diff --git a/quantstats/_plotting/core.py b/quantstats/_plotting/core.py
index 818aca55..74a759e6 100644
--- a/quantstats/_plotting/core.py
+++ b/quantstats/_plotting/core.py
@@ -50,9 +50,10 @@
'ytick.color': '#666666'
})
-_FLATUI_COLORS = ["#fedd78", "#348dc1", "#af4b64",
- "#4fa487", "#9b59b6", "#808080"]
-_GRAYSCALE_COLORS = ['silver', '#222222', 'gray'] * 3
+_FLATUI_COLORS = ['#FEDD78', '#348DC1', '#BA516B', '#4FA487', '#9B59B6',
+ '#613F66', '#84B082', '#DC136C', '#559CAD', '#4A5899']
+_GRAYSCALE_COLORS = ['#000000', '#222222', '#555555', '#888888', '#AAAAAA',
+ '#CCCCCC', '#EEEEEE', '#333333', '#666666', '#999999']
def _get_colors(grayscale):
@@ -108,11 +109,11 @@ def plot_returns_bars(returns, benchmark=None,
ax.spines['left'].set_visible(False)
# use a more precise date string for the x axis locations in the toolbar
- fig.suptitle(title+"\n", y=.99, fontweight="bold", fontname=fontname,
+ fig.suptitle(title, y=.94, fontweight="bold", fontname=fontname,
fontsize=14, color="black")
if subtitle:
- ax.set_title("\n%s - %s " % (
+ ax.set_title("%s - %s \n" % (
df.index.date[:1][0].strftime('%Y'),
df.index.date[-1:][0].strftime('%Y')
), fontsize=12, color='gray')
@@ -151,7 +152,7 @@ def plot_returns_bars(returns, benchmark=None,
ax.axhline(0, ls="--", lw=1, color="#000000", zorder=2)
# if isinstance(benchmark, _pd.Series) or hline:
- ax.legend(fontsize=12)
+ ax.legend(fontsize=11)
_plt.yscale("symlog" if log_scale else "linear")
@@ -163,6 +164,9 @@ def plot_returns_bars(returns, benchmark=None,
ax.yaxis.set_major_formatter(_FuncFormatter(format_pct_axis))
+ if benchmark is None and len(_pd.DataFrame(returns).columns) == 1:
+ ax.get_legend().remove()
+
try:
_plt.subplots_adjust(hspace=0, bottom=0, top=1)
except Exception:
@@ -238,11 +242,11 @@ def plot_timeseries(returns, benchmark=None,
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
- fig.suptitle(title+"\n", y=.99, fontweight="bold", fontname=fontname,
+ fig.suptitle(title, y=.94, fontweight="bold", fontname=fontname,
fontsize=14, color="black")
if subtitle:
- ax.set_title("\n%s - %s " % (
+ ax.set_title("%s - %s \n" % (
returns.index.date[:1][0].strftime('%e %b \'%y'),
returns.index.date[-1:][0].strftime('%e %b \'%y')
), fontsize=12, color='gray')
@@ -258,8 +262,8 @@ def plot_timeseries(returns, benchmark=None,
ax.plot(returns, lw=lw, label=returns.name, color=colors[1], alpha=alpha)
elif isinstance(returns, _pd.DataFrame):
# color_dict = {col: colors[i+1] for i, col in enumerate(returns.columns)}
- for col in returns.columns:
- ax.plot(returns[col], lw=lw, label=col, alpha=alpha)
+ for i, col in enumerate(returns.columns):
+ ax.plot(returns[col], lw=lw, label=col, alpha=alpha, color=colors[i+1])
if fill:
if isinstance(returns, _pd.Series):
@@ -287,8 +291,7 @@ def plot_timeseries(returns, benchmark=None,
color='white' if grayscale else 'black', zorder=2)
# if isinstance(benchmark, _pd.Series) or hline is not None:
- # ax.legend(fontsize=12)
- ax.legend(fontsize=12)
+ ax.legend(fontsize=11)
_plt.yscale("symlog" if log_scale else "linear")
@@ -303,6 +306,9 @@ def plot_timeseries(returns, benchmark=None,
fontweight='bold', fontsize=12, color="black")
ax.yaxis.set_label_coords(-.1, .5)
+ if benchmark is None and len(_pd.DataFrame(returns).columns) == 1:
+ ax.get_legend().remove()
+
try:
_plt.subplots_adjust(hspace=0, bottom=0, top=1)
except Exception:
@@ -336,9 +342,11 @@ def plot_histogram(returns, benchmark, resample="M", bins=20,
ylabel=True, subtitle=True, compounded=True,
savefig=None, show=True):
- colors = ['#348dc1', '#003366', 'red']
- if grayscale:
- colors = ['silver', 'gray', 'black']
+ # colors = ['#348dc1', '#003366', 'red']
+ # if grayscale:
+ # colors = ['silver', 'gray', 'black']
+
+ colors, _, _ = _get_colors(grayscale)
apply_fnc = _stats.comp if compounded else _np.sum
if benchmark is not None:
@@ -348,17 +356,19 @@ def plot_histogram(returns, benchmark, resample="M", bins=20,
returns = returns.fillna(0).resample(resample).apply(
apply_fnc).resample(resample).last()
+ figsize = (0.995 * figsize[0], figsize[1])
fig, ax = _plt.subplots(figsize=figsize)
+
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
- fig.suptitle(title+"\n", y=.99, fontweight="bold", fontname=fontname,
+ fig.suptitle(title, y=.94, fontweight="bold", fontname=fontname,
fontsize=14, color="black")
if subtitle:
- ax.set_title("\n%s - %s " % (
+ ax.set_title("%s - %s \n" % (
returns.index.date[:1][0].strftime('%Y'),
returns.index.date[-1:][0].strftime('%Y')
), fontsize=12, color='gray')
@@ -366,40 +376,60 @@ def plot_histogram(returns, benchmark, resample="M", bins=20,
fig.set_facecolor('white')
ax.set_facecolor('white')
- # Why do we need average?
