def eventmodMain():
start_month, start_day, start_year, end_month, end_day, end_year = modDatesReturn.get_dates(
)
dt_start = dt.datetime(start_year, start_month, start_day)
dt_end = dt.datetime(end_year, end_month, end_day)
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt.timedelta(hours=16))
dataobj = da.DataAccess('Yahoo')
ls_symbols = dataobj.get_symbols_from_list('mrtevent')
ls_symbols.append('SPY')
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
ldf_data = dataobj.get_data(ldt_timestamps, ls_symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
for s_key in ls_keys:
d_data[s_key] = d_data[s_key].fillna(method='ffill')
d_data[s_key] = d_data[s_key].fillna(method='bfill')
d_data[s_key] = d_data[s_key].fillna(1.0)
df_events = find_events(ls_symbols, d_data)
print "Creating Study"
ret = ep.eventprofiler(df_events,
d_data,
i_lookback=20,
i_lookforward=20,
s_filename='MyEventStudy.pdf',
b_market_neutral=True,
b_errorbars=True,
s_market_sym='SPY')
if ret == 0:
print 'No events'
basic.print_clrscr()
basic.print_logo()
eventmenuMain()
def bollingerplotMain():
'''Main Function'''
# List of symbols
#ls_symbols = ["AAPL", "GOOG", "IBM", "MSFT"]
ls_symbols = list()
symbol = str(raw_input('<<< Enter the list of symbol: '))
symbols = symbol.upper()
ls_symbols.append(symbols)
start_month,start_day,start_year,end_month,end_day,end_year = modDatesReturn.get_dates()
# We need closing prices so the timestamp should be hours=16.
dt_timeofday = dt.timedelta(hours=16)
# Get a list of trading days between the start and the end.
dt_start = dt.datetime(start_year, start_month, start_day)
dt_end = dt.datetime(end_year, end_month, end_day)
# Creating an object of the dataaccess class with Yahoo as the source.
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
c_dataobj = da.DataAccess('Yahoo')
# Keys to be read from the data, it is good to read everything in one go.
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
# Reading the data, now d_data is a dictionary with the keys above.
# Timestamps and symbols are the ones that were specified before.
ldf_data = c_dataobj.get_data(ldt_timestamps, ls_symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
# Copying close price into separate dataframe to find rets
df_close = d_data['close']
df_mean = pd.rolling_mean(d_data['close'], 20)
df_std = pd.rolling_std(d_data['close'], 20)
df_bollinger = (df_close - df_mean) / (df_std)
print df_bollinger.tail()
# Plotting the prices with x-axis=timestamps
plt.clf()
plt.subplot(211)
plt.plot(ldt_timestamps, df_close[symbols], label=symbols)
plt.legend()
plt.ylabel('Price')
plt.xlabel('Date')
plt.xticks(size='xx-small')
plt.xlim(ldt_timestamps[0], ldt_timestamps[-1])
plt.grid(axis='both')
plt.subplot(212)
plt.plot(ldt_timestamps, df_bollinger[symbols], label='Bollinger')
plt.axhline(1.0, color='r')
plt.axhline(-1.0, color='r')
plt.legend()
plt.ylabel('Bollinger')
plt.xlabel('Date')
plt.xticks(size='xx-small')
plt.xlim(ldt_timestamps[0], ldt_timestamps[-1])
#plt.savefig('bollingerplot.pdf', format='pdf')
plt.grid(axis='both')
plt.show()
def eventmodMain():
start_month,start_day,start_year,end_month,end_day,end_year = modDatesReturn.get_dates()
dt_start = dt.datetime(start_year, start_month, start_day)
dt_end = dt.datetime(end_year, end_month, end_day)
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt.timedelta(hours=16))
dataobj = da.DataAccess('Yahoo')
ls_symbols = dataobj.get_symbols_from_list('mrtevent')
