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 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 getPortfolioOpt(self, rets):
basic.print_clrscr()
basic.print_logo()
#l_period = input('<<< Enter period to compress return i.e 7 = Weekly : ')
f_target = input('<<< Target Return: ')
na_lower = np.zeros(rets.shape[1])
na_upper = np.ones(rets.shape[1])
weight_port,min_ret, max_ret = tsu.OptPort(rets, f_target, na_lower, na_upper,s_type="long")
print "<<< Weight of Portfolio:", weight_port
print "<<< Error:", max_ret
print "<<< Minimum Return:", min_ret
def usrHelpMain():
print '\t\t\t <<< Help >>> \n '
print '\t [1] Load Data [2] General Calculations [3] Fund Analysis'
print '\t [4] Reserach Stock [5] About [7] Main Menu'
try:
sel = input('Select: ')
except SyntaxError:
basic.print_clrscr()
basic.go_back()
except ValueError:
basic.print_clrscr()
basic.go_back()
except NameError:
basic.print_clrscr()
basic.go_back()
if sel == 1:
help_yahooDataPull()
elif sel == 2:
help_generalCalc()
elif sel == 3:
help_fundAnalysis()
elif sel == 4:
help_researchStock()
elif sel == 5:
help_about()
elif sel == 7:
basic.go_back()
else:
basic.print_clrscr()
basic.go_back()
def usrHelpMain():
print '\t\t\t <<< Help >>> \n '
print '\t [1] Load Data [2] General Calculations [3] Fund Analysis'
print '\t [4] Reserach Stock [5] About [7] Main Menu'
try:
sel = input('Select: ')
except SyntaxError:
basic.print_clrscr()
basic.go_back()
except ValueError:
basic.print_clrscr()
basic.go_back()
except NameError:
basic.print_clrscr()
basic.go_back()
if sel == 1:
help_yahooDataPull()
elif sel == 2:
help_generalCalc()
elif sel == 3:
help_fundAnalysis()
elif sel == 4:
help_researchStock()
elif sel == 5:
help_about()
elif sel == 7:
basic.go_back()
else:
basic.print_clrscr()
basic.go_back()
def getPortfolioOpt(self, rets):
basic.print_clrscr()
basic.print_logo()
#l_period = input('<<< Enter period to compress return i.e 7 = Weekly : ')
f_target = input('<<< Target Return: ')
na_lower = np.zeros(rets.shape[1])
na_upper = np.ones(rets.shape[1])
weight_port, min_ret, max_ret = tsu.OptPort(rets,
f_target,
na_lower,
na_upper,
s_type="long")
print "<<< Weight of Portfolio:", weight_port
print "<<< Error:", max_ret
print "<<< Minimum Return:", min_ret
def eventmenuMain():
print '\t\t\t <<< Event Analyzer >>> \n '
print ' [1] Event based on Market [2] Event based on Bollinger'
print ' [7] Main Menu '
try:
sel_opt = input('\n<<< Select: ')
except SyntaxError:
basic.print_clrscr()
basic.print_logo()
eventmenuMain()
except NameError:
basic.print_clrscr()
basic.print_logo()
eventmenuMain()
if sel_opt == 1:
eventmodMain()
elif sel_opt == 2:
eventBollinger.eventBollingerMain()
elif sel_opt == 7:
basic.go_back()
else:
basic.print_clrscr()
basic.print_logo()
eventmenuMain()
def fundanalysisMain():
print '\t\t\t <<< Fund Analysis >>> \n'
print '\t [1] Analyze Portfolio Allocation [2] Event Analyzer [3] Analyze Portfolio'
print '\t [7] Main Menu '
try:
sel_opt = input('\n<<< Select: ')
except SyntaxError:
basic.print_clrscr()
basic.print_logo()
fundanalysisMain()
except NameError:
basic.print_clrscr()
basic.print_logo()
fundanalysisMain()
if sel_opt == 1:
basic.print_logo()
portfolioAnalyz()
elif sel_opt == 2:
basic.print_logo()
eventmenuMain()
elif sel_opt == 3:
basic.print_logo()
print '<<<(s) Update orders.csv file.. \n'
marketsimMain()
elif sel_opt == 7:
basic.go_back()
else:
basic.print_clrscr()
basic.print_logo()
fundanalysisMain()
def eventmenuMain():
print '\t\t\t <<< Event Analyzer >>> \n '
print ' [1] Event based on Market [2] Event based on Bollinger'
print ' [7] Main Menu '
try:
sel_opt = input('\n<<< Select: ')
except SyntaxError:
basic.print_clrscr()
basic.print_logo()
eventmenuMain()
except NameError:
basic.print_clrscr()
basic.print_logo()
eventmenuMain()
if sel_opt == 1:
eventmodMain()
