class "data.frame" where the chunks have been rolled up into one row/order per security.
final:: Object of class "trades" containing the most basic information on the set of trades in the tradelist.
chunks.actual:: Object of class "data.frame" where "swaps" have been turned back into chunks and each chunk has its own row.
sorts:: Object of class "optionalList" which may be interpreted as a list of key-value pairs. The key is the name of the sort and must exist as a column in the "data" slot. The numeric value expresses the relative weight of the sort.
rank.sorts:: Object of class "list", where the names of the elements are the names of the sorts defined in the "sorts" list and the elements are data frames, each of which contains a ranking of the candidate trades created by applying an individual sort.
regions:: Object of class "character"
chunk.usd:: Object of class "numeric" that expresses the minimum unsigned market value in US dollars of a chunk. Defaults to $10,000.
trade.usd.min:: Object of class "numeric" that expresses the minimum unsigned market value a trade must have in order to be placed. Trades of lower market value are removed from the "candidates" data.frame and appended to the "restricted" data frame.
restrictions:: Object of class "data.frame" with 1 row for each trade and three columns, "id", "type", and "reason". "id" uniquely identifies the trade, "type" accepts a value of B, S, C, or X (buy, sell, cover, or short), expressing the type of trade that is prohibited, and "reason" is a label expressing why the restriction exists.
restricted:: Object of class "data.frame" that contains one row for every trade for which a restriction exists
to.equity:: Object of class "logical" expressing whether or not the algorithm should trade towards the value of target.equity
turnover:: Object of class "numeric" that expresses the maximum unsigned market value of all trades effected in one session.
tca:: Object of class "character" expressing whether or not to use trade cost adjustment.
rank.gain.min:: Object of class "numeric" specifying the minimum amount of difference between two trades' rank required in order for the pair to be considered as a swap.
target.equity:: Object of class "numeric" expressing the unsigned market value of the target portfolio.
mv.long.orig:: Object of class "numeric" The market value of the long side of the original portfolio.
mv.short.orig:: Object of class "numeric" The unsigned market value of the short side of the original portfolio
unrestricted:: Object of class "logical" specifying whether any restrictions should be applied, including checks for price and volume.
data:: Object of class "data.frame" containing supplementary information about the "tradelist". Must contain an "id" column, a "price.usd" column, a "volume" column, and a column named after each element listed in "sorts".
verbose:: Object of class "logical" controlling whether methods on this object should be verbose. Defaults to FALSE.

Methods

actualCols: signature(object = "tradelist"): Returns a vector with the following elements: "id", "side", "shares", "mv", names(object@sorts), and "rank.t"
calcActual: signature(object = "tradelist"): Rolls up the chunks calculated in calcChunksActual into single orders and stores the result as a data frame in the "actual" slot.
calcCandidates: signature(object = "tradelist", orig = "portfolio", target = "portfolio"): Builds a data frame of candidate trades with one row per trade by determining which positions have different numbers of shares in the original and target portfolios. Removes trades in the "restrictions" data frame, trades with a market value below "trade.usd.min", and trades that would cause a side change in one session, and appends these trades to the "restricted data frame."
calcChunksActual: signature(object = "tradelist"): Turns the swaps calculated in calcSwapsActual back into chunks and stores the results in a data frame in the "actual.chunks" slot.
calcChunks: signature(object = "tradelist"): Examines the data frame stored in the "ranks" slot, breaks the candidate trades into chunks of size "chunk.usd" or smaller, and stores the results in the "chunks" slot
calcSwapsActual: signature(object = "tradelist"): Examines the data frame stored in the "swaps" slot and removes swaps, which had they been processed as orders, would have exceeded the specified "turnover" of the tradelist. Stores the results as a data frame in the "swaps.actual" slot.
calcSwaps: signature(object = "tradelist"): Using the "chunks" data frame created by the calcChunks method, pairs attractive chunks with other attractive chunks of similar market value and stores the results as a data frame in the swaps slot.
calcRanks: signature(object = "tradelist"): Using information from the candidates data frame, interleaves the trades and calculates an absolute rank for each trade, "rank.t". Stores the results in the ranks slot
candidatesCols: signature(object = "tradelist"): Returns a vector of class character containing the following elements: "id", "orig", "target", "side", "shares", "mv"
chunksCols: signature(object = "tradelist"): Returns a vector of class character containing the following elements: rankCols(object), "tca.rank", "chunk.shares", "chunk.mv", "chunk".
dummyChunks: signature(object = "tradelist"): Creates a data frame of dummy chunks for a given side and dollar amount (total.usd). The supplied dollar amount, together with the tradelist object's chunk size, determines the number of rows in the resulting data frame.
initialize: signature(.Object = "tradelist"): Transparently calls calcCandidates, calcRanks, calcChunks, calcSwaps, calcSwapsActual, calcChunksActual, calcActual to construct the tradelist object.
ranksCols: signature(object = "tradelist"): Returns a vector of class character containing the following elements: "id", "orig", "target", "side", "shares", "mv", names(object@sorts), "rank.t"
restrictedCols: signature(object = "tradelist"): Returns a vector of class character containing the following elements: candidatesCols(object), "reason".
securityInfo: signature(object = "tradelist", id = "character"): Returns detailed information regarding a security in the tradelist.
show: signature(object = "tradelist"): Prints a detailed summary of tradelist attributes.
trimSide: signature(object = "tradelist"): If the market value of the side passed as the "side" parameter to this function is is greater than market value of the side as specified by the "value" parameter, excises the least desirable trades on that side until the the market value of that side is less than value. Returns a copy of the data frame stored in the "actual" slot with the trades that meet the forementioned conditions removed.

