def plottingMonthlyRetDist(returns, ax, title="Distribution of Monthly Returns"):
x_axis_formatter = FuncFormatter(zero_dec_percentage)
ax.xaxis.set_major_formatter(FuncFormatter(x_axis_formatter))
ax.tick_params(axis='x', which='major', labelsize=10)
monthlyRetTable = aggregateReturns(returns, 'monthly')
monthlyRetTable = np.exp(monthlyRetTable) - 1.
if len(monthlyRetTable) > 1:
ax.hist(
monthlyRetTable,
color='orange',
alpha=0.8,
bins=20
)
ax.axvline(
monthlyRetTable.mean(),
color='steelblue',
linestyle='--',
lw=4,
alpha=1.0
)
ax.axvline(0.0, color='black', linestyle='-', lw=3, alpha=0.75)
ax.legend(['mean'], loc='best')
ax.set_ylabel('Number of months')
ax.set_xlabel('Returns')
ax.set_title(title)
return ax
def plottingAnnualReturns(returns, ax, title='Annual Returns'):
x_axis_formatter = FuncFormatter(zero_dec_percentage)
ax.xaxis.set_major_formatter(FuncFormatter(x_axis_formatter))
ax.tick_params(axis='x', which='major', labelsize=10)
annulaReturns = pd.DataFrame(aggregateReturns(returns, 'yearly'))
annulaReturns = np.exp(annulaReturns) - 1.
ax.axvline(annulaReturns.values.mean(),
color='steelblue',
linestyle='--',
lw=4,
alpha=0.7)
annulaReturns.sort_index(ascending=False).plot(
ax=ax,
kind='barh',
alpha=0.7
)
ax.axvline(0.0, color='black', linestyle='-', lw=3)
ax.set_ylabel('Year')
ax.set_xlabel('Returns')
ax.set_title(title)
ax.legend(['mean'], loc='best')
return ax
def plottingMonthlyRetDist(returns,
ax,
title="Distribution of Monthly Returns"):
x_axis_formatter = FuncFormatter(zero_dec_percentage)
ax.xaxis.set_major_formatter(FuncFormatter(x_axis_formatter))
ax.tick_params(axis='x', which='major', labelsize=10)
monthlyRetTable = aggregateReturns(returns, 'monthly')
monthlyRetTable = np.exp(monthlyRetTable) - 1.
if len(monthlyRetTable) > 1:
ax.hist(
monthlyRetTable,
color='orange',
alpha=0.8,
bins=20
)
ax.axvline(
monthlyRetTable.mean(),
color='steelblue',
linestyle='--',
lw=4,
alpha=1.0
)
ax.axvline(0.0, color='black', linestyle='-', lw=3, alpha=0.75)
ax.legend(['mean'], loc='best')
ax.set_ylabel('Number of months')
ax.set_xlabel('Returns')
ax.set_title(title)
return ax
def plottingAnnualReturns(returns, ax, title='Annual Returns'):
x_axis_formatter = FuncFormatter(zero_dec_percentage)
ax.xaxis.set_major_formatter(FuncFormatter(x_axis_formatter))
ax.tick_params(axis='x', which='major', labelsize=10)
annulaReturns = pd.DataFrame(aggregateReturns(returns, 'yearly'))
annulaReturns = np.exp(annulaReturns) - 1.
ax.axvline(annulaReturns.values.mean(),
color='steelblue',
linestyle='--',
lw=4,
alpha=0.7)
annulaReturns.sort_index(ascending=False).plot(
ax=ax,
kind='barh',
alpha=0.7
)
ax.axvline(0.0, color='black', linestyle='-', lw=3)
ax.set_ylabel('Year')
ax.set_xlabel('Returns')
ax.set_title(title)
ax.legend(['mean'], loc='best')
return ax
def plottingMonthlyReturnsHeapmap(returns, ax, title='Monthly Returns (%)'):
x_axis_formatter = FuncFormatter(integer_format)
ax.xaxis.set_major_formatter(FuncFormatter(x_axis_formatter))
monthlyRetTable = pd.DataFrame(aggregateReturns(returns, 'monthly'))
monthlyRetTable = monthlyRetTable.unstack()
monthlyRetTable.columns = monthlyRetTable.columns.droplevel()
sns.heatmap((np.exp(monthlyRetTable.fillna(0)) - 1.0) * 100.0,
annot=True,
fmt=".1f",
annot_kws={"size": 9},
alpha=1.0,
center=0.0,
cbar=False,
cmap=matplotlib.cm.RdYlGn_r,
ax=ax)
ax.set_ylabel('Year')
ax.set_xlabel('Month')
ax.set_title(title)
return ax
def plottingMonthlyReturnsHeapmap(returns, ax, title='Monthly Returns (%)'):
x_axis_formatter = FuncFormatter(integer_format)
ax.xaxis.set_major_formatter(FuncFormatter(x_axis_formatter))
monthlyRetTable = pd.DataFrame(aggregateReturns(returns, 'monthly'))
monthlyRetTable = monthlyRetTable.unstack()
monthlyRetTable.columns = monthlyRetTable.columns.droplevel()
sns.heatmap((np.exp(monthlyRetTable.fillna(0)) - 1.0) * 100.0,
annot=True,
fmt=".1f",
annot_kws={"size": 9},
alpha=1.0,
center=0.0,
cbar=False,
cmap=matplotlib.cm.RdYlGn_r,
ax=ax)
ax.set_ylabel('Year')
ax.set_xlabel('Month')
ax.set_title(title)
return ax
def createPerformanceTearSheet(prices=None, returns=None, benchmark=None, benchmarkReturns=None, plot=True):
if prices is not None and not isinstance(prices, pd.Series):
raise TypeError("prices series should be a pandas time series.")
