def plot_rets(self, ls=1, ax=None):
"""Plot each of the position returns
:param ls: True, if positions should be broken into long/short
:param ax: Axes
:param regr: True, if regression line is shown
"""
import matplotlib.pyplot as plt
from tia.util.mplot import AxesFormat
if ax is None:
ax = plt.gca()
frame = self.frame
if not ls:
ax.scatter(frame.index, frame.ret, c="k", marker="o", label="All")
else:
if len(self.long_pids) > 0:
lframe = frame.ix[frame.index.isin(self.long_pids)]
ax.scatter(lframe.index, lframe.ret, c="k", marker="o", label="Long")
if len(self.short_pids) > 0:
sframe = frame.ix[frame.index.isin(self.short_pids)]
ax.scatter(sframe.index, sframe.ret, c="r", marker="o", label="Short")
# set some boundaries
AxesFormat().Y.percent().apply()
ax.set_xlim(0, frame.index.max() + 3)
ax.set_xlabel("pid")
ax.set_ylabel("return")
ax.legend(loc="upper left")
return ax
def plot_ltd(self, ax=None, style='k', label='ltd', show_dd=1, title=True, legend=1):
ltd = self.ltd_rets
ax = ltd.plot(ax=ax, style=style, label=label)
if show_dd:
dd = self.drawdowns
dd.plot(style='r', label='drawdowns', alpha=.5, ax=ax)
ax.fill_between(dd.index, 0, dd.values, facecolor='red', alpha=.25)
fmt = PercentFormatter
AxesFormat().Y.percent().X.label("").apply(ax)
legend and ax.legend(loc='upper left', prop={'size': 12})
# show the actualy date and value
mdt, mdd = self.maxdd_dt, self.maxdd
bbox_props = dict(boxstyle="round", fc="w", ec="0.5", alpha=0.25)
try:
dtstr = '{0}'.format(mdt.to_period())
except:
# assume daily
dtstr = '{0}'.format(hasattr(mdt, 'date') and mdt.date() or mdt)
ax.text(mdt, dd[mdt], "{1} \n {0}".format(fmt(mdd), dtstr).strip(), ha="center", va="top", size=8,
bbox=bbox_props)
if title is True:
pf = new_percent_formatter(1, parens=False, trunc_dot_zeros=True)
ff = new_float_formatter(precision=1, parens=False, trunc_dot_zeros=True)
total = pf(self.ltd_ann)
vol = pf(self.std_ann)
sh = ff(self.sharpe_ann)
mdd = pf(self.maxdd)
title = 'ret$\mathregular{_{ann}}$ %s vol$\mathregular{_{ann}}$ %s sharpe %s maxdd %s' % (
total, vol, sh, mdd)
title and ax.set_title(title, fontdict=dict(fontsize=10, fontweight='bold'))
return ax
def plot_hist(self, ax=None, **histplot_kwargs):
pf = new_percent_formatter(precision=1,
parens=False,
trunc_dot_zeros=1)
ff = new_float_formatter(precision=1, parens=False, trunc_dot_zeros=1)
ax = self.rets.hist(ax=ax, **histplot_kwargs)
AxesFormat().X.percent(1).apply(ax)
m, s, sk, ku = pf(self.mean), pf(self.std), ff(self.skew), ff(
self.kurtosis)
txt = (
"$\mathregular{\mu}$=%s $\mathregular{\sigma}$=%s skew=%s kurt=%s"
% (m, s, sk, ku))
bbox = dict(facecolor="white", alpha=0.5)
ax.text(
0,
1,
txt,
fontdict={"fontweight": "bold"},
bbox=bbox,
ha="left",
va="top",
transform=ax.transAxes,
)
return ax
def plot_ltd(self,
ax=None,
style="k",
label="ltd",
show_dd=1,
guess_xlabel=1,
title=True):
ltd = self.ltd_frame.pl
ax = ltd.plot(ax=ax, style=style, label=label)
if show_dd:
dd = self.drawdowns
dd.plot(style="r", label="drawdowns", alpha=0.5)
ax.fill_between(dd.index,
0,
dd.values,
facecolor="red",
alpha=0.25)
fmt = lambda x: x
# guess the formatter
if guess_xlabel:
from tia.util.fmt import guess_formatter
from tia.util.mplot import AxesFormat
fmt = guess_formatter(ltd.abs().max(), precision=1)
AxesFormat().Y.apply_format(fmt).apply(ax)
ax.legend(loc="upper left", prop={"size": 12})
# show the actualy date and value
mdt, mdd = self.maxdd_dt, self.maxdd
bbox_props = dict(boxstyle="round", fc="w", ec="0.5", alpha=0.25)
try:
dtstr = "{0}".format(mdt.to_period())
except:
# assume daily
dtstr = "{0}".format(
hasattr(mdt, "date") and mdt.date() or mdt)
ax.text(
mdt,
dd[mdt],
"{1} \n {0}".format(fmt(mdd), dtstr).strip(),
ha="center",
va="top",
size=8,
bbox=bbox_props,
)
if title is True:
df = new_dynamic_formatter(precision=1,
parens=False,
trunc_dot_zeros=True)
total = df(ltd.iloc[-1])
vol = df(self.std)
mdd = df(self.maxdd)
title = "pnl %s vol %s maxdd %s" % (total, vol, mdd)
title and ax.set_title(title,
fontdict=dict(fontsize=10, fontweight="bold"))
return ax
def plot_ret_range(self, ax=None, ls=0, dur=0):
"""Plot the return range for each position
:param ax: Axes
"""
import matplotlib.pyplot as plt
from tia.util.mplot import AxesFormat
if ax is None:
ax = plt.gca()
frame = self.frame
pids = frame.index
min_rets = pd.Series(
[self[pid].performance.ltd_txn.min() for pid in pids], index=pids
)
max_rets = pd.Series(
[self[pid].performance.ltd_txn.max() for pid in pids], index=pids
)
if not ls:
s = frame.duration + 20 if dur else 20
ax.scatter(frame.index, frame.ret, s=s, c="k", marker="o", label="All")
ax.vlines(pids, min_rets, max_rets)
else:
if len(self.long_pids) > 0:
lframe = frame.ix[frame.index.isin(self.long_pids)]
s = lframe.duration + 20 if dur else 20
ax.scatter(
lframe.index, lframe.ret, s=s, c="k", marker="o", label="Long"
)
ax.vlines(lframe.index, min_rets[lframe.index], max_rets[frame.index])
if len(self.short_pids) > 0:
sframe = frame.ix[frame.index.isin(self.short_pids)]
s = sframe.duration + 20 if dur else 20
ax.scatter(
sframe.index, sframe.ret, s=s, c="r", marker="o", label="Short"
)
ax.vlines(sframe.index, min_rets[sframe.index], max_rets[sframe.index])
AxesFormat().Y.percent().apply()
ax.axhline(color="k", linestyle="--")
ax.set_xlim(0, frame.index.max() + 3)
ax.set_xlabel("pid")
ax.set_ylabel("return")
ax.legend(loc="upper left")
return ax
def plot_return_on_dollar(rets, title='Return on $1', show_maxdd=0, figsize=None, ax=None, append=0, label=None, **plot_args):
""" Show the cumulative return of specified rets and max drawdowns if selected."""
crets = (1. + returns_cumulative(rets, expanding=1))
if isinstance(crets, pd.DataFrame):
tmp = crets.copy()
for c in tmp.columns:
s = tmp[c]
fv = s.first_valid_index()
fi = s.index.get_loc(fv)
if fi != 0:
tmp.ix[fi - 1, c] = 1.
else:
if not s.index.freq:
# no frequency set
freq = guess_freq(s.index)
s = s.asfreq(freq)
first = s.index.shift(-1)[0]
tmp = pd.concat([pd.DataFrame({c: [1.]}, index=[first]), tmp])
crets = tmp
if append:
toadd = crets.index.shift(1)[-1]
crets = pd.concat([crets, pd.DataFrame(np.nan, columns=crets.columns, index=[toadd])])
else:
fv = crets.first_valid_index()
fi = crets.index.get_loc(fv)
if fi != 0:
crets = crets.copy()
crets.iloc[fi - 1] = 1.
