def setUp(self):
order_list = []
with open("orders.csv", 'rU') as csvfile:
order_reader = csv.reader(csvfile, delimiter=',', skipinitialspace=True)
for row in order_reader:
date = dt.datetime(int(row[0]),int(row[1]), int(row[2]), 16)
o = Order(action=row[4], date=date, tick=row[3], shares=row[5])
order_list.append(o)
# order_list needs to be sorted. Otherwise the algorithm won't work.
date_list = [x.date for x in order_list]
date_list.sort()
dt_timeofday = dt.timedelta(hours=16)
dt_start = date_list[0]
dt_end = date_list[-1]
tick_set = sets.Set([x.tick for x in order_list])
ls_symbols = ['$SPX']
while(tick_set):
ls_symbols.append(tick_set.pop())
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
cash = 1000000
all_stocks = get_tickdata(ls_symbols=ls_symbols, ldt_timestamps=ldt_timestamps)
self.pf = Portfolio(equities=all_stocks, cash=cash, dates=ldt_timestamps, order_list=order_list)
self.benchmark = self.pf.equities['$SPX']
self.pf.sim()
o = Order(action=row[2], date=date, tick=row[1], shares=row[3])
else:
o = Order(action=row[2], date=date, tick=row[1], shares=row[3], price=row[4])
order_list.append(o)
# order_list needs to be sorted. Otherwise the algorithm won't work.
date_list = [x.date for x in order_list]
date_list.sort()
dt_start = date_list[0]
dt_end = date_list[-1]
tick_set = sets.Set([x.tick for x in order_list])
ls_symbols = ['$GSPC']
while(tick_set):
ls_symbols.append(tick_set.pop())
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
all_stocks = get_tickdata(ls_symbols=ls_symbols, ldt_timestamps=ldt_timestamps)
pf = Portfolio(equities=all_stocks, cash=cash, dates=ldt_timestamps, order_list=order_list)
pf.sim()
equity_col = ['buy', 'sell', 'close']
pf.csvwriter(csv_file=value_file, d=',', cash=False)
print "The final value of the portfolio using the sample file is -- ", pf.total[-1]
print "Details of the Performance of the portfolio :"
print "Data Range :", ldt_timestamps[0], "to", ldt_timestamps[-1]
print "Sharpe Ratio of Fund :", pf.sharpe_ratio()
print "Sortino Ratio of Fund :", pf.sortino()
print "Sharpe Ratio of $GSPC :", pf.equities['$GSPC'].sharpe_ratio()
print "Total Return of Fund :", pf.return_ratio()
print " Total Return of $GSPC :", pf.equities['$GSPC'].return_ratio()
print "Standard Deviation of Fund :", pf.std()
print " Standard Deviation of $GSPC :", pf.equities['$GSPC'].std()
print "Average Daily Return of Fund :", pf.avg_daily_return()
print "Average Daily Return of $GSPC :", pf.equities['$GSPC'].avg_daily_return()
class TestPortfolioFunctions(unittest.TestCase):
"""
_AAPL, _GOOG, _IBM and _XOM are for testing only.
The real tickers are AAPL, GOOG, IBM and XOM.
"""
def setUp(self):
order_list = []
with open("orders.csv", 'rU') as csvfile:
order_reader = csv.reader(csvfile, delimiter=',', skipinitialspace=True)
for row in order_reader:
date = dt.datetime(int(row[0]),int(row[1]), int(row[2]), 16)
o = Order(action=row[4], date=date, tick=row[3], shares=row[5])
order_list.append(o)
# order_list needs to be sorted. Otherwise the algorithm won't work.
date_list = [x.date for x in order_list]
date_list.sort()
dt_timeofday = dt.timedelta(hours=16)
dt_start = date_list[0]
dt_end = date_list[-1]
tick_set = sets.Set([x.tick for x in order_list])
ls_symbols = ['$SPX']
while(tick_set):
ls_symbols.append(tick_set.pop())
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
cash = 1000000
all_stocks = get_tickdata(ls_symbols=ls_symbols, ldt_timestamps=ldt_timestamps)
self.pf = Portfolio(equities=all_stocks, cash=cash, dates=ldt_timestamps, order_list=order_list)
self.benchmark = self.pf.equities['$SPX']
self.pf.sim()
def test_total(self):
print "test total..."
self.assertEqual(self.pf.total[-1], 1133860.0)
def test_sharpe_ratio(self):
print "test sharpe..."
self.assertEqual(round(self.pf.sharpe_ratio(), 4), 0.066)
def test_totalReturn(self):
print "test total return..."
self.assertEqual(self.pf.return_ratio(), 1.13386)
def test_std(self):
print "test Standard Deviation..."
self.assertEqual(round(self.pf.std(),6), 0.007175)
def test_avg_daily_return(self):
print "test daily return..."
self.assertEqual(round(self.pf.avg_daily_return(),8), 0.00054935)
def test_random_choose_tick(self):
print "test random choose tick..."
exclude_ls = ["_GOOG", "_AAPL"]
choice = self.pf.random_choose_tick(exclude=exclude_ls)
print "All equities are ", [x for x in self.pf.equities]
print "The choice is ", choice
self.assertTrue(choice in [x for x in self.pf.equities])
self.assertFalse(choice in [x for x in exclude_ls])
def test_equities_own(self):
print "test equities owned..."
