def _plot_txt_time(self, stats, ax=None, **kwargs):
"""
Outputs the statistics for various time frames.
"""
returns = stats['returns']
mly_ret = perf.aggregate_returns(returns, 'monthly')
yly_ret = perf.aggregate_returns(returns, 'yearly')
mly_pct = mly_ret[mly_ret >= 0].shape[0] / float(mly_ret.shape[0])
mly_avg_win_pct = np.mean(mly_ret[mly_ret >= 0])
mly_avg_loss_pct = np.mean(mly_ret[mly_ret < 0])
mly_max_win_pct = np.max(mly_ret)
mly_max_loss_pct = np.min(mly_ret)
yly_pct = yly_ret[yly_ret >= 0].shape[0] / float(yly_ret.shape[0])
yly_max_win_pct = np.max(yly_ret)
yly_max_loss_pct = np.min(yly_ret)
header = ["Performance", "Value"]
rows = [["Winning Months %", "{:.0%}".format(mly_pct)],
["Average Winning Month %", "{:.2%}".format(mly_avg_win_pct)],
["Average Losing Month %", "{:.2%}".format(mly_avg_loss_pct)],
["Best Month %", "{:.2%}".format(mly_max_win_pct)],
["Worst Month %", "{:.2%}".format(mly_max_loss_pct)],
["Winning Years %", '{:.0%}'.format(yly_pct)],
["Best Year %", '{:.2%}'.format(yly_max_win_pct)],
["Worst Year %", '{:.2%}'.format(yly_max_loss_pct)]]
table = (Table().add(header, rows).set_global_opts(
title_opts=opts.ComponentTitleOpts(title="Time")))
return table
def _plot_yearly_returns(self, stats, ax=None, **kwargs):
"""
Plots a barplot of returns by year.
"""
def format_perc(x, pos):
return '%.0f%%' % x
returns = stats['returns']
if ax is None:
ax = plt.gca()
y_axis_formatter = FuncFormatter(format_perc)
ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
ax.yaxis.grid(linestyle=':')
yly_ret = perf.aggregate_returns(returns, 'yearly') * 100.0
yly_ret.plot(ax=ax, kind="bar")
ax.set_title('Yearly Returns (%)', fontweight='bold')
ax.set_ylabel('')
ax.set_xlabel('')
ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
ax.xaxis.grid(False)
return ax
def _plot_monthly_returns(self, stats, ax=None, **kwargs):
"""
Plots a heatmap of the monthly returns.
"""
returns = stats['returns']
if ax is None:
ax = plt.gca()
monthly_ret = perf.aggregate_returns(returns, 'monthly')
monthly_ret = monthly_ret.unstack()
monthly_ret = np.round(monthly_ret, 3)
monthly_ret.rename(
columns={1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr',
5: 'May', 6: 'Jun', 7: 'Jul', 8: 'Aug',
9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'},
inplace=True
)
sns.heatmap(
monthly_ret.fillna(0) * 100.0,
annot=True,
fmt="0.1f",
annot_kws={"size": 8},
alpha=1.0,
center=0.0,
cbar=False,
cmap=cm.RdYlGn,
ax=ax, **kwargs)
ax.set_title('Monthly Returns (%)', fontweight='bold')
ax.set_ylabel('')
ax.set_yticklabels(ax.get_yticklabels(), rotation=0)
ax.set_xlabel('')
return ax
def _plot_yearly_returns(self, stats, ax=None, **kwargs):
"""
Plots a barplot of returns by year.
"""
def format_perc(x, pos):
return '%.0f%%' % x
returns = stats['returns']
if ax is None:
ax = plt.gca()
y_axis_formatter = FuncFormatter(format_perc)
ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
ax.yaxis.grid(linestyle=':')
yly_ret = perf.aggregate_returns(returns, 'yearly') * 100.0
yly_ret.plot(ax=ax, kind="bar")
ax.set_title('Yearly Returns (%)', fontweight='bold')
ax.set_ylabel('')
ax.set_xlabel('')
ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
ax.xaxis.grid(False)
return ax
def _plot_monthly_returns(self, stats, ax=None, **kwargs):
"""
Plots a heatmap of the monthly returns.
