def check_analysis(self, name, start_date, end_date, symbols,
allocations, sv, rfr, sf,
v_cr, v_adr, v_sddr, v_sr, v_ev):
print name
cr, adr, sddr, sr, ev = assess_portfolio(sd=start_date, ed=end_date,
syms=symbols, allocs=allocations,
sv=sv, rfr=rfr, gen_plot=False)
np.testing.assert_almost_equal([v_cr, v_adr, v_sddr, v_sr, v_ev],
[cr, adr, sddr, sr, ev], 5)
print ev
def _run_test(self, params, results):
start_date = params['start_date']
end_date = params['end_date']
symbols = params['symbols']
allocations = params['allocations']
start_value = params['start_value']
risk_free_rate = params['risk_free_rate']
sample_freq = params['sample_freq']
cumulative_return, \
average_daily_return, \
volatility, \
sharpe_ratio, \
end_value = analysis.assess_portfolio(sd=start_date, ed=end_date, syms=symbols, allocs=allocations,
sv=start_value, rfr=risk_free_rate, sf=sample_freq, gen_plot=False)
self.assertAlmostEqual(
cumulative_return,
results['cumulative_return'],
delta=1e-8,
msg="Incorrect cumulative return {}, expected to be {}".format(
cumulative_return, results['cumulative_return']))
self.assertAlmostEqual(
average_daily_return,
results['average_daily_return'],
delta=1e-8,
msg="Incorrect avg. daily return {}, expected to be {}".format(
average_daily_return, results['average_daily_return']))
self.assertAlmostEqual(
volatility,
results['volatility'],
delta=1e-8,
msg="Incorrect volatility {}, expected to be {}".format(
volatility, results['volatility']))
self.assertAlmostEqual(
sharpe_ratio,
results['sharpe_ratio'],
delta=1e-8,
msg="Incorrect sharpe ratio {}, expected to be {}".format(
sharpe_ratio, results['sharpe_ratio']))
if results['end_value']:
# The reason behind lowering the delta to 1e-5 here is
# because portfolio values are given only up to the 5th decimal
self.assertAlmostEqual(
end_value,
results['end_value'],
delta=1e-5,
msg="Incorrect end value {}, expected to be {}".format(
end_value, results['end_value']))
def test_results1(self):
start_date = dt.datetime(2010, 1, 1)
end_date = dt.datetime(2010, 12, 31)
symbols = ['GOOG', 'AAPL', 'GLD', 'XOM']
allocations = [0.2, 0.3, 0.4, 0.1]
start_val = 1000000
risk_free_rate = 0.0
sample_freq = 252
cr, adr, sddr, sr, ev = assess_portfolio(sd=start_date, ed=end_date, \
syms=symbols, \
allocs=allocations, \
sv=start_val, \
gen_plot=False)
np.testing.assert_almost_equal([0.255646784534, 0.000957366234238, 0.0100104028, 1.51819243641, 1255646.78453],
[cr, adr, sddr, sr, ev], 5)
def test_results3(self):
start_date = dt.datetime(2010, 6, 1)
end_date = dt.datetime(2010, 12, 31)
symbols = ['GOOG', 'AAPL', 'GLD', 'XOM']
allocations = [0.2, 0.3, 0.4, 0.1]
start_val = 1000000
risk_free_rate = 0.0
sample_freq = 252
cr, adr, sddr, sr, ev = assess_portfolio(sd=start_date, ed=end_date, \
syms=symbols, \
allocs=allocations, \
sv=start_val, \
gen_plot=False)
np.testing.assert_almost_equal(
[0.205113938792, 0.00129586924366, 0.00929734619707, 2.21259766672, 1205113.93879],
[cr, adr, sddr, sr, ev], 5)
def test_results2(self):
start_date = dt.datetime(2010, 1, 1)
end_date = dt.datetime(2010, 12, 31)
symbols = ['AXP', 'HPQ', 'IBM', 'HNZ']
allocations = [0.0, 0.0, 0.0, 1.0]
start_val = 1000000
risk_free_rate = 0.0
sample_freq = 252
cr, adr, sddr, sr, ev = assess_portfolio(sd=start_date, ed=end_date, \
syms=symbols, \
allocs=allocations, \
sv=start_val, \
gen_plot=False)
np.testing.assert_almost_equal(
[0.198105963655, 0.000763106152672, 0.00926153128768, 1.30798398744, 1198105.96365],
[cr, adr, sddr, sr, ev], 5)
def test_example1(self):
start_date = dt.datetime(2010, 1, 1)
end_date = dt.datetime(2010, 12, 31)
symbols = ['GOOG', 'AAPL', 'GLD', 'XOM']