ls_symbols.append('SPY')
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
ldf_data = dataobj.get_data(ldt_timestamps, ls_symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
for s_key in ls_keys:
d_data[s_key] = d_data[s_key].fillna(method = 'ffill')
d_data[s_key] = d_data[s_key].fillna(method = 'bfill')
d_data[s_key] = d_data[s_key].fillna(1.0)
df_events = find_events(ls_symbols, d_data)
print "Creating Study"
ret = ep.eventprofiler(df_events, d_data, i_lookback=20, i_lookforward=20,
s_filename='MyEventStudy.pdf', b_market_neutral=True, b_errorbars=True,
s_market_sym='SPY')
if ret == 0:
print 'No events'
basic.print_clrscr()
basic.print_logo()
eventmenuMain()
def _read_data(symbols, dates):
timeofday = dt.timedelta(hours=16)
timestamps = du.getNYSEdays(dates[0], dates[-1] + dt.timedelta(days=1), timeofday)
dataobj = da.DataAccess('Yahoo')
close = dataobj.get_data(timestamps, symbols, "close", verbose=True)
close = close.fillna(method='ffill')
close = close.fillna(method='bfill')
return close, timestamps
def daily(lfFunds):
"""
@summary Computes daily returns centered around 0
@param funds: A time series containing daily fund values
@return an array of daily returns
"""
if type(lfFunds) == type(pd.Series()):
ldt_timestamps = du.getNYSEdays(lfFunds.index[0], lfFunds.index[-1], dt.timedelta(hours=16))
lfFunds = lfFunds.reindex(index=ldt_timestamps, method='ffill')
nds = np.asarray(deepcopy(lfFunds))
s= np.shape(nds)
if len(s)==1:
nds=np.expand_dims(nds,1)
returnize0(nds)
return(nds)
def __init__(self):
# List of symbols
self.ls_symbols = list()
len_sym = basic.get_num_sym()
for i in range(len_sym):
symbols = str(raw_input('<<< Enter symbols:' ))
self.ls_symbols.append(symbols)
start_month,start_day,start_year,end_month,end_day,end_year = modDatesReturn.get_dates()
dt_start = dt.datetime(start_year, start_month, start_day)
dt_end = dt.datetime(end_year, end_month, end_day)
# We need closing prices so the timestamp should be hours=16.
dt_timeofday = dt.timedelta(hours=16)
# Get a list of trading days between the start and the end.
self.ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
# Creating an object of the dataaccess class with Yahoo as the source.
c_dataobj = da.DataAccess('Yahoo')
# Keys to be read from the data, it is good to read everything in one go.
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
# Reading the data, now d_data is a dictionary with the keys above.
# Timestamps and symbols are the ones that were specified before.
ldf_data = c_dataobj.get_data(self.ldt_timestamps, self.ls_symbols, ls_keys)
self.d_data = dict(zip(ls_keys, ldf_data))
# Getting the numpy ndarray of close prices.
self.na_price = self.d_data['close'].values
def __init__(self):
# List of symbols
self.ls_symbols = list()
len_sym = basic.get_num_sym()
for i in range(len_sym):
symbols = str(raw_input('<<< Enter symbols:'))
self.ls_symbols.append(symbols)
start_month, start_day, start_year, end_month, end_day, end_year = modDatesReturn.get_dates(
)
dt_start = dt.datetime(start_year, start_month, start_day)
dt_end = dt.datetime(end_year, end_month, end_day)
# We need closing prices so the timestamp should be hours=16.
dt_timeofday = dt.timedelta(hours=16)
# Get a list of trading days between the start and the end.
self.ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
# Creating an object of the dataaccess class with Yahoo as the source.
c_dataobj = da.DataAccess('Yahoo')
