else:
f1 = plt.figure(1)
pf.plot_rolling_returns(strat_ret, factor_returns=bm_ret)
f1.show()
f2 = plt.figure(2)
pf.plot_rolling_volatility(strat_ret, factor_returns=bm_ret)
f2.show()
f3 = plt.figure(3)
pf.plot_rolling_sharpe(strat_ret)
f3.show()
f4 = plt.figure(4)
pf.plot_drawdown_periods(strat_ret)
f4.show()
f5 = plt.figure(5)
pf.plot_monthly_returns_heatmap(strat_ret)
f5.show()
f6 = plt.figure(6)
pf.plot_annual_returns(strat_ret)
f6.show()
f7 = plt.figure(7)
pf.plot_monthly_returns_dist(strat_ret)
plt.show()
f8 = plt.figure(8)
pf.create_position_tear_sheet(strat_ret, df_positions)
plt.show()
f9 = plt.figure(9)
pf.create_txn_tear_sheet(strat_ret, df_positions, df_trades)
plt.show()
f10 = plt.figure(10)
pf.create_round_trip_tear_sheet(strat_ret, df_positions, df_trades)
plt.show()
def plot_positions_tear_sheet():
pf.create_position_tear_sheet(returns=returns, positions=positions)
plt.xlabel("Date")
plt.savefig("positions")
plt.show()
vol_annual = pf.timeseries.annual_volatility(rets.tz_localize('UTC'))
sharpe = ret_annual / vol_annual
bmrk = rets.copy()
bmrk[:] = np.random.rand() / 10000
wts_dlr = wts * 1000000
wts_dlr['cash'] = 0
leverage = wts_dlr.sum(axis=1) / 1000000
pf.create_full_tear_sheet(
rets.tz_localize('UTC'),
benchmark_rets=bmrk.tz_localize('UTC'),
positions=wts_dlr,
)
pf.create_position_tear_sheet(rets.tz_localize('UTC'),
wts_dlr.tz_localize('UTC'),
gross_lev=leverage)
daily_turnover = wts.diff().abs().sum(axis=1)
daily_turnover.index.month
#dataset_names = dbm.bloom_dataset_namesm
#prev_table_name = dataset_names[0]
#
#cummulative_strategy, _ = dbm.get_table(prev_table_name)
#cummulative_strategy['PX_LAST_' + prev_table_name] = cummulative_strategy['PX_LAST']
#cummulative_strategy['PX_LAST_' + prev_table_name].fillna(method='ffill', inplace=True)
#cummulative_strategy = cummulative_strategy[['PX_LAST_' + prev_table_name]]
#
#table_present = np.zeros(len(dataset_names))
#table_present[0] = 1
def create_report(perf,
filename,
now,
doc=None,
duration=None,
param=None,
info=None,
show_image=True):
if not hasattr(perf, 'returns'):
perf['returns'] = perf['pnl'] / (perf['portfolio_value'] - perf['pnl'])
perf['returns'] = perf['returns'].replace([np.nan, np.inf, -np.inf],
0.0)
tot_positions = sum([len(x) for x in perf.positions])
if tot_positions == 0:
log.warn("No positions available")
return
rets, positions, transactions = pf.utils.extract_rets_pos_txn_from_zipline(
perf)
date_rows = OrderedDict()
if len(rets.index) > 0:
date_rows['Start date'] = rets.index[0].strftime('%Y-%m-%d')
date_rows['End date'] = rets.index[-1].strftime('%Y-%m-%d')
date_rows['Total months'] = int(len(rets) / 21)
perf_stats_series = pf.timeseries.perf_stats(rets,
positions=positions,
transactions=transactions)
benchmark_rets = returns(symbol('SPY'), rets.index[0], rets.index[-1])
benchmark_perf_stats = pf.timeseries.perf_stats(benchmark_rets)
perf_stats_df = pd.DataFrame(perf_stats_series, columns=['Backtest'])
perf_stats_df['Benchmark'] = benchmark_perf_stats
perf_stats_df[
'Spread'] = perf_stats_df['Backtest'] - perf_stats_df['Benchmark']
format_perf_stats(perf_stats_df)
drawdown_df = pf.timeseries.gen_drawdown_table(rets, top=5)
rets_interesting = pf.timeseries.extract_interesting_date_ranges(rets)
positions = utils.check_intraday('infer', rets, positions, transactions)
transactions_closed = rt.add_closing_transactions(positions, transactions)
trades = rt.extract_round_trips(
transactions_closed,
portfolio_value=positions.sum(axis='columns') / (1 + rets))
if show_image:
fig0 = None
fig1 = None
fig2 = None
fig3 = None
fig4 = None
fig5 = None
try:
fig0 = create_log_returns_chart(rets, benchmark_rets)
except Exception as e:
log.warn(e)
try:
fig1 = pf.create_returns_tear_sheet(rets,
positions,
transactions,
benchmark_rets=benchmark_rets,
return_fig=True)
except Exception as e:
log.warn(e)
try:
fig2 = pf.create_position_tear_sheet(rets,
positions,
return_fig=True)
except Exception as e:
log.warn(e)
try:
fig3 = pf.create_txn_tear_sheet(rets,
positions,
transactions,
return_fig=True)
except Exception as e:
log.warn(e)
try:
fig4 = pf.create_interesting_times_tear_sheet(rets,
return_fig=True)
except Exception as e:
log.warn(e)
try:
fig5 = pf.create_round_trip_tear_sheet(rets,
positions,
transactions,
return_fig=True)
except Exception as e:
log.warn(e)
report_suffix = "_%s_%.2f_report.htm" % (
now.strftime(DATETIME_FMT), 100. * perf_stats_series['Annual return'])
reportfile = change_extension(filename, report_suffix)
with open(reportfile, 'w') as f:
print("""<!DOCTYPE html>
<html>
<head>
<title>Performance Report</title>
<style >
body {
font-family: Arial, Helvetica, sans-serif;
}
table {
border-collapse: collapse;
}
tbody tr:nth-child(odd) {
background-color: lightgrey;
}
tbody tr:nth-child(even) {
background-color: white;
}
tr th {
border: none;
text-align: right;
padding: 2px 5px 2px;
}
tr td {
border: none;
text-align: right;
padding: 2px 5px 2px;
}
</style>
<script type="text/javascript">
function showElement() {
element = document.getElementById('code');
element.style.visibility = 'visible';
}
function hideElement() {
element = document.getElementById('code');
element.style.visibility = 'hidden';
}
</script>
</head>
<body>""",
file=f)
print("<h1>Performance report for " + os.path.basename(filename) +
"</h1>",
file=f)
print("<p>Created on %s</p>" % (now), file=f)
if duration is not None:
print("<p>Backtest executed in %s</p>" %
(time.strftime("%H:%M:%S", time.gmtime(duration))),
file=f)
if doc is not None:
print('<h3>Description</h3>', file=f)
print('<p style="white-space: pre">%s</p>' % doc.strip(), file=f)
if param is not None and len(param) > 0:
print('<h3>Parameters</h3>', file=f)
print('<pre>%s</pre><br/>' % str(param), file=f)
if info is not None and len(info) > 0:
print('<h3>Info</h3>', file=f)
print('<pre>%s</pre><br/>' % str(info), file=f)
print(to_html_table(perf_stats_df,
float_format='{0:.2f}'.format,
header_rows=date_rows),
file=f)
print("<br/>", file=f)
if show_image:
if fig0 is not None:
print("<h3>Log Returns</h3>", file=f)
print(_to_img(fig0), file=f)
print("<br/>", file=f)
print(to_html_table(
drawdown_df.sort_values('Net drawdown in %', ascending=False),
name='Worst drawdown periods',
float_format='{0:.2f}'.format,
),
file=f)
print("<br/>", file=f)
print(to_html_table(pd.DataFrame(rets_interesting).describe().
transpose().loc[:, ['mean', 'min', 'max']] * 100,
name='Stress Events',
float_format='{0:.2f}%'.format),
file=f)
print("<br/>", file=f)
if len(trades) >= 5:
stats = rt.gen_round_trip_stats(trades)
print(to_html_table(stats['summary'],
float_format='{:.2f}'.format,
name='Summary stats'),
file=f)
print("<br/>", file=f)
print(to_html_table(stats['pnl'],
float_format='${:.2f}'.format,
name='PnL stats'),
file=f)
print("<br/>", file=f)
print(to_html_table(stats['duration'],
float_format='{:.2f}'.format,
name='Duration stats'),
file=f)
print("<br/>", file=f)
print(to_html_table(stats['returns'] * 100,
float_format='{:.2f}%'.format,
name='Return stats'),
file=f)
print("<br/>", file=f)
stats['symbols'].columns = stats['symbols'].columns.map(
format_asset)
print(to_html_table(stats['symbols'] * 100,
float_format='{:.2f}%'.format,
name='Symbol stats'),
file=f)
if show_image:
if fig1 is not None:
print("<h3>Returns</h3>", file=f)
print(_to_img(fig1), file=f)
if fig2 is not None:
print("<h3>Positions</h3>", file=f)
print(_to_img(fig2), file=f)
if fig3 is not None:
print("<h3>Transactions</h3>", file=f)
print(_to_img(fig3), file=f)
if fig4 is not None:
print("<h3>Interesting Times</h3>", file=f)
print(_to_img(fig4), file=f)
if fig5 is not None:
print("<h3>Trades</h3>", file=f)
print(_to_img(fig5), file=f)
print('<br/>', file=f)
print(
'<button onclick="showElement()">Show Code</button> <button onclick="hideElement()">Hide Code</button>',
file=f)
print('<pre id="code" style="visibility: hidden">', file=f)
print(open(filename, "r").read(), file=f)
print('</pre>', file=f)
print("</body>\n</html>", file=f)
def position_tear_sheet(returns, positions, sect_map):
ret = returns
pos = positions
#pos.index = pos.index.tz_localize('UTC')
#ret.index = ret.index.tz_localize('UTC')
return pyf.create_position_tear_sheet(ret, pos, sector_mappings=sect_map)
