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6 208 distribution with a kurtosis value of 3 is known as mesokurtic, of which, the normal distribution is the prime example [20]. Kurtosis indicates whether the tails of the distribution are normal; high kurtosis signifies fat tails, a higher than normal probability of extreme positive or negative events [2]. Kurtosis is calculated as follows [20]: 4 E( X X ) Kurt= (5) 2 [ E( X X ) ] 2 Extreme negative returns can be particularly damaging to a trading strategy, potentially wiping out all previous profits and even equity capital [2]. b) Technical analysis: Z-Score: A statistical measure that quantifies the distance a data point is from the mean of a data set. This distance is measured in standard deviations. Z-Score is also called z-value, normal score, standard score and standardized variable. Z- Score is calculated as follows: X X z = (6) σ Moving Average Convergence/Divergence (MACD): MACD is a technical momentum indicator that belongs to a family of indicators called oscillators. An oscillator gets its name from the fact that it moves or oscillates between two fixed values based on the price movement of a security or index. Here, taking the difference between two exponential moving averages (EMAs) with different periods, MACD produces an oscillator because the resulting curve swings back and forth across a zero line [21]. The MACD is calculated by subtracting the 26-day EMA from the 12-day EMA. A 9-day EMA of the MACD, called the "signal line" or trigger line, is then plotted on top of the MACD, functioning as a trigger for buy and sell signals [22]. The MACD Indicator mathematical formulae are as follows [22]: MACD + ( MACD % = ( C.% [ 0] [ 0] MACD [ 1].(1 % MACD)) 2 = ( ) Interval + 1 ) (7) MACD (8) where: C [ 0] = Closing price of the most recent period. % = Percentage used to determine the MACD exponential moving average length. MACD = The MACD value from one period [ 1] previous. Interval = Exponential moving average length in periods. There are three popular ways to interpret the MACD indicator: o Crossovers: The basic trading rule is to sell when the indicator falls below the trigger line [22]. Similarly, a buy signal occurs when it rises above the trigger o line [22]. Overbought / Oversold: When the shorter moving average pulls away dramatically from the longer moving average (i.e., it rises), it is likely that the price is overextending and will soon return to more realistic levels [22]. o Divergence: A bearish divergence occurs when it is making new lows while prices fail to reach new lows [22]. A bullish divergence occurs when it is making new highs while prices fail to reach new highs [22]. Relative Strength Index (RSI): Another technical momentum indicator that belongs to the oscillators family. An RSI ranges between 0 and 100 and compares the magnitude of recent gains to recent losses in an attempt to determine overbought and oversold conditions of an asset [23]. RSI is calculated using the following formula: 100 RSI = 100 (9) 1+ RS where: Average of days' up closes RS = (10) Average of days'down closes When the RSI turns up, developing a trough below 30, it suggests the price is oversold and likely to rally [23]. Conversely, when the RSI turns down, reaching a peak above 70, it suggests that the price is overbought and likely to drop [23]. 3) Development and Execution Environments Comparative analysis methodologies are used for this research. Basic descriptive statistics and technical analysis methods are developed as executable algorithms. These algorithms are deployed and executed on an on-premise hardware system and on a cloud system to determine the feasibility and effectiveness of cloud versus on-premise deployments.

11 213 TABLE IV. R EXECUTON CODE FOR BASC DESCRPTVE STATSTCS ## HFTBasicStatistics calculates the mean, median, variance, standart deviation, kurtosis and skewness of the xu30 high-frequency ## data. HFTBasicStatistics <- function() { x = data.matrix(data.frame((my.csv.data))) for (i in 1:length(x)){ if (i>1){ a[i-1] = x[i]/x[i-1] a[i-1]= log(a[i-1]) } } print(mean(a)) print(median(a)) print(var(a)) print(sd(a)) print(kurtosis(a)) print(skewness(a)) } ## "System.time" provides execution time of HFTBasicStatistics function. system.time(hftbasicstatistics()) TABLE V. R EXECUTON CODE FOR Z-SCORE ## CalcZscore function calculates zscore of new fictional record by using real XU30 data in the recordset. CalcZscore <- function() { x = data.matrix(data.frame((my.csv.data))) m <- mean(x) print(m) s <- sd(x,na.rm=true) z = (72000-m)/s print(z) } ## "System.time" provides execution time of CalcZscore function. system.time(calczscore())

