Package 'strand' reference manual

Title:	A Framework for Investment Strategy Simulation
Description:	Provides a framework for performing discrete (share-level) simulations of investment strategies. Simulated portfolios optimize exposure to an input signal subject to constraints such as position size and factor exposure. For background see L. Chincarini and D. Kim (2010, ISBN:978-0-07-145939-6) "Quantitative Equity Portfolio Management".
Authors:	Jeff Enos [cre, aut, cph], David Kane [aut], Ben Czekanski [ctb], Robert Hoover [ctb], Jack Luby [ctb], Nils Wallin [ctb]
Maintainer:	Jeff Enos <[email protected]>
License:	GPL-3
Version:	0.2.0
Built:	2025-03-12 05:48:30 UTC
Source:	https://github.com/strand-tech/strand

strand: a framework for investment strategy simulation

Description

The strand package provides a framework for performing discrete (share-level) simulations of investment strategies. Simulated portfolios optimize exposure to an input signal subject to constraints such as position size and factor exposure.

For an introduction to running simulations using the package, see vignette("strand"). For details on available methods see the documentation for the Simulation class.

Author(s)

Jeff Enos [email protected] and David Kane [email protected]

Examples

# Load up sample data
data(sample_secref)
data(sample_pricing)
data(sample_inputs)

# Load sample configuration
config <- example_strategy_config()

# Override config file end date to run a one-week sim
config$to <- as.Date("2020-06-05")

# Create the Simulation object and run
sim <- Simulation$new(config,
                      raw_input_data = sample_inputs,
                      raw_pricing_data = sample_pricing,
                      security_reference_data = sample_secref)
sim$run()

# Print overall statistics
sim$overallStatsDf()

# Access tabular result data
head(sim$getSimSummary())
head(sim$getSimDetail())
head(sim$getPositionSummary())
head(sim$getInputStats())
head(sim$getOptimizationSummary())
head(sim$getExposures())

# Plot results
## Not run: 
sim$plotPerformance()
sim$plotMarketValue()
sim$plotCategoryExposure("sector") 
sim$plotFactorExposure(c("value", "size"))
sim$plotNumPositions()

## End(Not run)
# Load up sample data
data(sample_secref)
data(sample_pricing)
data(sample_inputs)

# Load sample configuration
config <- example_strategy_config()

# Override config file end date to run a one-week sim
config$to <- as.Date("2020-06-05")

# Create the Simulation object and run
sim <- Simulation$new(config,
                      raw_input_data = sample_inputs,
                      raw_pricing_data = sample_pricing,
                      security_reference_data = sample_secref)
sim$run()

# Print overall statistics
sim$overallStatsDf()

# Access tabular result data
head(sim$getSimSummary())
head(sim$getSimDetail())
head(sim$getPositionSummary())
head(sim$getInputStats())
head(sim$getOptimizationSummary())
head(sim$getExposures())

# Plot results
## Not run: 
sim$plotPerformance()
sim$plotMarketValue()
sim$plotCategoryExposure("sector") 
sim$plotFactorExposure(c("value", "size"))
sim$plotNumPositions()

## End(Not run)

Run an example shiny app

Description

Runs a shiny app that allows interactively configuring and running a simulation. Once the simulation is finished results, such as performance statistics and plots of exposures, are available in a results panel.

Usage

example_shiny_app()
example_shiny_app()

Examples


if (interactive()) {
  example_shiny_app()
}

if (interactive()) {
  example_shiny_app()
}

Load example strategy configuration

Description

Loads an example strategy configuration file for use in examples.

Usage

example_strategy_config()
example_strategy_config()

Value

An object of class list that contains the example configuration. The list object is the result of loading the package's example yaml configuration file application/strategy_config.yaml.

Examples


config <- example_strategy_config()
names(config$strategies)
show(config$strategies$strategy_1)

config <- example_strategy_config()
names(config$strategies)
show(config$strategies$strategy_1)

Make Basic Flextable

Description

Make a flextable with preferred formatting

Usage

make_ft(x, title = NULL, col_names = NULL, hlines = "all")
make_ft(x, title = NULL, col_names = NULL, hlines = "all")

Arguments

`x`	The data.frame to use for flextable
`title`	The string to use as the table title
`col_names`	A character vector of preferred column names for flextable. Length of character vector must be equal to the number of columns. Defaults to NULL, in which case the column names of x are used in the flextable.
`hlines`	The row numbers to draw horizontal lines beneath. Defaults to "all", can be "all", "none", or a numeric vector.