elif sel_opt == 2:
eventBollinger.eventBollingerMain()
elif sel_opt == 7:
basic.go_back()
else:
basic.print_clrscr()
basic.print_logo()
eventmenuMain()
def marketsimMain():
try:
i_start_cash = float(input('<<< Enter Starting Cash($): '))
except ValueError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except SyntaxError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except NameError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
symbols, dates = _csv_read_sym_dates('orders.csv')
close, timestamps = _read_data(symbols, dates)
share_matrix = _share_holdings('orders.csv', symbols, timestamps, close)
share_matrix = _share_value_cash(share_matrix, close, i_start_cash)
ts_fund = _fund_value(share_matrix, close)
_write_fund(ts_fund, 'values.csv')
analyze.analyzeMain()
def fundanalysisMain():
print '\t\t\t <<< Fund Analysis >>> \n'
print '\t [1] Analyze Portfolio Allocation [2] Event Analyzer [3] Analyze Portfolio'
print '\t [7] Main Menu '
try:
sel_opt = input('\n<<< Select: ')
except SyntaxError:
basic.print_clrscr()
basic.print_logo()
fundanalysisMain()
except NameError:
basic.print_clrscr()
basic.print_logo()
fundanalysisMain()
if sel_opt == 1:
basic.print_logo()
portfolioAnalyz()
elif sel_opt == 2:
basic.print_logo()
eventmenuMain()
elif sel_opt == 3:
basic.print_logo()
print '<<<(s) Update orders.csv file.. \n'
marketsimMain()
elif sel_opt == 7:
basic.go_back()
else:
basic.print_clrscr()
basic.print_logo()
fundanalysisMain()
def generalcalc():
print '\t\t\t <<< General Calculations >>> \n '
print ' [1] Plot Adjusted Close [2] Calculate Daily Return [3] Calculate Standard Devation'
print ' [4] Optimized Portfolio [5] Get Sharpe Ration [6] Best Portfolio'
print ' [7] Main Menu '
try:
sel_opt = input('\n<<< Select: ')
except SyntaxError:
basic.print_clrscr()
basic.print_logo()
generalcalc()
except NameError:
basic.print_clrscr()
basic.print_logo()
generalcalc()
if sel_opt == 1:
objcalc = GeneralCalc()
objcalc.plotAdjustedClose()
elif sel_opt == 2:
objcalc = GeneralCalc()
objcalc.calDailyReturn()
elif sel_opt == 3:
objcalc = GeneralCalc()
d_ret = objcalc.calDailyReturn()
objcalc.calStdDev(d_ret)
elif sel_opt == 4:
objcalc = GeneralCalc()
rets = objcalc.calDailyReturn()
objcalc.getPortfolioOpt(rets)
elif sel_opt == 5:
objcalc = GeneralCalc()
rets = objcalc.calDailyReturn()
objcalc.getSharpeRatio(rets)
elif sel_opt == 6:
bestPortfolio.bestPort()
elif sel_opt == 7:
basic.go_back()
else:
basic.print_clrscr()
basic.print_logo()
generalcalc()
def generalcalc():
print '\t\t\t <<< General Calculations >>> \n '
print ' [1] Plot Adjusted Close [2] Calculate Daily Return [3] Calculate Standard Devation'
print ' [4] Optimized Portfolio [5] Get Sharpe Ration [6] Best Portfolio'
print ' [7] Main Menu '
try:
sel_opt = input('\n<<< Select: ')
except SyntaxError:
basic.print_clrscr()
basic.print_logo()
generalcalc()
except NameError:
basic.print_clrscr()
basic.print_logo()
generalcalc()
if sel_opt == 1:
objcalc = GeneralCalc()
objcalc.plotAdjustedClose()
elif sel_opt == 2:
objcalc = GeneralCalc()
objcalc.calDailyReturn()
elif sel_opt == 3:
objcalc = GeneralCalc()
d_ret = objcalc.calDailyReturn()
objcalc.calStdDev(d_ret)
elif sel_opt == 4:
objcalc = GeneralCalc()
rets = objcalc.calDailyReturn()
objcalc.getPortfolioOpt(rets)
elif sel_opt == 5:
objcalc = GeneralCalc()
rets = objcalc.calDailyReturn()
objcalc.getSharpeRatio(rets)
elif sel_opt == 6:
bestPortfolio.bestPort()
elif sel_opt == 7:
basic.go_back()
else:
basic.print_clrscr()
basic.print_logo()
generalcalc()
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 find_events(ls_symbols, d_data):
''' Finding the event dataframe '''
df_close = d_data['actual_close']
ts_market = df_close['SPY']
print "Finding Events"
# Creating an empty dataframe
df_events = copy.deepcopy(df_close)
df_events = df_events * np.NAN
