quantstrat v0.14.6

Quantitative Strategy Model Framework

Specify, build, and back-test quantitative financial trading and portfolio strategies.


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Transaction-oriented infrastructure for constructing trading systems and simulation. Provides support for multi-asset class and multi-currency portfolios for backtesting and other financial research.


quantstrat provides a generic infrastructure to model and backtest signal-based quantitative strategies. It is a high-level abstraction layer (built on xts, FinancialInstrument, blotter, etc.) that allows you to build and test strategies in very few lines of code. quantstrat is still under heavy development but is being used every day on real portfolios. We encourage you to send contributions and test cases via the appropriate GitHub mediums (Pull requests and Issue tracker).


In order to install quantstrat from GitHub, you will first need to install devtools and blotter from GitHub.

install.packages("devtools") # if not installed
install.packages("FinancialInstrument") #if not installed
install.packages("PerformanceAnalytics") #if not installed

# next install blotter from GitHub
# next install quantstrat from GitHub

Example: maCross

The demos in the demo folder are great for learning how to use quantstrat specifically. Below is an example of the maCross strategy, a simple moving average strategy using a short-term SMA of 50 days and a long-term SMA of 200 days.

We specify the symbol/s and currency/ies before defining the stock metadata using the stock() function from the FinancialInstrument package, available on CRAN.

stock.str='AAPL' # what are we trying it on
#> [1] "USD"
#> [1] "AAPL"

Next we set up the rest of the backtest charateristics:

  • start date
  • initial equity
  • portfolio and account names
  • initialize Portfolio, Account and Orders objects
  • assign strategy object to “stratMACROSS”
#> [1] "macross"
initAcct(account.st,portfolios=portfolio.st, initEq=initEq)
#> [1] "macross"
stratMACROSS<- strategy(portfolio.st)

We are now ready to add indicators, signals and rules. For more information on the theory of this approach, see below sections “About Signal-Based Strategy Modeling” and “How quantstrat Models Strategies”.

stratMACROSS <- add.indicator(strategy = stratMACROSS, name = "SMA", arguments = list(x=quote(Cl(mktdata)), n=50),label= "ma50" )
stratMACROSS <- add.indicator(strategy = stratMACROSS, name = "SMA", arguments = list(x=quote(Cl(mktdata)[,1]), n=200),label= "ma200")

stratMACROSS <- add.signal(strategy = stratMACROSS,name="sigCrossover",arguments = list(columns=c("ma50","ma200"), relationship="gte"),label="ma50.gt.ma200")
stratMACROSS <- add.signal(strategy = stratMACROSS,name="sigCrossover",arguments = list(column=c("ma50","ma200"),relationship="lt"),label="ma50.lt.ma200")

stratMACROSS <- add.rule(strategy = stratMACROSS,name='ruleSignal', arguments = list(sigcol="ma50.gt.ma200",sigval=TRUE, orderqty=100, ordertype='market', orderside='long'),type='enter')
stratMACROSS <- add.rule(strategy = stratMACROSS,name='ruleSignal', arguments = list(sigcol="ma50.lt.ma200",sigval=TRUE, orderqty='all', ordertype='market', orderside='long'),type='exit')

# if you want a long/short Stops and Reverse MA cross strategy, you would add two more rules for the short side:

# stratMACROSS <- add.rule(strategy = stratMACROSS,name='ruleSignal', arguments = list(sigcol="ma50.lt.ma200",sigval=TRUE, orderqty=-100, ordertype='market', orderside='short'),type='enter')
# stratMACROSS <- add.rule(strategy = stratMACROSS,name='ruleSignal', arguments = list(sigcol="ma50.gt.ma200",sigval=TRUE, orderqty=100, ordertype='market', orderside='short'),type='exit')

Now all we need to do is add our market data before calling the applyStrategy function to initiate the backtest.

#> [1] "AAPL"
for(i in stock.str)
  assign(i, adjustOHLC(get(i),use.Adjusted=TRUE))

out<-applyStrategy(strategy=stratMACROSS , portfolios=portfolio.st)
#> [1] "2001-06-27 00:00:00 AAPL 100 @ 1.124248"
#> [1] "2001-09-07 00:00:00 AAPL -100 @ 0.832348"
#> [1] "2002-01-07 00:00:00 AAPL 100 @ 1.103054"
#> [1] "2002-07-10 00:00:00 AAPL -100 @ 0.834275"
#> [1] "2003-05-16 00:00:00 AAPL 100 @ 0.905564"
#> [1] "2006-06-22 00:00:00 AAPL -100 @ 5.739735"
#> [1] "2006-09-26 00:00:00 AAPL 100 @ 7.476684"
#> [1] "2008-03-07 00:00:00 AAPL -100 @ 11.777149"
#> [1] "2008-05-19 00:00:00 AAPL 100 @ 17.687405"
#> [1] "2008-09-24 00:00:00 AAPL -100 @ 12.399479"
#> [1] "2009-05-14 00:00:00 AAPL 100 @ 11.844582"
#> [1] "2012-12-18 00:00:00 AAPL -100 @ 54.763748"
#> [1] "2013-08-26 00:00:00 AAPL 100 @ 59.079327"
#> [1] "2015-08-31 00:00:00 AAPL -100 @ 107.24485"
#> [1] "2016-08-31 00:00:00 AAPL 100 @ 103.068153"
#> Time difference of 0.183964 secs

