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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
[](https://travis-ci.org/braverock/quantstrat)
[](https://codecov.io/gh/braverock/quantstrat?branch=master)
# quantstrat
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.
## Overview
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).
## Installation
In order to install [quantstrat](https://github.com/braverock/quantstrat) from [GitHub](https://github.com/), you will first need to install devtools and blotter from GitHub.
``` r
install.packages("devtools") # if not installed
install.packages("FinancialInstrument") #if not installed
install.packages("PerformanceAnalytics") #if not installed
# next install blotter from GitHub
devtools::install_github("braverock/blotter")
# next install quantstrat from GitHub
devtools::install_github("braverock/quantstrat")
```
## Example: maCross
The demos in the [demo](https://github.com/braverock/quantstrat/tree/master/demo) folder are great for learning how to use quantstrat specifically. Below is an example of the [maCross](https://github.com/braverock/quantstrat/blob/master/demo/maCross.R) strategy, a simple moving average strategy using a short-term SMA of 50 days and a long-term SMA of 200 days.
```{r demo_maCross, include=FALSE}
require(quantstrat)
require(blotter)
# workaround to xts Date handling, remove later
ttz<-Sys.getenv('TZ')
Sys.setenv(TZ='UTC')
suppressWarnings(rm("order_book.macross",pos=.strategy))
suppressWarnings(rm("account.macross","portfolio.macross",pos=.blotter))
suppressWarnings(rm("account.st","portfolio.st","stock.str","stratMACROSS",'start_t','end_t'))
```
We specify the symbol/s and currency/ies before defining the stock metadata using the stock() function from the [FinancialInstrument](https://cran.r-project.org/web/packages/FinancialInstrument/FinancialInstrument.pdf) package, available on CRAN.
```{r FinancialInstrument, echo=TRUE, message=FALSE}
stock.str='AAPL' # what are we trying it on
currency('USD')
stock(stock.str,currency='USD',multiplier=1)
```
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"
```{r setup_backtest, echo=TRUE, message=FALSE}
startDate="1999-12-31"
initEq=1000000
portfolio.st='macross'
account.st='macross'
initPortf(portfolio.st,symbols=stock.str)
initAcct(account.st,portfolios=portfolio.st, initEq=initEq)
initOrders(portfolio=portfolio.st)
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".
```{r indicators_rules_signals, echo=TRUE, message=FALSE}
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.
```{r mktdata_applyStrategy, echo=TRUE, message=FALSE, warning=FALSE}
getSymbols(stock.str,from=startDate)
for(i in stock.str)
assign(i, adjustOHLC(get(i),use.Adjusted=TRUE))
start_t<-Sys.time()
out<-applyStrategy(strategy=stratMACROSS , portfolios=portfolio.st)
end_t<-Sys.time()
print(end_t-start_t)
```
Before we can review results using chart.Posn(), we update the portfolio.
```{r update_review, echo=TRUE, message=FALSE, warning=FALSE}
start_t<-Sys.time()
updatePortf(Portfolio='macross',Dates=paste('::',as.Date(Sys.time()),sep=''))
end_t<-Sys.time()
print("trade blotter portfolio update:")
print(end_t-start_t)
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().
## quantmod::zoomChart()
```{r charts3, echo=TRUE, warning=FALSE}
zoom_Chart('2014::2018')
```
## Warning
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](https://www.researchgate.net/publication/319298448_Developing_Backtesting_Systematic_Trading_Strategies).
## Resources
Below is a growing list of resources (some actively being developed) as relates to quantstrat:
- The demo scripts in the demo folder
- [Datacamp course](https://www.datacamp.com/community/blog/financial-trading-in-r-with-ilya-kipnis) presented by quantstrat contributor Ilya Kipnis covering the basics of strategy development using quantstrat and R.
- [2018 R/Finance quantstrat seminar](http://past.rinfinance.com/agenda/2018/BrianPeterson.html) workshop presented at R/Finance 2018. The markdown source for this workshop is included with quantstrat in the vignettes directory.
- [Backtesting Strategies with R](https://timtrice.github.io/backtesting-strategies/index.html) by Tim Trice
- Guy Yollin [presentations](http://www.r-programming.org/papers)
- 2013 [presentation](https://docs.google.com/presentation/d/1fGzDc-LFfCQJKHHzaonspuX1_TTm1EB5hlvCEDsz7zw/pub#slide=id.p) by quantstrat authors Jan Humme and Brian Peterson
## 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.
### Contributing
Please see the [contributing guide](.github/CONTRIBUTING.md).