# Time stamps for the event range
ldt_timestamps = df_close.index
writer = csv.writer(open('orders.csv', 'wb'), delimiter=',')
f_symreturn_cutoff = input('<<< Enter the cutoff in decimel for symbol return today: ')
f_marketreturn_cutoff = input('<<< Enter the cutoiff in decimel for market return today: ')
print '1 -> SYMBOL_RETURN_TODAY > ', f_symreturn_cutoff, '& MARKET_RETURN_TODAY < ', f_marketreturn_cutoff
print '2 -> SYMBOL_RETURN_TODAY < ', f_symreturn_cutoff, '& MARKET_RETURN_TODAY > ', f_marketreturn_cutoff
print '3 -> SYMBOL_RETURN_TODAY > ', f_symreturn_cutoff, '& MARKET_RETURN_TODAY > ', f_marketreturn_cutoff
print '4 -> SYMBOL_RETURN_TODAY <', f_symreturn_cutoff, '& MARKET_RETURN_TODAY < ', f_marketreturn_cutoff
try:
select = input('Select: ')
except ValueError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except SyntaxError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except NameError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
for s_sym in ls_symbols:
for i in range(1, len(ldt_timestamps)):
# Calculating the returns for this timestamp
f_symprice_today = df_close[s_sym].ix[ldt_timestamps[i]]
f_symprice_yest = df_close[s_sym].ix[ldt_timestamps[i - 1]]
f_marketprice_today = ts_market.ix[ldt_timestamps[i]]
f_marketprice_yest = ts_market.ix[ldt_timestamps[i - 1]]
f_symreturn_today = (f_symprice_today / f_symprice_yest) - 1
f_marketreturn_today = (f_marketprice_today / f_marketprice_yest) - 1
i_shares = 100
if select == 1:
if f_symreturn_today > float(f_symreturn_cutoff) and f_marketreturn_today < float(f_marketreturn_cutoff):
df_events[s_sym].ix[ldt_timestamps[i]] = 1
row_to_enter = [str(ldt_timestamps[i].year), str(ldt_timestamps[i].month), \
str(ldt_timestamps[i].day), s_sym, 'Buy', i_shares]
writer.writerow(row_to_enter)
try:
time_n = ldt_timestamps[i + 5]
except:
time_n = ldt_timestamps[-1]
row_to_enter = [str(time_n.year), str(time_n.month), \
str(time_n.day), s_sym, 'Sell', i_shares]
writer.writerow(row_to_enter)
elif select == 2:
if f_symreturn_today < float(f_symreturn_cutoff) and f_marketreturn_today > float(f_marketreturn_cutoff):
df_events[s_sym].ix[ldt_timestamps[i]] = 1
row_to_enter = [str(ldt_timestamps[i].year), str(ldt_timestamps[i].month), \
str(ldt_timestamps[i].day), s_sym, 'Buy', i_shares]
writer.writerow(row_to_enter)
try:
time_n = ldt_timestamps[i + 5]
except:
time_n = ldt_timestamps[-1]
row_to_enter = [str(time_n.year), str(time_n.month), \
str(time_n.day), s_sym, 'Sell', i_shares]
writer.writerow(row_to_enter)
elif select == 3:
if f_symreturn_today > float(f_symreturn_cutoff) and f_marketreturn_today > float(f_marketreturn_cutoff):
df_events[s_sym].ix[ldt_timestamps[i]] = 1
row_to_enter = [str(ldt_timestamps[i].year), str(ldt_timestamps[i].month), \
str(ldt_timestamps[i].day), s_sym, 'Buy', i_shares]
writer.writerow(row_to_enter)
try:
time_n = ldt_timestamps[i + 5]
except:
time_n = ldt_timestamps[-1]
row_to_enter = [str(time_n.year), str(time_n.month), \
str(time_n.day), s_sym, 'Sell', i_shares]
writer.writerow(row_to_enter)
else:
if f_symreturn_today < float(f_symreturn_cutoff) and f_marketreturn_today < float(f_marketreturn_cutoff):
df_events[s_sym].ix[ldt_timestamps[i]] = 1
row_to_enter = [str(ldt_timestamps[i].year), str(ldt_timestamps[i].month), \
str(ldt_timestamps[i].day), s_sym, 'Buy', i_shares]
writer.writerow(row_to_enter)
try:
time_n = ldt_timestamps[i + 5]
except:
time_n = ldt_timestamps[-1]
row_to_enter = [str(time_n.year), str(time_n.month), \
str(time_n.day), s_sym, 'Sell', i_shares]
writer.writerow(row_to_enter)
return df_events
def get_dates():
print '------------------------------------------------------------- '
try:
start_month = int(input('<<< Enter Start Month: '))
if int(start_month) > 12 or int(start_month) <= 0:
basic.print_clrscr()
basic.go_back()
start_day = int(input('<<< Enter Start Day: '))
if int(start_day) > 31 or int(start_day) <= 0:
basic.print_clrscr()
basic.go_back()
start_year = int(input('<<< Enter Start Year: '))
now = datetime.datetime.now()
if int(start_year) > now.year or int(start_year) < 2010:
basic.print_clrscr()
basic.go_back()
end_month = input('<<< Enter End Month: ')
if int(end_month) > 12 or int(end_month) <= 0:
basic.print_clrscr()
basic.go_back()
end_day = input('<<< Enter End Day: ')
if int(end_day) > 31 or int(end_day ) <= 0:
basic.print_clrscr()
basic.go_back()
end_year = input('<<< Enter End Year: ')
if int(end_year) > now.year or int(end_year) < 2010:
basic.print_clrscr()
basic.go_back()
if start_year > end_year:
print '<<<(w)End Year should be greater than start year'
basic.print_clrscr()
basic.go_back()
except ValueError:
basic.print_clrscr()
basic.go_back()
except SyntaxError:
basic.print_clrscr()
basic.go_back()
except NameError:
basic.print_clrscr()
basic.go_back()
leap_year_start = start_year%4
leap_year_end = end_year%4
if leap_year_start != 0 or leap_year_end != 0:
if start_day == 29 or end_day == 29:
print '<<(w) Not leap year..\n'
basic.print_clrscr()
basic.go_back()
return start_month,start_day,start_year,end_month,end_day,end_year
def analyzeMain():
ts_fund = _csv_read_fund('values.csv')
try:
benchmark = raw_input('Enter Benchmark: ')
except ValueError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except SyntaxError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except NameError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
bench_vals = _read_bench(benchmark, list(ts_fund.index))
# print bench_vals
multiple = ts_fund[0] / bench_vals[0]
bench_vals = bench_vals * multiple
print "Details of the Performance of the portfolio"
print "Total Fund:", ts_fund[0]
print 'Data Range : ', ts_fund.index[0], ' to ', ts_fund.index[-1]
print 'Sharpe Ratio of Fund :', tsu.get_sharpe_ratio(tsu.daily(ts_fund))[0]
print 'Sharpe Ratio of ' + benchmark + ' :', tsu.get_sharpe_ratio(
tsu.daily(bench_vals))[0]
print 'Total Return of Fund : ', (((ts_fund[-1] / ts_fund[0]) - 1) + 1)
print 'Total Return of ' + benchmark + ' :', ((
(bench_vals[-1] / bench_vals[0]) - 1) + 1)
print 'Standard Deviation of Fund : ', np.std(tsu.daily(ts_fund.values))
print 'Standard Deviation of ' + benchmark + ' :', np.std(
tsu.daily(bench_vals.values))
print 'Average Daily Return of Fund : ', np.mean(tsu.daily(ts_fund.values))
print 'Average Daily Return of ' + benchmark + ' :', np.mean(
tsu.daily(bench_vals.values))
KS, P = ks_statistic(ts_fund)
print "KS P:", KS, P
plt.clf()
plt.plot(ts_fund.index, ts_fund.values)
plt.plot(ts_fund.index, bench_vals)
plt.ylabel('Fund Value', size='xx-small')
plt.xlabel('Date', size='xx-small')
plt.legend(['Fund', 'Benchmark'], loc='best')
plt.xticks(size='xx-small')
plt.yticks(size='xx-small')
plt.grid(axis='both')
plt.show()
savefig('funds.png', format='png')
try:
report = input("Want to generate report(Press 1): ")
except ValueError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except SyntaxError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except NameError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
if report == 1:
name = str(dt.datetime.now())
file_conv = name.replace(" ", "")
file_conv1 = file_conv.replace(":", "_")
file_conv2 = file_conv1.replace("-", "_")
filename = file_conv2 + ".html"
print 'Generating report..', filename
html_file = open(filename, "w+")
html_file.write("<HTML>\n")
html_file.write("<HEAD>\n")
html_file.write("<TITLE>MSim Generated Report:" + name + "</TITLE>\n")
html_file.write("<H1>Details of the Performance of the portfolio</H1>")
html_file.write("</HEAD>\n\n")
html_file.write("<BODY>\n\n")
html_file.write(
"<IMG SRC = \'./funds.png\' title=Funds vs Benchmark align=right height= 500 width = 600/>\n"
