均線模型量化股票交易

. 15金融專碩 15720708 仰軍均線模型量化股票交易摘 要:移動平均線(MA)是股市中最常用的一種分析技術(shù)，用于在大行情的波動段找到有效的交易信號。移動平均線簡單、有效，對股市操作具有較好的指導(dǎo)作用。均線模型能有效地打敗了大部分的主觀策略，成為炒股，炒期貨的必備基本工具。本文選用阿里巴巴 2014 年 9 月至今的股票數(shù)據(jù)，對其每日收盤價(jià)進(jìn)行了 5 日、20 日和60 日移動平均。先用每日收盤價(jià)和 20 日平均分析一條均線的交易策略，通過找出交易信號并模擬交易，發(fā)現(xiàn)一條均線交易策略會有些許的虧損，但通過圖表分析，此策略能夠較好的預(yù)測股價(jià)走勢，相對主觀投資更為合理；然后用 5 日平均和 20 日平均做兩條均線的交易策略，這是對一條均線模型的優(yōu)化，模擬交易后發(fā)現(xiàn)此交易能夠盈利。關(guān)鍵詞：股票移動平均均線交易策略模擬交易II 1.均線模型的簡單介紹1.1移動平均線本文采用統(tǒng)計(jì)學(xué)中”移動平均”的原理，計(jì)算一股票每日收盤價(jià)的簡單移動平均，將一段時(shí)期內(nèi)的股票價(jià)格平均值連成曲線，來顯示股價(jià)的歷史波動情況，進(jìn)而反映股價(jià)指數(shù)未來發(fā)展趨勢。1.2均線的特性（1） 平滑性。通過均線的平滑移動來修復(fù)股價(jià)的不規(guī)則偶然變動。（2） 靈敏性、穩(wěn)定性。均線參數(shù)取值不同，其特性也不同。參數(shù)越小，靈敏性越強(qiáng)，穩(wěn)定性越差；參數(shù)越大，穩(wěn)定性越強(qiáng)，靈敏性越差。（3） 助漲性、助跌性。上升趨勢線有助漲的特性，下降趨勢線有助跌的特性。1.3均線模型在日K線圖中除了標(biāo)準(zhǔn)的價(jià)格K線以外，另外還有4條線，分別是白線、黃線、紫線、綠線依次分別表示：5日、10日、20日和60日移動平均線，通過這4條線與價(jià)格K線的交叉，就可以形成不同的均線模型。利用均線平滑的特點(diǎn)，可以發(fā)現(xiàn)均線與價(jià)格K線會有叉，各均線之間也有交叉，我們可以通過這些交叉點(diǎn)判斷交易信號：黃金交叉，當(dāng)10日均線由下往上穿越30日均線，10日均線在上，30日均線在下，其交叉點(diǎn)就是黃金交叉，黃金交叉是多頭的表現(xiàn)，出現(xiàn)黃金交叉后，后市會有一定的漲幅空間，這是進(jìn)場的最佳時(shí)機(jī)；死亡交叉，當(dāng)30日均線與10日平均線交叉時(shí)，30日均線由下住上穿越10日平均線，形成30日平均線在上，10日均線在下時(shí)，其交點(diǎn)稱之為”死亡交叉”，”死亡交叉”預(yù)示空頭市場來臨，股市將下跌此時(shí)是出場的最佳時(shí)機(jī)。2.用R語言實(shí)現(xiàn)模型建立2.1從互聯(lián)網(wǎng)下載股票數(shù)據(jù)利用quantmod包的getSymbols()函數(shù)，默認(rèn)會通過Yahoo的金融開放API下載數(shù)據(jù)，我們選擇阿里巴巴的股票數(shù)據(jù)，從2014-09-19至今的1年多的日間交易數(shù)據(jù)。數(shù)據(jù)類型為 xts格式的時(shí)間序列，數(shù)據(jù)包括7個(gè)列，以日期做索引列，其他6列分別為 開盤價(jià)(Open), 最高價(jià)(High), 最低價(jià)(Low), 收盤價(jià)(Close), 交易量(Volume), 調(diào)整價(jià)(Adjusted)。2.2自定義均線圖自定義均線指標(biāo)：以日期時(shí)間序列為索引，收盤價(jià)做為價(jià)格指標(biāo)，不考慮成交量及其他維度字段。取2014-09-19至今，形成趨勢的數(shù)據(jù)，畫出價(jià)格曲線，5日均線，20日均線，60 日均線。通過自己封裝的移動平均函數(shù)和可視化函數(shù)，就實(shí)現(xiàn)了與交易軟件中類似的日K線圖和多條均線結(jié)合的可視化輸出。2.3一條均線的交易策略模型設(shè)計(jì)思路：1. 以股價(jià)和20日均線的交叉，進(jìn)行交易信號的判斷。2. 當(dāng)股價(jià)上穿20日均線則買入(紅色)，下穿20日均線賣出(藍(lán)色)。首先畫出股價(jià)和20日均線圖以散點(diǎn)覆蓋20日均線，紅色點(diǎn)為買入持有，藍(lán)色點(diǎn)為賣出空倉。用股價(jià)和20日均線價(jià)格做比較，把股價(jià)大于均線的部分用藍(lán)色表示，股價(jià)小于均線的部分用紅色表示。我們看到圖中，藍(lán)色點(diǎn)和紅色點(diǎn)在20日均線上交替出現(xiàn)，我們可以在每次紅色出現(xiàn)的第一個(gè)點(diǎn)買入股票，然后在藍(lán)色的第一個(gè)點(diǎn)賣出股票。找出交易信號點(diǎn)，并以100000本金做模擬交易。為簡化操作假定在信號點(diǎn)滿倉買入或賣出，手續(xù)費(fèi)為0。運(yùn)算結(jié)果顯示，虧損的有11筆而盈利的只有3筆，一年下來一共虧損了17038元。似乎一條均線模型是失敗的，因?yàn)樗粌H沒能盈利反而帶來的虧損。下面就從股價(jià)和模擬現(xiàn)金投入來進(jìn)行簡單的分析虧損原因。該圖示，上半紅色部分為日收盤價(jià)，下半藍(lán)色部分為模擬交易的現(xiàn)金流，對比可見，阿里股價(jià)在14年年底開始走低，而根據(jù)一條均線模型進(jìn)行的投資策略比較合理的預(yù)測了股市的走勢并進(jìn)行了股票的買入賣出，在一定程度上是減少了虧損。2.4兩條均線的交易策略一條均線模型，在大的趨勢下是可以進(jìn)行穩(wěn)定投資的，但由于一條均線對于波動非常敏感性，如果小波動過于頻繁，不僅會增加交易次數(shù)，而且會讓模型失效。然后，就有二條均線的策略模型，可以減低對波動的敏感性。