Author(s)

Daniel Gerlanc [email protected]

Class "trades"

Description

An object of the class "trades" contains a data frame with columns "id", "side", and "shares" describing a simple list of trades to be performed.

Objects from the Class

Objects can be created by calls of the form new("trades", ...).

Slots

trades:: Object of class "data.frame" with columns "id", "side", and "shares".

Methods

No methods defined with class "trades" in the signature.

Author(s)

Kyle Campbell and Daniel Gerlanc

Examples


df <- data.frame(id = c(1,2,3), side = c("B","X","C"), shares = c(10,20,30))

t <- new("trades", trades = df)

df <- data.frame(id = c(1,2,3), side = c("B","X","C"), shares = c(10,20,30))

t <- new("trades", trades = df)

Calculate Position Weights

Description

Compute position weights of various types from an input variable.

Usage

weight(x, in.var, type, size, sides,
       weight.var = NULL, verbose = FALSE)
weight(x, in.var, type, size, sides,
       weight.var = NULL, verbose = FALSE)

Arguments

`x`	A data.frame containing the columns `in.var` and `weight.var` (if necessary).
`in.var`	Character vector specifying the column in `x` that contains a ranking from which weights can be computed.
`type`	Character vector specifying the method to use for weight creation. Can be one of `c("relative", "equal", "linear", "sigmoid", "centroid", "complex")`.
`size`	Character or numeric vector specifying the number of desired non-na weights per side in the result. Can either be a positive number or one of `"all", "decile", "quintile", "tercile", "demile")`.
`sides`	Character vector specifying the sides for which to create weights. May be any nonempty subset of `"long", "short"`.
`weight.var`	Numeric vector containing specifying the column in `x` that contains weight overrides. Overrides are applied after weights have been computed. Defaults to NULL.
`verbose`	Be verbose. Defaults to FALSE.

Value

A numeric vector of weights the same length as x.

Author(s)

Jeff Enos [email protected]

Examples

data <- data.frame(in.var = 1:50, weight.var = NA)
data$in.var <- as.numeric(data$in.var)

weight(data, in.var = "in.var", type = "linear", size = "quintile",
       sides = c("long", "short"))

data$weight.var[25] <- -0.05
weight(data, in.var = "in.var", type = "linear", size = "quintile",
       sides = c("long", "short"), weight.var = "weight.var")
data <- data.frame(in.var = 1:50, weight.var = NA)
data$in.var <- as.numeric(data$in.var)

weight(data, in.var = "in.var", type = "linear", size = "quintile",
       sides = c("long", "short"))

data$weight.var[25] <- -0.05
weight(data, in.var = "in.var", type = "linear", size = "quintile",
       sides = c("long", "short"), weight.var = "weight.var")

Package 'portfolio'

Help Index

Analysing equity portfolios

Description

Details

Author(s)

Assay Research rankings as of 2004-12-31

Description

Usage

Format

Examples

Class "contribution"

Description

Objects from the Class

Slots

Methods

Author(s)

DJIA for January, 2005

Description

Usage

Format

Examples

Class "exposure"

Description

Objects from the Class

Slots

Methods

Author(s)

Security data of large global companies for 2004

Description

Usage

Format

Examples

Create a Map of the Market

Description

Usage

Arguments

Author(s)

Examples

Class "matchedPortfolio"

Description

Objects from the Class

Slots

Methods

Details

Author(s)

See Also

Examples

Class "matchedPortfolioCollection"

Description

Objects from the Class

Slots

Methods

Author(s)

Class "performance"

Description

Objects from the Class

Slots

Methods

Author(s)

Class "portfolio"

Description

Objects from the Class

Slots

Extends

Methods

Author(s)

Class "portfolioBasic"

Description

Objects from the Class

Slots

Methods

Matched portfolios

Author(s)

Examples

Class "tradelist"

Description

Objects from the Class

Slots

Methods