Create backtestStrategy
object to define portfolio allocation
strategy
Create a backtestStrategy
object which defines a portfolio
allocation strategy.
Use this workflow to develop and run a backtest:
Define the strategy logic using a backtestStrategy
object to specify how the strategy rebalances a portfolio of assets.
Use backtestEngine
to create a backtestEngine
object that specifies the
parameters of the backtest.
Use runBacktest
to
run the backtest against historical asset price data and, optionally,
trading signal data.
Use summary
to
summarize the backtest results in a table format.
For more detailed information on this workflow, see Backtest Investment Strategies.
creates a strategy
= backtestStrategy(name
,rebalanceFcn
)backtestStrategy
object.
sets properties using
name-value pair arguments and any of the arguments in the previous syntax.
You can specify multiple name-value pair arguments. For example,
strategy
= backtestStrategy(___,Name,Value
)strat =
backtestStrategy('MyStrategy',@myRebalFcn,'TransactionCost',0.005,'LookbackWindow',20)
.
name
— Strategy nameStrategy name, specified as a string.
Data Types: string
rebalanceFcn
— Rebalance functionRebalance function, specified as a function handle. A function handle computes new portfolio weights during the backtest. The rebalanceFcn argument implements the core logic of the trading strategy and must have one of the following signatures:
new_weights =
rebalanceFcn(weights,assetPrices)
new_weights =
rebalanceFcn(weights,assetPrices,signalData)
The rebalance function is called by the backtestEngine
object each time the strategy must be
rebalanced. The backtestEngine
object calls the rebalance function with
the following arguments:
weights
— The current portfolio weights
before rebalancing, specified as decimal percentages.
assetPrices
— A timetable
containing a rolling window of adjusted asset prices.
signalData
— A timetable
containing a rolling window of signal data. If you provide
signal data is to the backtestEngine
object, then the engine object
passes it to the strategy rebalance function using the three
input argument syntax. If do not provide signal data the
backtestEngine
object, then the engine object
calls the rebalance function with the two input argument
syntax.
The rebalance function must return a single output
argument for new_weights
which is a
vector of asset weights specified as decimal percentages.
If the new_weights
sum to
1
, then the portfolio is fully
invested.
If the new_weights
sum to
less than 1
, then the portfolio
will has the remainder in cash, earning the
RiskFreeRate
specified in the
backtestEngine
object.
If the new_weights
sum to
more than 1
, then there is a
negative cash position (margin) and the cash
borrowed accrues interest at the cash borrowing
rate specified in the
CashBorrowRate
property of the
backtestEngine
object.
For more information on developing a
rebalanceFcn
function handle, see
Backtest Investment Strategies.
Data Types: function_handle
Specify optional
comma-separated pairs of Name,Value
arguments. Name
is
the argument name and Value
is the corresponding value.
Name
must appear inside quotes. You can specify several name and value
pair arguments in any order as
Name1,Value1,...,NameN,ValueN
.
strat =
backtestStrategy('MyStrategy',@myRebalFcn,'TransactionCost',0.005,'LookbackWindow',20)
'RebalanceFrequency'
— Rebalance frequency during backtest1
(default) | numericRebalance frequency during the backtest, specified as the
comma-separated pair consisting of
'RebalanceFrequency'
and a scalar numeric
representing the number of time steps between rebalancing. For
example, if you provide the backtestEngine
object with daily price data, then the
RebalanceFrequency
specifies the number of
days between rebalancing. The default is 1
,
meaning the strategy rebalances with each time step.
Data Types: double
'TransactionCosts'
— Transaction costs for trades0
(not computed) (default) | numeric | vector | function handleTransaction costs for trades, specified as the comma-separated
pair consisting of 'TransactionCosts'
and a
scalar numeric, vector, or function handle. You can specify
transaction costs in three ways:
rate
— A scalar decimal percentage
charge to both purchases and sales of assets. For
example ,if you set TransactionCosts
to 0.001
, then each transaction (buys
and sells) would pay 0.1% in transaction fees.