"""
returns = stats['returns']
if ax is None:
ax = plt.gca()
monthly_ret = perf.aggregate_returns(returns, 'monthly')
monthly_ret = monthly_ret.unstack()
monthly_ret = np.round(monthly_ret, 3)
monthly_ret.rename(
columns={1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr',
5: 'May', 6: 'Jun', 7: 'Jul', 8: 'Aug',
9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'},
inplace=True
)
sns.heatmap(
monthly_ret.fillna(0) * 100.0,
annot=True,
fmt="0.1f",
annot_kws={"size": 8},
alpha=1.0,
center=0.0,
cbar=False,
cmap=cm.RdYlGn,
ax=ax, **kwargs)
ax.set_title('Monthly Returns (%)', fontweight='bold')
ax.set_ylabel('')
ax.set_yticklabels(ax.get_yticklabels(), rotation=0)
ax.set_xlabel('')
return ax
def _plot_monthly_returns(self, stats):
"""
Plots the monthly returns heatmap
"""
returns = stats['returns']
monthly_ret = perf.aggregate_returns(returns, 'monthly')
monthly_ret = monthly_ret.unstack()
monthly_ret = np.round(monthly_ret, 3)
monthly_ret = monthly_ret.fillna(0) * 100
row_count = len(monthly_ret)
month = [
"Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep",
"Oct", "Nov", "Dec"
]
values = []
for i in range(12):
for j in range(row_count):
v = monthly_ret.iloc[j, i]
s = "%.3f" % v
values.append([i, j, round(v, 3), s])
c = (HeatMap().add_xaxis(month).add_yaxis("", list(
monthly_ret.index), values).set_global_opts(
title_opts=opts.TitleOpts(title="Monthly Returns (%)"),
visualmap_opts=opts.VisualMapOpts(
min_=min(values, key=lambda x: x[2])[2],
max_=max(values, key=lambda x: x[2])[2]),
))
return c
def _calculate_monthly_aggregated_returns_hc(self, returns):
"""
Calculate the monthly aggregated returns in the format
utilised by Highcharts. 0% -> 0.0, 100% -> 100.0
Parameters
----------
returns : `pd.Series`
The Series of daily returns values.
Returns
-------
`list[list]`
The list of list-based returns: [[month, year, return]]
"""
month_returns = perf.aggregate_returns(returns, 'monthly')
data = []
years = month_returns.index.levels[0].tolist()
years_range = range(0, len(years))
months_range = range(0, 12)
for month in months_range:
for year in years_range:
try:
data.append([
month, year,
100.0 * month_returns.loc[(years[year], month + 1)]
])
except KeyError: # Truncated year, so no data available
pass
return data
def _plot_monthly_returns(self, stats, ax=None, **kwargs):
"""
Plots a heatmap of the monthly returns.
"""
returns = stats['returns']
if ax is None:
ax = plt.gca()
# print(returns)
# https://www.geeksforgeeks.org/pandas-groupby/
# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html
# http://www.datasciencemadesimple.com/reshape-using-stack-unstack-function-pandas-python/
# def cumulate_returns(x):
# return np.exp(np.log(1 + x).cumsum())[-1] - 1
# rtn = returns.groupby([lambda x: x.year, lambda x: x.month]).apply(cumulate_returns)
# print(rtn.unstack())
#print(rtn.groups)
#print(rtn.first())
monthly_ret = perf.aggregate_returns(returns, 'monthly')
monthly_ret = monthly_ret.unstack()
monthly_ret = np.round(monthly_ret, 3)
monthly_ret.rename(columns={
1: 'Jan',
2: 'Feb',
3: 'Mar',
4: 'Apr',
5: 'May',
6: 'Jun',
7: 'Jul',
8: 'Aug',
9: 'Sep',
10: 'Oct',
11: 'Nov',
12: 'Dec'
},
inplace=True)
#print(monthly_ret)
sns.heatmap(monthly_ret.fillna(0) * 100.0,
annot=True,
fmt="0.1f",
annot_kws={"size": 8},
alpha=1.0,
center=0.0,
cbar=False,
cmap=cm.RdYlGn,
ax=ax,
**kwargs)
ax.set_title('Monthly Returns (%)', fontweight='bold')
ax.set_ylabel('')
ax.set_yticklabels(ax.get_yticklabels(), rotation=0)
ax.set_xlabel('')
return ax
def _plot_yearly_returns(self, stats):
"""
Plot the barplot of returns by year
"""
returns = stats["returns"]
yly_ret = perf.aggregate_returns(returns, 'yearly') * 100.0
yly_ret = yly_ret.map(lambda x: round(x, 3))
c = (Bar().add_xaxis(list(yly_ret.index)).add_yaxis(
"", list(yly_ret)).set_global_opts(title_opts=opts.TitleOpts(
title="Yearly Returns (%)")))
return c
def _calculate_yearly_aggregated_returns(self, returns):
"""
Calculate the yearly aggregated returns as a list of tuples,
with the first entry being the year integer and the
second entry the returns. 0% -> 0.0, 100% -> 1.0
Parameters
----------
returns : `pd.Series`
The Series of daily returns values.