# Keys to be read from the data, it is good to read everything in one go.
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
# Reading the data, now d_data is a dictionary with the keys above.
# Timestamps and symbols are the ones that were specified before.
ldf_data = c_dataobj.get_data(self.ldt_timestamps, self.ls_symbols,
ls_keys)
self.d_data = dict(zip(ls_keys, ldf_data))
# Getting the numpy ndarray of close prices.
self.na_price = self.d_data['close'].values
def bollingerplotMain():
'''Main Function'''
# List of symbols
#ls_symbols = ["AAPL", "GOOG", "IBM", "MSFT"]
ls_symbols = list()
symbol = str(raw_input('<<< Enter the list of symbol: '))
symbols = symbol.upper()
ls_symbols.append(symbols)
start_month, start_day, start_year, end_month, end_day, end_year = modDatesReturn.get_dates(
)
# We need closing prices so the timestamp should be hours=16.
dt_timeofday = dt.timedelta(hours=16)
# Get a list of trading days between the start and the end.
dt_start = dt.datetime(start_year, start_month, start_day)
dt_end = dt.datetime(end_year, end_month, end_day)
# Creating an object of the dataaccess class with Yahoo as the source.
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
c_dataobj = da.DataAccess('Yahoo')
# Keys to be read from the data, it is good to read everything in one go.
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
# Reading the data, now d_data is a dictionary with the keys above.
# Timestamps and symbols are the ones that were specified before.
ldf_data = c_dataobj.get_data(ldt_timestamps, ls_symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
# Copying close price into separate dataframe to find rets
df_close = d_data['close']
df_mean = pd.rolling_mean(d_data['close'], 20)
df_std = pd.rolling_std(d_data['close'], 20)
df_bollinger = (df_close - df_mean) / (df_std)
print df_bollinger.tail()
# Plotting the prices with x-axis=timestamps
plt.clf()
plt.subplot(211)
plt.plot(ldt_timestamps, df_close[symbols], label=symbols)
plt.legend()
plt.ylabel('Price')
plt.xlabel('Date')
plt.xticks(size='xx-small')
plt.xlim(ldt_timestamps[0], ldt_timestamps[-1])
plt.grid(axis='both')
plt.subplot(212)
plt.plot(ldt_timestamps, df_bollinger[symbols], label='Bollinger')
plt.axhline(1.0, color='r')
plt.axhline(-1.0, color='r')
plt.legend()
plt.ylabel('Bollinger')
plt.xlabel('Date')
plt.xticks(size='xx-small')
plt.xlim(ldt_timestamps[0], ldt_timestamps[-1])
#plt.savefig('bollingerplot.pdf', format='pdf')
plt.grid(axis='both')
plt.show()
def portfolioAnalyz():
''' Main Function
This method will analyze your performance of portfolio from date specified.
Create Excel sheet with your portfolio allocation and it will be read by this method to perform analysis
'''
# Reading the portfolio
print '<<<(s) Update your portfolioanalyze.csv file..'
if os.path.isfile("C:\MRT3.0\portfolioanalyze.csv"):
na_portfolio = np.loadtxt('portfolioanalyze.csv',
dtype='S5,f4',
delimiter=',',
comments="#",
skiprows=1)
else:
print '<<<(w) File Not Found: portfolioanalyze.csv '
sleep(2)
basic.go_back()
# Sorting the portfolio by symbol name
na_portfolio = sorted(na_portfolio, key=lambda x: x[0])
print na_portfolio
# Create two list for symbol names and allocation
ls_port_syms = []
lf_port_alloc = []
for port in na_portfolio:
ls_port_syms.append(port[0])
lf_port_alloc.append(port[1])
# Creating an object of the dataaccess class with Yahoo as the source.
c_dataobj = da.DataAccess('Yahoo')
ls_all_syms = c_dataobj.get_all_symbols()
# Bad symbols are symbols present in portfolio but not in all syms
ls_bad_syms = list(set(ls_port_syms) - set(ls_all_syms))
if len(ls_bad_syms) != 0:
print "<<<(w) Portfolio contains bad symbols : ", ls_bad_syms
for s_sym in ls_bad_syms:
i_index = ls_port_syms.index(s_sym)
ls_port_syms.pop(i_index)
lf_port_alloc.pop(i_index)
# Reading the historical data.
print '<<<(i) Reading Historical data...'