else:
if not crets.index.freq:
first = crets.asfreq(guess_freq(crets.index)).index.shift(-1)[0]
else:
first = crets.index.shift(-1)[0]
tmp = pd.Series([1.], index=[first])
tmp = tmp.append(crets)
crets = tmp
if append:
toadd = pd.Series(np.nan, index=[crets.index.shift(1)[-1]])
crets = crets.append(toadd)
ax = crets.plot(figsize=figsize, title=title, ax=ax, label=label, **plot_args)
AxesFormat().Y.apply_format(new_float_formatter()).X.label("").apply(ax)
#ax.tick_params(labelsize=14)
if show_maxdd:
# find the max drawdown available by using original rets
if isinstance(rets, pd.DataFrame):
iterator = rets.iteritems()
else:
iterator = iter([('', rets)])
for c, col in iterator:
dd, dt = max_drawdown(col, inc_date=1)
lbl = c and c + ' maxdd' or 'maxdd'
# get cret to place annotation correctly
if isinstance(crets, pd.DataFrame):
amt = crets.ix[dt, c]
else:
amt = crets[dt]
bbox_props = dict(boxstyle="round", fc="w", ec="0.5", alpha=0.7)
# sub = lambda c: c and len(c) > 2 and c[:2] or c
try:
dtstr = '{0}'.format(dt.to_period())
except:
dtstr = '{0}'.format(dt)
ax.text(dt, amt, "mdd {0}".format(dtstr).strip(), ha="center",
va="center", size=10, bbox=bbox_props)
plt.tight_layout()
def add_position_page(self, result):
def dofmt(t):
t.apply_basic_style(cmap=self.table_style)
[
row.guess_format(pcts=1, trunc_dot_zeros=1)
for row in t.cells.iter_rows()
]
ncols = len(t.formatted_values.columns)
t.set_col_widths(pcts=[1. / ncols] * ncols)
return t
def do_rename(df):
d = {
'consecutive_win_cnt_max': 'win_streak',
'consecutive_loss_cnt_max': 'lose_streak'
}
return df.rename(index=lambda c: d.get(c, c))
pdf = self.pdf
figures = self.figures
port = result.port
buyhold = result.buyhold
sframe = pd.DataFrame({
'all': port.positions.stats.series,
'long': port.long.positions.stats.series,
'short': port.short.positions.stats.series
})
tf = pdf.table_formatter(insert_level(sframe, 'Position', copy=True))
stable = dofmt(tf).build()
s = PortfolioSummary()
s.include_long_short().include_win_loss()
dframe = s(port, PortfolioSummary.analyze_returns).T.ix['pos']
tf = pdf.table_formatter(
do_rename(insert_level(dframe, 'Position', copy=True)))
dtable = dofmt(tf).build()
# Plot Position Returns
f, ax = self.create_ax()
port.positions.plot_rets(ax=ax)
plt.tight_layout()
figures.savefig(key='pos_ls', clear=1)
# Plot Position Ranges
f, ax = self.create_ax(figsize=(8, 3))
port.positions.plot_ret_range(ls=1, dur=1, ax=ax)
plt.tight_layout()
figures.savefig(key='pos_rng', clear=1)
# Plot Long Short Positions with regression line
tmp = port.position_frame[['side', 'ret']].reset_index()
g = sns.lmplot("pid", "ret", col="side", hue="side", data=tmp, size=3)
AxesFormat().Y.percent().apply()
figures.savefig(key='pos_ls', clear=1)
# Plot Return vs Duration
tmp = port.position_frame[['ret', 'duration', 'side']]
diag_kws = {}
if len(port.position_frame.index) <= 1:
diag_kws = {'range': (-100, 100)}
sns.pairplot(tmp, hue="side", size=3, diag_kws=diag_kws)
figures.savefig(key='pos_pair', clear=1)
toimg = lambda path: rlab.new_dynamic_image(path)
itms = {
'F1':
toimg(figures['pos_rng']),
'F3':
toimg(figures['pos_ls']),
'F2':
toimg(figures['pos_pair']),
'F5':