12 214 TABLE VI. R EXECUTON CODE FOR MACD ## XU30macd function provides to implement Exit, Entry and Risk strategy based on Moving Average Convergence-Divergence ##(MACD) of XU30 high frequency data. It also draws final MACD graphic. XU30macd = function(){ stock.str='xu30_matrix' data(xu30_matrix) XU30_matrix<-as.xts(XU30_matrix) initdate=' ' initeq=60000 portfolio.st='macd' account.st='macd' initportf(portfolio.st,symbols=stock.str, initdate=initdate) initacct(account.st,portfolios=portfolio.st, initdate=initdate) initorders(portfolio=portfolio.st,initdate=initdate) matype="ema" signalma = 9 fastma = 12 slowma = 26 currency('tl') stock(stock.str,currency='tl',multiplier=1) stratmacd <- strategy(portfolio.st) stratmacd <- add.indicator(strategy = stratmacd, name = "MACD", arguments = list(x=quote(cl(xu30_matrix))) ) ## Enter to market strategy stratmacd <- add.rule(strategy = stratmacd,name='rulesignal', arguments = list(sigcol="signal.gt.zero",sigval=true, orderqty=70, ordertype='market', orderside='long', threshold=null),type='enter') ## Stop to buy strategy stratmacd <- add.rule(strategy = stratmacd,name='rulesignal', arguments = list(sigcol="signal.gt.zero",sigval=true, orderqty=-70, ordertype='stoplimit', orderside='long', threshold=.60,tmult=true),type='risk') ## Exit strategy stratmacd <- add.rule(strategy = stratmacd,name='rulesignal', arguments = list(sigcol="signal.lt.zero",sigval=true, orderqty='all', ordertype='market', orderside='long', threshold=null),type='exit') stratmacd <- add.signal(strategy = stratmacd,name="sigthreshold",arguments = list(column="signal",relationship="gt",threshold=0,cross=true),label="signal.gt.zero") stratmacd <- add.signal(strategy = stratmacd,name="sigthreshold",arguments = list(column="signal",relationship="lt",threshold=0,cross=true),label="signal.lt.zero") getsymbols(stock.str,from=initdate) start_t<-sys.time() out<-try(applystrategy(strategy=stratmacd, portfolios=portfolio.st,parameters=list(nfast=fastma, nslow=slowma, nsig=signalma,matype=matype))) end_t<-sys.time() print(end_t-start_t) start_t<-sys.time() updateportf(portfolio=portfolio.st,dates=paste('::',as.date(sys.time()),sep='')) end_t<-sys.time() print(end_t-start_t) chart.posn(portfolio=portfolio.st,symbol=stock.str) plot(add_macd(fast=fastma, slow=slowma, signal=signalma,matype="ema")) } ## "System.time" provides execution time of XU30macd function. system.time(xu30macd())

13 215 TABLE VII. R EXECUTON CODE FOR RSI ## XU30rsi provides to implement Relative Strength Index (RSI) based entry / exit trading strategy. ##It uses xu30 high frequency data. It also helps to add thresholds, rules and signals. XU30rsi = function(){ # Strategy object stratrsi <- strategy("rsi") # Indicator stratrsi <- add.indicator(strategy = stratrsi, name = "RSI", arguments = list(price = quote(getprice(xu30_matrix))), label="rsi") # RSI is greater than 70 stratrsi <- add.signal(strategy = stratrsi, name="sigthreshold",arguments = list(threshold=70, column="rsi",relationship="gt", cross=true),label="rsi.gt.70") # RSI is less than 30 stratrsi <- add.signal(strategy = stratrsi, name="sigthreshold",arguments = list(threshold=30, column="rsi",relationship="lt",cross=true),label="rsi.lt.30") # Buy when the RSI crosses below the threshold 30 stratrsi <- add.rule(strategy = stratrsi, name='rulesignal', arguments = list(sigcol="rsi.lt.30", sigval=true, orderqty= 500, ordertype='market', orderside='long', pricemethod='market', replace=false), type='enter', path.dep=true) stratrsi <- add.rule(strategy = stratrsi, name='rulesignal', arguments = list(sigcol="rsi.gt.70", sigval=true, orderqty='all', ordertype='market', orderside='long', pricemethod='market', replace=false), type='exit', path.dep=true) # Sell when the RSI crosses above the threshold 70 stratrsi <- add.rule(strategy = stratrsi, name='rulesignal', arguments = list(sigcol="rsi.gt.70", sigval=true, orderqty=-500, ordertype='market', orderside='short', pricemethod='market', replace=false), type='enter', path.dep=true) stratrsi <- add.rule(strategy = stratrsi, name='rulesignal', arguments = list(sigcol="rsi.lt.30", sigval=true, orderqty='all', ordertype='market', orderside='short', pricemethod='market', replace=false), type='exit', path.dep=true) currency("tl") symbols = c("xu30") for(symbol in symbols){ stock(symbol, currency="tl",multiplier=1) getsymbols(symbol) } applysignals(strategy=stratrsi, xu30_matrix=applyindicators(strategy=stratrsi, xu30_matrix=symbols[1])) initeq=60000 port.st<-'rsi' initdate=' ' initorders(portfolio=port.st, initdate=initdate) initacct(port.st, portfolios=port.st, initdate=initdate) initportf(port.st, symbols=symbols, initdate=initdate) start_t<-sys.time() out<-try(applystrategy(strategy=stratrsi, portfolios=port.st, parameters=list(n=2) ) ) end_t<-sys.time() start_t<-sys.time() updateportf(portfolio=port.st,dates=paste('::',as.date(sys.time()),sep='')) end_t<-sys.time() } ## "System.time" provides execution time of XU30rsi function. system.time(xu30rsi())

14 216 Figure 4. MACD graph for 2 months data. Figure 5. RSI graph for 2 months data.

15 217 Figure 6. Biocep-R within the Technology Environment [29].

Financial Business Cloud for High-Frequency Trading Arden Agopyan Software Group IBM Central & Eastern Europe Istanbul, TURKEY arden@tr.ibm.com Emrah Şener Center for Computational Finance Özyeğin University

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