def check_analysis(self, name, start_date, end_date, symbols, v_cr, v_adr, v_sddr, v_sr, v_allocs):
print name
allocs, cr, adr, sddr, sr = optimize_portfolio(sd=start_date, ed=end_date, syms=symbols, gen_plot=False)
np.testing.assert_almost_equal([v_cr, v_adr, v_sddr, v_sr], [cr, adr, sddr, sr], 5)
np.testing.assert_almost_equal(v_allocs, allocs, 5)
print allocs
print sr
def _run_test(self, params, results):
start_date = params['start_date']
end_date = params['end_date']
symbols = params['symbols']
allocations, \
cumulative_return, \
average_daily_return, \
volatility, \
sharpe_ratio = optimization.optimize_portfolio(sd=start_date, ed=end_date, syms=symbols, gen_plot=False)
self.assertAlmostEqual(np.sum(allocations), 1.0, delta=1e-8, msg="Allocations do not sum to 1.0")
self.assertTrue(np.allclose(allocations, results['allocations']),
msg="Incorrect allocations {}, expected to be {}".format(allocations, results['allocations']))
self.assertAlmostEqual(cumulative_return, results['cumulative_return'], delta=1e-5,
msg="Incorrect cumulative return {}, expected to be {}".format(
cumulative_return, results['cumulative_return']))
self.assertAlmostEqual(average_daily_return, results['average_daily_return'], delta=1e-5,
msg="Incorrect avg. daily return {}, expected to be {}".format(
average_daily_return, results['average_daily_return']))
self.assertAlmostEqual(volatility, results['volatility'], delta=1e-5,
msg="Incorrect volatility {}, expected to be {}".format(
volatility, results['volatility']))
self.assertAlmostEqual(sharpe_ratio, results['sharpe_ratio'], delta=1e-5,
msg="Incorrect sharpe ratio {}, expected to be {}".format(
sharpe_ratio, results['sharpe_ratio']))
def check_analysis(self, name, start_date, end_date, symbols, v_cr, v_adr,
v_sddr, v_sr, v_allocs):
print name
allocs, cr, adr, sddr, sr = optimize_portfolio(sd=start_date,
ed=end_date,
syms=symbols,
gen_plot=False)
np.testing.assert_almost_equal([v_cr, v_adr, v_sddr, v_sr],
[cr, adr, sddr, sr], 5)
np.testing.assert_almost_equal(v_allocs, allocs, 5)
print allocs
print sr
def test_example1(self):
start_date = dt.datetime(2010,01,01)
end_date = dt.datetime(2010,12,31)
symbols = ['GOOG', 'AAPL', 'GLD', 'XOM']
start_val = 1000000
risk_free_rate = 0.0
sample_freq = 252.0
# Assess the portfolio
allocations, cr, adr, sddr, sr = o.optimize_portfolio(sd = start_date,
ed = end_date,
syms = symbols,
gen_plot = False)
self.assertAlmostEqual(0.360090826885, cr, 3, "Cumulative Return {} is incorrect".format(cr), delta=None)
self.assertAlmostEqual(2.00401501102, sr, 3, "Sharpe Ratio {} is incorrect".format(sr), delta=None)
self.assertAlmostEqual(0.00127710312803, adr, 5, "Avg Daily Return {} is incorrect".format(adr), delta=None)
self.assertAlmostEqual(0.0101163831312, sddr, 5,
"Volatility (stdev of daily returns) {} is incorrect".format(sddr), delta=None)
def test_example4(self):
start_date = dt.datetime(2005,12,01)
end_date = dt.datetime(2006,05,31)
symbols = ['YHOO', 'HPQ', 'GLD', 'HNZ']
start_val = 1000000
risk_free_rate = 0.0
sample_freq = 252.0
# Assess the portfolio
allocations, cr, adr, sddr, sr = o.optimize_portfolio(sd = start_date,
ed = end_date,
syms = symbols,
gen_plot = False)
self.assertAlmostEqual(0.229471589743, cr, 3, "Cumulative Return {} is incorrect".format(cr), delta=None)
self.assertAlmostEqual(3.2334265871, sr, 3, "Sharpe Ratio {} is incorrect".format(sr), delta=None)
self.assertAlmostEqual(0.00171589132005, adr, 5, "Avg Daily Return {} is incorrect".format(adr), delta=None)
self.assertAlmostEqual(0.00842416845541, sddr, 5,
"Volatility (stdev of daily returns) {} is incorrect".format(sddr), delta=None)
def test_example3(self):
start_date = dt.datetime(2004,12,01)
end_date = dt.datetime(2006,05,31)
symbols = ['YHOO', 'XOM', 'GLD', 'HNZ']
start_val = 1000000
risk_free_rate = 0.0
sample_freq = 252.0
# Assess the portfolio
allocations, cr, adr, sddr, sr = o.optimize_portfolio(sd = start_date,
ed = end_date,
syms = symbols,
gen_plot = False)
self.assertAlmostEqual(0.315973959221, cr, 3, "Cumulative Return {} is incorrect".format(cr), delta=None)
self.assertAlmostEqual(1.5178365773, sr, 3, "Sharpe Ratio {} is incorrect".format(sr), delta=None)
self.assertAlmostEqual(0.000762170576913, adr, 5, "Avg Daily Return {} is incorrect".format(adr), delta=None)
self.assertAlmostEqual(0.00797126844855, sddr, 5,
"Volatility (stdev of daily returns) {} is incorrect".format(sddr), delta=None)
def test_example2(self):
start_date = dt.datetime(2004,01,01)
end_date = dt.datetime(2006,01,01)
symbols = ['AXP', 'HPQ', 'IBM', 'HNZ']
start_val = 1000000
risk_free_rate = 0.0
sample_freq = 252.0
# Assess the portfolio
allocations, cr, adr, sddr, sr = o.optimize_portfolio(sd = start_date,
ed = end_date,
syms = symbols,
gen_plot = False)
self.assertAlmostEqual(0.255021425162, cr, 3, "Cumulative Return {} is incorrect".format(cr), delta=None)
self.assertAlmostEqual(0.842697383626, sr, 3, "Sharpe Ratio {} is incorrect".format(sr), delta=None)
self.assertAlmostEqual(0.000494944887734, adr, 5, "Avg Daily Return {} is incorrect".format(adr), delta=None)
self.assertAlmostEqual(0.0093236393828, sddr, 5,
"Volatility (stdev of daily returns) {} is incorrect".format(sddr), delta=None)
def test_example3(self):
start_date = dt.datetime(2004, 12, 01)
end_date = dt.datetime(2006, 05, 31)
symbols = ['YHOO', 'XOM', 'GLD', 'HNZ']
start_val = 1000000
risk_free_rate = 0.0
sample_freq = 252.0
# Assess the portfolio
allocations, cr, adr, sddr, sr = o.optimize_portfolio(sd=start_date,
ed=end_date,
syms=symbols,
gen_plot=False)
self.assertAlmostEqual(0.315973959221,
cr,
3,
"Cumulative Return {} is incorrect".format(cr),
delta=None)
self.assertAlmostEqual(1.5178365773,
sr,
3,
"Sharpe Ratio {} is incorrect".format(sr),
delta=None)
self.assertAlmostEqual(0.000762170576913,
adr,
5,
"Avg Daily Return {} is incorrect".format(adr),
delta=None)
self.assertAlmostEqual(
0.00797126844855,
sddr,
5,
"Volatility (stdev of daily returns) {} is incorrect".format(sddr),
delta=None)
def test_example1(self):
start_date = dt.datetime(2010, 01, 01)
end_date = dt.datetime(2010, 12, 31)
symbols = ['GOOG', 'AAPL', 'GLD', 'XOM']
start_val = 1000000
risk_free_rate = 0.0
sample_freq = 252.0
# Assess the portfolio
allocations, cr, adr, sddr, sr = o.optimize_portfolio(sd=start_date,
ed=end_date,
syms=symbols,
gen_plot=False)
self.assertAlmostEqual(0.360090826885,
cr,
3,
"Cumulative Return {} is incorrect".format(cr),
delta=None)
self.assertAlmostEqual(2.00401501102,
sr,
3,
"Sharpe Ratio {} is incorrect".format(sr),
delta=None)
self.assertAlmostEqual(0.00127710312803,
adr,
5,
"Avg Daily Return {} is incorrect".format(adr),
delta=None)
self.assertAlmostEqual(
0.0101163831312,
sddr,
5,
"Volatility (stdev of daily returns) {} is incorrect".format(sddr),
delta=None)
def test_example2(self):
start_date = dt.datetime(2004, 01, 01)
end_date = dt.datetime(2006, 01, 01)
symbols = ['AXP', 'HPQ', 'IBM', 'HNZ']
start_val = 1000000
risk_free_rate = 0.0
sample_freq = 252.0
# Assess the portfolio
allocations, cr, adr, sddr, sr = o.optimize_portfolio(sd=start_date,
ed=end_date,
syms=symbols,
gen_plot=False)
self.assertAlmostEqual(0.255021425162,
cr,
3,
"Cumulative Return {} is incorrect".format(cr),
delta=None)
self.assertAlmostEqual(0.842697383626,
sr,
3,
"Sharpe Ratio {} is incorrect".format(sr),
delta=None)
self.assertAlmostEqual(0.000494944887734,
adr,
5,
"Avg Daily Return {} is incorrect".format(adr),
delta=None)
self.assertAlmostEqual(
0.0093236393828,
sddr,
5,
"Volatility (stdev of daily returns) {} is incorrect".format(sddr),
delta=None)
def test_example4(self):
start_date = dt.datetime(2005, 12, 01)
end_date = dt.datetime(2006, 05, 31)
symbols = ['YHOO', 'HPQ', 'GLD', 'HNZ']
start_val = 1000000
risk_free_rate = 0.0
sample_freq = 252.0
# Assess the portfolio
allocations, cr, adr, sddr, sr = o.optimize_portfolio(sd=start_date,
ed=end_date,
syms=symbols,
gen_plot=False)
self.assertAlmostEqual(0.229471589743,
cr,
3,
"Cumulative Return {} is incorrect".format(cr),
delta=None)
self.assertAlmostEqual(3.2334265871,
sr,
3,
"Sharpe Ratio {} is incorrect".format(sr),
delta=None)
self.assertAlmostEqual(0.00171589132005,
adr,
5,
"Avg Daily Return {} is incorrect".format(adr),
delta=None)
self.assertAlmostEqual(
0.00842416845541,
sddr,
5,
"Volatility (stdev of daily returns) {} is incorrect".format(sddr),
delta=None)
sma_bb_ratio = np.array(
OSEBX.get_sma_bb_ratios(promising_stocks)
) #np.concatenate((OSEBX.get_sma_bb_ratios(requested_stocks), np.zeros((1,6))[0]))#
# Get historic stock covariances
cov_matrix = OSEBX.get_stock_covariances(returns)
# filter = OSEBX.filter(requested_stocks, [stock for stock, prob in promising_stocks])
# Generate optimization problem
P, q, G, h, A, b, means, stds = opt.generate_ProblemMatrices(
returns, cov_matrix, sma_bb_ratio, rho, phi, kappa)
# Solve optimization problem
optimized_portfolio = opt.optimize_portfolio(OSEBX, promising_stocks,
P, q, G, h, A, b, means,
stds, sma_bb_ratio, mu)
stock_algorithms.trade(OSEBX, my_portfolio, optimized_portfolio,
'historic', run_no)
#
OSEBX.fetch_benchmark(run_no)
#
if run_no == 10 or run_no == 20 or run_no == 30 or run_no == 40:
plot(my_portfolio.earnings, OSEBX.benchmark, run_no)
#
#
run_no += 1
#
OSEBX.fetch_stock_data(requested_stocks, run_no, 'historic', N)
#
def _run_test(self, params, results):
# the margins below are taken from grade_optimization.py
abs_margins = dict(
sum_to_one=0.02,
alloc_range=0.02,
alloc_match=0.1,
sddr_match=0.05) # absolute margin of error for each component
start_date = params['start_date']
end_date = params['end_date']
symbols = params['symbols']
start_time = time()
allocations, \
cumulative_return, \
average_daily_return, \
volatility, \
sharpe_ratio = optimization.optimize_portfolio(sd=start_date, ed=end_date, syms=symbols, gen_plot=False)
end_time = time()
function_runtime = end_time - start_time
# check runtime < 5 seconds
# =======================================
self.assertLessEqual(
function_runtime,
5,
msg="Runtime violation, expected < 5.0s, runtime was {:1.3f} s".
format(end_time - start_time))
# check volatility
# =======================================
self.assertLessEqual(
(volatility / results['volatility']) - 1,
abs_margins['sddr_match'],
msg="Sddr too large: {:1.6f} (expected < {:1.6f} + {:1.6f})".
format(volatility, results['volatility'],
results['volatility'] * abs_margins['sddr_match']))
# check that sddr is not significantly less than expected, this would be the case if the student answers are
# wrong or you are calculating sddr incorrectly. OR you found a global minimum below what other students have
# =======================================
# Note I was able to slightly beat the grade_optimization.py volatility slightly using a nifty trick so I
# created results "min_student_volatility" as well to capture this. The testcase below will error if you beat
# either the latest students volatility, if provided, or the benchmark volatility if there it is not a testcase
# from the grade_optimization.py
test_volatility = results['volatility']
if 'min_student_volatility' in results:
test_volatility = results['min_student_volatility']
self.assertGreaterEqual(
volatility +
1e-8, # add small delta to account for floating point errors
test_volatility,
msg=
"WARNING: your Volatility {} is less than the expected minimum {}, Verify your code and notify your classmates on Reddit"
.format(volatility, test_volatility))
# check allocation sum is ~1.0
# =======================================
self.assertLessEqual(
abs(sum(allocations) - 1),
abs_margins['sum_to_one'],
msg="sum of allocations: {:1.5f} (expected: 1.0)".format(
sum(allocations)))
# check allocations are within tolerance
# =======================================
# This loops through each symbol and errors if the allocation is out range
for symbol, alloc in zip(symbols, allocations):
self.assertLessEqual(
alloc,
1 + abs_margins['alloc_range'],
msg="{} - allocation out of range: {:1.5f} (expected [0.0, 1.0)"
.format(symbol, alloc))
self.assertGreaterEqual(
alloc,
-1 * abs_margins['alloc_range'],
msg="{} - allocation out of range: {:1.5f} (expected [0.0, 1.0)"
.format(symbol, alloc))
def t_generator(*args, **kwargs):
def t(self):
self.assertAlmostEqual(*args, **kwargs)
return t
def range_generator(value):
def t(self):
self.assertGreaterEqual(value, -0.02)
self.assertLessEqual(value, 1.02)
return t
for name, test in tests.iteritems():
allocations, cr, adr, sddr, sr = opt.optimize_portfolio(
sd = test['args']['start_date'],
ed = test['args']['end_date'],
syms = test['args']['symbols'],
gen_plot = False,
)
t = t_generator(cr, test['results']['cumulative_return'], places=4)
setattr(TestMCP2, "test_{0}_cumulative_return".format(name), t)
t = t_generator(adr, test['results']['adr'], places=4)
setattr(TestMCP2, "test_{0}_adr".format(name), t)
t = t_generator(sddr, test['results']['volatility'], places=4)
setattr(TestMCP2, "test_{0}_volatility".format(name), t)
t = t_generator(sr, test['results']['sharpe'], places=4)
setattr(TestMCP2, "test_{0}_sharpe_ratio".format(name), t)
def portfolio_page():
if request.method=='GET':
return render_template('portfolio.html')
else:
app_quantfy.vars={} # This is a dictionary
# Define the variables. This is a local variable, but in Flask it will be passed to the plot route I guess
app_quantfy.vars['sym'] = request.form['sym'].upper().strip(';').split(';') # 'sym' should be defined in html file as name
if (app_quantfy.vars['sym'][0]=='') : # sym is a list delimited by ;
return render_template('portfolio.html',error_sym='<font size="3" color="red" > Provide at least one ticker symbol </font>')
if len(request.form['start_date'])!=0: # Here start and end date are keys are coming even if they are empty
try:
app_quantfy.vars['start_date']=dt.datetime.strptime(request.form['start_date'],'%m/%d/%Y')
except ValueError:
return render_template('portfolio.html',error_start_date='<font size="3" color="red" > Wrong date format </font>')
else:
# Take 1 years ago of the current date
app_quantfy.vars['start_date']=dt.datetime.today()-dt.timedelta(days=365) # This does not give the accurate 5 years
if len(request.form['end_date'])!=0:
try:
app_quantfy.vars['end_date']=dt.datetime.strptime(request.form['end_date'],'%m/%d/%Y')
except ValueError:
return render_template('portfolio.html',error_end_date='<font size="3" color="red" > Wrong date format </font>')
else:
# Take today as the default date
app_quantfy.vars['end_date']=dt.datetime.today()
#print app_quantfy.vars
if 'bench_sym' in request.form:
app_quantfy.vars['bench_sym']=request.form['bench_sym']
else:
app_quantfy.vars['bench_sym']='SPY'
symbols=list(app_quantfy.vars['sym']); # Create a new list as we are doing insert operation next
symbols.insert(0,app_quantfy.vars['bench_sym']); # Insert the default symbol in the symbols list
# Here just get the data for the 'Adj. Close'
full_data=[(sym, apicall_data.get_data_from_quandl(symbol=sym, features=['Adj. Close'], start_dt=app_quantfy.vars['start_date'],end_dt=app_quantfy.vars['end_date'])
) for sym in symbols]
# Convert this to required format
df_all_sym=util.get_data(full_data)
app_quantfy.vars['guess_alloc']=request.form['guess_alloc'].strip(';').split(';')
app_quantfy.vars['start_value']=float(request.form['start_value']); # It has a default value
if len(app_quantfy.vars['guess_alloc']) !=0 and (app_quantfy.vars['guess_alloc'][0]!='') : # app_quantfy.vars['guess_alloc'] is a list because of the strip function
# print app_quantfy.vars['guess_alloc']
# print len(app_quantfy.vars['guess_alloc'])
app_quantfy.vars['guess_alloc']=[float(i) for i in app_quantfy.vars['guess_alloc']]
try:
assert len(app_quantfy.vars['guess_alloc'])==len(app_quantfy.vars['sym'])
except AssertionError:
return render_template('portfolio.html',error_alloc='<font size="3" color="red" > Number of allocations should be same as symbols </font>')
# Sum should be equal to one
print app_quantfy.vars['guess_alloc']
try:
assert abs(sum(app_quantfy.vars['guess_alloc'])-1.0)<=1e-5 # Sometimes the rounding does not work correctly
except AssertionError:
return render_template('portfolio.html',error_alloc='<font size="3" color="red" > Sum should be 1 </font>')
else:
# Generate random numbers
allocs=np.random.random(len(app_quantfy.vars['sym']))
allocs /=allocs.sum()
app_quantfy.vars['guess_alloc']=allocs
#print allocs
cr,adr,sddr,sr,ev,normalized_plot_df=optimization.access_portfolio(df_all_sym, app_quantfy.vars['bench_sym'],
app_quantfy.vars['guess_alloc'],
sv=app_quantfy.vars['start_value'])
#print cr,adr,sddr,sr,ev
param_not_opt=pd.DataFrame([cr,adr,sddr,sr,ev],index=['Cumulative Return','Average Daily Return','Stand. Deviation Daily return',
'Sharpe Ratio','End value'], columns=['Unoptimized'])
script_not_opt_table,div_not_opt_table=convert_pd_bokeh_html(param_not_opt)