- # ax.axvline(returns.mean(), ls="--", lw=1.5,
- # zorder=2, label="Average")
+ if isinstance(returns, _pd.DataFrame) and len(returns.columns) == 1:
+ returns = returns[returns.columns[0]]
+
+ pallete = colors[1:2] if benchmark is None else colors[:2]
+ alpha = 0.7
+ if isinstance(returns, _pd.DataFrame):
+ pallete = colors[1:len(returns.columns)+1] if benchmark is None else colors[:len(returns.columns)+1]
+ if len(returns.columns) > 1:
+ alpha = 0.5
if benchmark is not None:
if isinstance(returns, _pd.Series):
combined_returns = benchmark.to_frame().join(returns.to_frame()) \
.stack().reset_index() \
- .rename(columns={'level_1': 'Portfolio', 0: 'Returns'})
+ .rename(columns={'level_1': '', 0: 'Returns'})
elif isinstance(returns, _pd.DataFrame):
combined_returns = benchmark.to_frame().join(returns) \
.stack().reset_index() \
- .rename(columns={'level_1': 'Portfolio', 0: 'Returns'})
+ .rename(columns={'level_1': '', 0: 'Returns'})
+ x = _sns.histplot(data=combined_returns, x='Returns',
+ bins=bins, alpha=alpha, kde=kde,
+ stat="density", hue='',
+ palette=pallete,
+ ax=ax)
- _sns.histplot(data=combined_returns, x='Returns',
- bins=bins, alpha=0.4, kde=kde,
- stat="density", hue='Portfolio',
- ax=ax)
else:
if isinstance(returns, _pd.Series):
combined_returns = returns.copy()
- _sns.histplot(data=combined_returns, bins=bins,
- alpha=0.4, kde=kde,
- stat="density",
- ax=ax)
- ax.legend(fontsize=12)
+ if kde:
+ _sns.kdeplot(data=combined_returns, color='black', ax=ax)
+ x = _sns.histplot(data=combined_returns, bins=bins,
+ alpha=alpha,
+ kde=False,
+ stat="density",
+ color=colors[1],
+ ax=ax)
+
elif isinstance(returns, _pd.DataFrame):
combined_returns = returns.stack().reset_index() \
- .rename(columns={'level_1': 'Portfolio', 0: 'Returns'})
- _sns.histplot(data=combined_returns, x='Returns',
- bins=bins, alpha=0.4, kde=kde,
- stat="density", hue='Portfolio',
- ax=ax)
+ .rename(columns={'level_1': '', 0: 'Returns'})
+ # _sns.kdeplot(data=combined_returns, color='black', ax=ax)
+ x = _sns.histplot(data=combined_returns, x='Returns',
+ bins=bins, alpha=alpha, kde=kde,
+ stat="density", hue='',
+ palette=pallete,
+ ax=ax)
+
+ # Why do we need average?