Value

A flextable object with the argued formatting

Portfolio optimization class

Description

The PortOpt object is used to set up and solve a portfolio optimization problem.

Details

A PortOpt object is configured in the same way as a Simulation object, by supplying configuration in a yaml file or list to the object constructor. Methods are available for adding constraints and retrieving information about the optimization setup and results. See the package vignette for information on configuration file setup.

Methods

Method `new()`

Create a new PortOpt object.

Usage

PortOpt$new(config, input_data)

Arguments

config

An object of class list or character. If the value passed is a character vector, it should be of length 1 and specify the path to a yaml configuration file that contains the object's configuration info. If the value passed is of class list(), the list should contain the object's configuration info in list form (e.g, the return value of calling yaml.load_file on the configuration file).

input_data

A data.frame that contains all necessary input for the optimization.

If the top-level configuration item price_var is not set, prices will be expected in the ref_price column of input_data.

Returns

A new PortOpt object.

Examples

library(dplyr)
data(sample_secref)
data(sample_inputs)
data(sample_pricing)

# Construct optimization input for one day from sample data. The columns
# of the input data must match the input configuration.
optim_input <-
  inner_join(sample_inputs, sample_pricing,
             by = c("id", "date")) %>%
  left_join(sample_secref, by = "id") %>%
  filter(date %in% as.Date("2020-06-01")) %>%
  mutate(ref_price = price_unadj,
                shares_strategy_1 = 0)

opt <-
  PortOpt$new(config = example_strategy_config(),
              input_data = optim_input)

# The problem is not solved until the \code{solve} method is called
# explicitly.
opt$solve()

Method `setVerbose()`

Set the verbose flag to control the amount of informational output.

Usage

PortOpt$setVerbose(verbose)

Arguments

verbose: Logical flag indicating whether to be verbose or not.

Returns

No return value, called for side effects.

Method `addConstraints()`

Add optimization constraints.

Usage

PortOpt$addConstraints(constraint_matrix, dir, rhs, name)

Arguments

constraint_matrix

Matrix with one row per constraint and $(S+1) \times N$ columns, where S is number of strategies and N is the number of stocks.

The variables in the optimization are

$x_{1,1}, x_{2,1}, \ldots, x_{N,1},$

$x_{1,2}, x_{2,2}, \ldots, x_{N,2},$

$\vdots$

$x_{1,S}, x_{2,S}, \ldots, x_{N,S},$

$y_1, \ldots, y_N$

The first $N \times S$ variables are the individual strategy trades. Variable $x_{i,s}$ represents the signed trade for stock i in strategy s. The following N auxillary variables $y_1, \ldots, y_N$ represent the absolute value of the net trade in each stock. So for a stock i, we have:

$y_i = \sum_s |x_{i,s}|$

dir

Vector of class character of length nrow(constraint_matrix) that specifies the direction of the constraints. All elements must be one of ">=", "==", or "<=".

rhs

Vector of class numeric of length nrow(constraint_matrix) that specifies the bounds of the constraints.

name

PortOpt$getMaxPosition()

Returns

An object of class data.frame that contains the limits on size for long and short positions for each strategy and security. The columns in the data frame are:

id: Security identifier.
strategy: Strategy name.
max_pos_lmv: Maximum net market value for a long position.
max_pos_smv: Maximum net market value for a short position.

Method `getMaxOrder()`

Provide information about the maximum order size allowed for each security and strategy.

Usage

PortOpt$getMaxOrder()

Returns

An object of class data.frame that contains the limit on order size for each strategy and security. The columns in the data frame are:

id: Security identifier.
strategy: Strategy name.
max_order_gmv: Maximum gross market value allowed for an order.

Method `summaryDf()`

Provide aggregate level optimization information if the problem has been solved.

Usage

PortOpt$summaryDf()

Returns

A data frame with one row per strategy, including the joint (net) level, and columns for starting and ending market values and factor expoure values.

Method `print()`

Print summary information.

Usage

PortOpt$print()

Returns

No return value, called for side effects.

Method `clone()`

The objects of this class are cloneable with this method.