Before we can review results using chart.Posn(), we update the portfolio.

#> [1] "macross"
print("trade blotter portfolio update:")
#> [1] "trade blotter portfolio update:"
#> Time difference of 0.04124045 secs

chart.Posn(Portfolio='macross',Symbol=stock.str, TA=c("add_SMA(n=50,col='red')","add_SMA(n=200,col='blue')"))

If you would like to zoom into a particular period, you can use quantmod’s zoomChart().




A backtest cannot be unseen. In the words of Lopez de Prado from his book Advances in Financial Machine Learning, “Backtesting is one of the most essential, and yet least understood, techniques in the quant arsenal. A common misunderstanding is to think of backtesting as a research tool. Researching and backtesting is like drinking and driving. Do not research under the influence of a backtest. …A good backtest can be extremely helpful, but backtesting well is extremely hard.”

For a comprehensive overview of an hypothesis based approach to research and backtesting, see Developing & Backtesting Systematic Trading Strategies.


Below is a growing list of resources (some actively being developed) as relates to quantstrat:

About Signal-Based Strategy Modeling

A signal-based strategy model first generates indicators. Indicators are quantitative values derived from market data (e.g. moving averages, RSI, volatility bands, channels, momentum, etc.). Indicators should be applied to market data in a vectorized (for fast backtesting) or streaming (for live execution) fashion, and are assumed to be path-independent (i.e. they do not depend on account / portfolio characteristics, current positions, or trades).

The interaction between indicators and market data are used to generate signals (e.g. crossovers, thresholds, multiples, etc.). These signals are points in time at which you may want to take some action, even though you may not be able to. Like indicators, signals may be applied in a vectorized or streaming fashion, and are assumed to be path-independent.

Rules use market data, indicators, signals, and current account / portfolio characteristics to generate orders. Notice that rules about position sizing, fill simulation, order generation / management, etc. are separate from the indicator and signal generation process. Unlike indicators and signals, rules are generally evaluated in a path-dependent fashion (path-independent rules are supported but are rare in real life) and are aware of all prior market data and current positions at the time of evaluation. Rules may either generate new or modify existing orders ( e.g. risk management, fill, rebalance, entry, exit).

How quantstrat Models Strategies

quantstrat uses FinancialInstrument to specify instruments (including their currencies) and uses blotter to keep track of transactions, valuations, and P\&L across portfolios and accounts.

Indicators are often standard technical analysis functions like those found in TTR; and signals are often specified by the quantstrat sig* functions (i.e. sigComparison, sigCrossover, sigFormula, sigPeak, sigThreshold). Rules are typically specified with the quantstrat ruleSignal function.

The functions used to specify indicators, signals, and rules are not limited to those mentioned previously. The name parameter to add.indicator, add.signal, and add.rule can be any R function. Because the supporting toolchain is built using xts objects, custom functions will integrate most easily if they return xts objects.

The strategy model is created in layers and makes use of delayed execution. This means strategies can be applied–unmodified–to several different portfolios. Before execution, quantstrat strategy objects do not know what instruments they will be applied to or what parameters will be passed to them.

For example, indicator parameters such as moving average periods or thresholds are likely to affect strategy performance. Default values for parameters may (optionally) be set in the strategy object, or set at call-time via the parameters argument of applyStrategy (parameters is a named list, used like the arguments lists).

quantstrat models orders, which may or may not become transactions. This provides a lot of extra ability to evaluate how the strategy is actually working, not working, or could be improved. For example, the performance of strategies are often affected by how often resting limit orders are changed / replaced / canceled. An order book allows the quantitative strategist to examine market conditions at the time these decisions are made. Also, the order history allows for easy computation of things that are important for many strategies, like order-to-fill ratios.