+ if isinstance(combined_returns, _pd.Series) or len(combined_returns.columns) == 1:
+ ax.axvline(combined_returns.mean(), ls="--", lw=1.5,
+ zorder=2, label="Average", color="red")
+
+ # _plt.setp(x.get_legend().get_texts(), fontsize=11)
ax.xaxis.set_major_formatter(_plt.FuncFormatter(
lambda x, loc: "{:,}%".format(int(x*100))))
@@ -408,10 +438,9 @@ def plot_histogram(returns, benchmark, resample="M", bins=20,
# ax.axvline(0, lw=1, color="#000000", zorder=2)
ax.set_xlabel('')
- if ylabel:
- ax.set_ylabel("Occurrences", fontname=fontname,
- fontweight='bold', fontsize=12, color="black")
- ax.yaxis.set_label_coords(-.1, .5)
+ ax.set_ylabel("Occurrences", fontname=fontname,
+ fontweight='bold', fontsize=12, color="black")
+ ax.yaxis.set_label_coords(-.1, .5)
# fig.autofmt_xdate()
@@ -473,9 +502,8 @@ def plot_rolling_stats(returns, benchmark=None, title="",
elif isinstance(returns, _pd.DataFrame):
col_names = ['Benchmark', returns_label]
df = df[list(_pd.core.common.flatten(col_names))].dropna()
- for col in returns_label:
- ax.plot(df[col], lw=lw,
- label=col)
+ for i, col in enumerate(returns_label):
+ ax.plot(df[col], lw=lw, label=col, color=colors[i+1])
ax.plot(df['Benchmark'], lw=lw, label=benchmark.name,
color=colors[0], alpha=.8)
else:
@@ -485,20 +513,19 @@ def plot_rolling_stats(returns, benchmark=None, title="",
label=returns.name, color=colors[1])
elif isinstance(returns, _pd.DataFrame):
df = df[returns_label].dropna()
- for col in returns_label:
- ax.plot(df[col], lw=lw,
- label=col)
+ for i, col in enumerate(returns_label):
+ ax.plot(df[col], lw=lw, label=col, color=colors[i+1])
# rotate and align the tick labels so they look better
fig.autofmt_xdate()
# use a more precise date string for the x axis locations in the toolbar
# ax.fmt_xdata = _mdates.DateFormatter('%Y-%m-%d')\
- fig.suptitle(title+"\n", y=.99, fontweight="bold", fontname=fontname,
+ fig.suptitle(title, y=.94, fontweight="bold", fontname=fontname,
fontsize=14, color="black")
if subtitle:
- ax.set_title("\n%s - %s " % (
+ ax.set_title("%s - %s \n" % (
df.index.date[:1][0].strftime('%e %b \'%y'),
df.index.date[-1:][0].strftime('%e %b \'%y')
), fontsize=12, color='gray')
@@ -519,7 +546,10 @@ def plot_rolling_stats(returns, benchmark=None, title="",
ax.yaxis.set_major_formatter(_FormatStrFormatter('%.2f'))
- ax.legend(fontsize=12)
+ ax.legend(fontsize=11)
+
+ if benchmark is None and len(_pd.DataFrame(returns).columns) == 1:
+ ax.get_legend().remove()
try:
_plt.subplots_adjust(hspace=0, bottom=0, top=1)
@@ -562,15 +592,16 @@ def plot_rolling_beta(returns, benchmark,
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
- fig.suptitle(title+"\n", y=.99, fontweight="bold", fontname=fontname,
+ fig.suptitle(title, y=.94, fontweight="bold", fontname=fontname,
fontsize=14, color="black")
if subtitle:
- ax.set_title("\n%s - %s " % (
+ ax.set_title("%s - %s \n" % (
returns.index.date[:1][0].strftime('%e %b \'%y'),
returns.index.date[-1:][0].strftime('%e %b \'%y')
), fontsize=12, color='gray')
+ i = 1
if isinstance(returns, _pd.Series):
beta = _stats.rolling_greeks(returns, benchmark, window1)['beta'].fillna(0)
ax.plot(beta, lw=lw, label=window1_label, color=colors[1])
@@ -578,9 +609,12 @@ def plot_rolling_beta(returns, benchmark,
beta = ({col: _stats.rolling_greeks(returns[col], benchmark, window1)['beta'].fillna(0)
for col in returns.columns})
for name, b in beta.items():
- ax.plot(b, lw=lw, label=name + " " + f"({window1_label})")
+ ax.plot(b, lw=lw, label=name + " " + f"({window1_label})", color=colors[i])
+ i += 1
+ i = 1
if window2:
+ lw = lw - 0.5
if isinstance(returns, _pd.Series):
ax.plot(_stats.rolling_greeks(returns, benchmark, window2)['beta'],
lw=lw, label=window2_label, color="gray", alpha=0.8)
@@ -588,9 +622,8 @@ def plot_rolling_beta(returns, benchmark,
betas_w2 = ({col: _stats.rolling_greeks(returns[col], benchmark, window2)['beta']
for col in returns.columns})
for name, beta_w2 in betas_w2.items():