二條均線策略模型，與一條均線模型思路類似，以5日均線價(jià)格替換股價(jià)，是通過5日均線和20日均線交叉來進(jìn)行信號交易的。畫出股價(jià)、5日均線和20日均線圖以散點(diǎn)覆蓋20日均線，紅色點(diǎn)為買入持有，藍(lán)色點(diǎn)為賣出空倉。以同樣的條件進(jìn)行兩條均線交易策略的模擬交易。根據(jù)運(yùn)算結(jié)果，雖然依舊虧損了11筆盈利3筆，但最終帶來了總盈利7408元。2.5兩種策略的簡單分析策略一在模擬交易中一共進(jìn)行了30次交易，最終虧損10000元左右，而策略二只進(jìn)行了20次交易，最終帶來盈利7000左右。很明顯兩條均線的交易策略能更好的追蹤股價(jià)趨勢，帶給投資者回報(bào)。看起來均線模型是如此的簡單，但實(shí)盤交易時(shí)真能在趨勢行情中跑贏雙均線(優(yōu)化)模型，也真不是一件容易的事情。參考文獻(xiàn)： 1常寧，徐國祥.金融高頻數(shù)據(jù)分析的現(xiàn)狀與問題研究.財(cái)經(jīng)研究，2004，（3）： 31-39 2武振，鄭丕諤基于遺傳神經(jīng)網(wǎng)絡(luò)的股價(jià)波動預(yù)測天津大學(xué)學(xué)報(bào)，2004， 6(4)：307310 3馬超群，張明良.ACD 族計(jì)量模型的分類與比較分析.金融經(jīng)濟(jì)，2005，（8） 86-87 4蔣學(xué)雷，陳敏，王國明等.股票市場的流動性度量的動態(tài) ACD 模型.統(tǒng)計(jì)研究，2004，（4）：42-44 5 王 晶,王玉玲,向東進(jìn),阮曙芬. 自回歸條件持續(xù)期(ACD)模型研究 統(tǒng)計(jì)與決策 2006（6） 6 Economist. 2007a. Ahead of the TapeAlgorithmic Trading.Economist. June 23, 2007. 2007b. Dodgy TickersStock Exchanges. Economist. March 10, 2007. 7 M. Kearns and L. Ortiz. The PennLehman automated trading project. IEEE Intelligent Systems, 2003. To appear. 8 Domowitz, I., and H. Yegerman. 2005. “The Cost of Algorithmic Trading: A First Look at Comparative Performance.” Edited by Brian R. Bruce, Algorithmic Trading: Precision,Control, Execution. Institutional Investor. 附表一：R語言代碼#加載必須的函數(shù)包library(plyr) library(quantmod) library(TTR) library(ggplot2) library(scales)library（reshape2）#設(shè)置存儲位置setwd(E:/Statistical modeling/)#下載數(shù)據(jù)download-function(stock,from=2013-01-01) df-getSymbols(stock,from=from,env=environment(),auto.assign=FALSE)#下載數(shù)據(jù) names(df)-c(Open,High,Low,Close,Volume,Adjusted) write.zoo(df,file=paste(stock,.csv,sep=),sep=,quote=FALSE) #保存到本地 #本地讀數(shù)據(jù)read-function(stock)as.xts(read.zoo(file=paste(stock,.csv,sep=),header = TRUE,sep=, format=%Y-%m-%d)stock-BABAdownload(stock,from=2013-01-01) BABA-read(stock)#定義移動平均函數(shù)ma-function(cdata,mas=c(5,20,60) ldata-cdata for(m in mas) ldata-merge(ldata,SMA(cdata,m) ldata-na.locf(ldata, fromLast=TRUE) names(ldata)-c(Value,paste(ma,mas,sep=) return(ldata)#定義均線圖函數(shù)drawLine-function(ldata,titie=Stock_MA,sDate=min(index(ldata),eDate=max(index(ldata),out=FALSE) g-ggplot(aes(x=Index, y=Value),data=fortify(ldata,1,melt=TRUE) g-g+geom_line() g-g+geom_line(aes(colour=Series),data=fortify(ldata,-1,melt=TRUE) g-g+scale_x_date(labels=date_format(%Y-%m),breaks=date_breaks(2 months),limits =c(sDate,eDate) g-g+xlab() + ylab(Price)+ggtitle(title)if(out) ggsave(g,file=paste(titie,.png,sep=) else g#選取數(shù)據(jù)并運(yùn)行cdata-BABA2014/2015$Close title-Stock_BABA #圖片標(biāo)題 sDate-as.Date(2014-9-19) #開始日期 eDate-as.Date(2015-10-23) #結(jié)束日期ldata-ma(cdata,c(5,20,60) #選擇滑動平均指標(biāo)p0-drawLine(ldata,title,sDate,eDate) #畫圖 