# How to backtest a strategy in R

This is the third post in the Backtesting in Excel and R series and it will show how to backtest a simple strategy in R. It will follow the 4 steps Damian outlined in his post on how to backtest a simple strategy in Excel.

**Step 1: Get the data**

The `getSymbols`

function in quantmod makes this step easy if you can use daily data from Yahoo Finance. There are also “methods” (not in the strict sense) to pull data from other sources (FRED, Google, Oanda, R save files, databases, etc.). You could also use them as a template to write a custom function for a particular vendor you use.

```
# run the command below if quantmod isn't already installed
# install.packages("quantmod")
# use the quantmod package (loads TTR, xts, and zoo)
require(quantmod)
# pull SPX data from Yahoo (getSymbols returns an xts object)
getSymbols("^GSPC")
```

**Step 2: Create your indicator**

The TTR package contains a multitude of indicators. The indicators are written to make it easy to combine them in creative and unconventional ways. Starting with revision 106 on R-forge, TTR has a DVI indicator.

```
# calculate DVI indicator
dvi <- DVI(Cl(GSPC)) # Cl() extracts the close price column
```

**Step 3: Construct your trading rule**

Since this trading rule is simple–we’re long 100% if the DVI is below 0.5 and short 100% otherwise–it can be written in a single line. More elaborate rules and/or position sizings can be done as well, but require more code (RSI(2) with Position Sizing is an example of more complex position sizing rules). Also notice that the signal vector is lagged, which avoids look-ahead bias.

```
# create signal: (long (short) if DVI is below (above) 0.5)
# lag so yesterday's signal is applied to today's returns
sig <- Lag(ifelse(dvi$dvi < 0.5, 1, -1))
```

**Step 4: The trading rules/equity curve**

As in Damian’s example, the code below is a simplified approach that is frictionless and does not account for slippage. The code below takes today’s percentage return and multiplies it by yesterday’s signal / position size (always +/- 100% in this example). I also subset the system returns to match the results in the Excel file.

```
# calculate signal-based returns
ret <- ROC(Cl(GSPC))*sig
# subset returns to match data in Excel file
ret <- ret["2009-06-02/2010-09-07"]
```

**Step 5: Evaluate strategy performance**

Damian mentioned the importance of evaluating your strategy. Fortunately for R users, the PerformanceAnalytics package makes this easy. With a few lines of code we can view the drawdowns, downside risks, and a performance summary.

```
# use the PerformanceAnalytics package
# install.packages("PerformanceAnalytics")
require(PerformanceAnalytics)
# create table showing drawdown statistics
table.Drawdowns(ret, top=10)
# create table of downside risk estimates
table.DownsideRisk(ret)
# chart equity curve, daily performance, and drawdowns
charts.PerformanceSummary(ret)
```

That’s all there is to backtesting a simple strategy in R. It wasn’t that intimidating, was it? Please leave feedback if you’re moving your backtesting from Excel to R and there’s something you’re hung up on or you have an awesome tip you’d like to share.

Here’s a succinct version of the code in the above post if you want to be able to copy / paste it all in one block:

```
require(quantmod)
require(PerformanceAnalytics)
# Step 1: Get the data
getSymbols("^GSPC")
# Step 2: Create your indicator
dvi <- DVI(Cl(GSPC))
# Step 3: Construct your trading rule
sig <- Lag(ifelse(dvi$dvi < 0.5, 1, -1))
# Step 4: The trading rules/equity curve
ret <- ROC(Cl(GSPC))*sig
ret <- ret["2009-06-02/2010-09-07"]
eq <- exp(cumsum(ret))
plot(eq)
# Step 5: Evaluate strategy performance
table.Drawdowns(ret, top=10)
table.DownsideRisk(ret)
charts.PerformanceSummary(ret)
```