# Pre-computing a trading plan in parallel

November 11, 2011
By

(This article was first published on The Average Investor's Blog » R, and kindly contributed to R-bloggers)

R version 2.14 introduced a new package, called parallel. This new package combines the functionality from two previous packages: snow and multicore. Since I was using multicore to parallelise my computations, I had to migrate to the new package and decided to publish some code.

Often trading strategies are tested using the daily closing price both to determine the position and to perform the trading. Since we need to pre-compute an action plan, parallelisation may be necessary if the computations are heavy.

The code at the end of this post is pre-computing the actions for the CSS Analytics’ DVI indicator. The entry point in the code is as follows:

library( quantmod )
library( parallel )

# Load the code at the end of this post

# Get the SPY ETF from Yahoo
getSymbols( "SPY", from="1900-01-01" )

# Compute the actions
computeDVIActionPar( Cl( SPY ), range=10, cores=8 )

This basically requests to compute the position for all possible closing prices between -10% and +10%, parallelising the work 8 fold. The output of the command is something like:

Price    Pct Position
1 111.59 -10.00        1
2 127.97   3.21       -1
3 136.38  10.00       -1

This output tells us that if the SPY doesn’t advance more than 3.21%, closing above \$127.97, we should establish a long position at the close, otherwise – short. With that knowledge, and depending on the current position, what is left to do is to go to our Interactice Broker account and to put a limit on-close order. The complete code for the missing functions follows.

computeOneDVIAction = function( close, x )
{
x[tail( index( x ), 1 )] = close
dvi = DVI( x )
val = as.numeric( tail( dvi\$dvi, 1 ) )

# Short if DVI > 0.5, long otherwise
if( is.na( val ) )
{
return( 0 )
}
else if( val > 0.5 )
{
return( -1 )
}

return( 1 )
}

computeDVIActionPar = function( x, step=0.01, range=5, cores )
{
require( quantmod, quietly=TRUE )
require( parallel, quietly=TRUE )

prices = c( )
positions = c( )

latestClose = as.numeric( coredata( last( x ) ) )

# Shift to the left to use the last entry as the "guessed" close
yy = lag( x, -1 )

# range is percentages
range = range / 100

# Compute the vector with all closing prices within the range
close = latestClose * ( 1 - range )
lastClose = latestClose * ( 1 + range )

close = round( close / step ) * step
numSteps = ( close - latestClose ) / step + 1

close = round( close, 2 )
lastClose = ceiling( lastClose * 100 ) / 100

closes = close

repeat
{
if( close >= lastClose ) break

close = round( latestClose + step*numSteps, 2 )

numSteps = numSteps + 1

closes = c( closes, close )
}

# Detect the cores if not supplied
if( missing( cores ) )
{
cores = parallel:::detectCores()
}

res = mclapply( closes,
computeOneDVIAction,
x = yy,
mc.cores = cores )

# Summarize the positions
prices = c()
pcts = c()
positions = c()

# Impossible position
lastPosition = -1e9

len = length( closes )
for( ii in 1:(len - 1) )
{
if( res[[ii]] != lastPosition )
{
positions = append( positions, res[[ii]] )
prices = append( prices, closes[ii] )
pcts = append( pcts, round( ( closes[ii] - latestClose ) /
latestClose * 100, 2 ) )
lastPosition = res[[ii]]
}
}

positions = append( positions, res[[len]] )
prices = append( prices, closes[ii] )
pcts = append( pcts, round( ( closes[len] - latestClose ) /
latestClose * 100, 2 ) )

df = data.frame( prices, pcts, positions )
colnames( df ) = c( "Price", "Pct", "Position" )
return( df )
}

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