对投资组合进行压力测试
This example shows how to conduct a stress test on a set of stocks using transaction cost analysis from the Kissell Research Group.
Estimate historical market-impact costs and the corresponding dollar values for the specified date range.
Use trading costs to screen stocks in a portfolio and estimate the cost to liquidate or purchase a specified number of shares.
Analyze trading costs during volatile periods of time such as a financial crisis, flash crash, or debt crisis.
To access the example code, enteredit KRGStressTestingExample.mat the command line.
检索市场影响参数并加载历史数据
Retrieve the market-impact data from the Kissell Research Group FTP site. Connect to the FTP site using theftp使用用户名和密码函数。导航到MI_Parametersfolder and retrieve the market-impact data in theMI_Encrypted_Parameters.csv文件。miData包含加密的市场影响日期，代码和参数。
f = ftp('ftp.kissellresearch.com','用户名','PWD'）；mget(f,'MI_Encrypted_Parameters.csv'）；关闭（f）midata =可读取（'MI_Encrypted_Parameters.csv',“定界符”,。。。'，','ReadRowNames'，错误的，'ReadVariableNames',true);
Load the example dataTradeDataStressTest从文件krgexampledata.mat, which is included with the Datafeed Toolbox™.
loadKrgexampledataTradeDataStressTest
有关示例数据的描述，请参见Kissell研究小组数据集。
创建Kissell研究小组交易成本分析对象k。Specify initial settings for the date, market-impact code, and number of trading days.
k = krg(miData,datetime('today'),1,250);
准备压力测试的数据
Specify the date range from May 1, 2015, through July 31, 2015.
StartDate ='5/1/2015'; endDate ='7/31/2015';
确定股票数量numStocks在portfolio. Create a date rangedateRange从指定的日期。找到天数数字in the date range.
numStocks = length(TradeDataStressTest.Symbol); dateRange = (datenum(startDate):datenum(endDate))'; numDates = length(dateRange);
Preallocate the output data tableo。
outlength = numStocks*numdates;符号= tradeDataStressTest.symbol（：，ones（1，numdates））;side = tradeDataStressTest.side（：，ones（1，numdates））;dates = depaterange（：，一个（1，numstocks））';o = table（符号（:)，侧（:)，日期（:)，nan（outlength，1），nan（utlength，1），。。。'VariableNames'，{'象征','Side','Date','MI','MIDollar'});
确保使用的股票数量是正值absfunction.
TradeDataStressTest.Shares = abs(TradeDataStressTest.Shares);
Convert trade time trade strategy to the percentage of volume trade strategy.
TradeDataStressTest.TradeTime = TradeDataStressTest.TradeTime。。。。TradeDataStressTest.pov = Krg.TradeTime2Pov（TradeDataStressTest.TradEtime，TradeDateTime，。。。TradeDataStressTest.Shares);
Conduct Stress Test by Estimating Historical Market-Impact Costs
Estimate the historical market-impact costs for each stock in the portfolio for the date range usingmarketImpact。将市场影响的成本从十进制转换为本地美元。在输出数据表中检索结果数据o。
kk = 1;forii =过多（1）：过多（结束）forjj = 1:numStocks k.MiCode = TradeDataStressTest.MICode(jj); k.MiDate = ii; o.MI(kk) = marketImpact(k,TradeDataStressTest(jj,:)); o.MIDollar(kk) = (TradeDataStressTest.Shares(jj)。。。* TradeDataStressTest.Price（JJ））。。。* o.mi（kk） /10000 * tradeDataStressTest.fxrate（JJ）;kk = kk + 1;endend
显示前三行输出数据。
o(1:3,:)
ans = Symbol Side Date MI MIDollar ______ ____ _________ _____ ________ 'A' 1.00 736085.00 3.84 384.31 'B' 1.00 736085.00 11.43 14292.24 'C' 1.00 736085.00 32.69 20430.65
The output data contains these variables:
库存符号
边
历史交易日期
Historical market-impact cost in basis points
Historical market-impact value in local dollars
Retrieve the daily market-impact costdailyCost。Determine the number of daysnumDaysin the output data. Loop through the data and sum the market-impact costs for individual stocks for each day.
numdays =长度（o.date）/numstocks;idx = 1;fori = 1:numDays dailyCost.Date(i) = o.Date(idx); dailyCost.DailyMiCost(i) = sum(o.MI(idx:idx+(numStocks-1))); idx = idx+numStocks;end
Display the daily market-impact cost in the specified date range. This figure demonstrates how market-impact costs change over time.
plot(b.Date,b.DailyMiCost) ylabel({“每日费用”,'（bps）'}) title(“每日市场影响力压力测试”）xlabel（'Date'） 网格上xData = linspace（b.date（1），b.date（92），11）;a = gca;a.xaxis.ticklabels = datester（xdata，'mm/dd/yyyy'）；a.XTickLabelRotation = 45;
参考
[1] Kissell, Robert. “Creating Dynamic Pre-Trade Models: Beyond the Black Box.”Journal of Trading。卷。6，第4号，2011年秋季，第8-15页。
[2] Kissell, Robert. “TCA in the Investment Process: An Overview.”Journal of Index Investing。卷。2, Number 1, Summer 2011, pp. 60–64.
[3] Kissell, Robert.算法交易和投资组合管理科学。马萨诸塞州剑桥：Elsevier/Academic Press，2013年。
[4] Chung, Grace and Robert Kissell. “An Application of Transaction Costs in the Portfolio Optimization Process.”Journal of Trading。卷。11, Number 2, Spring 2016, pp. 11–20.
See Also
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