- ax.plot(beta_w2, lw=lw, ls='--', label=name + " " + f"({window2_label})", alpha=0.8)
- # ax.plot(_stats.rolling_greeks(returns, benchmark, window2)['beta'],
- # lw=lw, label=window2_label, color="gray", alpha=0.8)
+ ax.plot(beta_w2, lw=lw, ls='--', label=name + " " + f"({window2_label})", alpha=0.5, color=colors[i])
+ i += 1
beta_min = beta.min() if isinstance(returns, _pd.Series) else min([b.min() for b in beta.values()])
beta_max = beta.max() if isinstance(returns, _pd.Series) else max([b.max() for b in beta.values()])
@@ -616,7 +649,10 @@ def plot_rolling_beta(returns, benchmark,
fontweight='bold', fontsize=12, color="black")
ax.yaxis.set_label_coords(-.1, .5)
- ax.legend(fontsize=12)
+ ax.legend(fontsize=11)
+ if benchmark is None and len(_pd.DataFrame(returns).columns) == 1:
+ ax.get_legend().remove()
+
try:
_plt.subplots_adjust(hspace=0, bottom=0, top=1)
except Exception:
@@ -665,11 +701,11 @@ def plot_longest_drawdowns(returns, periods=5, lw=1.5,
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
- fig.suptitle(f"{title} - Worst %.0f Drawdown Periods\n" %
- periods, y=.99, fontweight="bold", fontname=fontname,
+ fig.suptitle(f"{title} - Worst %.0f Drawdown Periods" %
+ periods, y=.94, fontweight="bold", fontname=fontname,
fontsize=14, color="black")
if subtitle:
- ax.set_title("\n%s - %s " % (
+ ax.set_title("%s - %s \n" % (
returns.index.date[:1][0].strftime('%e %b \'%y'),
returns.index.date[-1:][0].strftime('%e %b \'%y')
), fontsize=12, color='gray')
@@ -767,12 +803,16 @@ def plot_distribution(returns, figsize=(10, 6),
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
- fig.suptitle(f"{title} - Return Quantiles\n", y=.99,
+ if title:
+ title = f"{title} - Return Quantiles"
+ else:
+ title = "Return Quantiles"
+ fig.suptitle(title, y=.94,
fontweight="bold", fontname=fontname,
fontsize=14, color="black")
if subtitle:
- ax.set_title("\n%s - %s " % (
+ ax.set_title("%s - %s \n" % (
returns.index.date[:1][0].strftime('%e %b \'%y'),
returns.index.date[-1:][0].strftime('%e %b \'%y')
), fontsize=12, color='gray')
@@ -780,7 +820,14 @@ def plot_distribution(returns, figsize=(10, 6),
fig.set_facecolor('white')
ax.set_facecolor('white')
- port.boxplot(ax=ax)#, colo=tuple(colors[:5]))
+ _sns.boxplot(data=port, ax=ax,
+ palette={
+ 'Daily': colors[0],
+ 'Weekly': colors[1],
+ 'Monthly': colors[2],
+ 'Quarterly': colors[3],
+ 'Yearly': colors[4]
+ })
ax.yaxis.set_major_formatter(_plt.FuncFormatter(
lambda x, loc: "{:,}%".format(int(x*100))))
diff --git a/quantstats/_plotting/wrappers.py b/quantstats/_plotting/wrappers.py
index 889b6f50..6f595220 100644
--- a/quantstats/_plotting/wrappers.py
+++ b/quantstats/_plotting/wrappers.py
@@ -64,16 +64,23 @@ def snapshot(returns, grayscale=False, figsize=(10, 8),
mode="comp", subtitle=True, savefig=None, show=True,
log_scale=False, **kwargs):
- strategy_colname = kwargs.get("strategy_title", "Strategy")
+ strategy_colname = kwargs.get("strategy_col", "Strategy")
+ multi_column = False
if isinstance(returns, _pd.Series):
returns.name = strategy_colname
elif isinstance(returns, _pd.DataFrame):
+ if len(returns.columns) > 1:
+ if strategy_colname in returns.columns:
+ returns = returns[strategy_colname]
+ else:
+ multi_column = True
+ returns = returns.mean(axis=1)
+ title = title + " (daily equal-weighted*)"
returns.columns = strategy_colname
colors = _GRAYSCALE_COLORS if grayscale else _FLATUI_COLORS
-
- returns = _utils.make_portfolio(returns, 1, mode).pct_change().fillna(0)
+ returns = _utils.make_portfolio(returns.dropna(), 1, mode).pct_change().fillna(0)
if figsize is None:
size = list(_plt.gcf().get_size_inches())
@@ -82,20 +89,27 @@ def snapshot(returns, grayscale=False, figsize=(10, 8),
fig, axes = _plt.subplots(3, 1, sharex=True, figsize=figsize,
gridspec_kw={'height_ratios': [3, 1, 1]})
+ if multi_column:
+ _plt.figtext(
+ 0, -.05,
+ " * When a multi-column DataFrame is passed, the mean of all columns will be used as returns.\n"
