def print_monthly_ks(fund_ts, years, ostream):
"""
@summary prints monthly returns for given fund and years to the given stream
@param fund_ts: pandas fund time series
@param years: list of years to print out
@param ostream: stream to print to
"""
ostream.write(" ")
month_names = du.getMonthNames()
for name in month_names:
ostream.write(" " + str(name))
ostream.write("\n")
# mrets = tsu.monthly(fund_ts)
m_str = []
for i, year in enumerate(years):
months = du.getMonths(fund_ts, year)
for j, month in enumerate(months):
if i == 0 and j < 3:
m_str.append(' ')
else:
# dt_st = max(fund_ts.index[0], dt.datetime(year, month, 1)-relativedelta(months=6))
dt_st = fund_ts.index[0]
dt_today = dt.datetime(year, month, 1) - relativedelta(months=2)
dt_end = min(dt.datetime(year, month, 1) + relativedelta(months=1) + dt.timedelta(hours=-5), fund_ts.index[-1])
fund_ts_past = fund_ts.ix[dt_st: dt_today]
fund_ts_month = fund_ts.ix[dt_today: dt_end]
ks, p = ks_statistic_calc(fund_ts_past, fund_ts_month)
if not(ks == -1 or p == -1):
if ks < p:
m_str.append('PASS')
else:
m_str.append('FAIL')
else:
m_str.append(' ')
i = 0
for year in years:
ostream.write(str(year))
months = du.getMonths(fund_ts, year)
for k in range(1, months[0]):
ostream.write(" ")
for month in months:
ostream.write("%7s" % (m_str[i]))
i = i + 1
ostream.write("\n")
def monthly(funds):
"""
@summary Computes monthly returns centered around 0
@param funds: A time series containing daily fund values
@return an array of monthly returns
"""
funds2 = []
last_last_month = -1
years = qsdateutil.getYears(funds)
for year in years:
months = qsdateutil.getMonths(funds, year)
for month in months:
last_this_month = qsdateutil.getLastDay(funds, year, month)
if last_last_month == -1 :
last_last_month=qsdateutil.getFirstDay(funds, year, month)
if type(funds).__name__=='TimeSeries':
funds2.append(funds[last_this_month]/funds[last_last_month]-1)
else:
funds2.append(funds.xs(last_this_month)/funds.xs(last_last_month)-1)
last_last_month = last_this_month
return(funds2)
def average_monthly(funds):
"""
@summary Computes average monthly returns centered around 0
@param funds: A time series containing daily fund values
@return an array of average monthly returns
"""
rets = daily(funds)
ret_i = 0
years = qsdateutil.getYears(funds)
averages = []
for year in years:
months = qsdateutil.getMonths(funds, year)
for month in months:
avg = 0
count = 0
days = qsdateutil.getDays(funds, year, month)
for day in days:
avg += rets[ret_i]
ret_i += 1
count += 1
averages.append(float(avg) / count)
return (averages)
def average_monthly(funds):
"""
@summary Computes average monthly returns centered around 0
@param funds: A time series containing daily fund values
@return an array of average monthly returns
"""
rets = daily(funds)
ret_i = 0
years = qsdateutil.getYears(funds)
averages = []
for year in years:
months = qsdateutil.getMonths(funds, year)
for month in months:
avg = 0
count = 0
days = qsdateutil.getDays(funds, year, month)
for day in days:
avg += rets[ret_i]
ret_i += 1
count += 1
averages.append(float(avg) / count)
return(averages)
def print_monthly_returns(fund_ts, years, ostream):
"""
@summary prints monthly returns for given fund and years to the given stream
@param fund_ts: pandas fund time series
@param years: list of years to print out
@param ostream: stream to print to
"""
ostream.write(" ")
month_names = du.getMonthNames()
for name in month_names:
ostream.write(" " + str(name))
ostream.write("\n")
i = 0
mrets = tsu.monthly(fund_ts)
for year in years:
ostream.write(str(year))
months = du.getMonths(fund_ts, year)
for k in range(1, months[0]):
ostream.write(" ")
for month in months:
ostream.write(" % + 6.2f" % (mrets[i]*100))
i += 1
ostream.write("\n")
def reportFunctionality(funds, symbols,filename=sys.stdout):
