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1 18 Effective ness of technical indicators in an automatic stock trade program
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3 . MACP VR MACD 3 MACP. 3. MACD RSI 3 MACP VR 2.. 5, ,1.,,... MACD MACP. i
4 . MACP, VR, MACD, 3, RSI, ii
5 Abstract Effective ness of technical indicators in an automatic stock trade program Nami Watanabe The purpose of this research is to develope a new investment technique. I pursued the combination which can effectively use each index characteristic of the technical indicators (the detection techniques of a stock trade timing). I had verified the practicality of my investment method on my automatic stock trade program. This proposed method uses the investment technique in which MACP (Principal axis), VR and MACD are combined. With this combination, we can ascertain stock prices in three viewpoint Conversion with, Turnover, and Directivity. Possible faults of each index can be covereel by each other. For this reason, we will be able to get profits more certainly. In order to confirm advantage of this investment technique in comparison, I also programmed The three point charge investment technique (the previous investment technique by combination of MACP Principal axis, VR and RSI). and The Base technique (the investment technique by combination of MACP and VR). I carried out imitation stock trade by using these three technique, and Nikkei225 with stock price data in As a management environment, initial property was set to 50 million yen. The trading was only once per day using market information until the previous day, and continued it for one year. When the buy signal was detected, the purchase number was determined by a formula tradingpower T henumberofpurchasebrandontheday stockprice. iii
6 The result of performance in imitation stock trade, this proposal detected signals moderately, and sufficient profits were obtained. But compared with the Nikkei225 or the Base, the maximum drawdown was higher. It means that this technique has a defect, too. Because of the combination of MACD and MACP, once a rise market is detected the sell signal is activated before a possession brand improves to some extent. These, it shows the fault of selling rarely at a low price than the price of purchase. Also, for better results in a practical use, it should be adjuseed more precisely the intial condition of parameters in sell signals and buy signals.there is a possibility that the formula to determine the number of stocks maybe insufficient. key words MACP, VR, MACD, The three point charge investment technique, RSI, Nikkei225 iv
7 (MACP : Moving Average Conversion Premium) VR (VolumeRatio) RSI (Relative Strength Index) MACD (Moving Average Convergence Divergence) ( ) v
8 A 39 A JAL (3 ) A JAL (3 ) A.3 ANA (3 ) A.4 (3 ) A.5 NTT (3 ) vi
9 1.1 MACP VR RSI MACD A JAL A JAL A.3 ANA A vii
10 A.5 NTT viii
11 (20 ) (50 ) ix
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14 (MACP : Moving Average Conversion Premium) MACP, N MACP,,, MACP,. MACP ( ) = (A B) B 100 A = B = N 26, -5-10, MACP 2
15 VR (VolumeRatio) VR N VR 0, 100,,, VR, V R( ) = A+ 1 2 C B+ 1 2 C 100 A = B = C = 0 14, 30 70, VR 3
16 RSI (Relative Strength Index) RSI N ( - ), RSI 0, 100,, RSI RSI( ) = A A+B 100 A =, B = RSI( ) = A A +B 100 C =, D = A = A (n 1) C N, B = B (n 1) D N 14, 20 30, RSI 4
17 MACD (Moving Average Convergence Divergence) MACD 2, MACD ( ) EMA ( ) EMA, 0, 0 MACD =N = EMA- EMA A = ( 0 1) = 2 N+1. EMA = EMA + A EMA EMA 12, EMA MACD 5
18 ( ) MACP,VR,RSI,MACD,,, 1,. 1, 3 3,MACP VR RSI 3, MACP,. : MACP, VR, RSI : MACP VR 2, MACP RSI
19 2 1 3 MACP RSI,, , 3. RSI,VR MACD 3, , 2.1 7
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21 ,, SDK( ) java, [1]., 3,3 RSI MACP VR RSI MAC-D EMA 12 EMA ( ) ( ) 3.1 MACP 26 MACP MACP VR RSI MACP VR MACP VR MAC-D MACP VR or MACP RSI MACP VR MACP VR or MACP MAC-D 9