+ " To change this behavior, use a pandas Series or pass the column name in the `strategy_col` parameter.",
+ ha="left", fontsize=11, color='black', alpha=0.6, linespacing=1.5)
+
for ax in axes:
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
- fig.suptitle(title, fontsize=14, y=.995,
+ fig.suptitle(title, fontsize=14, y=.97,
fontname=fontname, fontweight='bold', color='black')
fig.set_facecolor('white')
if subtitle:
if isinstance(returns, _pd.Series):
- axes[0].set_title("\n%s - %s ; Sharpe: %.2f " % (
+ axes[0].set_title("%s - %s ; Sharpe: %.2f \n" % (
returns.index.date[:1][0].strftime('%e %b \'%y'),
returns.index.date[-1:][0].strftime('%e %b \'%y'),
_stats.sharpe(returns)
@@ -118,10 +132,10 @@ def snapshot(returns, grayscale=False, figsize=(10, 8),
axes[0].axhline(0, color='silver', lw=1, zorder=0)
axes[0].set_yscale("symlog" if log_scale else "linear")
- axes[0].legend(fontsize=12)
+ # axes[0].legend(fontsize=12)
dd = _stats.to_drawdown_series(returns) * 100
- ddmin = min(_utils._round_to_closest(abs(dd.min()), 5))
+ ddmin = _utils._round_to_closest(abs(dd.min()), 5)
ddmin_ticks = 5
if ddmin > 50:
ddmin_ticks = ddmin / 4
@@ -141,13 +155,13 @@ def snapshot(returns, grayscale=False, figsize=(10, 8),
axes[1].axhline(0, color='silver', lw=1, zorder=0)
if not grayscale:
if isinstance(dd, _pd.Series):
- axes[1].fill_between(dd.index, 0, dd, color=colors[1], alpha=.25)
+ axes[1].fill_between(dd.index, 0, dd, color=colors[2], alpha=.25)
elif isinstance(dd, _pd.DataFrame):
for i, col in enumerate(dd.columns):
axes[1].fill_between(dd[col].index, 0, dd[col], color=colors[i + 1], alpha=.25)
axes[1].set_yscale("symlog" if log_scale else "linear")
- axes[1].legend(fontsize=12)
+ # axes[1].legend(fontsize=12)
axes[2].set_ylabel('Daily Return', fontname=fontname,
fontweight='bold', fontsize=12)
@@ -160,17 +174,17 @@ def snapshot(returns, grayscale=False, figsize=(10, 8),
axes[2].axhline(0, color=colors[-1], linestyle='--', lw=1, zorder=2)
axes[2].set_yscale("symlog" if log_scale else "linear")
- axes[2].legend(fontsize=12)
+ # axes[2].legend(fontsize=12)
- retmax = max(_utils._round_to_closest(returns.max() * 100, 5))
- retmin = min(_utils._round_to_closest(returns.min() * 100, 5))
+ retmax = _utils._round_to_closest(returns.max() * 100, 5)
+ retmin = _utils._round_to_closest(returns.min() * 100, 5)
retdiff = (retmax - retmin)
steps = 5
if retdiff > 50:
steps = retdiff / 5
elif retdiff > 30:
steps = retdiff / 4
- steps = int(_utils._round_to_closest(steps, 5))
+ steps = _utils._round_to_closest(steps, 5)
axes[2].set_yticks(_np.arange(retmin, retmax, step=steps))
for ax in axes:
@@ -463,7 +477,7 @@ def distribution(returns, fontname='Arial', grayscale=False, ylabel=True,
return fig
-def histogram(returns, benchmark, resample='M', fontname='Arial',
+def histogram(returns, benchmark=None, resample='M', fontname='Arial',
grayscale=False, figsize=(10, 5), ylabel=True,
subtitle=True, compounded=True, savefig=None, show=True,
prepare_returns=True):
@@ -673,7 +687,7 @@ def monthly_heatmap(returns, benchmark, annot_size=10, figsize=(10, 5),
returns = _stats.monthly_returns(returns, eoy=eoy,
compounded=compounded) * 100
- fig_height = len(returns) / 3
+ fig_height = len(returns) / 2.5
if figsize is None:
size = list(_plt.gcf().get_size_inches())
@@ -682,7 +696,7 @@ def monthly_heatmap(returns, benchmark, annot_size=10, figsize=(10, 5),
figsize = (figsize[0], max([fig_height, figsize[1]]))
if cbar:
- figsize = (figsize[0]*1.04, max([fig_height, figsize[1]]))
+ figsize = (figsize[0]*1.051, max([fig_height, figsize[1]]))
fig, ax = _plt.subplots(figsize=figsize)
ax.spines['top'].set_visible(False)
diff --git a/quantstats/reports.py b/quantstats/reports.py
index 58b624d9..a8fd0462 100644
--- a/quantstats/reports.py
+++ b/quantstats/reports.py
@@ -26,7 +26,6 @@
)
from base64 import b64encode as _b64encode
import re as _regex
-
from tabulate import tabulate as _tabulate
from . import (
__version__, stats as _stats,
@@ -34,7 +33,6 @@