[buyRate, sellRate]
— A
1
-by-2
vector
of decimal percentage rates that specifies separate
rates for buying and selling of assets.
computeTransactionCostsFcn
— A
function handle to compute customized transaction fees.
If you specify a function handle, the backtestEngine
object calls the
TransactionCosts
function to
compute the fees for each rebalance. The user-defined
function handle must have the following
signature:
[buyCosts,sellCosts] = computeCostsFcn(deltaPositions)
The user-defined function handle takes a single input
argument deltaPositions
, which is a
vector of changes in asset positions for all assets (in
currency units) as a result of a rebalance. Positive
elements in the deltaPositions
vector
indicate purchases while negative entries represent
sales. The user-defined function handle must return two
output arguments buyCosts
and
sellCosts
, which contain the
total costs (in currency) for the entire rebalance for
each type of transaction.
Data Types: double
| function_handle
'LookbackWindow'
— Lookback window[0 Inf]
(default) | vectorLookback window, specified as the comma-separated pair consisting
of 'LookbackWindow'
and a
1
-by-2
vector that defines
the minimum and maximum size of the rolling window of data (asset
prices and signal data) that you provide to the
rebalanceFcn
argument. You specify these
limits in terms of the number of time steps. For example, if the
backtestEngine
object is provided with daily price
data, then LookbackWindow
specifies the size
bounds of the rolling window in days. The default is [0
Inf]
, meaning that all available past data is given to
the rebalance function. If you specify a non-zero minimum, then the
software does not call rebalanceFcn
until
enough time steps process to meet the minimum size.
If you specify LookbackWindow
as a single
scalar value, then the value is both the minimum and maximum of the
LookbackWindow
(that is, a fixed-sized
window).
Data Types: double
'InitialWeights'
— Initial portfolio weights[ ]
(default) | vectorInitial portfolio weights, specified as the comma-separated pair
consisting of 'InitialWeights'
and a vector. The
InitialWeights
vector sets the portfolio
weights before the backtestEngine
object begins the backtest. The size of
the initial weights vector must match the number of assets used in
the backtest.
Alternatively, you can set the InitialWeights
name-value pair argument to empty ([ ]
) to
indicate the strategy will begin with no investments and in a 100%
cash position. The default for InitialWeights
is empty ([ ]
).
Data Types: double
Name
— Strategy nameStrategy name, specified as a string.
Data Types: string
RebalanceFcn
— Rebalance functionRebalance function, specified as a function handle.
Data Types: function_handle
RebalanceFrequency
— Rebalance frequency during backtest1
(default) | numericRebalance frequency during the backtest, specified as a scalar numeric.
Data Types: double
TransactionCosts
— Transaction costs0
(default) | numeric | vector | function handleTransaction costs, specified as a scalar numeric, vector, or function handle.
Data Types: double
| function_handle
LookbackWindow
— Lookback window[0 Inf]
(default) | numeric | vectorLookback window, specified as a scalar numeric or vector.
Data Types: double
InitialWeights
— Initial weights[ ]
(default) | vectorInitial weights, specified as a vector.
Data Types: double
Define a backtest strategy by using a backtestStrategy
object. backtestStrategy
objects contain properties specific to a trading strategy, such as the rebalance frequency, transaction costs, and a rebalance function. The rebalance function implements the core logic of the strategy and is used by the backtesting engine during the backtest to allow the strategy to change its asset allocation and to make trades. In this example, to illustrate how to create and use backtest strategies in MATLAB®, you prepare two simple strategies for backtesting:
An equal weighted strategy
A strategy that attempts to "chase returns"
The strategy logic for these two strategies is defined in the rebalance functions.
Set Strategy Properties
A backtestStrategy
object has several properties that you set using parameters for the backtestStrategy
function.