stable,
'F6':
dtable,
'HDR':
self.title_bar('{0} - {1} - position summary'.format(
result.sid, result.desc))
}
pdf.build_page('positions', itms)
def add_portfolio_page(self, result):
def alpha_beta(p, bm):
model = pd.ols(x=bm.rets, y=p.rets)
beta = model.beta[0]
alpha = p.total_ann - beta * bm.total_ann
s = pd.Series({'alpha': alpha, 'beta': beta})
return s
def rs(port1, port2, kind='dly_ret_stats'):
stats = getattr(port1, kind)
ab = alpha_beta(stats, getattr(port2, kind))
tmp = stats.series.append(ab)
tmp.name = stats.series.name
return tmp
def dofmt(t):
t.apply_basic_style(cmap=self.table_style)
[
row.guess_format(pcts=1, trunc_dot_zeros=1)
for row in t.cells.iter_rows()
]
ncols = len(t.formatted_values.columns)
t.set_col_widths(pcts=[1. / ncols] * ncols)
def do_rename(df):
d = {
'consecutive_win_cnt_max': 'win_streak',
'consecutive_loss_cnt_max': 'lose_streak'
}
return df.rename(index=lambda c: d.get(c, c))
# Build the pdf tables
pdf = self.pdf
figures = self.figures
port = result.port
buyhold = result.buyhold
sframe = pd.DataFrame([
rs(port, buyhold, 'dly_ret_stats'),
rs(port, buyhold, 'weekly_ret_stats'),
rs(port, buyhold, 'monthly_ret_stats'),
rs(port, buyhold, 'quarterly_ret_stats')
]).T
tf = pdf.table_formatter(insert_level(sframe, 'Portfolio', copy=True))
dofmt(tf)
stable = tf.build()
s = PortfolioSummary()
s.include_long_short().include_win_loss()
dframe = s(port, PortfolioSummary.analyze_returns).T
tf = pdf.table_formatter(
do_rename(insert_level(dframe.ix['port'], 'Portfolio', copy=True)))
dofmt(tf)
dtable = tf.build()
# Return on $1 image
f, ax = self.create_ax()
buyhold.plot_ret_on_dollar('B', label='Buy & Hold', ax=ax)
port.plot_ret_on_dollar('B', label=result.desc, ax=ax, color='k')
ax.legend(loc='upper left')
ax.set_title('vs Buy & Hold')
plt.tight_layout()
figures.savefig(key='buyhold', clear=1)
# Drawdown image
f, ax = self.create_ax()
port.dly_ret_stats.plot_ltd(ax=ax)
plt.tight_layout()
figures.savefig(key='dd', clear=1)
# Long / Short Returns
f, ax = self.create_ax()
port.plot_ret_on_dollar('B', label='All', color='k', ax=ax)
port.long.plot_ret_on_dollar('B', label='Long', ax=ax)
port.short.plot_ret_on_dollar('B', label='Short', ax=ax)
ax.legend(loc='upper left')
figures.savefig(key='ls', clear=1)
# Sharpe / Ann Vol
f, ax = self.create_ax()
perf.sharpe_annualized(port.monthly_rets,
expanding=1).iloc[3:].plot(ax=ax,
color='k',
label='sharpe')
ax.set_ylabel('sharpe ann', color='k')
ax2 = ax.twinx()
perf.std_annualized(port.monthly_rets,
expanding=1).iloc[3:].plot(ax=ax2,
label='vol',
color='b',
alpha=1)
ax2.set_ylabel('vol ann', color='b')
plt.tight_layout()
figures.savefig(key='sharpe', clear=1)
# Monthly Returns Bar Chart
f, ax = self.create_ax()
tmp = pd.DataFrame({
'All': port.monthly_rets.to_period('M'),
'Long': port.long.monthly_rets.to_period('M'),
'Short': port.short.monthly_rets.to_period('M')
})
tmp.plot(kind='bar',
ax=ax,
color=['k', self.long_color, self.short_color])
AxesFormat().Y.percent().X.rotate().apply()
plt.tight_layout()
ax.set_title('Monthly Returns')
figures.savefig(key='mrets', clear=1)
# Monthly Returns Box Plot
f, ax = self.create_ax()
sns.boxplot(tmp,
ax=ax,