)
from dateutil.relativedelta import relativedelta
from io import StringIO
-
try:
from IPython.display import (
display as iDisplay, HTML as iHTML
@@ -51,10 +49,12 @@ def _get_trading_periods(periods_per_year=252):
def _match_dates(returns, benchmark):
- returns = returns.loc[
- max(returns.ne(0).idxmax(), benchmark.ne(0).idxmax()):]
- benchmark = benchmark.loc[
- max(returns.ne(0).idxmax(), benchmark.ne(0).idxmax()):]
+ if isinstance(returns, _pd.DataFrame):
+ loc = max(returns[returns.columns[0]].ne(0).idxmax(), benchmark.ne(0).idxmax())
+ else:
+ loc = max(returns.ne(0).idxmax(), benchmark.ne(0).idxmax())
+ returns = returns.loc[loc:]
+ benchmark = benchmark.loc[loc:]
return returns, benchmark
@@ -62,11 +62,14 @@ def _match_dates(returns, benchmark):
def html(returns, benchmark=None, rf=0., grayscale=False,
title='Strategy Tearsheet', output=None, compounded=True,
periods_per_year=252, download_filename='quantstats-tearsheet.html',
- figfmt='svg', template_path=None, match_dates=False, **kwargs):
+ figfmt='svg', template_path=None, match_dates=True, **kwargs):
if output is None and not _utils._in_notebook():
raise ValueError("`output` must be specified")
+ if match_dates:
+ returns = returns.dropna()
+
win_year, win_half_year = _get_trading_periods(periods_per_year)
tpl = ""
@@ -75,9 +78,15 @@ def html(returns, benchmark=None, rf=0., grayscale=False,
f.close()
# prepare timeseries
+ if match_dates:
+ returns = returns.dropna()
returns = _utils._prepare_returns(returns)
strategy_title = kwargs.get('strategy_title', 'Strategy')
+ if isinstance(returns, _pd.DataFrame):
+ if len(returns.columns) > 1 and isinstance(strategy_title, str):
+ strategy_title = list(returns.columns)
+
if benchmark is not None:
benchmark_title = kwargs.get('benchmark_title', 'Benchmark')
if kwargs.get('benchmark_title') is None:
@@ -220,7 +229,7 @@ def html(returns, benchmark=None, rf=0., grayscale=False,
figfile = _utils._file_stream()
_plots.histogram(returns, benchmark, grayscale=grayscale,
- figsize=(8, 4), subtitle=False,
+ figsize=(7, 4), subtitle=False,
savefig={'fname': figfile, 'format': figfmt},
show=False, ylabel=False, compounded=compounded,
prepare_returns=False)
@@ -345,9 +354,11 @@ def html(returns, benchmark=None, rf=0., grayscale=False,
def full(returns, benchmark=None, rf=0., grayscale=False,
figsize=(8, 5), display=True, compounded=True,
- periods_per_year=252, match_dates=False, **kwargs):
+ periods_per_year=252, match_dates=True, **kwargs):
# prepare timeseries
+ if match_dates:
+ returns = returns.dropna()
returns = _utils._prepare_returns(returns)
if benchmark is not None:
benchmark = _utils._prepare_benchmark(benchmark, returns.index, rf)
@@ -360,6 +371,10 @@ def full(returns, benchmark=None, rf=0., grayscale=False,
strategy_title = kwargs.get('strategy_title', 'Strategy')
active = kwargs.get('active_returns', 'False')
+ if isinstance(returns, _pd.DataFrame):
+ if len(returns.columns) > 1 and isinstance(strategy_title, str):
+ strategy_title = list(returns.columns)
+
if benchmark is not None:
benchmark.name = benchmark_title
if isinstance(returns, _pd.Series):
@@ -396,19 +411,19 @@ def full(returns, benchmark=None, rf=0., grayscale=False,
prepare_returns=False,
benchmark_title=benchmark_title,
strategy_title=strategy_title))
- iDisplay(iHTML('5 Worst Drawdowns
'))
if isinstance(dd, _pd.Series):
+ iDisplay(iHTML('Worst 5 Drawdowns
'))
if dd_info.empty:
iDisplay(iHTML("(no drawdowns)
"))
else:
iDisplay(dd_info)
elif isinstance(dd, _pd.DataFrame):
for ptf, dd_info in dd_info_dict.items():
+ iDisplay(iHTML('%s - Worst 5 Drawdowns
' % ptf))
if dd_info.empty:
iDisplay(iHTML("(no drawdowns)
"))
else:
- iDisplay(iHTML("%s
" % ptf))
iDisplay(dd_info)
iDisplay(iHTML('Strategy Visualization
'))
@@ -422,7 +437,7 @@ def full(returns, benchmark=None, rf=0., grayscale=False,
benchmark_title=benchmark_title,
strategy_title=strategy_title)
print('\n\n')
- print('[5 Worst Drawdowns]\n')