elif returns is not None and prices is not None:
raise ValueError("prices series and returns series can't be both set.")
if benchmark is not None and not (isinstance(benchmark, pd.Series) or isinstance(benchmark, str)):
raise TypeError("benchmark series should be a pandas time series or a string ticker.")
if returns is None:
returns = np.log(prices / prices.shift(1))
returns.fillna(0, inplace=True)
returns = returns[~np.isinf(returns)]
if benchmark is not None and isinstance(benchmark, str) and benchmarkReturns is None:
startDate = advanceDateByCalendar("China.SSE", prices.index[0], '-1b', BizDayConventions.Preceding)
benchmarkPrices = get_benchmark_data(benchmark,
start_date=startDate.strftime('%Y-%m-%d'),
end_data=returns.index[-1].strftime("%Y-%m-%d"))
# do the linear interpolation on the target time line
date_index = prices.index
new_index = benchmarkPrices.index.union(date_index)
benchmarkPrices = benchmarkPrices.reindex(new_index)
benchmarkPrices = benchmarkPrices.interpolate().ix[date_index].dropna()
benchmarkReturns = np.log(benchmarkPrices['closePrice'] / benchmarkPrices['closePrice'].shift(1))
benchmarkReturns.name = benchmark
benchmarkReturns.fillna(0, inplace=True)
benchmarkReturns.index = pd.to_datetime(benchmarkReturns.index.date)
elif benchmark is not None and isinstance(benchmark, pd.Series):
benchmarkReturns = np.log(benchmark / benchmark.shift(1))
try:
benchmarkReturns.name = benchmark.name
except AttributeError:
benchmarkReturns.name = "benchmark"
benchmarkReturns.dropna(inplace=True)
benchmarkReturns.index = pd.to_datetime(benchmarkReturns.index.date)
aggregateDaily = aggregateReturns(returns)
drawDownDaily = drawDown(aggregateDaily)
# perf metric
annualRet = annualReturn(aggregateDaily)
annualVol = annualVolatility(aggregateDaily)
sortino = sortinoRatio(aggregateDaily)
sharp = sharpRatio(aggregateDaily)
maxDrawDown = np.min(drawDownDaily['draw_down'])
winningDays = np.sum(aggregateDaily > 0.)
lossingDays = np.sum(aggregateDaily < 0.)
