## 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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