ggsave(p0,file=paste(title,.png,sep=)#存圖#畫出股價(jià)和20日均線圖ldata1-ma(cdata,c(20) #選擇滑動平均指標(biāo) p11-drawLine(ldata1,title,sDate,eDate) #畫圖#以散點(diǎn)覆蓋20日均線，紅色點(diǎn)為買入持有，藍(lán)色點(diǎn)為賣出空倉# 定義均線圖+散點(diǎn)函數(shù)drawPoint-function(ldata,pdata,titie,sDate,eDate) g-ggplot(aes(x=Index, y=Value),data=fortify(ldata,1,melt=TRUE) g-g+geom_line() g-g+geom_line(aes(colour=Series),data=fortify(ldata,-1,melt=TRUE) g-g+geom_point(aes(x=Index,y=pdata,3,colour=compare),data=pdata)g-g+scale_x_date(labels=date_format(%Y-%m),breaks=date_breaks(2 months),limits = c(sDate,eDate) g-g+xlab() + ylab(Price)+ggtitle(title) g#定義獲取散點(diǎn)數(shù)據(jù)函數(shù)getPoint-function(ldata) data-fortify(ldata) n-nrow(data)data-data.frame(data,compare=vector(length=n) v1-data,2 v2-data,3 dist-(v2-v1)data$comparewhich(dist0)=0)-c(up) pdata-data#運(yùn)行并獲取數(shù)據(jù)pdata-getPoint(ldata1)p1-drawPoint(ldata1,pdata,title,sDate,eDate)#定義找出交易信號點(diǎn)函數(shù)Signal-function(cdata,pdata) n-nrow(pdata) pdata1-data.frame(pdata,Sigs=vector(length=n),comp=vector(length=n),p=vector(length=n) pdata1$compwhich(pdata1$compare=up)-1 pdata1$compwhich(pdata1$compare=down)-0 for(i in 2:(n-1) pdata1$pi-(pdata1$compi-pdata1$compi-1) pdata1$p1-pdata1$p2 pdata1$Sigswhich(pdata1$p0)+10)+1-c(S) temp-pdata1-c(1,which(pdata1$p=0)+1), x-c(4,6,7) Signals-temp,-x#運(yùn)行并獲取數(shù)據(jù)tdata-Signal(cdata,pdata)#模擬交易#利用交易信號數(shù)據(jù)，進(jìn)行模擬交易。設(shè)定交易參數(shù)，以$10W為本金，滿倉買入或賣出，手續(xù)為0， #傳入交易信號。#參數(shù)：交易信號,本金,持倉比例,手續(xù)費(fèi)比例#規(guī)定數(shù)據(jù)格式以及小數(shù)點(diǎn)位數(shù)options(scipen=4) options(digits=4)#定義模擬交易函數(shù)trade-function(tdata,capital) n=nrow(tdata) cash-vector(length=n) amount-vector(length=n) asset-vector(length=n) diff-vector(length=n)t-data.frame(tdata,cash,amount,asset,diff)if(t1,4=S) t1,5-capital else t1,5-(capital%t1,2) if(t1,4=S) t1,6-0 else t1,6-floor(capital/t1,2) t1,7-capital t1,8-0 for(i in 2:n) if(t$Sigsi=B) t$amounti-floor(t$cashi-1/t,2i)+amounti-1 t$cashi-t$cashi-1%t,2i t$asseti-t$amounti*t,2i+t$cashi t$diffi-t$asseti-t$asseti-1 else t$amounti-0 t$cashi-t$amounti-1*t,2i+t$cashi-1 t$asseti-t$cashi t$diffi-t$asseti-t$asseti-1t#定義數(shù)據(jù)，運(yùn)行capital=100000 result1-trade(tdata,capital) rise0) fall-length(which(result1$diff0)#畫出資金曲線#定義畫出股價(jià)+現(xiàn)金流量對比函數(shù)drawCash-function(result,ldata)n-which(result1$Sigs=S) m-c(1,5) xx-resultn,mcolnames(xx)-c(Time,cash) xx-melt(xx,id=Time)yy-fortify(ldata) k-c(1,2) yy-yy,kcolnames(yy)-c(Time,close) yy-melt(yy,id=Time) zz-rbind(yy,xx) g-ggplot(aes(Time,value),data=zz) g-g+geom_line(aes(group=1,colour=variable) g-g+facet_grid(variable.,scales=free_y)g-g+scale_x_date(labels=date_format(%Y-%m),breaks=date_breaks(2 month),limits=c(sDate,eDate) g-g+xlab()+ylab(Price)+ggtitle(title)g#運(yùn)行p2-drawCash(result1,ldata1)ggsave(p2,file=paste(close+cash,.png,sep=)#存圖#一條均線模型，在大的趨勢下是可以穩(wěn)定賺錢的，但由于一條均線對于波動非常敏感性，#如果小波動過于頻繁，不僅會增加交易次數(shù)，而且會讓模型失效。