#dt_end = dt.datetime(2011, 1, 1)
#dt_start = dt_end - dt.timedelta(days=95) # Three years
# We need closing prices so the timestamp should be hours=16.
dt_timeofday = dt.timedelta(hours=16)
start_month, start_day, start_year, end_month, end_day, end_year = modDatesReturn.get_dates(
)
dt_start = dt.datetime(start_year, start_month, start_day)
dt_end = dt.datetime(end_year, end_month, end_day)
# Get a list of trading days between the start and the end.
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
# Keys to be read from the data, it is good to read everything in one go.
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
# Reading the data, now d_data is a dictionary with the keys above.
# Timestamps and symbols are the ones that were specified before.
ldf_data = c_dataobj.get_data(ldt_timestamps, ls_port_syms, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
# Copying close price into separate dataframe to find rets
df_rets = d_data['close'].copy()
# Filling the data.
df_rets = df_rets.fillna(method='ffill')
df_rets = df_rets.fillna(method='bfill')
# Numpy matrix of filled data values
na_rets = df_rets.values
# returnize0 works on ndarray and not dataframes.
tsu.returnize0(na_rets)
# Estimate portfolio returns
na_portrets = np.sum(na_rets * lf_port_alloc, axis=1)
na_port_total = np.cumprod(na_portrets + 1)
na_component_total = np.cumprod(na_rets + 1, axis=0)
# Plotting the results
plt.clf()
#fig = plt.figure()
#fig.add_subplot(111)
plt.plot(ldt_timestamps, na_component_total)
plt.plot(ldt_timestamps, na_port_total)
ls_names = ls_port_syms
ls_names.append('Portfolio')
plt.legend(ls_names)
plt.ylabel('Cumulative Returns')
plt.xlabel('Date')
#fig.autofmt_xdate(rotation=45)
plt.show()
def bestPort():
'''Main Function'''
# List of symbols
#ls_symbols = ["AXP", "HPQ", "IBM", "HNZ"]
ls_symbols = list()
try:
len_sym = input('<<< How many symbol(Works only for 4 symbols): ')
if len_sym > 4 or len_sym <= 0 or len_sym < 4:
print '<<<(w) Works only for 4 symbols..\n'
basic.print_logo()
basic.print_clrscr()
basic.go_back()
except ValueError:
basic.print_logo()
basic.print_clrscr()
basic.go_back()
except SyntaxError:
basic.print_logo()
basic.print_clrscr()
basic.go_back()
except NameError:
basic.print_logo()
basic.print_clrscr()
basic.go_back()
for i in range(len_sym):
symbols = str(raw_input('<<< Enter the list of symbols: '))
ls_symbols.append(symbols)
## Start and End date of the charts
start_month, start_day, start_year, end_month, end_day, end_year = modDatesReturn.get_dates(
)
dt_start = dt.datetime(start_year, start_month, start_day)
dt_end = dt.datetime(end_year, end_month, end_day)
# Start and End date of the charts
#dt_start = dt.datetime(2010, 1, 1)
#dt_end = dt.datetime(2010, 12, 31)
# We need closing prices so the timestamp should be hours=16.
dt_timeofday = dt.timedelta(hours=16)
# Get a list of trading days between the start and the end.
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
# Creating an object of the dataaccess class with Yahoo as the source.
c_dataobj = da.DataAccess('Yahoo')
# Keys to be read from the data, it is good to read everything in one go.
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
# Reading the data, now d_data is a dictionary with the keys above.
# Timestamps and symbols are the ones that were specified before.
ldf_data = c_dataobj.get_data(ldt_timestamps, ls_symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
# Copying close price into separate dataframe to find rets
df_rets = d_data['close'].copy()