perf_metric = pd.DataFrame([annualRet, annualVol, sortino, sharp, maxDrawDown, winningDays, lossingDays],
index=['annual_return',
'annual_volatiltiy',
'sortino_ratio',
'sharp_ratio',
'max_draw_down',
'winning_days',
'lossing_days'],
columns=['metrics'])
perf_df = pd.DataFrame(index=aggregateDaily.index)
perf_df['daily_return'] = aggregateDaily
perf_df['daily_cum_return'] = np.exp(aggregateDaily.cumsum()) - 1.0
perf_df['daily_draw_down'] = drawDownDaily['draw_down']
if benchmarkReturns is not None:
perf_df['benchmark_return'] = benchmarkReturns
perf_df['benchmark_cum_return'] = benchmarkReturns.cumsum()
perf_df.dropna(inplace=True)
perf_df['benchmark_cum_return'] = np.exp(perf_df['benchmark_cum_return']
- perf_df['benchmark_cum_return'][0]) - 1.0
perf_df['access_return'] = aggregateDaily - benchmarkReturns
perf_df['access_cum_return'] = (1.0 + perf_df['daily_cum_return']) \
/ (1.0 + perf_df['benchmark_cum_return']) - 1.0
perf_df.fillna(0.0, inplace=True)
accessDrawDownDaily = drawDown(perf_df['access_return'])
else:
accessDrawDownDaily = None
if 'benchmark_cum_return' in perf_df:
benchmarkCumReturns = perf_df['benchmark_cum_return']
benchmarkCumReturns.name = benchmarkReturns.name
accessCumReturns = perf_df['access_cum_return']
accessReturns = perf_df['access_return']
index = perf_df.index
length1 = len(bizDatesList('China.SSE', index[0], index[-1]))
length2 = len(perf_df)
factor = length1 / float(length2)
rb = RollingBeta(perf_df['daily_return'], perf_df['benchmark_return'], [1, 3, 6], factor=factor)
rs = RollingSharp(perf_df['daily_return'], [1, 3, 6], factor=factor)
else:
benchmarkCumReturns = None
accessReturns = None
accessCumReturns = None
if len(perf_df['daily_return']) > APPROX_BDAYS_PER_MONTH and benchmarkCumReturns is not None:
rollingRisk = pd.concat([pd.concat(rs, axis=1), pd.concat(rb, axis=1)], axis=1)
else:
rollingRisk = None
if plot:
verticalSections = 2
plt.figure(figsize=(16, 7 * verticalSections))
gs = gridspec.GridSpec(verticalSections, 3, wspace=0.5, hspace=0.5)
axRollingReturns = plt.subplot(gs[0, :])
axDrawDown = plt.subplot(gs[1, :], sharex=axRollingReturns)
plottingRollingReturn(perf_df['daily_cum_return'], benchmarkCumReturns, axRollingReturns)
plottingDrawdownPeriods(perf_df['daily_cum_return'], drawDownDaily, 5, axDrawDown)
if rollingRisk is not None:
plt.figure(figsize=(16, 7 * verticalSections))
gs = gridspec.GridSpec(verticalSections, 3, wspace=0.5, hspace=0.5)
axRollingBeta = plt.subplot(gs[0, :])
axRollingSharp = plt.subplot(gs[1, :])
bmName = benchmarkReturns.name
plottingRollingBeta(rb, bmName, ax=axRollingBeta)
plottingRollingSharp(rs, ax=axRollingSharp)
plt.figure(figsize=(16, 7 * verticalSections))
gs = gridspec.GridSpec(verticalSections, 3, wspace=0.5, hspace=0.5)
axUnderwater = plt.subplot(gs[0, :])
axMonthlyHeatmap = plt.subplot(gs[1, 0])
axAnnualReturns = plt.subplot(gs[1, 1])
axMonthlyDist = plt.subplot(gs[1, 2])
plottingUnderwater(drawDownDaily['draw_down'], axUnderwater)
plottingMonthlyReturnsHeapmap(returns, axMonthlyHeatmap)
plottingAnnualReturns(returns, axAnnualReturns)
plottingMonthlyRetDist(returns, axMonthlyDist)
if accessReturns is not None and plot:
plt.figure(figsize=(16, 7 * verticalSections))
gs = gridspec.GridSpec(verticalSections, 3, wspace=0.5, hspace=0.5)
axRollingAccessReturns = plt.subplot(gs[0, :])
axAccessDrawDown = plt.subplot(gs[1, :], sharex=axRollingAccessReturns)
plottingRollingReturn(accessCumReturns, None, axRollingAccessReturns, title='Access Cumulative Returns w.r.t. ' + benchmarkReturns.name)
plottingDrawdownPeriods(accessCumReturns, accessDrawDownDaily, 5, axAccessDrawDown, title=('Top 5 Drawdown periods w.r.t. ' + benchmarkReturns.name))
plt.figure(figsize=(16, 7 * verticalSections))
gs = gridspec.GridSpec(verticalSections, 3, wspace=0.5, hspace=0.5)
axAccessUnderwater = plt.subplot(gs[0, :])
axAccessMonthlyHeatmap = plt.subplot(gs[1, 0])
axAccessAnnualReturns = plt.subplot(gs[1, 1])
axAccessMonthlyDist = plt.subplot(gs[1, 2])
plottingUnderwater(accessDrawDownDaily['draw_down'], axAccessUnderwater, title='Underwater Plot w.r.t. '
+ benchmarkReturns.name)
plottingMonthlyReturnsHeapmap(accessReturns, ax=axAccessMonthlyHeatmap, title='Monthly Access Returns (%)')
plottingAnnualReturns(accessReturns, ax=axAccessAnnualReturns, title='Annual Access Returns')
plottingMonthlyRetDist(accessReturns, ax=axAccessMonthlyDist, title='Distribution of Monthly Access Returns')
return perf_metric, perf_df, rollingRisk
def createPerformanceTearSheet(prices=None,
returns=None,
benchmark=None,
benchmarkReturns=None,
other_curves=None,
turn_over=None,
tc_cost=0.,
plot=True):
if prices is not None and not isinstance(prices, pd.Series):
raise TypeError("prices series should be a pandas time series.")
