def analyzeValue(valueFilename=valueFilename, benchmark=benchmark,figureName=figureName, closefield=closefield):
valueCSV = open(valueFilename, 'rU')
reader = csv.reader(valueCSV, delimiter=',')
timestamp = []
values = []
for row in reader:
timestamp.append(dt.date(int(row[0]),int(row[1]),int(row[2])))
values.append(float(row[3]))
#
# Plot portfolio value
plt.clf()
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(timestamp, values)
# adjust tick size
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(6)
plt.plot(timestamp, values)
savefig(figureName,format='pdf')
#
# Compute Cumulative Return, Sharpe ratio, std of daily return for the portfolio
#
if debug:
print "-----Before calculate-----"
for v in values:
print v
daily_returns0 = tsu.daily(values)
if debug:
print "-----After calculate-----"
for v in daily_returns0:
print v
final_return = values[len(values)-1]/values[0]-1
print "Total Return:" , final_return
mean = np.average(daily_returns0)
print "Expected Return:", mean
std2 = np.std(daily_returns0)
print "Standard Deviation:", std2
sharp_ratio = tsu.get_sharpe_ratio(daily_returns0)
print "Calculated Sharpe Ratio:", tsu.sqrt(252) * mean/std2
print "Get Sharpe Ration from QSTK:", sharp_ratio
#
# Calculate Benchmark metrics
#
timeofday = dt.timedelta(hours=h)
timestamps = du.getNYSEdays(timestamp[0],timestamp[len(timestamp)-1],timeofday)
dataobj = da.DataAccess('Yahoo')
print "reading benchmark data...",benchmark
close = dataobj.get_data(timestamps, benchmark, closefield)
if debug:
for time in timestamps:
print time, close[benchmark[0]][time]
close = (close.fillna(method='ffill')).fillna(method='backfill')
bValues = close.values
print
final_return_b = (bValues[len(bValues)-1]/bValues[0])-1
print benchmark,"Total Return:" , final_return_b
# daily returns
b_daily_returns0 = tsu.daily(bValues)
mean = np.average(b_daily_returns0)
print benchmark, "Expected Return:", mean
std2 = np.std(b_daily_returns0)
print benchmark,"Standard Deviation:", std2
sharp_ratio = tsu.get_sharpe_ratio(b_daily_returns0)
print benchmark,"Calculated Sharpe Ratio:", tsu.sqrt(252) * mean/std2
print benchmark,"Get Sharpe Ration from QSTK:", sharp_ratio
if debug:
print "-----After calculate-----"
for v in daily_returns0:
print v
final_return = values[len(values)-1]/values[0]-1
print "Total Return:" , final_return
mean = np.average(daily_returns0)
print "Expected Return:", mean
std2 = np.std(daily_returns0)
print "Standard Deviation:", std2
sharp_ratio = tsu.get_sharpe_ratio(daily_returns0)
print "Calculated Sharpe Ratio:", tsu.sqrt(252) * mean/std2
print "Get Sharpe Ration from QSTK:", sharp_ratio
#
# Calculate Benchmark metrics
#
timeofday = dt.timedelta(hours=h)
timestamps = du.getNYSEdays(timestamp[0],timestamp[len(timestamp)-1],timeofday)
dataobj = da.DataAccess('Yahoo')
print "reading benchmark data...",benchmark
close = dataobj.get_data(timestamps, benchmark, closefield)
if debug:
for time in timestamps:
print time, close[benchmark[0]][time]
