def add_summary_page(self):
"""Build a table which is shown on the first page which gives an overview of the portfolios"""
s = PortfolioSummary()
s.include_long_short()
pieces = []
for r in self.results:
tmp = s(r.port, PortfolioSummary.analyze_returns)
tmp['desc'] = r.desc
tmp['sid'] = r.sid
tmp = tmp.set_index(['sid', 'desc'],
append=1).reorder_levels([2, 1, 0])
pieces.append(tmp)
frame = pd.concat(pieces)
tf = self.pdf.table_formatter(frame)
tf.apply_basic_style(cmap=self.table_style)
# [col.guess_format(pcts=1, trunc_dot_zeros=1) for col in tf.cells.iter_cols()]
tf.cells.match_column_labels(
['nmonths', 'cnt', 'win cnt', 'lose cnt', 'dur max']).int_format()
tf.cells.match_column_labels(['sharpe ann', 'sortino',
'dur avg']).float_format(precision=1)
tf.cells.match_column_labels(['maxdd dt']).apply_format(
new_datetime_formatter('%d-%b-%y'))
tf.cells.match_column_labels([
'cagr', 'mret avg', 'mret std ann', 'ret std', 'mret avg ann',
'maxdd', 'avg dd', 'winpct', 'ret avg', 'ret min', 'ret max'
]).percent_format()
self.pdf.build_page('summary', {'F1': tf.build()})
def add_summary_page(self):
"""Build a table which is shown on the first page which gives an overview of the portfolios"""
s = PortfolioSummary()
s.include_long_short()
pieces = []
for r in self.results:
tmp = s(r.port, PortfolioSummary.analyze_returns)
tmp["desc"] = r.desc
tmp["sid"] = r.sid
tmp = tmp.set_index(["sid", "desc"],
append=1).reorder_levels([2, 1, 0])
pieces.append(tmp)
frame = pd.concat(pieces)
tf = self.pdf.table_formatter(frame)
tf.apply_basic_style(cmap=self.table_style)
# [col.guess_format(pcts=1, trunc_dot_zeros=1) for col in tf.cells.iter_cols()]
tf.cells.match_column_labels(
["nmonths", "cnt", "win cnt", "lose cnt", "dur max"]).int_format()
tf.cells.match_column_labels(["sharpe ann", "sortino",
"dur avg"]).float_format(precision=1)
tf.cells.match_column_labels(["maxdd dt"]).apply_format(
new_datetime_formatter("%d-%b-%y"))
tf.cells.match_column_labels([
"cagr",
"mret avg",
"mret std ann",
"ret std",
"mret avg ann",
"maxdd",
"avg dd",
"winpct",
"ret avg",
"ret min",
"ret max",
]).percent_format()
self.pdf.build_page("summary", {"F1": tf.build()})
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)