Calculate profit and loss for trades - r

Question

I have this script which calculates profit and loss of trades. It works fine
but I think it can be improved. It will be great to get rid of the for loops at least to make the code look compact.
Can anyone please help me out ?
The logic to calculate profit/loss is first to match the sell trades with potential buy trades. A single sell trade can be matched with multiple buys. So the cost might be distributed to multiple buys.
Steps :
separate the trades into buy and sell in increasing dates.
calculate the average cost price
calculate profit/loss = (selling price - cost price)*matching vol
Thanks
Here is the sample data set
> structure(list(AsxCode = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "QAN", class = "factor"), Order.Type = structure(c(1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L), .Label = c("Buy", "Sell"), class = "factor"), Trade.Date = structure(c(13L, 12L, 12L, 11L, 10L, 9L, 8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L), .Label = c("2014-03-28", "2014-05-22", "2014-11-07", "2014-11-18", "2014-12-04", "2015-03-02", "2015-03-24", "2015-03-27", "2015-05-11", "2015-05-15", "2015-08-21", "2016-04-15", "2016-04-18"), class = "factor"), Price = c(3.75, 4.05, 4.01, 3.55, 3.68, 3.38, 2.9, 2.98, 2.9, 2.05, 1.8, 1.65, 1.25, 1.07), Quantity = c(850L, 1350L, 150L, 1000L, 1500L, 1400L, 1091L, 2000L, 1750L, 600L, 366L, 375L, 500L, 500L), Consideration = c(3198.5, 5456.5, 590.5, 3561, 5531, 4743, 3152.9, 5949, 5086, 1241, 669.8, 629.75, 614, 546), match_status = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), match_vol = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), avg_price = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), profit_loss = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("AsxCode", "Order.Type", "Trade.Date", "Price", "Quantity", "Consideration", "match_status", "match_vol", "avg_price", "profit_loss"), row.names = c(NA, -14L), class = "data.frame")
AsxCode Order.Type Trade.Date Price Quantity Consideration match_status match_vol avg_price profit_loss
1 QAN Buy 2016-04-18 3.75 850 3198.50 NA 0 0 0
2 QAN Sell 2016-04-15 4.05 1350 5456.50 NA 0 0 0
3 QAN Sell 2016-04-15 4.01 150 590.50 NA 0 0 0
4 QAN Buy 2015-08-21 3.55 1000 3561.00 NA 0 0 0
5 QAN Buy 2015-05-15 3.68 1500 5531.00 NA 0 0 0
6 QAN Buy 2015-05-11 3.38 1400 4743.00 NA 0 0 0
7 QAN Sell 2015-03-27 2.90 1091 3152.90 NA 0 0 0
8 QAN Sell 2015-03-24 2.98 2000 5949.00 NA 0 0 0
9 QAN Buy 2015-03-02 2.90 1750 5086.00 NA 0 0 0
10 QAN Buy 2014-12-04 2.05 600 1241.00 NA 0 0 0
11 QAN Buy 2014-11-18 1.80 366 669.80 NA 0 0 0
12 QAN Buy 2014-11-07 1.65 375 629.75 NA 0 0 0
13 QAN Sell 2014-05-22 1.25 500 614.00 NA 0 0 0
14 QAN Buy 2014-03-28 1.07 500 546.00 NA 0 0 0
calculate.profit <- function(trades){
trades$match_vol <- 0
s <- trades[trades$Order.Type== 'Sell', ]
sell.trades <- s[order(s$Trade.Date, decreasing=FALSE),]
b <- trades[trades$Order.Type== 'Buy', ]
buy.trades <- b[order(b$Trade.Date, decreasing=FALSE),]
# Don't want to execute the for loop when there is no sell trades. In other words when there is no profit/loss unless you sell
if(nrow(sell.trades)==0){
return (buy.trades)
}
# for each sell find the associated buys
for(i in 1:nrow(sell.trades))
{
# calculate average price. The Consideration column contains total cost
s.price <- sell.trades[i, 'Consideration']/sell.trades[i,'Quantity']
for(j in 1:nrow(buy.trades))
{
# this part matches sell with a buy trade
# if sell volume and buy volume are same, the sell is fully matched otherwise it has to find the remaining sell units.
s.vol <- sell.trades[i,'Quantity'] - sell.trades[i,'match_vol']
b.vol <- buy.trades[j, 'Quantity'] - buy.trades[j, 'match_vol']
if (b.vol != 0)
{
b.price <- buy.trades[j, 'Consideration']/buy.trades[j, 'Quantity']
# contains the volume which is matched between buy and sell
# trades
match.vol <- min(s.vol, b.vol)
profit <- match.vol * (s.price - b.price)
buy.trades[j, 'match_vol'] <- match.vol + buy.trades[j, 'match_vol']
sell.trades[i, 'profit_loss'] <- profit + sell.trades[i, 'profit_loss']
sell.trades[i, 'match_vol'] <- match.vol + sell.trades[i, 'match_vol']
}
# sell parcel fully processed
if (sell.trades[i ,'match_vol'] == sell.trades[i ,'Quantity'])
{
j=1
break;
}
}
}
return (rbind(buy.trades, sell.trades))
}

Answer 1

There is a number of improvements that could be made.
Preallocating object sizes
The most obvious thing would be to preallocate objet sizes. The received wisdom is that it is inefficient to expand objects in loops. Hence you would do:
# On example of a single column
sell.trades.vec <- vector(mode = "numeric", length = nrow(buy.trades))
in order to avoid objects being expended within loops.
seq_along()
Broadly speaking, it is neat to use seq_along instead of 1:something, have a look:
>> a <- NULL
>> 1:length(a)
[1] 1 0
>> seq_along(a)
integer(0)
>>
as well:
>> 1:0
[1] 1 0
>> seq_along(0)
[1] 1
>>
I'm guessing that you will (most probably) always have some sensible nrow value but seq_along maybe worth reflecting on, in case there is a risk to get some odd data.