# Filling the data.
df_rets = df_rets.fillna(method='ffill')
df_rets = df_rets.fillna(method='bfill')
# Numpy matrix of filled data values
na_rets = df_rets.values
na_rets = na_rets / na_rets[0, :]
lf_alloc = [0.0, 0.0, 0.0, 0.0]
max_sharpe = -1000
final_stddev = -1000
final_daily_ret = -1000
final_cum_ret = -1000
best_portfolio = lf_alloc
for i in range(0, 101, 10):
left_after_i = 101 - i
for j in range(0, left_after_i, 10):
left_after_j = 101 - i - j
for k in range(0, left_after_j, 10):
left_after_k = 100 - i - j - k
lf_alloc = [i, j, k, left_after_k]
lf_alloc = [x * 0.01 for x in lf_alloc]
stddev, daily_ret, sharpe, cum_ret = simulate(
na_rets, lf_alloc)
if sharpe > max_sharpe:
max_sharpe = sharpe
final_stddev = stddev
final_cum_ret = cum_ret
final_daily_ret = daily_ret
best_portfolio = lf_alloc
print "<<< Symbols : ", ls_symbols
print "<<< Best Portfolio : ", best_portfolio
print "<<< Statistics : Std. Deviation : ", final_stddev
print "<<< Statistics : Daily Returns : ", final_daily_ret
print "<<< Statistics : Cum. Returns : ", final_cum_ret
print "<<< Statistics : Sharpe Ratio : ", max_sharpe
def portfolioAnalyz():
''' Main Function
This method will analyze your performance of portfolio from date specified.
Create Excel sheet with your portfolio allocation and it will be read by this method to perform analysis
'''
# Reading the portfolio
print '<<<(s) Update your portfolioanalyze.csv file..'
if os.path.isfile("C:\MRT3.0\portfolioanalyze.csv"):
na_portfolio = np.loadtxt('portfolioanalyze.csv', dtype='S5,f4',
delimiter=',', comments="#", skiprows=1)
else:
print '<<<(w) File Not Found: portfolioanalyze.csv '
sleep(2)
basic.go_back()
# Sorting the portfolio by symbol name
na_portfolio = sorted(na_portfolio, key=lambda x: x[0])
print na_portfolio
# Create two list for symbol names and allocation
ls_port_syms = []
lf_port_alloc = []
for port in na_portfolio:
ls_port_syms.append(port[0])
lf_port_alloc.append(port[1])
# Creating an object of the dataaccess class with Yahoo as the source.
c_dataobj = da.DataAccess('Yahoo')
ls_all_syms = c_dataobj.get_all_symbols()
# Bad symbols are symbols present in portfolio but not in all syms
ls_bad_syms = list(set(ls_port_syms) - set(ls_all_syms))
if len(ls_bad_syms) != 0:
print "<<<(w) Portfolio contains bad symbols : ", ls_bad_syms
for s_sym in ls_bad_syms:
i_index = ls_port_syms.index(s_sym)
ls_port_syms.pop(i_index)
lf_port_alloc.pop(i_index)
# Reading the historical data.
print '<<<(i) Reading Historical data...'
#dt_end = dt.datetime(2011, 1, 1)
#dt_start = dt_end - dt.timedelta(days=95) # Three years
# We need closing prices so the timestamp should be hours=16.
dt_timeofday = dt.timedelta(hours=16)
start_month,start_day,start_year,end_month,end_day,end_year = modDatesReturn.get_dates()
dt_start = dt.datetime(start_year, start_month, start_day)
dt_end = dt.datetime(end_year, end_month, end_day)
# Get a list of trading days between the start and the end.
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
# Keys to be read from the data, it is good to read everything in one go.
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
# Reading the data, now d_data is a dictionary with the keys above.
# Timestamps and symbols are the ones that were specified before.
ldf_data = c_dataobj.get_data(ldt_timestamps, ls_port_syms, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
# Copying close price into separate dataframe to find rets
df_rets = d_data['close'].copy()
# Filling the data.
df_rets = df_rets.fillna(method='ffill')
df_rets = df_rets.fillna(method='bfill')
# Numpy matrix of filled data values
na_rets = df_rets.values
# returnize0 works on ndarray and not dataframes.
tsu.returnize0(na_rets)
# Estimate portfolio returns
na_portrets = np.sum(na_rets * lf_port_alloc, axis=1)
na_port_total = np.cumprod(na_portrets + 1)
na_component_total = np.cumprod(na_rets + 1, axis=0)
# Plotting the results
plt.clf()
#fig = plt.figure()
#fig.add_subplot(111)
plt.plot(ldt_timestamps, na_component_total)
plt.plot(ldt_timestamps, na_port_total)
ls_names = ls_port_syms
ls_names.append('Portfolio')
plt.legend(ls_names)
plt.ylabel('Cumulative Returns')
plt.xlabel('Date')
#fig.autofmt_xdate(rotation=45)
plt.show()
def bestPort():
'''Main Function'''
# List of symbols
#ls_symbols = ["AXP", "HPQ", "IBM", "HNZ"]
ls_symbols = list()
try:
len_sym = input('<<< How many symbol(Works only for 4 symbols): ')
if len_sym > 4 or len_sym <= 0 or len_sym < 4:
print '<<<(w) Works only for 4 symbols..\n'
basic.print_logo()
basic.print_clrscr()
basic.go_back()
except ValueError:
basic.print_logo()
basic.print_clrscr()
basic.go_back()
except SyntaxError:
basic.print_logo()
basic.print_clrscr()
basic.go_back()
except NameError:
basic.print_logo()
basic.print_clrscr()
basic.go_back()
for i in range(len_sym):
symbols = str(raw_input('<<< Enter the list of symbols: '))
ls_symbols.append(symbols)
## Start and End date of the charts
start_month,start_day,start_year,end_month,end_day,end_year = modDatesReturn.get_dates()
dt_start = dt.datetime(start_year, start_month, start_day)
dt_end = dt.datetime(end_year, end_month, end_day)
# Start and End date of the charts
#dt_start = dt.datetime(2010, 1, 1)
#dt_end = dt.datetime(2010, 12, 31)
# We need closing prices so the timestamp should be hours=16.
dt_timeofday = dt.timedelta(hours=16)
# Get a list of trading days between the start and the end.
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
# Creating an object of the dataaccess class with Yahoo as the source.
c_dataobj = da.DataAccess('Yahoo')
# Keys to be read from the data, it is good to read everything in one go.
ls_keys = ['open', 'high', 'low', 'close', 'volume', 'actual_close']
# Reading the data, now d_data is a dictionary with the keys above.
# Timestamps and symbols are the ones that were specified before.
ldf_data = c_dataobj.get_data(ldt_timestamps, ls_symbols, ls_keys)
d_data = dict(zip(ls_keys, ldf_data))
# Copying close price into separate dataframe to find rets
df_rets = d_data['close'].copy()
# Filling the data.
df_rets = df_rets.fillna(method='ffill')
df_rets = df_rets.fillna(method='bfill')
# Numpy matrix of filled data values
na_rets = df_rets.values
na_rets = na_rets / na_rets[0, :]
lf_alloc = [0.0, 0.0, 0.0, 0.0]
max_sharpe = -1000
final_stddev = -1000
final_daily_ret = -1000
final_cum_ret = -1000
best_portfolio = lf_alloc
for i in range(0, 101, 10):
left_after_i = 101 - i
for j in range(0, left_after_i, 10):
left_after_j = 101 - i - j
for k in range(0, left_after_j, 10):
left_after_k = 100 - i - j - k
lf_alloc = [i, j, k, left_after_k]
lf_alloc = [x * 0.01 for x in lf_alloc]
stddev, daily_ret, sharpe, cum_ret = simulate(na_rets, lf_alloc)
if sharpe > max_sharpe:
max_sharpe = sharpe
final_stddev = stddev
final_cum_ret = cum_ret
final_daily_ret = daily_ret
best_portfolio = lf_alloc
print "<<< Symbols : ", ls_symbols
print "<<< Best Portfolio : ", best_portfolio
print "<<< Statistics : Std. Deviation : ", final_stddev
print "<<< Statistics : Daily Returns : ", final_daily_ret
print "<<< Statistics : Cum. Returns : ", final_cum_ret
print "<<< Statistics : Sharpe Ratio : ", max_sharpe