22 MACP VR MAC-D 3 MACP VR 2 MACP MAC-D (1 1 ),,.. 0.1,., 3.2 SDK Java,,.,,, 10
23 3.3. : Java, SDK Java SDK : 5, ( ) SDK : 50 1 ( ) : = * *( 50 5 )= = 11
24 import java.util.list; // List import java.util.arraylist; // ArrayList import jp.tradesc.superkaburobo.sdk.robot.abstractrobot; // SDK AbstractRobot import jp.tradesc.superkaburobo.sdk.driver.robotdriver; // SDK RobotDriver import jp.tradesc.superkaburobo.sdk.trade.informationmanager; // InformationManager import jp.tradesc.superkaburobo.sdk.trade.enumcurrentsession; // EnumCurrentSession import jp.tradesc.superkaburobo.sdk.trade.enumanalysisspan; // ( )EnumAnalysisSpan import jp.tradesc.superkaburobo.sdk.trade.tradeagent; // Manager TradeAgent import jp.tradesc.superkaburobo.sdk.trade.timemanager; // TimeManager import jp.tradesc.superkaburobo.sdk.trade.ordermanager; // OrderManager import jp.tradesc.superkaburobo.sdk.trade.portfoliomanager; // PortfolioManager import jp.tradesc.superkaburobo.sdk.trade.analysis.technicalindex.movingaverage; // MovingAverage import jp.tradesc.superkaburobo.sdk.trade.analysis.technicalindex.volumeratio; // VolumeRatio VolumeRatio import jp.tradesc.superkaburobo.sdk.trade.analysis.technicalindex.rsi; // Relative Strength Index RSI import jp.tradesc.superkaburobo.sdk.trade.analysis.technicalindex.macd; 12
25 3.4 // Moving Average Convergence Divergence MACD import jp.tradesc.superkaburobo.sdk.trade.data.stock; // Stock import jp.tradesc.superkaburobo.sdk.trade.data.portfolio; // Portfolio public class Original extends AbstractRobot { // AbstractRobot Original public static void main(string[] args) { // -n Original String[] v = { "-n", "Original" }; RobotDriver.main( v ); } public void order(tradeagent tradeagent) { // order TimeManager tm = TimeManager.getInstance(); InformationManager im = InformationManager.getInstance(); if(tm.getcurrentsession() == EnumCurrentSession.EARLY_SESSION){ //, checkportfolio(); } if(tm.getcurrentsession() == EnumCurrentSession.EARLY_SESSION){ //, List<Stock> targetstocklist = gettargetstock(); // if(targetstocklist.size()!= 0){ int priceforone = (int)(tradeagent.gettradablemoney()/(targetstocklist.size())); // 1 (/ ) for(stock stock: targetstocklist) { try{ int price = im.getstocksession(stock).getclosingprice(); // 13
26 3 1 int qty = priceforone/price; // (1 / ) OrderManager om = OrderManager.getInstance(); // om om.orderactualnowmarket(stock, Math.max(qty, stock.getunit())); // }catch(nullpointerexception e){ // NullPointerException } } } } } public void screening(tradeagent arg0) { // order screening } private List<Stock> gettargetstock() { // gettargetstock InformationManager im = InformationManager.getInstance(); List<Stock> targetstocklist = new ArrayList(); for(stock stock:im.getstocklist()){ try{ if(getbuysignal(stock) == 1){ // (1: ) targetstocklist.add(stock); } }catch(nullpointerexception e){ // NullPointerException } } return targetstocklist; // } 14
27 3.4 private void checkportfolio() { // checkportfolio PortfolioManager pm = PortfolioManager.getInstance(); OrderManager om = OrderManager.getInstance(); for(portfolio portfolio:pm.getportfolio()){ try{ if(getsellsignal(portfolio.getstock()) == -1) { // (-1: ) portfolio.orderreversenowmarketall(); } }catch(nullpointerexception e){ // NullPointerException } } } double getbuysignal(stock stock){ // if(getkairisignal(stock) > 0 && getmacdsignal(stock) > 0 && getvolumeratiosignal(stock) > 0) { return 1.0; } // (MACP VR MACD ) /* if(getkairisignal(stock) > 0 && getrsisignal(stock) > 0 && getvolumeratiosignal(stock) > 0) { return 1.0; } */ // 3 (MACP VR RSI ) 15
28 3 1 /* if(getkairisignal(stock) > 0 && getvolumeratiosignal(stock) > 0) { return 1.0; } */ // (MACP VR ) return 0.0; // } double getsellsignal(stock stock){ // if(getmacdsignal(stock) < 0 && getkairisignal(stock) < 0) { return -1.0; } else if(getvolumeratiosignal(stock) < 0 && getkairisignal(stock) < 0) { return -1.0; } // (MACP VR MACD ) /* if(getrsisignal(stock) < 0 && getkairisignal(stock) < 0) { return -1.0; } else if(getvolumeratiosignal(stock) < 0 && getkairisignal(stock) < 0) { return -1.0; } */ // 3 (MACP VR RSI ) /* if(getvolumeratiosignal(stock) < 0 && getkairisignal(stock) < 0) { return -1.0; } 16
29 3.4 */ // (MACP VR ) return 0.0; // } double getkairisignal(stock stock){ // 26 MACP InformationManager im = InformationManager.getInstance(); int price = im.getstocksession(stock).getclosingprice(); // MovingAverage kairi = new MovingAverage(EnumAnalysisSpan.DAILY, 26); // 26 if((price-kairi.getindexsimple(stock))/kairi.getindexsimple(stock)*100 < -7){ // -7 return 1.0; } else if((price-kairi.getindexsimple(stock))/kairi.getindexsimple(stock)*100 > 5){ // 5 return -1.0; // } return 0.0; // } double getvolumeratiosignal(stock stock){ // 25 VolumeRatio VolumeRatio volumeratio = new VolumeRatio(EnumAnalysisSpan.DAILY,25); double volumeratioindex = volumeratio.getindex(stock); //VolumeRatio if(volumeratioindex < 70){ return 1.0; // 70 } else if(volumeratioindex > 250){ return -1.0; // 250 } return 0.0; // 17
30 3 1 } double getmacdsignal(stock stock) { // MACD MACD macd = new MACD(EnumAnalysisSpan.DAILY,12,26,9); // 9 MACD if (macd.getindexmacd(stock) < 0) { return 1.0; // 0 } else if (macd.getindexmacd(stock) > 0) { return -1.0; // 0 } return 0.0; // } double getrsisignal(stock stock){ // 14 RSI RSI rsi = new RSI(EnumAnalysisSpan.DAILY,14); double rsiindex = rsi.getindexsimple(stock); //RSI if (rsiindex < 25) { return 1.0; // 25 } else if (rsiindex > 75) { return -1.0; // 75 } return 0.0; // } } 18
31 Eclipse /09/22 08:00 ( ) 50,000, ,000, // /09/22 09:00 ( ) ( ):
32 /09/22 11:30 ( ) // 1 1, 26,346,370 23,800,000 50,146,370 // ( ) : : : : 8766 : 2004/09/22 : 17 : 1,390,000 : 23,800,000 //. // 2004/09/22 12:30 ( ) ( ): /09/22 16:00 ( ) // (, ) 26,346,370 23,800,000 50,146,370 // ( ) : : : : 8766 : 2004/09/22 : 17 : 1,390,000 : 23,800, /09/22 23:59 ( ) ( ): 31 20
33 ,. 2004/11/16 08:00 ( ) 854,904 54,335,000 55,189,904 // ( ) : : : : 5108 : 2004/10/27 : 1000 : 1,926 : 1,996, : 2004/10/26 : 2000 : 1,874 : 3,992, : 2004/09/28 : 7 : 892,000 : 6,811, : 2004/09/22 : 17 : 1,390,000 : 26,350, : 2004/09/27 : 9 : 1,380,000 : 13,950, : 2004/10/27 : 1000 : 1,128 : 1,236, // 8306( 7 ) /11/16 09:00 ( ) ( ): /11/16 11:30 ( ) // 1 1, 48,753,957 21
34 3 1 7,161,000 55,914,957 // ( ) : : : : 5108 : 2004/10/26 : 2000 : 1,874 : 3,946, : 2004/10/27 : 1000 : 1,926 : 1,973, : 2004/10/27 : 1000 : 1,128 : 1,242, /11/16 12:30 ( ) ( ): /11/16 16:00 ( ) // (, ) 48,753,957 7,133,000 55,886,957 // ( ) : : : : 5108 : 2004/10/27 : 1000 : 1,926 : 1,964, : 2004/10/26 : 2000 : 1,874 : 3,928, : 2004/10/27 : 1000 : 1,128 : 1,241, /11/16 23:59 ( ) ( ): 0 22
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No. 19 January 19 2013 19 Regionalism at the 19 th National Assembly Elections Focusing on the Yeongnam and Honam Region Yasurou Mori As the biggest issue of contemporary politics at South Korea, there
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28 Docker Design and Implementation of Program Evaluation System Using Docker Virtualized Environment
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32 2008 pp. 93 106 1 Received October 30, 2008 The purpose of this study is to examine the effects of aerobics training class on weight loss for female students in HOKURIKU UNIVERSITY. Seventy four female
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Forecasting Model for Electricity Consumption in Residential House Based on Time Series Analysis * ** *** Shuhei Kondo Nobayasi Masamori Shuichi Hokoi ( 2015 7 3 2015 12 11 ) After the experience of electric
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