color=['gray', self.long_color, self.short_color])
ax.set_title('Monthly Returns')
AxesFormat().Y.percent().apply()
plt.tight_layout()
figures.savefig(key='mrets_box', clear=1)
# Build the PDF Page
toimg = lambda path: rlab.new_dynamic_image(path)
itms = {
'F1':
toimg(figures['buyhold']),
'F2':
toimg(figures['dd']),
'F3':
toimg(figures['ls']),
'F4':
toimg(figures['mrets']),
'F5':
toimg(figures['sharpe']),
'F6':
toimg(figures['mrets_box']),
'F7':
stable,
'F8':
dtable,
'HDR':
self.title_bar('{0} - {1} - portfolio summary'.format(
result.sid, result.desc))
}
pdf.build_page('portfolio', itms)
def add_position_page(self, result):
def dofmt(t):
t.apply_basic_style(cmap=self.table_style)
[
row.guess_format(pcts=1, trunc_dot_zeros=1)
for row in t.cells.iter_rows()
]
ncols = len(t.formatted_values.columns)
t.set_col_widths(pcts=[1.0 / ncols] * ncols)
return t
def do_rename(df):
d = {
"consecutive_win_cnt_max": "win_streak",
"consecutive_loss_cnt_max": "lose_streak",
}
return df.rename(index=lambda c: d.get(c, c))
pdf = self.pdf
figures = self.figures
port = result.port
buyhold = result.buyhold
sframe = pd.DataFrame({
"all": port.positions.stats.series,
"long": port.long.positions.stats.series,
"short": port.short.positions.stats.series,
})
tf = pdf.table_formatter(insert_level(sframe, "Position", copy=True))
stable = dofmt(tf).build()
s = PortfolioSummary()
s.include_long_short().include_win_loss()
dframe = s(port, PortfolioSummary.analyze_returns).T.ix["pos"]
tf = pdf.table_formatter(
do_rename(insert_level(dframe, "Position", copy=True)))
dtable = dofmt(tf).build()
# Plot Position Returns
f, ax = self.create_ax()
port.positions.plot_rets(ax=ax)
plt.tight_layout()
figures.savefig(key="pos_ls", clear=1)
# Plot Position Ranges
f, ax = self.create_ax(figsize=(8, 3))
port.positions.plot_ret_range(ls=1, dur=1, ax=ax)
plt.tight_layout()
figures.savefig(key="pos_rng", clear=1)
# Plot Long Short Positions with regression line
tmp = port.position_frame[["side", "ret"]].reset_index()
g = sns.lmplot("pid", "ret", col="side", hue="side", data=tmp, size=3)
AxesFormat().Y.percent().apply()
figures.savefig(key="pos_ls", clear=1)
# Plot Return vs Duration
tmp = port.position_frame[["ret", "duration", "side"]]
diag_kws = {}
if len(port.position_frame.index) <= 1:
diag_kws = {"range": (-100, 100)}
sns.pairplot(tmp, hue="side", size=3, diag_kws=diag_kws)
figures.savefig(key="pos_pair", clear=1)
toimg = lambda path: rlab.new_dynamic_image(path)
itms = {
"F1":
toimg(figures["pos_rng"]),
"F3":
toimg(figures["pos_ls"]),
"F2":
toimg(figures["pos_pair"]),
"F5":
stable,
"F6":
dtable,
"HDR":
self.title_bar("{0} - {1} - position summary".format(
result.sid, result.desc)),
}
pdf.build_page("positions", itms)
def add_portfolio_page(self, result):
def alpha_beta(p, bm):
model = pd.ols(x=bm.rets, y=p.rets)
beta = model.beta[0]
alpha = p.total_ann - beta * bm.total_ann
s = pd.Series({"alpha": alpha, "beta": beta})
return s
def rs(port1, port2, kind="dly_ret_stats"):
stats = getattr(port1, kind)
ab = alpha_beta(stats, getattr(port2, kind))
tmp = stats.series.append(ab)
tmp.name = stats.series.name
return tmp
def dofmt(t):
t.apply_basic_style(cmap=self.table_style)