Usage

PortOpt$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `PortOpt$new`
## ------------------------------------------------

library(dplyr)
data(sample_secref)
data(sample_inputs)
data(sample_pricing)

# Construct optimization input for one day from sample data. The columns
# of the input data must match the input configuration.
optim_input <-
  inner_join(sample_inputs, sample_pricing,
             by = c("id", "date")) %>%
  left_join(sample_secref, by = "id") %>%
  filter(date %in% as.Date("2020-06-01")) %>%
  mutate(ref_price = price_unadj,
                shares_strategy_1 = 0)

opt <-
  PortOpt$new(config = example_strategy_config(),
              input_data = optim_input)

# The problem is not solved until the \code{solve} method is called
# explicitly.
opt$solve()
## ------------------------------------------------
## Method `PortOpt$new`
## ------------------------------------------------

library(dplyr)
data(sample_secref)
data(sample_inputs)
data(sample_pricing)

# Construct optimization input for one day from sample data. The columns
# of the input data must match the input configuration.
optim_input <-
  inner_join(sample_inputs, sample_pricing,
             by = c("id", "date")) %>%
  left_join(sample_secref, by = "id") %>%
  filter(date %in% as.Date("2020-06-01")) %>%
  mutate(ref_price = price_unadj,
                shares_strategy_1 = 0)

opt <-
  PortOpt$new(config = example_strategy_config(),
              input_data = optim_input)

# The problem is not solved until the \code{solve} method is called
# explicitly.
opt$solve()

Sample security inputs for examples and testing

Description

A dataset containing sample security input data for 492 securities and 65 weekdays, from 2020-06-01 to 2020-08-31. Data items include average trading dollar volume, market cap, and normalized size and value factors. The pricing data used to construct the dataset was downloaded using the Tiingo Stock API and is used with permission. Fundamental data items were downloaded from EDGAR.

Usage

data(sample_inputs)
data(sample_inputs)

Format

A data frame with 31980 rows and 7 variables:

date: Input date. It is assumed that the input data for day X is known at the beginning of day X (e.g., the data is as-of the previous day's close).
id: Security identifier.
rc_vol: Average dollar trading volume for the security over the past 20 trading days.
market_cap: Market capitalization, in dollars. The shares outstanding value used to calculate market cap is the latest value available at the beginning of the month.
book_to_price: Ratio of total equity to market cap. The stockholders' equity value used to calculate book to price is the latest value available at the beginning of the month.
size: Market cap factor normalized to be N(0,1) for each day.
value: Book to price factor normalized to be N(0,1) for each day.

Details

Data for most members of the S&P 500 are present. Some securities have been omitted due to data processing complexities. For example, securities for companies with multiple share classes have been omitted in the current version.

Values for shares outstanding and stockholders' equity downloaded from EDGAR may be inaccurate due to XBRL parsing issues.

Full code for reconstructing the dataset can be found in the pystrand repository.

Sample security pricing data for examples and testing

Description

A dataset containing sample security pricing data for 492 securities and 65 weekdays, from 2020-06-01 to 2020-08-31. This data was downloaded using the Tiingo Stock API and is redistributed with permission.

Usage

data(sample_pricing)
data(sample_pricing)

Format

A data frame with 31980 rows and 8 variables:

date: Pricing date.
id: Security identifier.
price_unadj: The unadjusted price of the security.
prior_close_unadj: The unadjusted prior closing price of the security.
dividend_unadj: The dividend for the security on an unadjusted basis, if any.
distribution_unadj: The distribution (e.g., spin-off) for the security on an unadjusted basis (note that there is no spin-off information in this dataset, so all values are zero).
volume: Trading volume for the security, in shares.
adjustment_ratio: The adjustment ratio for the security. For example, AAPL has an adjustment ratio of 0.25 to account for its 4:1 split on 2020-08-31.

Details

Full code for reconstructing the dataset can be found in the pystrand repository.

Sample security reference data for examples and testing

Description

A dataset containing sample reference data for the securities of 492 large companies. All securities in the dataset were in the S&P 500 for most or all of the period June-August 2020.

Usage

data(sample_secref)
data(sample_secref)

Format

A data frame with 492 rows and 4 variables:

id: Unique security identifier (the security's ticker).
name: Company name.
symbol: Human-readable symbol for display and reporting purposes. In the case of this dataset it is the same as the id variable.
sector: GICS sector for the company according to the Wikipedia page List of S&P 500 companies.

Show Best/Worst Performers

Description

Build a flextable object showing a Simulation's best and worst performers

Usage

show_best_worst(sim)
show_best_worst(sim)

Arguments

sim

A Simulation object to show the best and worst performers for

Show Strategy Configuration

Description

Build a flextable object showing a Simulation's configuration

Usage

show_config(sim)
show_config(sim)

Arguments

sim

A Simulation object to show the configuration for

Show Strategy Constraints

Description

Build a flextable object showing a Simulation's risk constraints

Usage

show_constraints(sim)
show_constraints(sim)

Arguments

sim

A Simulation object to show the configuration for

Show monthly returns

Description

Build a flextable object that shows a simulation's return by month by formatting the output of 'Simulation$overallReturnsByMonthDf'.

Usage

show_monthly_returns(sim)
show_monthly_returns(sim)

Arguments

sim

A Simulation object with results to display

Show Overall Stats Table

Description

Build a flextable object showing a Simulation's overall statistics

Usage

show_stats(sim)
show_stats(sim)

Arguments

sim

A Simulation object to show the statistics for

Simulation class

Description

Class for running a simulation and getting results.

Details

The Simulation class is used to set up and run a daily simulation over a particular period. Portfolio construction parameters and other simulator settings can be configured in a yaml file that is passed to the object's constructor. See vignette("strand") for information on configuration file setup.

Methods

Public methods

Simulation$new()
Simulation$setVerbose()
Simulation$setShinyCallback()
Simulation$getSecurityReference()
Simulation$run()
Simulation$getSimDates()
Simulation$getSimSummary()
Simulation$getSimDetail()
Simulation$getPositionSummary()
Simulation$getInputStats()
Simulation$getLooseningInfo()
Simulation$getOptimizationSummary()
Simulation$getExposures()
Simulation$getDelistings()
Simulation$getSingleStrategySummaryDf()
Simulation$plotPerformance()
Simulation$plotContribution()
Simulation$plotMarketValue()
Simulation$plotCategoryExposure()
Simulation$plotFactorExposure()
Simulation$plotNumPositions()
Simulation$plotTurnover()
Simulation$plotUniverseSize()
Simulation$plotNonInvestablePct()
Simulation$overallStatsDf()
Simulation$overallReturnsByMonthDf()
Simulation$print()
Simulation$writeFeather()
Simulation$readFeather()
Simulation$getConfig()
Simulation$writeReport()
Simulation$clone()

Method `new()`

Create a new Simulation object.

Usage

Simulation$new(
  config = NULL,
  raw_input_data = NULL,
  input_dates = NULL,
  raw_pricing_data = NULL,
  security_reference_data = NULL,
  delisting_data = NULL
)

Arguments

config: An object of class list or character, or NULL. If the value passed is a character vector, it should be of length 1 and specify the path to a yaml configuration file that contains the object's configuration info. If the value passed is of class list(), the list should contain the object's configuration info in list form (e.g, the return value of calling yaml.load_file on the configuration file). If the value passed is NULL, then there will be no configuration information associated with the simulation and it will not possible to call the run method. Setting config = NULL is useful when creating simulation objects into which results will be loaded with readFeather.
raw_input_data: A data frame that contains all of the input data (for all periods) for the simulation. The data frame must have a date column. Data supplied using this parameter will be used if the configuration option simulator/input_data/type is set to object. Defaults to NULL.
input_dates: Vector of class Date that specifies when input data should be updated. If data is being supplied using the raw_input_data parameter, then input_dates defaults to set of dates present in this data.
raw_pricing_data: A data frame that contains all of the input data (for all periods) for the simulation. The data frame must have a date column. Data supplied using this parameter will only be used if the configuration option simulator/pricing_data/type is set to object. Defaults to NULL.
security_reference_data: A data frame that contains reference data on the securities in the simulation, including any categories that are used in portfolio construction constraints. Note that the simulator will throw an error if there are input data records for which there is no entry in the security reference. Data supplied using this parameter will only be used if the configuration option simulator/secref_data/type is set to object. Defaults to NULL.
delisting_data: A data frame that contains delisting dates and associated returns. It must contain three columns: id (character), delisting_date (Date), and delisting_return (numeric). The date in the delisting_date column means the day on which a stock will be removed from the simulation portfolio. It is typically the day after the last day of trading. The delisting_return column reflects what, if any, P&L should be recorded on the delisting date. A delisting_return of -1 means that the shares were deemed worthless. The delisting return is multiplied by the starting net market value of the position to determine P&L for the delisted position on the delisting date. Note that the portfolio optimization does not include stocks that are being removed due to delisting. Data supplied using this parameter will only be used if the configuration option simulator/delisting_data/type is set to object. Defaults to NULL.

Returns

A new Simulation object.

Method `setVerbose()`

Set the verbose flag to control info output.

Usage

Simulation$setVerbose(verbose)

Arguments

verbose: Logical flag indicating whether to be verbose or not.

Returns

No return value, called for side effects.

Method `setShinyCallback()`

Set the callback function for updating progress when running a simulation in shiny.

Usage

Simulation$setShinyCallback(callback)

Arguments

callback: A function suitable for updating a shiny Progress object. It must have two parameters: value, indicating the progress amount, and detail, and detail, a text string for display on the progress bar.

Returns

No return value, called for side effects.

Method `getSecurityReference()`

Get security reference information.

Usage

Simulation$getSecurityReference()

Returns

An object of class data.frame that contains the security reference data for the simulation.

Method `run()`

Run the simulation.

Usage

Simulation$run()

Returns

No return value, called for side effects.

Method `getSimDates()`

Get a list of all date for the simulation.

Usage

Simulation$getSimDates()

Returns

A vector of class Date over which the simulation currently iterates: all weekdays between the 'from' and 'to' dates in the simulation's config.

Method `getSimSummary()`

Get summary information.

Usage

Simulation$getSimSummary(strategy_name = NULL)

Arguments

strategy_name: Character vector of length 1 that specifies the strategy for which to get detail data. If NULL data for all strategies is returned. Defaults to NULL.

Returns

An object of class data.frame that contains summary data for the simulation, by period, at the joint and strategy level. The data frame contains the following columns:

strategy: Strategy name, or 'joint' for the aggregate strategy.
sim_date: Date of the summary data.
market_fill_nmv: Total net market value of fills that do not net down across strategies.
transfer_fill_nmv: Total net market value of fills that represent "internal transfers", i.e., fills in one strategy that net down with fills in another. Note that at the joint level this column by definition is 0.
market_order_gmv: Total gross market value of orders that do not net down across strategies.
market_fill_gmv: Total gross market value of fills that do not net down across strategies.
transfer_fill_gmv: Total gross market value of fills that represent "internal transfers", i.e., fills in one strategy that net down with fills in another.
start_nmv: Total net market value of all positions at the start of the period.
start_lmv: Total net market value of all long positions at the start of the period.
start_smv: Total net market value of all short positions at the start of the period.
end_nmv: Total net market value of all positions at the end of the period.
end_gmv: Total gross market value of all positions at the end of the period.
end_lmv: Total net market value of all long positions at the end of the period.
end_smv: Total net market value of all short positions at the end of the period.
end_num: Total number of positions at the end of the period.
end_num_long: Total number of long positions at the end of the period.
end_num_short: Total number of short positions at the end of the period.
position_pnl: The total difference between the end and start market value of positions.
trading_pnl: The total difference between the market value of trades at the benchmark price and at the end price. Note: currently assuming benchmark price is the closing price, so trading P&L is zero.
gross_pnl: Total P&L gross of costs, calculated as position_pnl + trading_pnl.
trade_costs: Total trade costs (slippage).
financing_costs: Total financing/borrow costs.
net_pnl: Total P&L net of costs, calculated as gross_pnl - trade_costs - financing_costs.
fill_rate_pct: Total fill rate across all market orders, calculated as 100 * market_fill_gmv / market_order_gmv.
num_investable: Number of investable securities (size of universe).

Method `getSimDetail()`

Get detail information.

Usage

Simulation$getSimDetail(
  sim_date = NULL,
  strategy_name = NULL,
  security_id = NULL,
  columns = NULL
)

Arguments

sim_date: Vector of length 1 of class Date or character that specifies the period for which to get detail information. If NULL then data from all periods is returned. Defaults to NULL.
strategy_name: Character vector of length 1 that specifies the strategy for which to get detail data. If NULL data for all strategies is returned. Defaults to NULL.
security_id: Character vector of length 1 that specifies the security for which to get detail data. If NULL data for all securities is returned. Defaults to NULL.
columns: Vector of class character specifying the columns to return. This parameter can be useful when dealing with very large detail datasets.

Returns

An object of class data.frame that contains security-level detail data for the simulation for the desired strategies, securities, dates, and columns. Available columns include:

id: Security identifier.
strategy: Strategy name, or 'joint' for the aggregate strategy.
sim_date: Date to which the data pertains.
shares: Shares at the start of the period.
int_shares: Shares at the start of the period that net down with positions in other strategies.
ext_shares: Shares at the start of the period that do not net down with positions in other strategies.
order_shares: Order, in shares.
market_order_shares: Order that does not net down with orders in other strategies, in shares.
transfer_order_shares: Order that nets down with orders in other strategies, in shares.
fill_shares: Fill, in shares.
market_fill_shares: Fill that does not net down with fills in other strategies, in shares.
transfer_fill_shares: Fill that nets down with fills in other strategies, in shares.
end_shares: Shares at the end of the period.
end_int_shares: Shares at the end of the period that net down with positions in other strategies.
end_ext_shares: Shares at the end of the period that do not net down with positions in other strategies.
start_price: Price for the security at the beginning of the period.
end_price: Price for the security at the end of the period.
dividend: Dividend for the security, if any, for the period.
distribution: Distribution (e.g., spin-off) for the security, if any, for the period.
investable: Logical indicating whether the security is part of the investable universe. The value of the flag is set to TRUE if the security has not been delisted and satisfies the universe criterion provided (if any) in the simulator/universe configuration option.
delisting: Logical indicating whether a position in the security was removed due to delisting. If delisting is set to TRUE, the gross_pnl and net_pnl columns will contain the P&L due to delisting, if any. P&L due to delisting is calculated as the delisting return times the start_nmv of the position.
position_pnl: Position P&L, calculated as shares * (end_price + dividend + distribution - start_price)
trading_pnl: The difference between the market value of trades at the benchmark price and at the end price. Note: currently assuming benchmark price is the closing price, so trading P&L is zero.
trade_costs: Trade costs, calculated as a fixed percentage (set in the simulation configuration) of the notional of the market trade (valued at the close).
financing_costs: Financing cost for the position, calculated as a fixed percentage (set in the simulation configuration) of the notional of the starting value of the portfolio's external positions. External positions are positions held on the street and are recorded in the ext_shares column.
gross_pnl: Gross P&L, calculated as position_pnl + trading_pnl.
net_pnl: Net P&L, calculated as gross_pnl - trade_costs - financing_costs.
market_order_nmv: Net market value of the order that does not net down with orders in other strategies.
market_fill_gmv: Gross market value of the order that does not net down with orders in other strategies.
market_fill_nmv: Net market value of the fill that does not net down with orders in other strategies.
market_fill_gmv: Gross market value of the fill that does not net down with orders in other strategies.
transfer_fill_nmv: Net market value of the fill that nets down with fills in other strategies.
transfer_fill_gmv: Gross market value of the fill that nets down with fills in other strategies.
start_nmv: Net market value of the position at the start of the period.
end_nmv: Net market value of the position at the end of the period.
end_gmv: Gross market value of the position at the end of the period.

Method `getPositionSummary()`

Get summary information by security. This method can be used, for example, to calculate the biggest winners and losers over the course of the simulation.

Usage

Simulation$getPositionSummary(strategy_name = NULL)

Arguments

strategy_name: Character vector of length 1 that specifies the strategy for which to get detail data. If NULL data for all strategies is returned. Defaults to NULL.

Returns

An object of class data.frame that contains summary information aggregated by security. The data frame contains the following columns:

id: Security identifier.
strategy: Strategy name, or 'joint' for the aggregate strategy.
gross_pnl: Gross P&L for the position over the entire simulation.
gross_pnl: Net P&L for the position over the entire simulation.
average_market_value: Average net market value of the position over days in the simulation where the position was not flat.
total_trading: Total gross market value of trades for the security.
trade_costs: Total cost of trades for the security over the entire simulation.
trade_costs: Total cost of financing for the position over the entire simulation.
days_in_portfolio: Total number of days there was a position in the security in the portfolio over the entire simulation.

Method `getInputStats()`

Get input statistics.

Usage

Simulation$getInputStats()

Returns

An object of class data.frame that contains statistics on select columns of input data. Statistics are tracked for the columns listed in the configuration variable simulator/input_data/track_metadata. The data frame contains the following columns:

period: Period to which statistics pertain.
input_rows: Total number of rows of input data, including rows carried forward from the previous period.
cf_rows: Total number of rows carried forward from the previous period.
num_na_column: Number of NA values in column. This measure appears for each element of track_metadata.
cor_column: Period-over-period correlation for column. This measure appears for each element of track_metadata.

Method `getLooseningInfo()`

Get loosening information.

Usage

Simulation$getLooseningInfo()

Returns

An object of class data.frame that contains, for each period, which constraints were loosened in order to solve the portfolio optimization problem, if any. The data frame contains the following columns:

date: Date for which the constraint was loosened.
constraint_name: Name of the constraint that was loosened.
pct_loosened: Percentage by which the constraint was loosened, where 100 means loosened fully (i.e., the constraint is effectively removed).

Method `getOptimizationSummary()`

Get optimization summary information.