Functions in quantstrat

Name Description
distributional.boxplot Visualization of Single Signal
apply.paramset Apply a paramset to the strategy
chart.forward Chart to analyse walk.forward() objective function
signal.plot Visualization of Signal Across Lookback
.profitHurdle internal implementation of profit hurdle code, see profit.hurdle
is.strategy test to see if object is of type 'strategy'
sigPeak signal function for peak/valley signals
get.orderbook get the order book object
sigThreshold generate a threshold signal
install.param.combo insert a specific parameter combo into a strategy object
get.strategy retrieve strategy from the container environment
rulePctEquity rule to base trade size on a percentage of available equity.
setParameterDistribution Function used to create an object that contains the distribution of parameters to be generated from, before testing parameters of a strategy. (deprecated)
spx sample spx daily OHLCVA data set 1970:1971
osNoOp default order sizing function
initOrders initialize order container
ruleRevoke rule to revoke(cancel) an unfilled limit order on a signal
osMaxPos order sizing function for position limits and level sizing
chart.forward.training Chart to analyse walk.forward() objective function
getPosLimit get position and level limits on timestamp
sigComparison generate comparison signal
portfolio.luxor sample portfolio output from running luxor demo in file data/luxor-p066.RData
delete.paramset Delete a paramset from a strategy
degrees.of.freedom calculate degrees of freedom used by a strategy and available from test data
walk.forward Rolling Walk Forward Analysis
updateStrategy run standard and custom strategy wrapup functions such as updating portfolio, account, and ending equity
applyRules apply the rules in the strategy to arbitrary market data
updateOrders update an order or orders
sigFormula generate a signal from a formula
paramConstraint Internal function used in applyParameter function for process constraints on relationship between two parameter values. (deprecated)
enable.rule enable a rule in the strategy
print.haircutSR print method for Harvey and Liu Haircut Sharpe Ratio
rm.strat Remove objects associated with a strategy
initStrategy run standard and custom strategy initialization functions
initSymbol Run standard and custom symbol initialization functions
load.strategy load a strategy object from disk into memory
match.names match names in data to a list of partial name matches
getOrders get orders by time span, status, type, and side
post.signal.returns Generate Post Signal Returns
print.dof print method for strategy degrees of freedom object
print.profitHurdle print method for Harvey and Liu Haircut Sharpe Ratio
save.strategy save a strategy object from memory onto disk
tradeGraphs Draw 3D graphs from tradeStats results using rgl
ruleOrderProc process open orders at time t, generating transactions or new orders
ruleSignal default rule to generate a trade order on a signal
.audit sample audit environment output from running luxor demo in file data/luxor.wfa.ples.RData
sample_random_multests Generate empirical p-value distributions
signal.path.plot Visualization of Signal Path
signal.obj.slope Signal Objective Function
stratFaber Faber market timing strategy
sigTimestamp generate a signal on a timestamp
setParameterConstraint Function to construct parameter constraint object. (deprecated)
strategy constructor for objects of type 'strategy'
sigCrossover generate a crossover signal
tradeOrderStats get order information associated with closing positions
getParameterTable Extract the parameter structure from a strategy object. (deprecated)
profitHurdle Profit Hurdle function - A Minimum Profitability Method for Proposed Trading Strategies
put.orderbook put an orderbook object in .strategy env
signal.generate.statistics Signal Objective Function Calculation
put.strategy put a strategy object in .strategy env
quantstrat-package Quantitative Strategy Model Framework
stats sample tradeStats output from running luxor demo in file data/luxor.parameters.1-10.30-55.RData
stratBBands Bollinger Bands Strategy
add.distribution.constraint Adds a constraint on 2 distributions within a paramset
add.rule add a rule to a strategy
add.indicator add an indicator to a strategy
addPosLimit add position and level limits at timestamp
deflatedSharpe Calculate a Deflated Sharpe Ratio using number of trials and portfolio moments
addOrder add an order to the order book
beanplot.signals Visualization of Signal Across Lookback with Beanplots
applyParameter Generate parameter sets for a specific strategy, test the strategy on each set of parameters, output result package. (deprecated)
applyStrategy.rebalancing apply the strategy to arbitrary market data, with periodic rebalancing
add.init add arbitrary initialization functions to a strategy
applyStrategy apply the strategy to arbitrary market data
haircutSharpe Haircut Sharpe Ratio to correct for number of trials and autocorrelation
add.signal add a signal to a strategy
applyIndicators apply the indicators in the strategy to arbitrary market data
apply.paramset.signal.analysis Signal Analysis With Parmeter Optimization
applySignals apply the signals in the strategy to arbitrary market data
add.distribution Adds a distribution to a paramset in a strategy
applyIndicatorSignals Calculate Indicators and Signals for a Strategy
No Results!

Vignettes of quantstrat

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Type Package
Date 2018-06-27 $Date$
Contributors Yu Chen, Joe Dunn, Dirk Eddelbuettel, Michael Guan, Jeffrey A. Ryan, Garrett See
LazyLoad yes
License GPL-3
Copyright (c) 2009-2017
ByteCompile TRUE
VignetteBuilder knitr

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