def cal_sharpe(value): #average = cal_average(value) #std_value = cal_std(value) #sharpe_ratio = (sqrt(days)*average)/std_value daily_ret = daily_return(value) sharpe_ratio = tsu.get_sharpe_ratio(daily_ret) return sharpe_ratio
def cal_sharpe(value):
#average = cal_average(value)
#std_value = cal_std(value)
#sharpe_ratio = (sqrt(days)*average)/std_value
daily_ret = daily_return(value)
sharpe_ratio = tsu.get_sharpe_ratio(daily_ret)
return sharpe_ratio
def simulate(symbols, allocations, startday, endday):
"""
@symbols: list of symbols
@allocations: list of weights
@startday: ...
@endday: ...
"""
timeofday = dt.timedelta(hours=16)
timestamps = du.getNYSEdays(startday,endday,timeofday)
dataobj = da.DataAccess('Yahoo')
close = dataobj.get_data(timestamps, symbols, "close", verbose=False)
close = close.values
norm_close = close / close[0, :]
allocations = allocations / np.sum(allocations)
portfolio_value = np.dot(norm_close, allocations)
portfolio_return = portfolio_value.copy()
tsu.returnize0(portfolio_return)
sharpe = tsu.get_sharpe_ratio(portfolio_return)
accum = portfolio_value[-1] / portfolio_value[0]
average = np.mean(portfolio_return)
stddev = np.std(portfolio_return)
result = {"sharpe":sharpe, "cumulative_return":accum, "average":average, "stddev":stddev}
return result
def overall_metrics(self, timestamp='one_month', metrics=None, save=False, db_id=None):
'''
Use zipline results to compute some performance indicators and store it in database
'''
perfs = dict()
# If no rolling perfs provided, computes it
if metrics is None:
metrics = self.rolling_performances(timestamp=timestamp, save=False, db_id=db_id)
riskfree = np.mean(metrics['Treasury.Returns'])
#NOTE Script args define default database table name (test), make it consistent
if db_id is None:
db_id = self.backtest_cfg['algorithm'] + pd.datetime.strftime(pd.datetime.now(), format='%Y%m%d')
perfs['Name'] = db_id
perfs['Sharpe.Ratio'] = tsu.get_sharpe_ratio(metrics['Returns'].values, risk_free = riskfree)
perfs['Returns'] = (((metrics['Returns'] + 1).cumprod()) - 1)[-1]
perfs['Max.Drawdown'] = max(metrics['Max.Drawdown'])
perfs['Volatility'] = np.mean(metrics['Volatility'])
perfs['Beta'] = np.mean(metrics['Beta'])
perfs['Alpha'] = np.mean(metrics['Alpha'])
perfs['Benchmark.Returns'] = (((metrics['Benchmark.Returns'] + 1).cumprod()) - 1)[-1]
if save:
self.feeds.stock_db.save_performances(perfs)
return perfs
def get_sharpe_ratio(fund_ts):
"""
@summary Returns daily computed Sharpe ratio of fund time series
@param fund_ts: pandas time series of daily fund values
@return Sharpe ratio of fund time series
"""
return tsu.get_sharpe_ratio(tsu.daily(fund_ts))
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
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:
