""" See http://qoppac.blogspot.co.uk/2015/11/using-random-data.html for more examples """ from common import arbitrary_timeseries from commonrandom import generate_trendy_price from matplotlib.pyplot import show ans = arbitrary_timeseries(generate_trendy_price(Nlength=180, Tlength=30, Xamplitude=10.0, Volscale=0.0)) print ans print len(ans) ans.plot() show() ans = arbitrary_timeseries(generate_trendy_price(Nlength=180, Tlength=30, Xamplitude=10.0, Volscale=0.1)) ans.plot() show() ans = arbitrary_timeseries(generate_trendy_price(Nlength=180, Tlength=30, Xamplitude=10.0, Volscale=0.5)) ans.plot() show()
import logging
"""
See http://qoppac.blogspot.co.uk/2015/11/using-random-data.html for more examples
"""
from common import arbitrary_timeseries
from commonrandom import generate_trendy_price
from matplotlib.pyplot import show
ans = arbitrary_timeseries(
generate_trendy_price(Nlength=180,
Tlength=30,
Xamplitude=10.0,
Volscale=0.0))
print ans
print len(ans)
ans.plot()
show()
ans = arbitrary_timeseries(
generate_trendy_price(Nlength=180,
Tlength=30,
Xamplitude=10.0,
Volscale=0.1))
ans.plot()
show()
ans = arbitrary_timeseries(
generate_trendy_price(Nlength=180,
Tlength=30,
Xamplitude=10.0,
want_skew=0.0
want_Sharpes=[0.5, 1.0]
length_period_years=20
# calculations
length_period=length_period_years*DAYS_IN_YEAR
want_ann_stdev=0.2
want_ann_mean=[ann_SR*want_ann_stdev for ann_SR in want_Sharpes]
want_mean_list=[this_ann_mean/DAYS_IN_YEAR for this_ann_mean in want_ann_mean]
want_stdev=want_ann_stdev/ROOT_DAYS_IN_YEAR
happywithcurve=False
while not happywithcurve:
## generate a new Curve
equitycurves=[arbitrary_timeseries(autocorr_skewed_returns(rho, want_mean, want_stdev,
want_skew, size=length_period), index_start=pd.datetime(1995,1,1))
for want_mean in want_mean_list]
equitycurves_pd=pd.concat(equitycurves, axis=1)
equitycurves_pd.columns=["A", "B"]
equitycurves_pd.cumsum().plot()
show()
ans=raw_input("Happy with this? Y / N? ")
if ans=="Y":
happywithcurve=True
sliced_data=slices_for_ts(equitycurves[0])
start_point=sliced_data[0]
for idx in range(len(sliced_data))[1:]:
sliced_curves=[eq_curve[start_point:sliced_data[idx]] for eq_curve in equitycurves]
equitycurves_pd=pd.concat(sliced_curves, axis=1)
want_stdev)
avg_annual_returns_this_scenario = []
avg_drawdowns_this_scenario = []
max_drawdowns_this_scenario = []
avg_avg_ratio_this_scenario = []
avg_max_ratio_this_scenario = []
## Window '1000' is no ovelay at all
for N_length in N_windows:
print "N_length: %d" % N_length
## Generate a lot of random data
many_curves_raw = [
arbitrary_timeseries(
autocorr_skewed_returns(want_rho,
adj_want_mean,
adj_want_stdev,
adj_want_skew,
size=curve_length))
for notused in range(monte_runs)
]
## Apply the overlay
many_curves = [
account_curve(apply_overlay(x, N_length, period_stdev, costs_SR))
for x in many_curves_raw
]
## Calc statistics
annual_returns_these_curves = [
np.mean(x) * period_adj for x in many_curves
]
want_skew=0.0
want_Sharpes=[0.5, 1.0]
length_period_years=20
# calculations
length_period=length_period_years*DAYS_IN_YEAR
want_ann_stdev=0.2
want_ann_mean=[ann_SR*want_ann_stdev for ann_SR in want_Sharpes]
want_mean_list=[this_ann_mean/DAYS_IN_YEAR for this_ann_mean in want_ann_mean]
want_stdev=want_ann_stdev/ROOT_DAYS_IN_YEAR
happywithcurve=False
while not happywithcurve:
## generate a new Curve
equitycurves=[arbitrary_timeseries(autocorr_skewed_returns(rho, want_mean, want_stdev,
want_skew, size=length_period), index_start=pd.datetime(1995,1,1))
for want_mean in want_mean_list]
equitycurves_pd=pd.concat(equitycurves, axis=1)
equitycurves_pd.columns=["A", "B"]
equitycurves_pd.cumsum().plot()
show()
ans=raw_input("Happy with this? Y / N? ")
if ans=="Y":
happywithcurve=True
sliced_data=slices_for_ts(equitycurves[0])
start_point=sliced_data[0]
for idx in range(len(sliced_data))[1:]:
sliced_curves=[eq_curve[start_point:sliced_data[idx]] for eq_curve in equitycurves]
equitycurves_pd=pd.concat(sliced_curves, axis=1)
"""
See http://qoppac.blogspot.co.uk/2015/11/using-random-data.html for more examples
"""
from common import arbitrary_timeseries
from commonrandom import skew_returns_annualised
from common import cum_perc
from matplotlib.pyplot import show
ans = arbitrary_timeseries(
skew_returns_annualised(annualSR=0.5, want_skew=0.0, size=2500))
cum_perc(ans).plot()
show()
ans = arbitrary_timeseries(
skew_returns_annualised(annualSR=0.5, want_skew=1.0, size=2500))
cum_perc(ans).plot()
show()
ans = arbitrary_timeseries(
skew_returns_annualised(annualSR=1.0, want_skew=-3.0, size=2500))
cum_perc(ans).plot()
show()
print "Scenario:"
print scenario
(notUsed, want_mean, want_stdev, want_skew, want_rho)=scenario
(adj_want_mean, adj_want_skew, adj_want_stdev)=adj_moments_for_rho(want_rho, want_mean, want_skew, want_stdev)
avg_annual_returns_this_scenario=[]
avg_drawdowns_this_scenario=[]
max_drawdowns_this_scenario=[]
avg_avg_ratio_this_scenario=[]
avg_max_ratio_this_scenario=[]
## Window '1000' is no ovelay at all
for N_length in N_windows:
print "N_length: %d" % N_length
## Generate a lot of random data
many_curves_raw=[arbitrary_timeseries(autocorr_skewed_returns(want_rho, adj_want_mean, adj_want_stdev, adj_want_skew, size=curve_length)) for notused in range(monte_runs)]
## Apply the overlay
many_curves=[account_curve(apply_overlay(x, N_length, period_stdev, costs_SR)) for x in many_curves_raw]
## Calc statistics
annual_returns_these_curves=[np.mean(x)*period_adj for x in many_curves]
avg_drawdowns_these_curves=[x.avg_drawdown() for x in many_curves]
max_drawdowns_these_curves=[x.worst_drawdown() for x in many_curves]
## Add results
avg_annual_returns_this_scenario.append(np.mean(annual_returns_these_curves))
avg_drawdowns_this_scenario.append(np.nanmean(avg_drawdowns_these_curves))
max_drawdowns_this_scenario.append(np.nanmean(max_drawdowns_these_curves))
avg_avg_ratio_this_scenario.append(np.nanmean([divxx(x) for x in zip(annual_returns_these_curves, avg_drawdowns_these_curves)]))
avg_max_ratio_this_scenario.append(np.nanmean([divxx(x) for x in zip(annual_returns_these_curves, max_drawdowns_these_curves)]))
import logging """ See http://qoppac.blogspot.co.uk/2015/11/using-random-data.html for more examples """ from common import arbitrary_timeseries from commonrandom import skew_returns_annualised from common import cum_perc from matplotlib.pyplot import show ans=arbitrary_timeseries(skew_returns_annualised(annualSR=0.5, want_skew=0.0, size=2500)) cum_perc(ans).plot() show() ans=arbitrary_timeseries(skew_returns_annualised(annualSR=0.5, want_skew=1.0, size=2500)) cum_perc(ans).plot() show() ans=arbitrary_timeseries(skew_returns_annualised(annualSR=1.0, want_skew=-3.0, size=2500)) cum_perc(ans).plot() show()
(notUsed, want_mean, want_stdev, want_skew, want_rho) = scenario
(adj_want_mean, adj_want_skew, adj_want_stdev) = adj_moments_for_rho(want_rho, want_mean, want_skew, want_stdev)
avg_annual_returns_this_scenario = []
avg_drawdowns_this_scenario = []
max_drawdowns_this_scenario = []
avg_avg_ratio_this_scenario = []
avg_max_ratio_this_scenario = []
## Window '1000' is no ovelay at all
for N_length in N_windows:
print "N_length: %d" % N_length
## Generate a lot of random data
many_curves_raw = [
arbitrary_timeseries(
autocorr_skewed_returns(want_rho, adj_want_mean, adj_want_stdev, adj_want_skew, size=curve_length)
)
for notused in range(monte_runs)
]
## Apply the overlay
many_curves = [account_curve(apply_overlay(x, N_length, period_stdev, costs_SR)) for x in many_curves_raw]
## Calc statistics
annual_returns_these_curves = [np.mean(x) * period_adj for x in many_curves]
avg_drawdowns_these_curves = [x.avg_drawdown() for x in many_curves]
max_drawdowns_these_curves = [x.worst_drawdown() for x in many_curves]
## Add results
avg_annual_returns_this_scenario.append(np.mean(annual_returns_these_curves))
avg_drawdowns_this_scenario.append(np.nanmean(avg_drawdowns_these_curves))