然后，就有二條均線的#策略模型，可以減低對波動的敏感性。二條均線策略模型，與一條均線模型思路類似，以 #5日均線價(jià)格替換股價(jià)，是通過5日均線和20日均線交叉來進(jìn)行信號交易的。#首先畫出股價(jià)，5日均線和20日均線圖。#選擇5日和20日滑動平均指標(biāo)ldata2-ma(cdata,c(5,20)#畫圖p33-drawLine(ldata2,title,sDate,eDate)#這步是為了將數(shù)據(jù)框與之前的函數(shù)格式對應(yīng)ldata2-ldata2,c(2,3)#獲取散點(diǎn)圖pdata2-getPoint(ldata2)#畫出散點(diǎn)圖p3-drawPoint(ldata2,pdata2,title,sDate,eDate)#找出交易信號tdata2-Signal(cdata,pdata2)#模擬交易result2-trade(tdata2,capital) rise20) fall2-length(which(result2$diff0)#畫出股價(jià)現(xiàn)金圖p4-drawCash(result2,ldata2)附表二：阿里股票數(shù)據(jù)IndexOpenHighLowCloseVolumeAdjusted2014/9/1992.69999799.69999789.94999793.88999927187940093.8899992014/9/2292.69999792.94999789.589.8899996665780089.8899992014/9/2388.94000290.48000386.62000387.1699983900980087.1699982014/9/2488.47000190.5787.22000190.573208800090.572014/9/2591.08999691.588.588.9199982859800088.9199982014/9/2689.73000390.45999988.66000490.4599991834000090.4599992014/9/2989.62000389.69999788.01999788.752530200088.752014/9/308990.87999788.45999988.8499982441940088.8499982014/10/188.69999788.94000286.04000186.0999982402960086.0999982014/10/286.26999788.19999785.61187.0599982146970087.0599982014/10/388.09999889.94000287.65000288.0999981848570088.0999982014/10/689.15000289.65000288.05999888.309998926840088.3099982014/10/787.94999789.69999787.05999887.6699981279170087.6699982014/10/88888.587.05999888.3000031025260088.3000032014/10/988.51000290.3499988888.7900012150700088.7900012014/10/1088.2588.73999885.23999885.8799971543140085.8799972014/10/1386.84999886.88999984.91999885.1200031484500085.1200032014/10/1485.80999885.87999783.22000184.9499971555920084.9499972014/10/1584.04000186.48999882.80999885.5999981682430085.5999982014/10/1684.98000389.17500384.01499988.8499981523200088.8499982014/10/1790.40000290.90000287.66999887.9100041736070087.9100042014/10/208889.587.86000188.260002989140088.2600022014/10/2189.09999892.588.590.9000022324380090.9000022014/10/2292.2593.591.01000291.6299972031250091.6299972014/10/2392.91999894.69999792.8294.4499972064100094.4499972014/10/2495.079894.77999995.7600023213260095.7600022014/10/279798.84999896.30000397.7900012830240097.7900012014/10/2899.839996100.66999898.61000199.683200360099.682014/10/2999.87999710096.8298.3099982865540098.3099982014/10/3098.48000399.44000297.30000398.7300031548460098.7300032014/10/31100.099998100.22000198.13600298.5999981812830098.5999982014/11/399.669998102.80000399.050003101.80000340883700101.8000032014/11/4100.425003106.35900199.510002106.0767814000106.072014/11/5108.480003110.139999106.480003108.66999848344000108.6699982014/11/6109.300003111.699997107.209999111.5733609000111.572014/11