)
html_file.write("<p><i> Total Fund:" + str(ts_fund[0]) + "</p\n")
html_file.write("<p><i>Data Range : </i>," + str(ts_fund.index[0]) +
"to ," + str(ts_fund.index[-1]) + "</p\n")
html_file.write("<p><i>Sharpe Ratio of Fund :</i>" +
str(tsu.get_sharpe_ratio(tsu.daily(ts_fund))[0]) +
"</p\n")
html_file.write("<p><i>Sharpe Ratio of </i>" + benchmark + ":" +
str(tsu.get_sharpe_ratio(tsu.daily(bench_vals))[0]) +
"</p\n")
html_file.write("<p><i>Sharpe Ratio of Fund : ,</i>" +
str(tsu.get_sharpe_ratio(tsu.daily(ts_fund))[0]) +
"</p\n")
html_file.write("<p><i>Sharpe Ratio of </i>" + benchmark + ":" +
str(tsu.get_sharpe_ratio(tsu.daily(bench_vals))[0]) +
"</p\n")
html_file.write("<p><i>Total Return of Fund :</i>" +
str((((ts_fund[-1] / ts_fund[0]) - 1) + 1)) + "</p\n")
html_file.write("<p><i>Total Return of </i>" + benchmark + " : " +
str((((bench_vals[-1] / bench_vals[0]) - 1) + 1)) +
"</p\n")
html_file.write("<p><i>Standard Deviation of Fund : </i>" +
str(np.std(tsu.daily(ts_fund.values))) + "</p\n")
html_file.write("<p><i>Standard Deviation of </i>" + benchmark + ":" +
str(np.std(tsu.daily(bench_vals.values))) + "</p\n")
html_file.write("<p><i>Average Daily Return of Fund : </i> " +
str(np.mean(tsu.daily(ts_fund.values))) + "</p\n")
html_file.write("<p><i>Average Daily Return of </i>" + benchmark +
" : " + str(np.mean(tsu.daily(bench_vals.values))) +
"</p\n")
html_file.write(
"<link type=text/csv rel=stylesheet href=D:\Sim\MSimv2.1/orders.csv />"
)
html_file.write("</BODY>\n\n")
html_file.write("</HTML>")
def analyzeMain():
ts_fund = _csv_read_fund('values.csv')
try:
benchmark = raw_input('Enter Benchmark: ')
except ValueError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except SyntaxError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except NameError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
bench_vals = _read_bench(benchmark, list(ts_fund.index))
# print bench_vals
multiple = ts_fund[0] / bench_vals[0]
bench_vals = bench_vals * multiple
print "Details of the Performance of the portfolio"
print "Total Fund:", ts_fund[0]
print 'Data Range : ', ts_fund.index[0], ' to ', ts_fund.index[-1]
print 'Sharpe Ratio of Fund :', tsu.get_sharpe_ratio(tsu.daily(ts_fund))[0]
print 'Sharpe Ratio of ' + benchmark + ' :', tsu.get_sharpe_ratio(
tsu.daily(bench_vals))[0]
print 'Total Return of Fund : ', (((ts_fund[-1] / ts_fund[0]) - 1) + 1)
print 'Total Return of ' + benchmark + ' :', (((bench_vals[-1]
/ bench_vals[0]) - 1) + 1)
print 'Standard Deviation of Fund : ', np.std(tsu.daily(
ts_fund.values))
print 'Standard Deviation of ' + benchmark + ' :', np.std(
tsu.daily(bench_vals.values))
print 'Average Daily Return of Fund : ', np.mean(tsu.daily(
ts_fund.values))
print 'Average Daily Return of ' + benchmark + ' :', np.mean(
tsu.daily(bench_vals.values))
KS, P = ks_statistic(ts_fund)
print "KS P:" , KS,P
plt.clf()
plt.plot(ts_fund.index, ts_fund.values)
plt.plot(ts_fund.index, bench_vals)
plt.ylabel('Fund Value', size='xx-small')
plt.xlabel('Date', size='xx-small')
plt.legend(['Fund', 'Benchmark'], loc='best')
plt.xticks(size='xx-small')
plt.yticks(size='xx-small')
plt.grid(axis='both')
plt.show()
savefig('funds.png', format = 'png')
try:
report = input("Want to generate report(Press 1): ")
except ValueError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except SyntaxError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except NameError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
if report == 1:
name = str(dt.datetime.now())
file_conv = name.replace(" ", "")
file_conv1 = file_conv.replace(":","_")
file_conv2 = file_conv1.replace("-","_")
filename = file_conv2+".html"
print 'Generating report..', filename
html_file = open(filename,"w+")
html_file.write("<HTML>\n")
html_file.write("<HEAD>\n")
