PortOpt: Portfolio optimization class
In strand: A Framework for Investment Strategy Simulation
Description
Details
Methods
Examples
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
Public methods
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Method new()
Create a new PortOpt object.
Usage
PortOpt$new(config, input_data)
Arguments
configAn object of class list or character. If the
value passed is a character vector, it should be of length 1 and
specify the path to a yaml configuration file that contains the
object's configuration info. If the value passed is of class list(),
the list should contain the object's configuration info in list form
(e.g, the return value of calling yaml.load_file on the
configuration file).
input_dataA data.frame that contains all necessary input
for the optimization.
If the top-level configuration item price_var is not set, prices will be expected
in the ref_price column of input_data.
Returns
A new PortOpt object.
Examples
library(dplyr)
data(sample_secref)
data(sample_inputs)
data(sample_pricing)
# Construct optimization input for one day from sample data. The columns
# of the input data must match the input configuration.
optim_input <-
inner_join(sample_inputs, sample_pricing,
by = c("id", "date")) %>%
left_join(sample_secref, by = "id") %>%
filter(date %in% as.Date("2020-06-01")) %>%
mutate(ref_price = price_unadj,
shares_strategy_1 = 0)
opt <-
PortOpt$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
verboseLogical flag indicating whether to be verbose or not.
Returns
No return value, called for side effects.
Method addConstraints()
Add optimization constraints.
Usage
PortOpt$addConstraints(constraint_matrix, dir, rhs, name)
Arguments
constraint_matrixMatrix with one row per constraint and (S+1) \times N
columns, where S is number of strategies and N is the number of stocks.
The variables in the optimization are
x_{1,1}, x_{2,1}, …, x_{N,1},
x_{1,2}, x_{2,2}, …, x_{N,2},
\vdots
x_{1,S}, x_{2,S}, …, x_{N,S},
y_1, …, y_N
The first N \times S variables are the individual strategy
trades. Variable x_{i,s} represents the signed trade for stock i
in strategy s. The following N auxillary variables y_1, …, y_N
represent the absolute value of the net trade in each stock. So
for a stock i, we have:
y_i = ∑_s |x_{i,s}|
dirVector of class character of length
nrow(constraint_matrix) that specifies the direction of the
constraints. All elements must be one of ">=", "==", or "<=".
rhsVector of class numeric of length
nrow(constraint_matrix) that specifies the bounds of the
constraints.
nameCharacter vector of length 1 that specifies a name for the
set of constraints that are being created.
Returns
No return value, called for side effects.
Method getConstraintMatrix()
Constraint matrix access.
Usage
PortOpt$getConstraintMatrix()
Returns
The optimization's constraint matrix.
Method getConstraintMeta()
Provide high-level constraint information.
Usage
PortOpt$getConstraintMeta()
Returns
A data frame that contains constraint metadata, such as current constraint value and
whether a constraint is currently within bounds, for all single-row
constraints. Explicitly exclude net trade constraints and constraints
that involve net trade variables.
Method solve()
Solve the optimization. After running solve(),
results can be retrieved using getResultData().
Usage
PortOpt$solve()
Returns
No return value, called for side effects.
Method getResultData()
Get optimization result.
Usage
PortOpt$getResultData()
Returns
A data frame that contains the number of shares and the net
market value of the trades at the strategy and joint (net) level
for each stock in the optimization's input.
Method getLoosenedConstraints()
Provide information about any constraints that were loosened
in order to solve the optimization.
Usage
PortOpt$getLoosenedConstraints()
Returns
Object of class list where keys are the names of the
loosened constraints and values are how much they were loosened toward
current values. Values are expressed as (current constraint value -
loosened constraint value) / (current constraint value - violated
constraint value). A value of 0 means a constraint was loosened 100%
and is not binding.
Method getMaxPosition()
Provide information about the maximum position size allowed
for long and short positions.