Usage

Simulation$getOptimizationSummary()

Returns

An object of class data.frame that contains optimization summary information, such as starting and ending factor constraint values, at the strategy and joint level. The data frame contains the following columns:

strategy: Strategy name, or 'joint' for the aggregate strategy.
sim_date: Date to which the data pertains.
order_gmv: Total gross market value of orders generated by the optimization.
start_smv: Total net market value of short positions at the start of the optimization.
start_lmv: Total net market value of long positions at the start of the optimization.
end_smv: Total net market value of short positions at the end of the optimization.
end_lmv: Total net market value of long positions at the end of the optimization.
start_factor: Total net exposure to factor at the start of the optimization, for each factor constraint.
end_factor: Total net exposure to factor at the start of the optimization, for each factor constraint.

Method `getExposures()`

Get end-of-period exposure information.

Usage

Simulation$getExposures(type = "net")

Arguments

type: Vector of length 1 that may be one of "net", "long", "short", and "gross".

Returns

An object of class data.frame that contains end-of-period exposure information for the simulation portfolio. The units of the exposures are portfolio weight relative to strategy_captial (i.e., net market value of exposure divided by strategy capital). The data frame contains the following columns:

strategy: Strategy name, or 'joint' for the aggregate strategy.
sim_date: Date of the exposure data.
category_level: Exposure to level within category, for all levels of all category constraints, at the end of the period.
factor: Exposure to factor, for all factor constraints, at the end of the period.

Method `getDelistings()`

Get information on positions removed due to delisting.

Usage

Simulation$getDelistings()

Returns

An object of class data.frame that contains a row for each position that is removed from the simulation portfolio due to a delisting. Each row contains the size of the position on the day on which it was removed from the portfolio.

Method `getSingleStrategySummaryDf()`

Get summary information for a single strategy suitable for plotting input.

Usage

Simulation$getSingleStrategySummaryDf(
  strategy_name = "joint",
  include_zero_row = TRUE
)

Arguments

strategy_name: Strategy for which to return summary data.
include_zero_row: Logical flag indicatiing whether to prepend a row to the summary data with starting values at zero. Defaults to TRUE.

Returns

A data frame that contains summary information for the desired strategy, as well as columns for cumulative net and gross total return, calculated as pnl divided by ending gross market value.

Method `plotPerformance()`

Draw a plot of cumulative gross and net return by date.

Usage

Simulation$plotPerformance(strategy_name = "joint")

Arguments

strategy_name: Character vector of length 1 specifying the strategy for the plot. Defaults to "joint".

Method `plotContribution()`

Draw a plot of contribution to net return on GMV for levels of a specified category.

Usage

Simulation$plotContribution(category_var, strategy_name = "joint")

Arguments

category_var: Plot performance contribution for the levels of category_var. category_var must be present in the simulation's security reference, and detail data must be present in the object's result data.
strategy_name: Character vector of length 1 specifying the strategy for the plot. Defaults to "joint".

Method `plotMarketValue()`

Draw a plot of total gross, long, short, and net market value by date.

Usage

Simulation$plotMarketValue(strategy_name = "joint")

Arguments

strategy_name: Character vector of length 1 specifying the strategy for the plot. Defaults to "joint".

Method `plotCategoryExposure()`

Draw a plot of exposure to all levels in a category by date.

Usage

Simulation$plotCategoryExposure(in_var, strategy_name = "joint")

Arguments

in_var: Category for which exposures are plotted. In order to plot exposures for category in_var, we must have run the simulation with in_var in the config setting simulator/calculate_exposures/category_vars.
strategy_name: Character vector of length 1 specifying the strategy for the plot. Defaults to "joint".

Method `plotFactorExposure()`

Draw a plot of exposure to factors by date.

Usage

Simulation$plotFactorExposure(in_var, strategy_name = "joint")

Arguments

in_var: Factors for which exposures are plotted.
strategy_name: Character vector of length 1 specifying the strategy for the plot. Defaults to "joint".

Method `plotNumPositions()`

Draw a plot of number of long and short positions by date.

Usage

Simulation$plotNumPositions(strategy_name = "joint")

Arguments

strategy_name: Character vector of length 1 specifying the strategy for the plot. Defaults to "joint".

Method `plotTurnover()`

Draw a plot of number of long and short positions by date.

Usage

Simulation$plotTurnover(strategy_name = "joint")

Arguments

strategy_name: Character vector of length 1 specifying the strategy for the plot. Defaults to "joint".

Method `plotUniverseSize()`

Draw a plot of the universe size, or number of investable stocks, over time.

Usage

Simulation$plotUniverseSize(strategy_name = "joint")

Arguments

strategy_name: Character vector of length 1 specifying the strategy for the plot. Defaults to joint.