html_file.write("<TITLE>MSim Generated Report:" + name + "</TITLE>\n")
html_file.write("<H1>Details of the Performance of the portfolio</H1>")
html_file.write("</HEAD>\n\n")
html_file.write("<BODY>\n\n")
html_file.write("<IMG SRC = \'./funds.png\' title=Funds vs Benchmark align=right height= 500 width = 600/>\n")
html_file.write("<p><i> Total Fund:" +str(ts_fund[0])+ "</p\n")
html_file.write("<p><i>Data Range : </i>," +str(ts_fund.index[0])+ "to ," +str(ts_fund.index[-1])+"</p\n")
html_file.write("<p><i>Sharpe Ratio of Fund :</i>"+str(tsu.get_sharpe_ratio(tsu.daily(ts_fund))[0])+"</p\n")
html_file.write("<p><i>Sharpe Ratio of </i>" + benchmark + ":" +str(tsu.get_sharpe_ratio(tsu.daily(bench_vals))[0])+"</p\n")
html_file.write("<p><i>Sharpe Ratio of Fund : ,</i>" +str(tsu.get_sharpe_ratio(tsu.daily(ts_fund))[0])+"</p\n")
html_file.write("<p><i>Sharpe Ratio of </i>" + benchmark + ":" +str(tsu.get_sharpe_ratio(tsu.daily(bench_vals))[0])+"</p\n")
html_file.write("<p><i>Total Return of Fund :</i>" +str((((ts_fund[-1] / ts_fund[0]) - 1) + 1))+"</p\n")
html_file.write("<p><i>Total Return of </i>" + benchmark + " : " +str((((bench_vals[-1]/ bench_vals[0]) - 1) + 1))+"</p\n")
html_file.write("<p><i>Standard Deviation of Fund : </i>" +str(np.std(tsu.daily(ts_fund.values)))+"</p\n")
html_file.write("<p><i>Standard Deviation of </i>"+ benchmark + ":" +str(np.std(tsu.daily(bench_vals.values)))+"</p\n")
html_file.write("<p><i>Average Daily Return of Fund : </i> " +str(np.mean(tsu.daily(ts_fund.values)))+"</p\n")
html_file.write("<p><i>Average Daily Return of </i>" + benchmark + " : " + str(np.mean(tsu.daily(bench_vals.values)))+"</p\n")
html_file.write("<link type=text/csv rel=stylesheet href=D:\Sim\MSimv2.1/orders.csv />")
html_file.write("</BODY>\n\n")
html_file.write("</HTML>")
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 find_events(ls_symbols, d_data):
''' Finding the event dataframe '''
df_close = d_data['actual_close']
# ts_market = df_close['SPY']
print "Finding Events"
# Creating an empty dataframe
df_events = copy.deepcopy(df_close)
df_events = df_events * np.NAN
# Time stamps for the event range
ldt_timestamps = df_close.index
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)
writer = csv.writer(open('bollingerorders.csv', 'wb'), delimiter=',')
f_symreturn_cutoff = input('<<< Enter the cutoff in decimel for symbol return today: ')
f_symyest_cutoff = input('<<< Enter the cutoff in decimel for symbol return yesterday: ')
print '1 -> SYMBOL_RETURN_TODAY > ', f_symreturn_cutoff, 'SYMBOL_RETURN_YESTERDAY < ',f_symyest_cutoff
print '2 -> SYMBOL_RETURN_TODAY < ', f_symreturn_cutoff, 'SYMBOL_RETURN_YESTERDAY > ',f_symyest_cutoff
print '3 -> SYMBOL_RETURN_TODAY > ', f_symreturn_cutoff, 'SYMBOL_RETURN_YESTERDAY > ',f_symyest_cutoff
print '4 -> SYMBOL_RETURN_TODAY <', f_symreturn_cutoff, 'SYMBOL_RETURN_YESTERDAY < ',f_symyest_cutoff
try:
select = input('Select: ')
except ValueError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except SyntaxError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except NameError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
for s_sym in ls_symbols:
for i in range(1, len(ldt_timestamps)):
# Calculating the returns for this timestamp
f_symboll_today = df_bollinger[s_sym].ix[ldt_timestamps[i]]
f_symboll_yest = df_bollinger[s_sym].ix[ldt_timestamps[i - 1]]
f_marketbol_today = df_bollinger['SPY'].ix[ldt_timestamps[i]]
# f_marketprice_yest = ts_market.ix[ldt_timestamps[i - 1]]
i_shares = 100
if select == 1:
if f_symboll_today > float(f_symreturn_cutoff) and f_symboll_yest < float(f_symyest_cutoff):
if f_marketbol_today > 1.0:
df_events[s_sym].ix[ldt_timestamps[i]] = 1
row_to_enter = [str(ldt_timestamps[i].year), str(ldt_timestamps[i].month), \
str(ldt_timestamps[i].day), s_sym, 'Buy', i_shares]
writer.writerow(row_to_enter)
try:
time_n = ldt_timestamps[i + 5]
except:
time_n = ldt_timestamps[-1]
row_to_enter = [str(time_n.year), str(time_n.month), \
str(time_n.day), s_sym, 'Sell', i_shares]
writer.writerow(row_to_enter)
elif select == 2:
if f_symboll_today > float(f_symreturn_cutoff) and f_symboll_yest < float(f_symyest_cutoff):
if f_marketbol_today > 1.0:
df_events[s_sym].ix[ldt_timestamps[i]] = 1
row_to_enter = [str(ldt_timestamps[i].year), str(ldt_timestamps[i].month), \
str(ldt_timestamps[i].day), s_sym, 'Buy', i_shares]
writer.writerow(row_to_enter)
try:
time_n = ldt_timestamps[i + 5]
except:
time_n = ldt_timestamps[-1]
row_to_enter = [str(time_n.year), str(time_n.month), \
str(time_n.day), s_sym, 'Sell', i_shares]
writer.writerow(row_to_enter)
elif select == 3:
if f_symboll_today > float(f_symreturn_cutoff) and f_symboll_yest < float(f_symyest_cutoff):
if f_marketbol_today > 1.0:
df_events[s_sym].ix[ldt_timestamps[i]] = 1
row_to_enter = [str(ldt_timestamps[i].year), str(ldt_timestamps[i].month), \
str(ldt_timestamps[i].day), s_sym, 'Buy', i_shares]
writer.writerow(row_to_enter)
try:
time_n = ldt_timestamps[i + 5]
except:
time_n = ldt_timestamps[-1]
row_to_enter = [str(time_n.year), str(time_n.month), \
str(time_n.day), s_sym, 'Sell', i_shares]
writer.writerow(row_to_enter)
else:
if f_symboll_today > float(f_symreturn_cutoff) and f_symboll_yest < float(f_symyest_cutoff):
if f_marketbol_today > 1.0:
df_events[s_sym].ix[ldt_timestamps[i]] = 1
row_to_enter = [str(ldt_timestamps[i].year), str(ldt_timestamps[i].month), \
str(ldt_timestamps[i].day), s_sym, 'Buy', i_shares]
writer.writerow(row_to_enter)
try:
time_n = ldt_timestamps[i + 5]
except:
time_n = ldt_timestamps[-1]
row_to_enter = [str(time_n.year), str(time_n.month), \
str(time_n.day), s_sym, 'Sell', i_shares]
writer.writerow(row_to_enter)
#if f_symboll_today < -2.0 and f_symboll_yest >= -2.0:
#if f_marketbol_today > 1.0:
#df_events[s_sym].ix[ldt_timestamps[i]] = 1
#row_to_enter = [str(ldt_timestamps[i].year), str(ldt_timestamps[i].month), \
#str(ldt_timestamps[i].day), s_sym, 'Buy', i_shares]
#writer.writerow(row_to_enter)
#try:
#time_n = ldt_timestamps[i + 5]
#except:
#time_n = ldt_timestamps[-1]
#row_to_enter = [str(time_n.year), str(time_n.month), \
#str(time_n.day), s_sym, 'Sell', i_shares]
#writer.writerow(row_to_enter)
return df_events
def find_events(ls_symbols, d_data):
''' Finding the event dataframe '''
df_close = d_data['actual_close']
ts_market = df_close['SPY']
print "Finding Events"
# Creating an empty dataframe
df_events = copy.deepcopy(df_close)
df_events = df_events * np.NAN
# Time stamps for the event range
ldt_timestamps = df_close.index
writer = csv.writer(open('orders.csv', 'wb'), delimiter=',')
f_symreturn_cutoff = input(
'<<< Enter the cutoff in decimel for symbol return today: ')
f_marketreturn_cutoff = input(
'<<< Enter the cutoiff in decimel for market return today: ')
print '1 -> SYMBOL_RETURN_TODAY > ', f_symreturn_cutoff, '& MARKET_RETURN_TODAY < ', f_marketreturn_cutoff
print '2 -> SYMBOL_RETURN_TODAY < ', f_symreturn_cutoff, '& MARKET_RETURN_TODAY > ', f_marketreturn_cutoff
print '3 -> SYMBOL_RETURN_TODAY > ', f_symreturn_cutoff, '& MARKET_RETURN_TODAY > ', f_marketreturn_cutoff
print '4 -> SYMBOL_RETURN_TODAY <', f_symreturn_cutoff, '& MARKET_RETURN_TODAY < ', f_marketreturn_cutoff
try:
select = input('Select: ')
except ValueError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except SyntaxError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
except NameError:
basic.print_clrscr()
basic.print_logo()
basic.go_back()
for s_sym in ls_symbols:
for i in range(1, len(ldt_timestamps)):
# Calculating the returns for this timestamp
f_symprice_today = df_close[s_sym].ix[ldt_timestamps[i]]
f_symprice_yest = df_close[s_sym].ix[ldt_timestamps[i - 1]]
f_marketprice_today = ts_market.ix[ldt_timestamps[i]]