Initial Weights
The InitialWeights
property contains the asset allocation weights at the start of the backtest. The default value for InitialWeights
is empty ([]
), which indicates that the strategy begins the backtest uninvested, meaning that 100% of the capital is in cash earning the risk-free rate.
Set the InitialWeights
to a specific asset allocation. The size of the initial weights vector must match the number of assets in the backtest.
% Initialize the strategies with 30 weights, since the backtest % data comes from a year of the 30 DJIA stocks. numAssets = 30; % Give the initial weights for both strategies equal weighting. Weights % must sum to 1 to be fully invested. initialWeights = ones(1,numAssets); initialWeights = initialWeights / sum(initialWeights);
Transaction Costs
The TransactionCosts
property allows you to set the fees that the strategy pays for trading assets. Transaction costs are paid as a percentage of the total change in position for each asset. Specify costs in decimal percentages. For example, if TransactionCosts
is set to 1% (0.01
) and the strategy buys $100 worth of a stock, then the transaction costs incurred are $1.
Transaction costs are set using a 1
-by-2
vector that sets separate fee rates for purchases and sales of assets. In this example, both strategies pay the same transaction costs — 25 basis points for asset purchases and 50 basis points for sales.
% Define the Transaction costs as [buyCosts sellCost] and specify the costs % as decimal percentages. tradingCosts = [0.0025 0.005];
You can also set the TransactionCosts property to a function handle if you need to implement arbitrarily complex transaction cost structures. For more information on creating transaction cost functions, see backtestStrategy
.
Rebalance Frequency
The RebalanceFrequency
property determines how often the backtesting engine rebalances and reallocates the portfolio of a strategy using the rebalance function. Set the RebalanceFrequency
in terms of time steps in the backtest. For example, if the backtesting engine is testing a strategy with a set of daily price data, then set the rebalance function in days. Essentially, RebalanceFrequency
represents the number of rows of price data to process between each call to the strategy rebalance function.
% Both strategies rebalance every 4 weeks (20 days).
rebalFreq = 20;
Lookback Window
Each time the backtesting engine calls a strategy rebalance function, a window of asset price data (and possibly signal data) is passed to the rebalance function. The rebalance function can then make trading and allocation decisions based on a rolling window of market data. The LookbackWindow
property sets the size of these rolling windows. Set the window in terms of time steps. The window determines the number of rows of data from the asset price timetable that are passed to the rebalance function.
The LookbackWindow
property can be set in two ways. For a fixed-sized rolling window of data (for example, "50 days of price history"), the LookbackWindow
property is set to a single scalar value (N
= 50
). The software then calls the rebalance function with a price timetable containing exactly N
rows of rolling price data.
Alternatively, you can define the LookbackWindow property by using a 1
-by-2
vector [min max]
that specifies the minimum and maximum size for an expanding window of data. In this way, you can set flexible window sizes. For example:
[10 Inf]
— At least 10 rows of data
[0 50]
— No more than 50 rows of data
[0 Inf]
— All available data (that is, no minimum, no maximum); this is the default value
[20 20]
— Exactly 20 rows of data; this is equivalent to setting LookbackWindow
to the scalar value 20
The software does not call the rebalance function if the data is insufficient to create a valid rolling window, regardless of the value of the RebalanceFrequency
property.
If the strategy does not require any price or signal data history, then you can indicate that the rebalance function requires no data by setting the LookbackWindow
property to 0
.
% The equal weight strategy does not require any price history data. ewLookback = 0; % The "chase returns" strategy bases its decisions on the trailing % 10-day asset returns. The lookback window is set to 11 since computing 10 days % of returns requires the close price from day 0. chaseLookback = 11;
Rebalance Function
The rebalance function (rebalanceFcn
) is the user-authored function that contains the logic of the strategy. The backtesting engine calls the strategy rebalance function with a fixed set of parameters and expects it to return a vector of asset weights representing the new, desired portfolio allocation after a rebalance. For more information, see the rebalance functions.