Returns
-------
`list[tuple]`
The list of tuple-based returns: [(year, return)]
"""
year_returns = perf.aggregate_returns(returns, 'yearly')
return list(zip(year_returns.index, year_returns))
def _calculate_monthly_aggregated_returns(self, returns):
"""
Calculate the monthly aggregated returns as a list of tuples,
with the first entry a further tuple of (year, month) and the
second entry the returns. 0% -> 0.0, 100% -> 1.0
Parameters
----------
returns : `pd.Series`
The Series of daily returns values.
Returns
-------
`list[tuple]`
The list of tuple-based returns: [((year, month), return)]
"""
month_returns = perf.aggregate_returns(returns, 'monthly')
return list(zip(month_returns.index, month_returns))
def _plot_daily_returns(self, stats, ax=None, **kwargs):
"""
Plots a heatmap of the daily returns.
"""
returns = stats['returns']
if ax is None:
ax = plt.gca()
daily_ret = perf.aggregate_returns(returns, 'daily')
daily_ret = daily_ret.unstack()
daily_ret = np.round(daily_ret, 3)
# daily_ret.rename(
# columns={1: '1', 2: '2', 3: '3', 4: '4',
# 5: '5', 6: '6', 7: '7', 8: '8',
# 9: '9', 10: '10', 11: '11', 12: '12'},
# columns={1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr',
# 5: 'May', 6: 'Jun', 7: 'Jul', 8: 'Aug',
# 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'},
# inplace=True
# )
sns.heatmap(daily_ret.fillna(0) * 100.0,
annot=True,
fmt="0.1f",
annot_kws={"size": 8},
alpha=1.0,
center=0.0,
cbar=True,
cmap=cm.RdYlGn_r,
ax=ax,
**kwargs)
ax.set_title('Daily Returns (%)', fontweight='bold')
ax.set_ylabel('')
ax.set_yticklabels(ax.get_yticklabels(), rotation=0)
ax.set_xlabel('')
return ax
def _plot_txt_time(self, stats, ax=None, **kwargs):
"""
Outputs the statistics for various time frames.
"""
def format_perc(x, pos):
return '%.0f%%' % x
returns = stats['returns']
if ax is None:
ax = plt.gca()
y_axis_formatter = FuncFormatter(format_perc)
ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
mly_ret = perf.aggregate_returns(returns, 'monthly')
yly_ret = perf.aggregate_returns(returns, 'yearly')
mly_pct = mly_ret[mly_ret >= 0].shape[0] / float(mly_ret.shape[0])
mly_avg_win_pct = np.mean(mly_ret[mly_ret >= 0])
mly_avg_loss_pct = np.mean(mly_ret[mly_ret < 0])
mly_max_win_pct = np.max(mly_ret)
mly_max_loss_pct = np.min(mly_ret)
yly_pct = yly_ret[yly_ret >= 0].shape[0] / float(yly_ret.shape[0])
yly_max_win_pct = np.max(yly_ret)
yly_max_loss_pct = np.min(yly_ret)
ax.text(0.5, 8.9, 'Winning Months %', fontsize=8)
ax.text(9.5, 8.9, '{:.0%}'.format(mly_pct), fontsize=8, fontweight='bold',
horizontalalignment='right')
ax.text(0.5, 7.9, 'Average Winning Month %', fontsize=8)
ax.text(9.5, 7.9, '{:.2%}'.format(mly_avg_win_pct), fontsize=8, fontweight='bold',
color='red' if mly_avg_win_pct < 0 else 'green',
horizontalalignment='right')
ax.text(0.5, 6.9, 'Average Losing Month %', fontsize=8)
ax.text(9.5, 6.9, '{:.2%}'.format(mly_avg_loss_pct), fontsize=8, fontweight='bold',
color='red' if mly_avg_loss_pct < 0 else 'green',
horizontalalignment='right')
ax.text(0.5, 5.9, 'Best Month %', fontsize=8)
ax.text(9.5, 5.9, '{:.2%}'.format(mly_max_win_pct), fontsize=8, fontweight='bold',
color='red' if mly_max_win_pct < 0 else 'green',
horizontalalignment='right')
ax.text(0.5, 4.9, 'Worst Month %', fontsize=8)
ax.text(9.5, 4.9, '{:.2%}'.format(mly_max_loss_pct), fontsize=8, fontweight='bold',
color='red' if mly_max_loss_pct < 0 else 'green',
horizontalalignment='right')
ax.text(0.5, 3.9, 'Winning Years %', fontsize=8)
ax.text(9.5, 3.9, '{:.0%}'.format(yly_pct), fontsize=8, fontweight='bold',
horizontalalignment='right')
ax.text(0.5, 2.9, 'Best Year %', fontsize=8)
ax.text(9.5, 2.9, '{:.2%}'.format(yly_max_win_pct), fontsize=8,
fontweight='bold', color='red' if yly_max_win_pct < 0 else 'green',
horizontalalignment='right')
ax.text(0.5, 1.9, 'Worst Year %', fontsize=8)
ax.text(9.5, 1.9, '{:.2%}'.format(yly_max_loss_pct), fontsize=8,
fontweight='bold', color='red' if yly_max_loss_pct < 0 else 'green',
horizontalalignment='right')
# ax.text(0.5, 0.9, 'Positive 12 Month Periods', fontsize=8)
# ax.text(9.5, 0.9, num_trades, fontsize=8, fontweight='bold', horizontalalignment='right')
ax.set_title('Time', fontweight='bold')
ax.grid(False)
ax.spines['top'].set_linewidth(2.0)
ax.spines['bottom'].set_linewidth(2.0)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.get_yaxis().set_visible(False)
ax.get_xaxis().set_visible(False)
ax.set_ylabel('')
ax.set_xlabel('')
ax.axis([0, 10, 0, 10])
return ax
def _plot_txt_time(self, stats, ax=None, **kwargs):
"""
Outputs the statistics for various time frames.