/7112.93114.769997111.75114.55999851457000114.5599982014/11/10117.269997119.449997115.199997119.15000275971000119.1500022014/11/11117.25117.550003113.690002114.54000170983600114.5400012014/11/12115.050003119.07114.019997118.19999753908000118.1999972014/11/13119.300003120114.550003114.83999662163000114.8399962014/11/14115.059998115.389999113.349998115.09999829849000115.0999982014/11/17115.440002115.629997113.129997114.2522573000114.252014/11/18114.330002114.379997110.410004110.80999841098000110.8099982014/11/19109.830002110.68107.220001108.8246841000108.822014/11/20107.809998112.440002107.260002109.8236489000109.822014/11/21113.209999113.5110.434998110.73000327432000110.7300032014/11/24112113.93111.550003113.91999820646000113.9199982014/11/25114.940002115.169998112.330002113.47000123485000113.4700012014/11/26113.330002113.739998112.330002112.66999811794700112.6699982014/11/28113.139999113.230003111.110001111.6399998077800111.6399992014/12/1110.019997110.050003103.896004105.98999838464200105.9899982014/12/2107.349998110106.610001109.88999918651700109.8899992014/12/3110.400002111.68108.797997110.63999916221100110.6399992014/12/4110.099998110.5108.910004109.16999810779300109.1699982014/12/5109.599998110.349998107.760002107.90000212148100107.9000022014/12/8105.970001107.400002104.209999105.0719217300105.072014/12/9102.269997107.949997101.199997107.48000324866800107.4800032014/12/10107.089996107.379997103.510002103.87999718466700103.8799972014/12/11104.440002106.839996104.290001104.97000115684300104.9700012014/12/12104.699997107.449997104.179001105.11000114537600105.1100012014/12/15106.389999107.769997103.699997104.69999716521800104.6999972014/12/16103.75107.68103.699997105.76999721700600105.7699972014/12/17107.110001109.190002106.279999109.01999717311100109.0199972014/12/18110.580002111.199997108.260002109.2522788100109.252014/12/19109.93110.650002108.040001110.65000214857800110.6500022014/12/22110.629997110.980003108.529999108.76999713041200108.7699972014/12/23108.300003108.470001103.879997105.51999719096300105.5199972014/12/24105.68107.209999105.599998105.9499975870400105.9499972014/12/26105.989998106.940002105.5105.9499976529100105.9499972014/12/29105.949997107.660004105.639999105.9800038068600105.9800032014/12/30105.639999106.709999105.129997105.7510205700105.752014/12/31106.459999106.470001103.690002103.94000210283400103.9400022015/1/2104.239998104.720001102.519997103.59999812303400103.5999982015/1/5102.760002103.01999799.900002101183370001012015/1/6101.25103.849998100.110001103.3215720400103.322015/1/7104.589996104.739998102.029999102.12999711052200102.1299972015/1/8102.949997105.339996102.68105.02999912942100105.0299992015/1/9105.239998105.300003102.889999103.01999710222200103.0199