date = dt.datetime(2011,1,12, 16)
self.assertEqual(self.pf.equities_long(date), ["_AAPL"])
date = dt.datetime(2011,3,4, 16)
self.assertEqual(self.pf.equities_long(date), ["_IBM"])
def test_beta(self):
print "test beta..."
beta = self.pf.beta(self.pf.equities['$SPX'])
self.assertEqual(round(beta,4), -0.4234)
def test_info_ratio(self):
print "test information ratio..."
self.assertEqual(round(self.pf.info_ratio(self.pf.equities['$SPX']), 4), 0.0467)
def test_appraisal_ratio(self):
print "test appraisal ratio..."
self.assertEqual(round(self.pf.appraisal_ratio(self.pf.equities['$SPX']), 4), 0.0363)
def test_residual_return(self):
print "test residual return..."
self.assertEqual(round(self.pf.mean_residual_return(self.pf.equities['$SPX']), 6), 0.000449)
def test_equity_total(self):
print "test equity total..."
self.assertEqual(round(self.pf.equities['_AAPL']['close'][-1],2), 394.26)
def test_equity_sharpe_ratio(self):
print "test equity sharpe..."
self.assertEqual(round(self.pf.equities['_AAPL'].sharpe_ratio(), 4), 0.0399)
def test_equity_totalReturn(self):
print "test equity total return..."
self.assertEqual(round(self.pf.equities['_AAPL'].total_return(), 8), 1.15622159)
def test_equity_std(self):
print "test equity Standard Deviation..."
self.assertEqual(round(self.pf.equities['_AAPL'].std(),6), 0.016765)
def test_equity_avg_daily_return(self):
print "test equity daily return..."
self.assertEqual(round(self.pf.equities['_AAPL'].avg_daily_return(),8), 0.00074551)
def test_equity_beta(self):
print "test equity beta..."
beta = self.pf.equities['_AAPL'].beta(self.benchmark)
self.assertEqual(round(beta,4), -0.8104)
def test_equity_info_ratio(self):
print "test equity information ratio..."
self.assertEqual(round(self.pf.equities['_AAPL'].info_ratio(self.benchmark), 4), 0.0569)
def test_equity_appraisal_ratio(self):
print "test equity appraisal ratio..."
self.assertEqual(round(self.pf.equities['_AAPL'].appraisal_ratio(self.benchmark), 4), 0.0235)
def test_equity_residual_return(self):
print "test equity residual return..."
self.assertEqual(round(self.pf.equities['_AAPL'].mean_residual_return(self.benchmark), 6), 0.000622)
def test_equity_avg_daily_return(self):
print "test equity average daily return..."
self.assertEqual(round(self.pf.equities['_AAPL'].avg_daily_return(), 8), 0.00074551)
def test_equity_std(self):
print "test equity standard deviation..."
self.assertEqual(round(self.pf.equities['_AAPL'].std(), 8), 0.01676517)
def test_equity_sharpe_ratio(self):
print "test equity sharpe ratio..."
self.assertEqual(round(self.pf.equities['_AAPL'].sharpe_ratio(), 8), 0.0399392)
def test_equity_info_ratio(self):
print "test equity info ratio..."
self.assertEqual(round(self.pf.equities['_AAPL'].info_ratio(self.benchmark), 8), 0.05687389)
def test_equity_appraisal_ratio(self):
print "test equity appraisal ratio..."
self.assertEqual(round(self.pf.equities['_AAPL'].appraisal_ratio(self.benchmark), 8), 0.02347507)
price=row[4])
order_list.append(o)
# order_list needs to be sorted. Otherwise the algorithm won't work.
date_list = [x.date for x in order_list]
date_list.sort()
dt_start = date_list[0]
dt_end = date_list[-1]
tick_set = sets.Set([x.tick for x in order_list])
ls_symbols = ['$GSPC']
while (tick_set):
ls_symbols.append(tick_set.pop())
ldt_timestamps = du.getNYSEdays(dt_start, dt_end, dt_timeofday)
all_stocks = get_tickdata(ls_symbols=ls_symbols,
ldt_timestamps=ldt_timestamps)
pf = Portfolio(equities=all_stocks,
cash=cash,
dates=ldt_timestamps,
order_list=order_list)
pf.sim()
equity_col = ['buy', 'sell', 'close']
pf.csvwriter(csv_file=value_file, d=',', cash=False)
print("The final value of the portfolio using the sample file is -- ",
pf.total[-1])
print("Details of the Performance of the portfolio :")
print("Data Range :", ldt_timestamps[0], "to", ldt_timestamps[-1])
print("Sharpe Ratio of Fund :", pf.sharpe_ratio())
print("Sortino Ratio of Fund :", pf.sortino())
print("Sharpe Ratio of $GSPC :", pf.equities['$GSPC'].sharpe_ratio())
print("Total Return of Fund :", pf.return_ratio())
print(" Total Return of $GSPC :", pf.equities['$GSPC'].return_ratio())
print("Standard Deviation of Fund :", pf.std())
print(" Standard Deviation of $GSPC :", pf.equities['$GSPC'].std())