+ print('[Worst 5 Drawdowns]\n')
if isinstance(dd, _pd.Series):
if dd_info.empty:
print("(no drawdowns)")
@@ -449,9 +464,11 @@ def full(returns, benchmark=None, rf=0., grayscale=False,
def basic(returns, benchmark=None, rf=0., grayscale=False,
figsize=(8, 5), display=True, compounded=True,
- periods_per_year=252, match_dates=False, **kwargs):
+ periods_per_year=252, match_dates=True, **kwargs):
# prepare timeseries
+ if match_dates:
+ returns = returns.dropna()
returns = _utils._prepare_returns(returns)
if benchmark is not None:
benchmark = _utils._prepare_benchmark(benchmark, returns.index, rf)
@@ -464,6 +481,10 @@ def basic(returns, benchmark=None, rf=0., grayscale=False,
strategy_title = kwargs.get('strategy_title', 'Strategy')
active = kwargs.get('active_returns', 'False')
+ if isinstance(returns, _pd.DataFrame):
+ if len(returns.columns) > 1 and isinstance(strategy_title, str):
+ strategy_title = list(returns.columns)
+
if _utils._in_notebook():
iDisplay(iHTML('Performance Metrics
'))
metrics(returns=returns, benchmark=benchmark,
@@ -495,24 +516,28 @@ def basic(returns, benchmark=None, rf=0., grayscale=False,
def metrics(returns, benchmark=None, rf=0., display=True,
mode='basic', sep=False, compounded=True,
periods_per_year=252, prepare_returns=True,
- match_dates=False, **kwargs):
+ match_dates=True, **kwargs):
+ if match_dates:
+ returns = returns.dropna()
returns.index = returns.index.tz_localize(None)
win_year, _ = _get_trading_periods(periods_per_year)
+ benchmark_colname = kwargs.get("benchmark_title", "Benchmark")
+ strategy_colname = kwargs.get("strategy_title", "Strategy")
+
if benchmark is not None:
if isinstance(benchmark, str):
- benchmark_col = f'Benchmark ({benchmark.upper()})'
+ benchmark_colname = f'Benchmark ({benchmark.upper()})'
elif isinstance(benchmark, _pd.DataFrame) and len(benchmark.columns) > 1:
raise ValueError("`benchmark` must be a pandas Series, "
"but a multi-column DataFrame was passed")
- benchmark_colname = kwargs.get("benchmark_title", "Benchmark")
- strategy_colname = kwargs.get("strategy_title", "Strategy")
-
if isinstance(returns, _pd.DataFrame):
if len(returns.columns) > 1:
blank = [''] * len(returns.columns)
+ if isinstance(strategy_colname, str):
+ strategy_colname = list(returns.columns)
else:
blank = ['']
@@ -522,7 +547,7 @@ def metrics(returns, benchmark=None, rf=0., display=True,
# returns = returns[returns.columns[0]]
if prepare_returns:
- returns = _utils._prepare_returns(returns)
+ df = _utils._prepare_returns(returns)
if isinstance(returns, _pd.Series):
df = _pd.DataFrame({"returns": returns})
@@ -543,27 +568,14 @@ def metrics(returns, benchmark=None, rf=0., display=True,
for i, strategy_col in enumerate(returns.columns):
df["returns_" + str(i+1)] = returns[strategy_col]
- df = df.fillna(0)
-
- # pct multiplier
- pct = 100 if display or "internal" in kwargs else 1
- if kwargs.get("as_pct", False):
- pct = 100
-
- # return df
- dd = _calc_dd(df, display=(display or "internal" in kwargs),
- as_pct=kwargs.get("as_pct", False))
-
- metrics = _pd.DataFrame()
-
if isinstance(returns, _pd.Series):
s_start = {'returns': df['returns'].index.strftime('%Y-%m-%d')[0]}
s_end = {'returns': df['returns'].index.strftime('%Y-%m-%d')[-1]}
s_rf = {'returns': rf}
elif isinstance(returns, _pd.DataFrame):
df_strategy_columns = [col for col in df.columns if col != "benchmark"]
- s_start = {strategy_col: df[strategy_col].index.strftime('%Y-%m-%d')[0] for strategy_col in df_strategy_columns}
- s_end = {strategy_col: df[strategy_col].index.strftime('%Y-%m-%d')[-1] for strategy_col in df_strategy_columns}
+ s_start = {strategy_col: df[strategy_col].dropna().index.strftime('%Y-%m-%d')[0] for strategy_col in df_strategy_columns}
+ s_end = {strategy_col: df[strategy_col].dropna().index.strftime('%Y-%m-%d')[-1] for strategy_col in df_strategy_columns}
s_rf = {strategy_col: rf for strategy_col in df_strategy_columns}
if "benchmark" in df:
@@ -571,6 +583,18 @@ def metrics(returns, benchmark=None, rf=0., display=True,