972015/1/12103.199997103.360001101.209999101.6200037997200101.6200032015/1/13102.589996102.849998100.010002100.76999711294400100.7699972015/1/1499.279999100.1898.05999899.5800021780800099.5800022015/1/1599.669998100.13999996.01999796.3099981826010096.3099982015/1/1696.08999697.80000395.51999796.8899991332790096.8899992015/1/2098.300003100.20999997.589996100.04000112105600100.0400012015/1/21100.75103.860001100.32103.29000115186900103.2900012015/1/22104.599998104.919998103.099998104114330001042015/1/23104.019997105.199997103.019997103.1100019873800103.1100012015/1/26104.400002105.129997103.330002103.98999810664900103.9899982015/1/27102.889999103.57100.580002102.94000215659900102.9400022015/1/28100.300003101.48999897.79000198.4499974214470098.4499972015/1/2990.52999990.73999887.36000189.8099987656140089.8099982015/1/3089.5999989288.11000189.0800023680690089.0800022015/2/291.12999791.66000488.61000190.1299971886530090.1299972015/2/391.65000291.65000289.90000290.6100011351230090.6100012015/2/490.98999891.87999789.4800039014643600902015/2/589.58000289.83999686.0999988728926200872015/2/687.11000187.40000285.41999885.681758760085.682015/2/985.83000286.7585.4700018612109500862015/2/1087.01000287.47000186.51999787.2600021204330087.2600022015/2/1187.58000287.69999785.828612391900862015/2/1285.59999888.30000385.55000387.0999981517780087.0999982015/2/1388.19999789.30000387.65000289.0500031466520089.0500032015/2/1788.77999988.98999886.69999786.8499981519170086.8499982015/2/1887.09999887.4386.586.739998742230086.7399982015/2/1986.80999887.87000386.70999986.889999760410086.8899992015/2/2087.2587.29000186.37999786.639999782170086.6399992015/2/2386.51000286.6885.2585.470001936790085.4700012015/2/2485.52999985.52999983.87999784.6900021584640084.6900022015/2/2584.37999786.83000284.36000186.1900021364570086.1900022015/2/2686.91999887.16000485.19999785.370003876330085.3700032015/2/2785.94999786.5599988585.120003837720085.1200032015/3/28585.01999783.758411213400842015/3/382.94999783.2580.02999981.5800023924610081.5800022015/3/480.26999785.82599680.16999885.4899983660720085.4899982015/3/585.7586.26999784.01000286.0999981851480086.0999982015/3/685.7399988684.05000384.4000021067370084.4000022015/3/984.34999884.34999881.48000382.5299991761650082.5299992015/3/1081.08999683.15000280.65000282.9700011381700082.9700012015/3/118383.37999781.19000281.9899981276870081.9899982015/3/1282.09999882.90000281.52999981.9199981123200081.9199982015/3/1381.80000381.91999880.76999781.8600011266330081.8600012015/3/1682.01000285.19999781.9400028416905700842015/3/1784.01000285.09999883.51000284.51763900084.52015/3/1883.87000385.94999783.30000384.5899963566300084.5899962015/3/1985.11000187.0400018585.7399983045470085.7399982015/3/