elif returns is not None and prices is not None:
raise ValueError("prices series and returns series can't be both set.")
if benchmark is not None and not (isinstance(benchmark, pd.Series) or isinstance(benchmark, str)):
raise TypeError("benchmark series should be a pandas time series or a string ticker.")
if returns is None:
returns = np.log(prices / prices.shift(1))
returns.fillna(0, inplace=True)
returns = returns[~np.isinf(returns)]
if benchmark is not None and isinstance(benchmark, str) and benchmarkReturns is None:
startDate = advanceDateByCalendar("China.SSE", prices.index[0], '-1b', BizDayConventions.Preceding)
benchmarkPrices = get_benchmark_data(benchmark,
start_date=startDate.strftime('%Y-%m-%d'),
end_data=returns.index[-1].strftime("%Y-%m-%d"))
# do the linear interpolation on the target time line
date_index = prices.index
new_index = benchmarkPrices.index.union(date_index)
benchmarkPrices = benchmarkPrices.reindex(new_index)
benchmarkPrices = benchmarkPrices.interpolate().ix[date_index].dropna()
benchmarkReturns = np.log(benchmarkPrices['closePrice'] / benchmarkPrices['closePrice'].shift(1))
benchmarkReturns.name = benchmark
benchmarkReturns.fillna(0, inplace=True)
benchmarkReturns.index = pd.to_datetime(benchmarkReturns.index.date)
elif benchmark is not None and isinstance(benchmark, pd.Series):
benchmarkReturns = np.log(benchmark / benchmark.shift(1))
try:
benchmarkReturns.name = benchmark.name
except AttributeError:
benchmarkReturns.name = "benchmark"
benchmarkReturns.dropna(inplace=True)
benchmarkReturns.index = pd.to_datetime(benchmarkReturns.index.date)
aggregateDaily, aggregateDailyAfterTC = aggregateReturns(returns, turn_over, tc_cost)
if aggregateDailyAfterTC is not None:
aggregateDailyBeforeTC = aggregateDaily
aggregateDaily = aggregateDailyAfterTC
else:
aggregateDailyBeforeTC = aggregateDaily
drawDownDaily = drawDown(aggregateDaily)
# perf metric
annualRet = annualReturn(aggregateDaily)
annualVol = annualVolatility(aggregateDaily)
sortino = sortinoRatio(aggregateDaily)
sharp = sharpRatio(aggregateDaily)
maxDrawDown = np.min(drawDownDaily['draw_down'])
winningDays = np.sum(aggregateDaily > 0.)
lossingDays = np.sum(aggregateDaily < 0.)
perf_metric = pd.DataFrame([annualRet, annualVol, sortino, sharp, maxDrawDown, winningDays, lossingDays],
index=['annual_return',
'annual_volatiltiy',
'sortino_ratio',
'sharp_ratio',
'max_draw_down',
'winning_days',
'lossing_days'],
columns=['metrics'])
perf_df = pd.DataFrame(index=aggregateDaily.index)
perf_df['daily_return'] = aggregateDaily
perf_df['daily_return (w/o tc)'] = aggregateDailyBeforeTC
perf_df['daily_cum_return'] = np.exp(aggregateDaily.cumsum()) - 1.0
perf_df['daily_cum_return (w/o tc)'] = np.exp(aggregateDailyBeforeTC.cumsum()) - 1.0
perf_df['daily_draw_down'] = drawDownDaily['draw_down']
if benchmarkReturns is not None:
perf_df['benchmark_return'] = benchmarkReturns
perf_df['benchmark_cum_return'] = benchmarkReturns.cumsum()
perf_df.fillna(0.0, inplace=True)
perf_df['benchmark_cum_return'] = np.exp(perf_df['benchmark_cum_return']
- perf_df['benchmark_cum_return'][0]) - 1.0
perf_df['access_return'] = aggregateDaily - perf_df['benchmark_return']
perf_df['access_cum_return'] = (1.0 + perf_df['daily_cum_return']) \
/ (1.0 + perf_df['benchmark_cum_return']) - 1.0
accessDrawDownDaily = drawDown(perf_df['access_return'])
else:
accessDrawDownDaily = None
if 'benchmark_cum_return' in perf_df:
benchmarkCumReturns = perf_df['benchmark_cum_return']
benchmarkCumReturns.name = benchmarkReturns.name
accessCumReturns = perf_df['access_cum_return']
accessReturns = perf_df['access_return']
index = perf_df.index
length1 = len(bizDatesList('China.SSE', index[0], index[-1]))
length2 = len(perf_df)
factor = length1 / float(length2)
rb = RollingBeta(perf_df['daily_return'], perf_df['benchmark_return'], [1, 3, 6], factor=factor)
rs = RollingSharp(perf_df['daily_return'], [1, 3, 6], factor=factor)
else:
benchmarkCumReturns = None
accessReturns = None
accessCumReturns = None
if len(perf_df['daily_return']) > APPROX_BDAYS_PER_MONTH and benchmarkCumReturns is not None:
rollingRisk = pd.concat([pd.concat(rs, axis=1), pd.concat(rb, axis=1)], axis=1)
else:
rollingRisk = None
if plot:
verticalSections = 2
plt.figure(figsize=(16, 7 * verticalSections))
gs = gridspec.GridSpec(verticalSections, 3, wspace=0.5, hspace=0.5)
axRollingReturns = plt.subplot(gs[0, :])
axDrawDown = plt.subplot(gs[1, :])
plottingRollingReturn(perf_df['daily_cum_return'],
perf_df['daily_cum_return (w/o tc)'],
benchmarkCumReturns,
other_curves, axRollingReturns)
plottingDrawdownPeriods(perf_df['daily_cum_return'], drawDownDaily, 5, axDrawDown)
if rollingRisk is not None:
plt.figure(figsize=(16, 7 * verticalSections))
gs = gridspec.GridSpec(verticalSections, 3, wspace=0.5, hspace=0.5)
axRollingBeta = plt.subplot(gs[0, :])
axRollingSharp = plt.subplot(gs[1, :])
bmName = benchmarkReturns.name
plottingRollingBeta(rb, bmName, ax=axRollingBeta)
plottingRollingSharp(rs, ax=axRollingSharp)
plt.figure(figsize=(16, 7 * verticalSections))
gs = gridspec.GridSpec(verticalSections, 3, wspace=0.5, hspace=0.5)
axUnderwater = plt.subplot(gs[0, :])
axMonthlyHeatmap = plt.subplot(gs[1, 0])
axAnnualReturns = plt.subplot(gs[1, 1])
axMonthlyDist = plt.subplot(gs[1, 2])
plottingUnderwater(drawDownDaily['draw_down'], axUnderwater)
plottingMonthlyReturnsHeapmap(aggregateDaily, axMonthlyHeatmap)
plottingAnnualReturns(aggregateDaily, axAnnualReturns)
plottingMonthlyRetDist(aggregateDaily, axMonthlyDist)
if accessReturns is not None and plot:
plt.figure(figsize=(16, 7 * verticalSections))
gs = gridspec.GridSpec(verticalSections, 3, wspace=0.5, hspace=0.5)
axRollingAccessReturns = plt.subplot(gs[0, :])
axAccessDrawDown = plt.subplot(gs[1, :], sharex=axRollingAccessReturns)
plottingRollingReturn(accessCumReturns, None, None, None, axRollingAccessReturns, title='Access Cumulative Returns w.r.t. ' + benchmarkReturns.name)
plottingDrawdownPeriods(accessCumReturns, accessDrawDownDaily, 5, axAccessDrawDown, title=('Top 5 Drawdown periods w.r.t. ' + benchmarkReturns.name))
plt.figure(figsize=(16, 7 * verticalSections))
gs = gridspec.GridSpec(verticalSections, 3, wspace=0.5, hspace=0.5)
axAccessUnderwater = plt.subplot(gs[0, :])
axAccessMonthlyHeatmap = plt.subplot(gs[1, 0])
axAccessAnnualReturns = plt.subplot(gs[1, 1])
axAccessMonthlyDist = plt.subplot(gs[1, 2])
plottingUnderwater(accessDrawDownDaily['draw_down'], axAccessUnderwater, title='Underwater Plot w.r.t. '
+ benchmarkReturns.name)
plottingMonthlyReturnsHeapmap(accessReturns, ax=axAccessMonthlyHeatmap, title='Monthly Access Returns (%)')
plottingAnnualReturns(accessReturns, ax=axAccessAnnualReturns, title='Annual Access Returns')
plottingMonthlyRetDist(accessReturns, ax=axAccessMonthlyDist, title='Distribution of Monthly Access Returns')
return perf_metric, perf_df, rollingRisk