if(len(symbols)!=0):
funds2=runOther(funds,symbols)
arg2=1
else:
arg2=0
if(filename==sys.stdout):
html_file=sys.stdout
else:
html_file = open(filename,"w")
#top
html_file.write("<HTML>\n")
html_file.write("<HEAD>\n")
html_file.write("<TITLE>QSTK Generated Report from "+readableDate(funds.index[0])+" to "+readableDate(funds.index[-1])+"</TITLE>\n")
html_file.write("</HEAD>\n\n")
html_file.write("<BODY><CENTER>\n\n")
years=du.getYears(funds)
html_file.write("<H2>Performance Summary for "+sys.argv[1]+"</H2>\n")
html_file.write("For the dates "+readableDate(funds.index[0])+" to "+readableDate(funds.index[-1])+"\n")
html_file.write("<H3>Yearly Performance Metrics</H3>\n")
html_file.write("<TABLE CELLPADDING=10>\n")
html_file.write("<TR><TH></TH>\n")
for year in years:
html_file.write("<TH>"+str(year)+"</TH>\n")
html_file.write("</TR>\n")
#yearly return
html_file.write("<TR>\n")
html_file.write("<TH>Annualized Return:</TH>\n")
for year in years:
retur=getYearReturn(funds,year)
html_file.write("<TD>\n")
print >>html_file, "%.2f\n" % (retur*100)
html_file.write("%</TD>\n")
html_file.write("</TR>\n")
#yearly winning days
html_file.write("<TR>\n")
html_file.write("<TH>Winning Days:</TH>\n")
for year in years:
# change to compare to inputs - ratio=tsu.getYearRatio(funds,year)
if(arg2!=0):
win=getWinningDays(funds,funds2,year)
html_file.write("<TD>\n")
print >>html_file, "%.2f\n" % (win*100)
html_file.write("%</TD>\n")
else:
html_file.write("<TD>No comparison.</TD>\n")
html_file.write("</TR>\n")
#max draw down
html_file.write("<TR>\n")
html_file.write("<TH>Max Draw Down:</TH>\n")
for year in years:
drop=getYearMaxDrop(funds,year)
html_file.write("<TD>\n")
print >>html_file, "%.2f" % (drop*100)
html_file.write("%</TD>\n")
html_file.write("</TR>\n")
#yearly sharpe ratio using daily rets
html_file.write("<TR>\n")
html_file.write("<TH>Daily Sharpe Ratio:</TH>\n")
for year in years:
ratio=tsu.getYearRatio(funds,year)
html_file.write("<TD>\n")
print >>html_file, "%.2f\n" % ratio
html_file.write("</TD>\n")
html_file.write("</TR>\n")
#yearly sharpe ratio using monthly rets
html_file.write("<TR>\n")
html_file.write("<TH>Monthly Sharpe Ratio:</TH>\n")
for year in years:
ratio=getYearRatioUsingMonth(funds,year)
html_file.write("<TD>\n")
print >>html_file, "%.2f\n" % ratio
html_file.write("</TD>\n")
html_file.write("</TR>\n")
html_file.write("</TABLE>\n")
html_file.write("<BR/>\n\n")
vals=funds.values;
vals2=np.append(vals,funds2.values,2)
df=DataMatrix(index=funds.index,data=funds.values, columns=['fund'])
df2=DataMatrix(index=funds2.index,data=funds2.values,columns=['other'])
df['other']=df2['other']
corrcoef=numpy.corrcoef(funds.values[0:-1],funds2.values)
html_file.write("<H3>Correlation=")
print >>html_file, "%.2f\n" % corrcoef[0][1]
html_file.write("<H3>\n")
html_file.write("<BR/>\n\n")
#montly returns
mrets=tsu.monthly(funds)
html_file.write("<H2>Monthly Returns</H2>\n")
html_file.write("<TABLE CELLPADDING=10>\n")
html_file.write("<TR>\n")
html_file.write("<TH></TH>\n")
month_names=du.getMonthNames()
for name in month_names:
html_file.write("<TH>"+str(name)+"</TH>\n")
html_file.write("</TR>\n")
i=0
for year in years:
html_file.write("<TR>\n")
html_file.write("<TH>"+str(year)+"</TH>\n")
months=du.getMonths(funds,year)
for month in months:
html_file.write("<TD>\n")
print >>html_file, "%.2f\n" % (mrets[i]*100)
html_file.write("%</TD>\n")
i+=1
html_file.write("</TR>\n")
html_file.write("</TABLE>\n")
html_file.write("<BR/>\n\n")
#fund value graph
fundlist=[];
fundlist.append(funds)
fundlist.append(funds2)
converter.fundsToPNG(fundlist,'funds.png')
html_file.write("<IMG SRC=\'./funds.png\'/>\n")
html_file.write("<BR/>\n\n")
#end
html_file.write("</CENTER></BODY>\n\n")
html_file.write("</HTML>")