Skip to content

Commit

Permalink
Merge pull request ranaroussi#199 from s0ap/table_plot_titles
Browse files Browse the repository at this point in the history 
report changes to portfolio names, plot titles, and handle multiple strategies
  • Loading branch information
@ranaroussi
ranaroussi authored Jun 22, 2023
2 parents 0287d8e + 115a777 commit 0c53b4d
Show file tree
Hide file tree
Showing 5 changed files with 642 additions and 231 deletions.
210 changes: 148 additions & 62 deletions quantstats/_plotting/core.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -83,10 +83,17 @@ def plot_returns_bars(returns, benchmark=None,

# ---------------
colors, _, _ = _get_colors(grayscale)
df = _pd.DataFrame(index=returns.index, data={returns_label: returns})
if isinstance(returns, _pd.Series):
df = _pd.DataFrame(index=returns.index, data={returns.name: returns})
elif isinstance(returns, _pd.DataFrame):
df = _pd.DataFrame(index=returns.index, data={col: returns[col] for col in returns.columns})
if isinstance(benchmark, _pd.Series):
df['Benchmark'] = benchmark[benchmark.index.isin(returns.index)]
df = df[['Benchmark', returns_label]]
df[benchmark.name] = benchmark[benchmark.index.isin(returns.index)]
if isinstance(returns, _pd.Series):
df = df[[benchmark.name, returns.name]]
elif isinstance(returns, _pd.DataFrame):
col_names = [benchmark.name, returns.columns]
df = df[list(_pd.core.common.flatten(col_names))]

df = df.dropna()
if resample is not None:
Expand Down Expand Up @@ -134,16 +141,17 @@ def plot_returns_bars(returns, benchmark=None,
# rotate and align the tick labels so they look better
fig.autofmt_xdate()

if hline:
if grayscale:
hlcolor = 'gray'
ax.axhline(hline, ls="--", lw=hlw, color=hlcolor,
label=hllabel, zorder=2)
if hline is not None:
if not isinstance(hline, _pd.Series):
if grayscale:
hlcolor = 'gray'
ax.axhline(hline, ls="--", lw=hlw, color=hlcolor,
label=hllabel, zorder=2)

ax.axhline(0, ls="--", lw=1, color="#000000", zorder=2)

if isinstance(benchmark, _pd.Series) or hline:
ax.legend(fontsize=12)
# if isinstance(benchmark, _pd.Series) or hline:
ax.legend(fontsize=12)

_plt.yscale("symlog" if log_scale else "linear")

Expand Down Expand Up @@ -243,33 +251,44 @@ def plot_timeseries(returns, benchmark=None,
ax.set_facecolor('white')

if isinstance(benchmark, _pd.Series):
ax.plot(benchmark, lw=lw, ls=ls, label="Benchmark", color=colors[0])
ax.plot(benchmark, lw=lw, ls=ls, label=benchmark.name, color=colors[0])

alpha = .25 if grayscale else 1
ax.plot(returns, lw=lw, label=returns_label, color=colors[1], alpha=alpha)
if isinstance(returns, _pd.Series):
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)

if fill:
ax.fill_between(returns.index, 0, returns, color=colors[1], alpha=.25)
if isinstance(returns, _pd.Series):
ax.fill_between(returns.index, 0, returns, color=colors[1], alpha=.25)
elif isinstance(returns, _pd.DataFrame):
for i, col in enumerate(returns.columns):
ax.fill_between(returns[col].index, 0, returns[col], color=colors[i+1], alpha=.25)

# 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')

if hline:
if grayscale:
hlcolor = 'black'
ax.axhline(hline, ls="--", lw=hlw, color=hlcolor,
label=hllabel, zorder=2)
if hline is not None:
if not isinstance(hline, _pd.Series):
if grayscale:
hlcolor = 'black'
ax.axhline(hline, ls="--", lw=hlw, color=hlcolor,
label=hllabel, zorder=2)

ax.axhline(0, ls="-", lw=1,
color='gray', zorder=1)
ax.axhline(0, ls="--", lw=1,
color='white' if grayscale else 'black', zorder=2)

if isinstance(benchmark, _pd.Series) or hline:
ax.legend(fontsize=12)
# if isinstance(benchmark, _pd.Series) or hline is not None:
# ax.legend(fontsize=12)
ax.legend(fontsize=12)