# print normalized_plot_df.head()
hover=HoverTool(
tooltips=[
("Portfolio",'$y')
]
)
TOOLS='pan,wheel_zoom,box_zoom,reset,save,box_select,crosshair'
not_opt_p = figure(width=900, height=500, x_axis_type="datetime",tools=[TOOLS,hover])
colors=['blue','red','green','#cc3300']
for (i,ftr) in enumerate(normalized_plot_df):
not_opt_p.line(normalized_plot_df.index,normalized_plot_df[ftr],legend=ftr,color=colors[i],line_width=2)
#not_opt_p.line(normalized_plot_df)
not_opt_p.title.text = "Un-optimized portfolio"
not_opt_p.legend.location = "top_left"
not_opt_p.xaxis.axis_label = 'Date'
not_opt_p.yaxis.axis_label = 'Relative portfolio value'
tab_not_opt=Panel(child=not_opt_p,title='Un-optimized portfolio')
# script_not_opt, div_not_opt=components(not_opt_p)
# print script_not_opt,div_not_opt
# Now run optimized
cr,adr,sddr,sr,ev,normalized_plot_df,optimal_alloc=optimization.optimize_portfolio(df_all_sym,app_quantfy.vars['bench_sym'],
app_quantfy.vars['start_value'])
# print cr,adr,sddr,sr,ev,optimal_alloc
# print normalized_plot_df.head()
hover=HoverTool(
tooltips=[
("Portfolio",'$y')
]
)
opt_p = figure(width=900, height=500, x_axis_type="datetime",tools=[TOOLS,hover])
for (i,ftr) in enumerate(normalized_plot_df):
opt_p.line(normalized_plot_df.index,normalized_plot_df[ftr],legend=ftr,color=colors[i],line_width=2)
# print normalized_plot_df
opt_p.title.text = "Optimized portfolio value"
opt_p.legend.location = "top_left"
opt_p.xaxis.axis_label = 'Date'
opt_p.yaxis.axis_label = 'Relative portfolio value'
tab_opt=Panel(child=opt_p,title='Optimized portfolio')
tabs=Tabs(tabs=[tab_not_opt,tab_opt])
script_opt, div_opt=components(tabs)
param_opt=pd.DataFrame([cr,adr,sddr,sr,ev],index=['Cummulative Return','Additive Daily Return','Stand. Deviation Daily return',
'Sharpe Ratio','End value'], columns=['Optimized'])
all_params=param_not_opt.join(param_opt)
script_opt_table,div_opt_table=convert_pd_bokeh_html(all_params)
alloc_df=pd.DataFrame([app_quantfy.vars['guess_alloc'],list(optimal_alloc)],index=['Random/Guess allocations','Optimized allocations'],columns=app_quantfy.vars['sym'])
#str_opt_alloc='Optimal allocations: '+', '.join([str(i) for i in optimal_alloc])
script_alloc_df,div_alloc_df=convert_pd_bokeh_html(alloc_df)
# script_not_opt_table=script_not_opt_table,div_not_opt_table=div_not_opt_table,
return render_template('portfolio.html',script_opt_table=script_opt_table, div_opt_table=div_opt_table,
script_alloc_df=script_alloc_df,div_alloc_df=div_alloc_df,
script_opt=script_opt,plot_opt=div_opt
)
return t
def range_generator(value):
def t(self):
self.assertGreaterEqual(value, -0.02)
self.assertLessEqual(value, 1.02)
return t
for name, test in tests.iteritems():
allocations, cr, adr, sddr, sr = opt.optimize_portfolio(
sd=test['args']['start_date'],
ed=test['args']['end_date'],
syms=test['args']['symbols'],
gen_plot=False,
)
t = t_generator(cr, test['results']['cumulative_return'], places=4)
setattr(TestMCP2, "test_{0}_cumulative_return".format(name), t)
t = t_generator(adr, test['results']['adr'], places=4)
setattr(TestMCP2, "test_{0}_adr".format(name), t)
t = t_generator(sddr, test['results']['volatility'], places=4)
setattr(TestMCP2, "test_{0}_volatility".format(name), t)
t = t_generator(sr, test['results']['sharpe'], places=4)
setattr(TestMCP2, "test_{0}_sharpe_ratio".format(name), t)