Usage
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$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
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## ------------------------------------------------
## Method `PortOpt$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()
strand documentation built on Nov. 20, 2020, 1:08 a.m.
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Description Details Methods Examples
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
Public methods
Method new()
Create a new PortOpt object.
Usage
PortOpt$new(config, input_data)
Arguments
configAn object of class
listorcharacter. If the value passed is a character vector, it should be of length 1 and specify the path to a yaml configuration file that contains the object's configuration info. If the value passed is of class list(), the list should contain the object's configuration info in list form (e.g, the return value of callingyaml.load_fileon the configuration file).input_dataA
data.framethat contains all necessary input for the optimization.If the top-level configuration item
price_varis not set, prices will be expected in theref_pricecolumn ofinput_data.
Returns
A new PortOpt object.
Examples
library(dplyr)
data(sample_secref)
data(sample_inputs)
data(sample_pricing)
# Construct optimization input for one day from sample data. The columns
# of the input data must match the input configuration.
optim_input <-
inner_join(sample_inputs, sample_pricing,
by = c("id", "date")) %>%
left_join(sample_secref, by = "id") %>%
filter(date %in% as.Date("2020-06-01")) %>%
mutate(ref_price = price_unadj,
shares_strategy_1 = 0)
opt <-
PortOpt$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
verboseLogical flag indicating whether to be verbose or not.
Returns
No return value, called for side effects.
Method addConstraints()
Add optimization constraints.
Usage
PortOpt$addConstraints(constraint_matrix, dir, rhs, name)
Arguments
constraint_matrixMatrix with one row per constraint and (S+1) \times N columns, where S is number of strategies and N is the number of stocks.
The variables in the optimization are
x_{1,1}, x_{2,1}, …, x_{N,1},
x_{1,2}, x_{2,2}, …, x_{N,2},
\vdots
x_{1,S}, x_{2,S}, …, x_{N,S},
y_1, …, y_N
The first N \times S variables are the individual strategy trades. Variable x_{i,s} represents the signed trade for stock i in strategy s. The following N auxillary variables y_1, …, y_N represent the absolute value of the net trade in each stock. So for a stock i, we have:
y_i = ∑_s |x_{i,s}|
dirVector of class character of length
nrow(constraint_matrix)that specifies the direction of the constraints. All elements must be one of ">=", "==", or "<=".rhsVector of class numeric of length
nrow(constraint_matrix)that specifies the bounds of the constraints.nameCharacter vector of length 1 that specifies a name for the set of constraints that are being created.
Returns
No return value, called for side effects.
Method getConstraintMatrix()
Constraint matrix access.
Usage
PortOpt$getConstraintMatrix()
Returns
The optimization's constraint matrix.
Method getConstraintMeta()
Provide high-level constraint information.
Usage
PortOpt$getConstraintMeta()
Returns
A data frame that contains constraint metadata, such as current constraint value and whether a constraint is currently within bounds, for all single-row constraints. Explicitly exclude net trade constraints and constraints that involve net trade variables.
Method solve()
Solve the optimization. After running solve(),
results can be retrieved using getResultData().
Usage
PortOpt$solve()
Returns
No return value, called for side effects.
Method getResultData()
Get optimization result.
Usage
PortOpt$getResultData()
Returns
A data frame that contains the number of shares and the net market value of the trades at the strategy and joint (net) level for each stock in the optimization's input.
Method getLoosenedConstraints()
Provide information about any constraints that were loosened in order to solve the optimization.
Usage
PortOpt$getLoosenedConstraints()
Returns
Object of class list where keys are the names of the
loosened constraints and values are how much they were loosened toward
current values. Values are expressed as (current constraint value -
loosened constraint value) / (current constraint value - violated
constraint value). A value of 0 means a constraint was loosened 100%
and is not binding.
Method getMaxPosition()
Provide information about the maximum position size allowed for long and short positions.
Usage
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$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ## ------------------------------------------------
## Method `PortOpt$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()
|
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