s_end['benchmark'] = df['benchmark'].index.strftime('%Y-%m-%d')[-1]
s_rf['benchmark'] = rf
+ df = df.fillna(0)
+
+ # pct multiplier
+ pct = 100 if display or "internal" in kwargs else 1
+ if kwargs.get("as_pct", False):
+ pct = 100
+
+ # return df
+ dd = _calc_dd(df, display=(display or "internal" in kwargs),
+ as_pct=kwargs.get("as_pct", False))
+
+ metrics = _pd.DataFrame()
metrics['Start Period'] = _pd.Series(s_start)
metrics['End Period'] = _pd.Series(s_end)
metrics['Risk-Free Rate %'] = _pd.Series(s_rf)*100
@@ -838,14 +862,23 @@ def metrics(returns, benchmark=None, rf=0., display=True,
def plots(returns, benchmark=None, grayscale=False,
figsize=(8, 5), mode='basic', compounded=True,
- periods_per_year=252, prepare_returns=True, match_dates=False, **kwargs):
+ periods_per_year=252, prepare_returns=True,
+ match_dates=True, **kwargs):
benchmark_colname = kwargs.get("benchmark_title", "Benchmark")
strategy_colname = kwargs.get("strategy_title", "Strategy")
active = kwargs.get('active', 'False')
+ if isinstance(returns, _pd.DataFrame):
+ if len(returns.columns) > 1:
+ if isinstance(strategy_colname, str):
+ strategy_colname = list(returns.columns)
+
win_year, win_half_year = _get_trading_periods(periods_per_year)
+ if match_dates is True:
+ returns = returns.dropna()
+
if prepare_returns:
returns = _utils._prepare_returns(returns)
@@ -854,6 +887,8 @@ def plots(returns, benchmark=None, grayscale=False,
elif isinstance(returns, _pd.DataFrame):
returns.columns = strategy_colname
+ returns = _pd.DataFrame(returns)
+
if mode.lower() != 'full':
_plots.snapshot(returns, grayscale=grayscale,
figsize=(figsize[0], figsize[0]),
@@ -867,7 +902,7 @@ def plots(returns, benchmark=None, grayscale=False,
compounded=compounded, active=active)
elif isinstance(returns, _pd.DataFrame):
for col in returns.columns:
- _plots.monthly_heatmap(returns[col], benchmark, grayscale=grayscale,
+ _plots.monthly_heatmap(returns[col].dropna(), benchmark, grayscale=grayscale,
figsize=(figsize[0], figsize[0] * .5),
show=True, ylabel=False, returns_label=col,
compounded=compounded, active=active)
@@ -909,29 +944,32 @@ def plots(returns, benchmark=None, grayscale=False,
show=True, ylabel=False,
prepare_returns=False)
+ small_fig_size = (figsize[0], figsize[0]*.35)
+ if len(returns.columns) > 1:
+ small_fig_size = (figsize[0], figsize[0]*(.33*(len(returns.columns)*.66)))
+
_plots.daily_returns(returns, benchmark, grayscale=grayscale,
- figsize=(figsize[0], figsize[0]*.3),
+ figsize=small_fig_size,
show=True, ylabel=False,
prepare_returns=False, active=active)
if benchmark is not None:
_plots.rolling_beta(returns, benchmark, grayscale=grayscale,
window1=win_half_year, window2=win_year,
- figsize=(figsize[0], figsize[0]*.3),
+ figsize=small_fig_size,
show=True, ylabel=False,
prepare_returns=False)
- _plots.rolling_volatility(
- returns, benchmark, grayscale=grayscale,
- figsize=(figsize[0], figsize[0]*.3), show=True, ylabel=False,
- period=win_half_year)
+ _plots.rolling_volatility(returns, benchmark, grayscale=grayscale,
+ figsize=small_fig_size, show=True, ylabel=False,
+ period=win_half_year)
_plots.rolling_sharpe(returns, grayscale=grayscale,
- figsize=(figsize[0], figsize[0]*.3),
+ figsize=small_fig_size,
show=True, ylabel=False, period=win_half_year)
_plots.rolling_sortino(returns, grayscale=grayscale,
- figsize=(figsize[0], figsize[0]*.3),
+ figsize=small_fig_size,
show=True, ylabel=False, period=win_half_year)
if isinstance(returns, _pd.Series):
diff --git a/quantstats/stats.py b/quantstats/stats.py
index 8f746b9b..1f8d9641 100644
--- a/quantstats/stats.py
+++ b/quantstats/stats.py
@@ -60,7 +60,7 @@ def get_outliers(data):
}
if isinstance(returns, _pd.DataFrame):
- warn("Pandas DataFrame was passed (Series expeted). "
+ warn("Pandas DataFrame was passed (Series expected). "
"Only first column will be used.")
returns = returns.copy()
returns.columns = map(str.lower, returns.columns)