Description Usage Arguments Details Value Author(s) References See Also Examples
This function receives as argument an object of class
tradeRecord
that is the result of a call to the
trading.simulator()
function and produces a set of evaluation
metrics of this simulation
1 |
t |
An object of call |
Given the result of a trading simulation this function calculates:
The number of trades.
The number of profitable trades.
The percentage of profitable trades.
The profit/loss of the simulation (i.e. the final result).
The return of the simulation.
The return over the buy and hold strategy.
The maximum draw down of the simulation.
The Sharpe Ration score.
The average percentage return of the profitable trades.
The average percentage return of the non-profitable trades.
The average percentage return of all trades.
The maximum return of all trades.
The maximum percentage loss of all trades.
A vector of evaluation metric values
Luis Torgo ltorgo@dcc.fc.up.pt
Torgo, L. (2016) Data Mining using R: learning with case studies, second edition, Chapman & Hall/CRC (ISBN-13: 978-1482234893).
tradeRecord
, trading.simulator
, trading.signals
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | ## An example partially taken from chapter 3 of my book Data Mining
## with R (Torgo,2010)
## First a trading policy function
## This function implements a strategy to trade on futures with
## long and short positions. Its main ideas are the following:
## - all decisions aretaken at the end of the day, that is, after
## knowing all daily quotes of the current session.
## - if at the end of day d our models issue a sell signal and we
## currently do not hold any opened position, we will open a short
## position by issuing a sell order. When this order is carried out by
## the market at a price pr sometime in the future, we will
## immediately post two other orders. The first is a buy limit order
## with a limit price of pr - p%, where p% is a target profit margin.
## We will wait 10 days for this target to be reached. If the order is
## not carried out by this deadline, we will buy at the closing price
## of the 10th day. The second order is a buy stop order with a price
## limit pr + l%. This order is placed with the goal of limiting our
## eventual losses with this position. The order will be executed if
## the market reaches the price pr + l%, thus limiting our possible
## losses to l%.
## - if the end of the day signal is buy the strategy is more or less
## the inverse
## Not run:
library(xts)
policy.1 <- function(signals,market,opened.pos,money,
bet=0.2,hold.time=10,
exp.prof=0.025, max.loss= 0.05
)
{
d <- NROW(market) # this is the ID of today
orders <- NULL
nOs <- NROW(opened.pos)
# nothing to do!
if (!nOs && signals[d] == 'h') return(orders)
# First lets check if we can open new positions
# i) long positions
if (signals[d] == 'b' && !nOs) {
quant <- round(bet*money/market[d,'Close'],0)
if (quant > 0)
orders <- rbind(orders,
data.frame(order=c(1,-1,-1),order.type=c(1,2,3),
val = c(quant,
market[d,'Close']*(1+exp.prof),
market[d,'Close']*(1-max.loss)
),
action = c('open','close','close'),
posID = c(NA,NA,NA)
)
)
# ii) short positions
} else if (signals[d] == 's' && !nOs) {
# this is the nr of stocks we already need to buy
# because of currently opened short positions
need2buy <- sum(opened.pos[opened.pos[,'pos.type']==-1,
"N.stocks"])*market[d,'Close']
quant <- round(bet*(money-need2buy)/market[d,'Close'],0)
if (quant > 0)
orders <- rbind(orders,
data.frame(order=c(-1,1,1),order.type=c(1,2,3),
val = c(quant,
market[d,'Close']*(1-exp.prof),
market[d,'Close']*(1+max.loss)
),
action = c('open','close','close'),
posID = c(NA,NA,NA)
)
)
}
# Now lets check if we need to close positions
# because their holding time is over
if (nOs)
for(i in 1:nOs) {
if (d - opened.pos[i,'Odate'] >= hold.time)
orders <- rbind(orders,
data.frame(order=-opened.pos[i,'pos.type'],
order.type=1,
val = NA,
action = 'close',
posID = rownames(opened.pos)[i]
)
)
}
orders
}
## Now let us play a bit with the SP500 quotes availabe in our package
data(GSPC)
## Let us select the last 3 months as the simulation period
market <- last(GSPC,'3 months')
## now let us generate a set of random trading signals for
## illustration purpose only
ndays <- nrow(market)
aRandomIndicator <- rnorm(sd=0.3,ndays)
theRandomSignals <- trading.signals(aRandomIndicator,b.t=0.1,s.t=-0.1)
## now lets trade!
tradeR <- trading.simulator(market,theRandomSignals,
'policy.1',list(exp.prof=0.05,bet=0.2,hold.time=10))
## a few stats on the trading performance
tradingEvaluation(tradeR)
## End(Not run)
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