_plt.yscale("symlog" if log_scale else "linear")

Expand Down Expand Up @@ -311,7 +330,7 @@ def plot_timeseries(returns, benchmark=None,
return None


def plot_histogram(returns, resample="M", bins=20,
def plot_histogram(returns, benchmark, resample="M", bins=20,
fontname='Arial', grayscale=False,
title="Returns", kde=True, figsize=(10, 6),
ylabel=True, subtitle=True, compounded=True,
Expand All @@ -322,8 +341,12 @@ def plot_histogram(returns, resample="M", bins=20,
colors = ['silver', 'gray', 'black']

apply_fnc = _stats.comp if compounded else _np.sum
if benchmark is not None:
benchmark = benchmark.fillna(0).resample(resample).apply(
apply_fnc).resample(resample).last()

returns = returns.fillna(0).resample(resample).apply(
apply_fnc).resample(resample).last()
apply_fnc).resample(resample).last()

fig, ax = _plt.subplots(figsize=figsize)
ax.spines['top'].set_visible(False)
Expand All @@ -343,31 +366,53 @@ def plot_histogram(returns, resample="M", bins=20,
fig.set_facecolor('white')
ax.set_facecolor('white')

ax.axvline(returns.mean(), ls="--", lw=1.5,
color=colors[2], zorder=2, label="Average")

_sns.histplot(returns, bins=bins,
color=colors[0],
alpha=1,
kde=kde,
stat="density",
ax=ax)
_sns.kdeplot(returns, color='black', linewidth=1.5)
# Why do we need average?
# ax.axvline(returns.mean(), ls="--", lw=1.5,
# zorder=2, label="Average")

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'})
elif isinstance(returns, _pd.DataFrame):
combined_returns = benchmark.to_frame().join(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)
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)
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)

ax.xaxis.set_major_formatter(_plt.FuncFormatter(
lambda x, loc: "{:,}%".format(int(x*100))))

ax.axhline(0.01, lw=1, color="#000000", zorder=2)
ax.axvline(0, lw=1, color="#000000", zorder=2)
# Removed static lines for clarity
# ax.axhline(0.01, lw=1, color="#000000", zorder=2)
# 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.legend(fontsize=12)

# fig.autofmt_xdate()

try:
Expand Down Expand Up @@ -412,15 +457,37 @@ def plot_rolling_stats(returns, benchmark=None, title="",
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)

df = _pd.DataFrame(index=returns.index, data={returns_label: returns})
if isinstance(returns, _pd.DataFrame):
returns_label = list(returns.columns)

if isinstance(returns, _pd.Series):
df = _pd.DataFrame(index=returns.index, data={returns_label: returns})
elif isinstance(returns, _pd.DataFrame):
df = _pd.DataFrame(index=returns.index, data={col: returns[col] for col in returns.columns})
if isinstance(benchmark, _pd.Series):
df['Benchmark'] = benchmark[benchmark.index.isin(returns.index)]
df = df[['Benchmark', returns_label]].dropna()
ax.plot(df['Benchmark'], lw=lw, label="Benchmark",
if isinstance(returns, _pd.Series):
df = df[['Benchmark', returns_label]].dropna()
ax.plot(df[returns_label].dropna(), lw=lw,
label=returns.name, color=colors[1])
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)
ax.plot(df['Benchmark'], lw=lw, label=benchmark.name,
color=colors[0], alpha=.8)

ax.plot(df[returns_label].dropna(), lw=lw,
label=returns_label, color=colors[1])
else:
if isinstance(returns, _pd.Series):
df = df[[returns_label]].dropna()
ax.plot(df[returns_label].dropna(), lw=lw,
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)

# rotate and align the tick labels so they look better
fig.autofmt_xdate()
Expand All @@ -436,11 +503,12 @@ def plot_rolling_stats(returns, benchmark=None, title="",
df.index.date[-1:][0].strftime('%e %b \'%y')
), fontsize=12, color='gray')

if hline:
if grayscale:
hlcolor = 'black'
ax.axhline(hline, ls="--", lw=hlw, color=hlcolor,
label=hllabel, zorder=2)
if hline is not None:
if not isinstance(hline, _pd.Series):
if grayscale:
hlcolor = 'black'
ax.axhline(hline, ls="--", lw=hlw, color=hlcolor,
label=hllabel, zorder=2)

ax.axhline(0, ls="--", lw=1, color="#000000", zorder=2)