[
row.guess_format(pcts=1, trunc_dot_zeros=1)
for row in t.cells.iter_rows()
]
ncols = len(t.formatted_values.columns)
t.set_col_widths(pcts=[1.0 / ncols] * ncols)
def do_rename(df):
d = {
"consecutive_win_cnt_max": "win_streak",
"consecutive_loss_cnt_max": "lose_streak",
}
return df.rename(index=lambda c: d.get(c, c))
# Build the pdf tables
pdf = self.pdf
figures = self.figures
port = result.port
buyhold = result.buyhold
sframe = pd.DataFrame([
rs(port, buyhold, "dly_ret_stats"),
rs(port, buyhold, "weekly_ret_stats"),
rs(port, buyhold, "monthly_ret_stats"),
rs(port, buyhold, "quarterly_ret_stats"),
]).T
tf = pdf.table_formatter(insert_level(sframe, "Portfolio", copy=True))
dofmt(tf)
stable = tf.build()
s = PortfolioSummary()
s.include_long_short().include_win_loss()
dframe = s(port, PortfolioSummary.analyze_returns).T
tf = pdf.table_formatter(
do_rename(insert_level(dframe.ix["port"], "Portfolio", copy=True)))
dofmt(tf)
dtable = tf.build()
# Return on $1 image
f, ax = self.create_ax()
buyhold.plot_ret_on_dollar("B", label="Buy & Hold", ax=ax)
port.plot_ret_on_dollar("B", label=result.desc, ax=ax, color="k")
ax.legend(loc="upper left")
ax.set_title("vs Buy & Hold")
plt.tight_layout()
figures.savefig(key="buyhold", clear=1)
# Drawdown image
f, ax = self.create_ax()
port.dly_ret_stats.plot_ltd(ax=ax)
plt.tight_layout()
figures.savefig(key="dd", clear=1)
# Long / Short Returns
f, ax = self.create_ax()
port.plot_ret_on_dollar("B", label="All", color="k", ax=ax)
port.long.plot_ret_on_dollar("B", label="Long", ax=ax)
port.short.plot_ret_on_dollar("B", label="Short", ax=ax)
ax.legend(loc="upper left")
figures.savefig(key="ls", clear=1)
# Sharpe / Ann Vol
f, ax = self.create_ax()
perf.sharpe_annualized(port.monthly_rets,
expanding=1).iloc[3:].plot(ax=ax,
color="k",
label="sharpe")
ax.set_ylabel("sharpe ann", color="k")
ax2 = ax.twinx()
perf.std_annualized(port.monthly_rets,
expanding=1).iloc[3:].plot(ax=ax2,
label="vol",
color="b",
alpha=1)
ax2.set_ylabel("vol ann", color="b")
plt.tight_layout()
figures.savefig(key="sharpe", clear=1)
# Monthly Returns Bar Chart
f, ax = self.create_ax()
tmp = pd.DataFrame({
"All": port.monthly_rets.to_period("M"),
"Long": port.long.monthly_rets.to_period("M"),
"Short": port.short.monthly_rets.to_period("M"),
})
tmp.plot(kind="bar",
ax=ax,
color=["k", self.long_color, self.short_color])
AxesFormat().Y.percent().X.rotate().apply()
plt.tight_layout()
ax.set_title("Monthly Returns")
figures.savefig(key="mrets", clear=1)
# Monthly Returns Box Plot
f, ax = self.create_ax()
sns.boxplot(tmp,
ax=ax,
color=["gray", self.long_color, self.short_color])
ax.set_title("Monthly Returns")
AxesFormat().Y.percent().apply()
plt.tight_layout()
figures.savefig(key="mrets_box", clear=1)
# Build the PDF Page
toimg = lambda path: rlab.new_dynamic_image(path)
itms = {
"F1":
toimg(figures["buyhold"]),
"F2":
toimg(figures["dd"]),
"F3":
toimg(figures["ls"]),
"F4":
toimg(figures["mrets"]),
"F5":
toimg(figures["sharpe"]),
"F6":
toimg(figures["mrets_box"]),
"F7":
stable,
"F8":
dtable,
"HDR":
self.title_bar("{0} - {1} - portfolio summary".format(
result.sid, result.desc)),
}
pdf.build_page("portfolio", itms)