allocations = [0.2, 0.3, 0.4, 0.1]
start_val = 1000000
risk_free_rate = 0.0
sample_freq = 252.0
# Assess the portfolio
cr, adr, sddr, sr, ev = a.assess_portfolio(sd=start_date, ed=end_date,
syms=symbols,
allocs=allocations,
sv=start_val,
gen_plot=False)
self.assertAlmostEqual(0.255646784534, cr, 3, "Cumulative Return {} is incorrect".format(cr), delta=None)
self.assertAlmostEqual(1.51819243641, sr, 3, "Sharpe Ratio {} is incorrect".format(sr), delta=None)
self.assertAlmostEqual(0.000957366234238, adr, 5, "Avg Daily Return {} is incorrect".format(adr), delta=None)
self.assertAlmostEqual(0.0100104028, sddr, 5,
"Volatility (stdev of daily returns) {} is incorrect".format(sddr), delta=None)
def test_example3(self):
start_date = dt.datetime(2010, 6, 1)
end_date = dt.datetime(2010, 12, 31)
symbols = ['GOOG', 'AAPL', 'GLD', 'XOM']
allocations = [0.2, 0.3, 0.4, 0.1]
start_val = 1000000
risk_free_rate = 0.0
sample_freq = 252.0
# Assess the portfolio
cr, adr, sddr, sr, ev = a.assess_portfolio(sd=start_date, ed=end_date,
syms=symbols,
allocs=allocations,
sv=start_val,
gen_plot=False)
self.assertAlmostEqual(0.205113938792, cr, 3, "Cumulative Return {} is incorrect".format(cr), delta=None)
self.assertAlmostEqual(2.21259766672, sr, 3, "Sharpe Ratio {} is incorrect".format(sr), delta=None)
self.assertAlmostEqual(0.00129586924366, adr, 5, "Avg Daily Return {} is incorrect".format(adr), delta=None)
self.assertAlmostEqual(0.00929734619707, sddr, 5,
"Volatility (stdev of daily returns) {} is incorrect".format(sddr), delta=None)
def test_example2(self):
start_date = dt.datetime(2010, 1, 1)
end_date = dt.datetime(2010, 12, 31)
symbols = ['AXP', 'HPQ', 'IBM', 'HNZ']
allocations = [0.0, 0.0, 0.0, 1.0]
start_val = 1000000
risk_free_rate = 0.0
sample_freq = 252.0
# Assess the portfolio
cr, adr, sddr, sr, ev = a.assess_portfolio(sd=start_date, ed=end_date,
syms=symbols,
allocs=allocations,
sv=start_val,
gen_plot=False)
self.assertAlmostEqual(0.198105963655, cr, 3, "Cumulative Return {} is incorrect".format(cr), delta=None)
self.assertAlmostEqual(1.30798398744, sr, 3, "Sharpe Ratio {} is incorrect".format(sr), delta=None)
self.assertAlmostEqual(0.000763106152672, adr, 5, "Avg Daily Return {} is incorrect".format(adr), delta=None)
self.assertAlmostEqual(0.00926153128768, sddr, 5,
"Volatility (stdev of daily returns) {} is incorrect".format(sddr), delta=None)
def run_test(start_date, end_date, symbols, allocations, start_val, risk_free_rate, sample_freq):
# Assess the portfolio
cr, adr, sddr, sr, ev = analysis.assess_portfolio(sd = start_date, ed = end_date,\
syms = symbols, \
allocs = allocations,\
sv = start_val, \
rfr = risk_free_rate, \
sf = sample_freq, \
gen_plot = False)
# Print statistics
print "Start Date:", start_date
print "End Date:", end_date
print "Symbols:", symbols
print "Allocations:", allocations
print "Sharpe Ratio:", sr
print "Volatility (stdev of daily returns):", sddr
print "Average Daily Return:", adr
print "Cumulative Return:", cr
print "End Value:", ev
def try_assess_portfolio(self, portvals):
statsMethodExists = True
# First try from previous assignment file
try:
cum_ret, avg_daily_ret, std_daily_ret, sharpe_ratio = assess_portfolio(
portvals)
except AttributeError:
statsMethodExists = False
if statsMethodExists:
return statsMethodExists, cum_ret, avg_daily_ret, std_daily_ret, sharpe_ratio
# Otherwise try this function
try:
cum_ret, avg_daily_ret, std_daily_ret, sharpe_ratio = marketsim_editedv4.assess_my_portfolio(
portvals)
except AttributeError:
statsMethodExists = False
if statsMethodExists:
return statsMethodExists, cum_ret, avg_daily_ret, std_daily_ret, sharpe_ratio
return False, 0., 0., 0., 0.