Expand Down Expand Up @@ -503,20 +571,38 @@ def plot_rolling_beta(returns, benchmark,
returns.index.date[-1:][0].strftime('%e %b \'%y')
), fontsize=12, color='gray')

beta = _stats.rolling_greeks(returns, benchmark, window1)['beta'].fillna(0)
ax.plot(beta, lw=lw, label=window1_label, color=colors[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])
elif isinstance(returns, _pd.DataFrame):
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})")

if window2:
ax.plot(_stats.rolling_greeks(returns, benchmark, window2)['beta'],
lw=lw, label=window2_label, color="gray", alpha=0.8)
mmin = min([-100, int(beta.min()*100)])
mmax = max([100, int(beta.max()*100)])
if isinstance(returns, _pd.Series):
ax.plot(_stats.rolling_greeks(returns, benchmark, window2)['beta'],
lw=lw, label=window2_label, color="gray", alpha=0.8)
elif isinstance(returns, _pd.DataFrame):
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)

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()])
mmin = min([-100, int(beta_min*100)])
mmax = max([100, int(beta_max*100)])
step = 50 if (mmax-mmin) >= 200 else 100
ax.set_yticks([x / 100 for x in list(range(mmin, mmax, step))])

hlcolor = 'black' if grayscale else hlcolor
ax.axhline(beta.mean(), ls="--", lw=1.5,
color=hlcolor, zorder=2)
if isinstance(returns, _pd.Series):
hlcolor = 'black' if grayscale else hlcolor
ax.axhline(beta.mean(), ls="--", lw=1.5,
color=hlcolor, zorder=2)

ax.axhline(0, ls="--", lw=1, color="#000000", zorder=2)

Expand Down Expand Up @@ -559,7 +645,7 @@ def plot_rolling_beta(returns, benchmark,


def plot_longest_drawdowns(returns, periods=5, lw=1.5,
fontname='Arial', grayscale=False,
fontname='Arial', grayscale=False, title=None,
log_scale=False, figsize=(10, 6), ylabel=True,
subtitle=True, compounded=True,
savefig=None, show=True):
Expand All @@ -579,7 +665,7 @@ def plot_longest_drawdowns(returns, periods=5, lw=1.5,
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)

fig.suptitle("Worst %.0f Drawdown Periods\n" %
fig.suptitle(f"{title} - Worst %.0f Drawdown Periods\n" %
periods, y=.99, fontweight="bold", fontname=fontname,
fontsize=14, color="black")
if subtitle:
Expand Down Expand Up @@ -646,7 +732,7 @@ def plot_longest_drawdowns(returns, periods=5, lw=1.5,

def plot_distribution(returns, figsize=(10, 6),
fontname='Arial', grayscale=False, ylabel=True,
subtitle=True, compounded=True,
subtitle=True, compounded=True, title=None,
savefig=None, show=True):

colors = _FLATUI_COLORS
Expand Down Expand Up @@ -681,7 +767,7 @@ def plot_distribution(returns, figsize=(10, 6),
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)

fig.suptitle("Return Quantiles\n", y=.99,
fig.suptitle(f"{title} - Return Quantiles\n", y=.99,
fontweight="bold", fontname=fontname,
fontsize=14, color="black")

Expand All @@ -694,13 +780,13 @@ def plot_distribution(returns, figsize=(10, 6),
fig.set_facecolor('white')
ax.set_facecolor('white')

_sns.boxplot(data=port, ax=ax, palette=tuple(colors[:5]))
port.boxplot(ax=ax)#, colo=tuple(colors[:5]))

ax.yaxis.set_major_formatter(_plt.FuncFormatter(
lambda x, loc: "{:,}%".format(int(x*100))))

if ylabel:
ax.set_ylabel('Rerurns', fontname=fontname,
ax.set_ylabel('Returns', fontname=fontname,
fontweight='bold', fontsize=12, color="black")
ax.yaxis.set_label_coords(-.1, .5)

Expand Down
Loading

0 comments on commit 0c53b4d

Please sign in to comment.