Create Strategies
Using the prepared strategy properties, you can create the two strategy objects.
% Create the equal weighted strategy. The rebalance function @equalWeights % is defined in the Rebalance Functions section at the end of this example. equalWeightStrategy = backtestStrategy("EqualWeight",@equalWeight,... 'RebalanceFrequency',rebalFreq,... 'TransactionCosts',tradingCosts,... 'LookbackWindow',ewLookback,... 'InitialWeights',initialWeights)
equalWeightStrategy = backtestStrategy with properties: Name: "EqualWeight" RebalanceFcn: @equalWeight RebalanceFrequency: 20 TransactionCosts: [0.0025 0.0050] LookbackWindow: 0 InitialWeights: [1x30 double]
% Create the "chase returns" strategy. The rebalance function % @chaseReturns is defined in the Rebalance Functions section at the end of this example. chaseReturnsStrategy = backtestStrategy("ChaseReturns",@chaseReturns,... 'RebalanceFrequency',rebalFreq,... 'TransactionCosts',tradingCosts,... 'LookbackWindow',chaseLookback,... 'InitialWeights',initialWeights)
chaseReturnsStrategy = backtestStrategy with properties: Name: "ChaseReturns" RebalanceFcn: @chaseReturns RebalanceFrequency: 20 TransactionCosts: [0.0025 0.0050] LookbackWindow: 11 InitialWeights: [1x30 double]
Set Up Backtesting Engine
To backtest the two strategies, use the backtestEngine
object. The backtesting engine sets parameters of the backtest that apply to all strategies, such as the risk-free rate and initial portfolio value. For more information, see backtestEngine
.
% Create an array of strategies for the backtestEngine. strategies = [equalWeightStrategy chaseReturnsStrategy]; % Create backtesting engine to test both strategies. backtester = backtestEngine(strategies);
Rebalance Functions
Strategy rebalance functions defined using the rebalanceFcn
argument for backtestStrategy
must adhere to a fixed API that the backtest engine expects when interacting with each strategy. Rebalance functions must implement one of the following two syntaxes:
function new_weights = exampleRebalanceFcn(current_weights,assetPriceTimeTable) function new_weights = exampleRebalanceFcn(current_weights,assetPriceTimeTable,signalDataTimeTable)
All rebalance functions take as their first input argument the current allocation weights of the portfolio. current_weights
represents the asset allocation just before the rebalance occurs. During a rebalance, you can use current_weights
in a variety of ways. For example, you can use current_weights
to determine how far the portfolio allocation has drifted from the target allocation or to size trades during the rebalance to limit turnover.
The second and third arguments of the rebalance function syntax are the rolling windows of asset prices and optional signal data. The two tables contain the trailing N
rows of the asset and signal timetables that are passed to the runBacktest
function, where N
is set using the LookbackWindow
property of each strategy.
If optional signal data is provided to the runBacktest
function, then the backtest engine passes the rolling window of signal data to each strategy that supports it.
The equalWeight
strategy simply invests equally across all assets.
function new_weights = equalWeight(current_weights,assetPrices) %#ok<INUSD> % Invest equally across all assets. num_assets = numel(current_weights); new_weights = ones(1,num_assets) / num_assets; end
The chaseReturns
strategy invests only in the top X stocks based on their rolling returns in the lookback window. This naive strategy is used simply as an illustrative example.
function new_weights = chaseReturns(current_weights,assetPrices) % Set number of stocks to invest in. numStocks = 15; % Compute rolling returns from lookback window. rollingReturns = assetPrices{end,:} ./ assetPrices{1,:}; % Select the X best performing stocks over the lookback window [~,idx] = sort(rollingReturns,'descend'); bestStocksIndex = idx(1:numStocks); % Initialize new weights to all zeros. new_weights = zeros(size(current_weights)); % Invest equally across the top performing stocks. new_weights(bestStocksIndex) = 1; new_weights = new_weights / sum(new_weights); end
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