f_marketprice_yest = ts_market.ix[ldt_timestamps[i - 1]]
f_symreturn_today = (f_symprice_today / f_symprice_yest) - 1
f_marketreturn_today = (f_marketprice_today /
f_marketprice_yest) - 1
i_shares = 100
if select == 1:
if f_symreturn_today > float(
f_symreturn_cutoff) and f_marketreturn_today < float(
f_marketreturn_cutoff):
df_events[s_sym].ix[ldt_timestamps[i]] = 1
row_to_enter = [str(ldt_timestamps[i].year), str(ldt_timestamps[i].month), \
str(ldt_timestamps[i].day), s_sym, 'Buy', i_shares]
writer.writerow(row_to_enter)
try:
time_n = ldt_timestamps[i + 5]
except:
time_n = ldt_timestamps[-1]
row_to_enter = [str(time_n.year), str(time_n.month), \
str(time_n.day), s_sym, 'Sell', i_shares]
writer.writerow(row_to_enter)
elif select == 2:
if f_symreturn_today < float(
f_symreturn_cutoff) and f_marketreturn_today > float(
f_marketreturn_cutoff):
df_events[s_sym].ix[ldt_timestamps[i]] = 1
row_to_enter = [str(ldt_timestamps[i].year), str(ldt_timestamps[i].month), \
str(ldt_timestamps[i].day), s_sym, 'Buy', i_shares]
writer.writerow(row_to_enter)
try:
time_n = ldt_timestamps[i + 5]
except:
time_n = ldt_timestamps[-1]
row_to_enter = [str(time_n.year), str(time_n.month), \
str(time_n.day), s_sym, 'Sell', i_shares]
writer.writerow(row_to_enter)
elif select == 3:
if f_symreturn_today > float(
f_symreturn_cutoff) and f_marketreturn_today > float(
f_marketreturn_cutoff):
df_events[s_sym].ix[ldt_timestamps[i]] = 1
row_to_enter = [str(ldt_timestamps[i].year), str(ldt_timestamps[i].month), \
str(ldt_timestamps[i].day), s_sym, 'Buy', i_shares]
writer.writerow(row_to_enter)
try:
time_n = ldt_timestamps[i + 5]
except:
time_n = ldt_timestamps[-1]
row_to_enter = [str(time_n.year), str(time_n.month), \
str(time_n.day), s_sym, 'Sell', i_shares]
writer.writerow(row_to_enter)
else:
if f_symreturn_today < float(
f_symreturn_cutoff) and f_marketreturn_today < float(
f_marketreturn_cutoff):
df_events[s_sym].ix[ldt_timestamps[i]] = 1
row_to_enter = [str(ldt_timestamps[i].year), str(ldt_timestamps[i].month), \
str(ldt_timestamps[i].day), s_sym, 'Buy', i_shares]
writer.writerow(row_to_enter)
try:
time_n = ldt_timestamps[i + 5]
except:
time_n = ldt_timestamps[-1]
row_to_enter = [str(time_n.year), str(time_n.month), \
str(time_n.day), s_sym, 'Sell', i_shares]
writer.writerow(row_to_enter)
return df_events
def get_dates():
print '------------------------------------------------------------- '
try:
start_month = int(input('<<< Enter Start Month: '))
if int(start_month) > 12 or int(start_month) <= 0:
basic.print_clrscr()
basic.go_back()
start_day = int(input('<<< Enter Start Day: '))
if int(start_day) > 31 or int(start_day) <= 0:
basic.print_clrscr()
basic.go_back()
start_year = int(input('<<< Enter Start Year: '))
now = datetime.datetime.now()
if int(start_year) > now.year or int(start_year) < 2010:
basic.print_clrscr()
basic.go_back()
end_month = input('<<< Enter End Month: ')
if int(end_month) > 12 or int(end_month) <= 0:
basic.print_clrscr()
basic.go_back()
end_day = input('<<< Enter End Day: ')
if int(end_day) > 31 or int(end_day) <= 0:
basic.print_clrscr()
basic.go_back()
end_year = input('<<< Enter End Year: ')
if int(end_year) > now.year or int(end_year) < 2010:
basic.print_clrscr()
basic.go_back()
if start_year > end_year:
print '<<<(w)End Year should be greater than start year'
basic.print_clrscr()
basic.go_back()
except ValueError:
basic.print_clrscr()
basic.go_back()
except SyntaxError:
basic.print_clrscr()
basic.go_back()
except NameError:
basic.print_clrscr()
basic.go_back()
leap_year_start = start_year % 4
leap_year_end = end_year % 4
if leap_year_start != 0 or leap_year_end != 0:
if start_day == 29 or end_day == 29:
print '<<(w) Not leap year..\n'
basic.print_clrscr()
basic.go_back()
return start_month, start_day, start_year, end_month, end_day, end_year