Method `plotNonInvestablePct()`

Draw a plot of the percentage of portfolio GMV held in non-investable stocks (e.g., stocks that do not satisfy universe criteria) for a given strategy. Note that this plot requires detail data.

Usage

Simulation$plotNonInvestablePct(strategy_name = "joint")

Arguments

strategy_name: Character vector of length 1 specifying the strategy for the plot. Defaults to "joint".

Method `overallStatsDf()`

Calculate overall simulation summary statistics, such as total P&L, Sharpe, average market values and counts, etc.

Usage

Simulation$overallStatsDf()

Returns

A data frame that contains summary statistics, suitable for reporting.

Method `overallReturnsByMonthDf()`

Calculate return for each month and summary statistics for each year, such as total return and annualized Sharpe. Return in data frame format suitable for reporting.

Usage

Simulation$overallReturnsByMonthDf()

Returns

The data frame contains one row for each calendar year in the simulation, and up to seventeen columns: one column for year, one column for each calendar month, and columns for the year's total return, annualized return, annualized volatility, and annualized Sharpe. Total return is the sum of daily net returns. Annualized return is the mean net return times 252. Annualized volatility is the standard deviation of net return times the square root of 252. Annualized Sharpe is the ratio of annualized return to annualized volatility. All returns are in percent.

Method `print()`

Print overall simulation statistics.

Usage

Simulation$print()

Method `writeFeather()`

Write the data in the object to feather files.

Usage

Simulation$writeFeather(out_loc)

Arguments

out_loc: Directory in which output files should be created.

Returns

No return value, called for side effects.

Method `readFeather()`

Load files created with writeFeather into the object. Note that because detail data is not re-split by period, it will not be possible to use the sim_date parameter when calling getSimDetail on the populated object.

Usage

Simulation$readFeather(in_loc)

Arguments

in_loc: Directory that contains files to be loaded.

Returns

No return value, called for side effects.

Method `getConfig()`

Get the object's configuration information.

Usage

Simulation$getConfig()

Returns

Object of class list that contains the simulation's configuration information.

Method `writeReport()`

Write an html document of simulation results.

Usage

Simulation$writeReport(
  out_dir,
  out_file,
  out_fmt = "html",
  contrib_vars = NULL
)

Arguments

out_dir: Directory in which output files should be created
out_file: File name for output
out_fmt: Format in which output files should be created. The default is html and that is currently the only option.
contrib_vars: Security reference variables for which to plot return contribution.
res: The object of class 'Simulation' which we want to write the report about.

Method `clone()`

The objects of this class are cloneable with this method.

Usage

Simulation$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Package 'strand'

Help Index

strand: a framework for investment strategy simulation

Description

Author(s)

Examples

Run an example shiny app

Description

Usage

Examples

Load example strategy configuration

Description

Usage

Value

Examples

Make Basic Flextable

Description

Usage

Arguments

Value

Portfolio optimization class

Description

Details

Methods

Public methods

Method new()

Usage

Arguments

Returns

Examples

Method setVerbose()

Usage

Arguments

Returns

Method addConstraints()

Usage

Arguments

Returns

Method getConstraintMatrix()

Usage

Returns

Method getConstraintMeta()

Usage

Returns

Method solve()

Usage

Returns

Method getResultData()

Usage

Returns

Method getLoosenedConstraints()

Usage

Returns

Method getMaxPosition()

Usage

Returns

Method getMaxOrder()

Usage

Returns

Method summaryDf()

Usage

Returns

Method print()

Usage

Returns

Method clone()

Usage

Arguments

Examples

Sample security inputs for examples and testing

Description

Usage

Format

Details

Sample security pricing data for examples and testing

Description

Usage

Format

Details

Sample security reference data for examples and testing

Method `new()`

Method `setVerbose()`

Method `addConstraints()`

Method `getConstraintMatrix()`

Method `getConstraintMeta()`

Method `solve()`

Method `getResultData()`

Method `getLoosenedConstraints()`

Method `getMaxPosition()`

Method `getMaxOrder()`

Method `summaryDf()`

Method `print()`

Method `clone()`

Method `new()`

Method `setVerbose()`

Method `setShinyCallback()`

Method `getSecurityReference()`

Method `run()`

Method `getSimDates()`

Method `getSimSummary()`

Method `getSimDetail()`

Method `getPositionSummary()`

Method `getInputStats()`

Method `getLooseningInfo()`

Method `getOptimizationSummary()`