"""
def format_perc(x, pos):
return '%.0f%%' % x
returns = stats['returns']
if ax is None:
ax = plt.gca()
y_axis_formatter = FuncFormatter(format_perc)
ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
mly_ret = perf.aggregate_returns(returns, 'monthly')
yly_ret = perf.aggregate_returns(returns, 'yearly')
mly_pct = mly_ret[mly_ret >= 0].shape[0] / float(mly_ret.shape[0])
mly_avg_win_pct = np.mean(mly_ret[mly_ret >= 0])
mly_avg_loss_pct = np.mean(mly_ret[mly_ret < 0])
mly_max_win_pct = np.max(mly_ret)
mly_max_loss_pct = np.min(mly_ret)
yly_pct = yly_ret[yly_ret >= 0].shape[0] / float(yly_ret.shape[0])
yly_max_win_pct = np.max(yly_ret)
yly_max_loss_pct = np.min(yly_ret)
ax.text(0.5, 8.9, 'Winning Months %', fontsize=8)
ax.text(9.5, 8.9, '{:.0%}'.format(mly_pct), fontsize=8, fontweight='bold',
horizontalalignment='right')
ax.text(0.5, 7.9, 'Average Winning Month %', fontsize=8)
ax.text(9.5, 7.9, '{:.2%}'.format(mly_avg_win_pct), fontsize=8, fontweight='bold',
color='red' if mly_avg_win_pct < 0 else 'green',
horizontalalignment='right')
ax.text(0.5, 6.9, 'Average Losing Month %', fontsize=8)
ax.text(9.5, 6.9, '{:.2%}'.format(mly_avg_loss_pct), fontsize=8, fontweight='bold',
color='red' if mly_avg_loss_pct < 0 else 'green',
horizontalalignment='right')
ax.text(0.5, 5.9, 'Best Month %', fontsize=8)
ax.text(9.5, 5.9, '{:.2%}'.format(mly_max_win_pct), fontsize=8, fontweight='bold',
color='red' if mly_max_win_pct < 0 else 'green',
horizontalalignment='right')
ax.text(0.5, 4.9, 'Worst Month %', fontsize=8)
ax.text(9.5, 4.9, '{:.2%}'.format(mly_max_loss_pct), fontsize=8, fontweight='bold',
color='red' if mly_max_loss_pct < 0 else 'green',
horizontalalignment='right')
ax.text(0.5, 3.9, 'Winning Years %', fontsize=8)
ax.text(9.5, 3.9, '{:.0%}'.format(yly_pct), fontsize=8, fontweight='bold',
horizontalalignment='right')
ax.text(0.5, 2.9, 'Best Year %', fontsize=8)
ax.text(9.5, 2.9, '{:.2%}'.format(yly_max_win_pct), fontsize=8,
fontweight='bold', color='red' if yly_max_win_pct < 0 else 'green',
horizontalalignment='right')
ax.text(0.5, 1.9, 'Worst Year %', fontsize=8)
ax.text(9.5, 1.9, '{:.2%}'.format(yly_max_loss_pct), fontsize=8,
fontweight='bold', color='red' if yly_max_loss_pct < 0 else 'green',
horizontalalignment='right')
# ax.text(0.5, 0.9, 'Positive 12 Month Periods', fontsize=8)
# ax.text(9.5, 0.9, num_trades, fontsize=8, fontweight='bold', horizontalalignment='right')
ax.set_title('Time', fontweight='bold')
ax.grid(False)
ax.spines['top'].set_linewidth(2.0)
ax.spines['bottom'].set_linewidth(2.0)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.get_yaxis().set_visible(False)
ax.get_xaxis().set_visible(False)
ax.set_ylabel('')
ax.set_xlabel('')
ax.axis([0, 10, 0, 10])
return ax
