def get_results(self):
"""
Return a dict with all important results & stats.
"""
# Equity
equity_s = pd.Series(self.equity).sort_index()
# Returns
returns_s = equity_s.pct_change().fillna(0.0)
# Rolling Annualised Sharpe
rolling = returns_s.rolling(window=self.periods)
rolling_sharpe_s = np.sqrt(
self.periods) * (rolling.mean() / rolling.std())
# Cummulative Returns
# YOE log(1+Rt)~Rt, por tanto exp(log(1+Rt)+log(1+Rt-1)+...))~Rt*Rt-1*...
cum_returns_s = np.exp(np.log(1 + returns_s).cumsum())
# Drawdown, max drawdown, max drawdown duration
dd_s, max_dd, dd_dur = perf.create_drawdowns(cum_returns_s)
statistics = {}
# Equity statistics
statistics["sharpe"] = perf.create_sharpe_ratio(
returns_s, self.periods)
statistics["drawdowns"] = dd_s
# TODO: need to have max_drawdown so it can be printed at end of test
statistics["max_drawdown"] = max_dd
statistics["max_drawdown_pct"] = max_dd
statistics["max_drawdown_duration"] = dd_dur
statistics["equity"] = equity_s
statistics["returns"] = returns_s
statistics["rolling_sharpe"] = rolling_sharpe_s
statistics["cum_returns"] = cum_returns_s
positions = self._get_positions()
if positions is not None:
statistics["positions"] = positions
# Benchmark statistics if benchmark ticker specified
if self.benchmark is not None:
equity_b = pd.Series(self.equity_benchmark).sort_index()
returns_b = equity_b.pct_change().fillna(0.0)
rolling_b = returns_b.rolling(window=self.periods)
rolling_sharpe_b = np.sqrt(
self.periods) * (rolling_b.mean() / rolling_b.std())
cum_returns_b = np.exp(np.log(1 + returns_b).cumsum())
dd_b, max_dd_b, dd_dur_b = perf.create_drawdowns(cum_returns_b)
statistics["sharpe_b"] = perf.create_sharpe_ratio(returns_b)
statistics["drawdowns_b"] = dd_b
statistics["max_drawdown_pct_b"] = max_dd_b
statistics["max_drawdown_duration_b"] = dd_dur_b
statistics["equity_b"] = equity_b
statistics["returns_b"] = returns_b
statistics["rolling_sharpe_b"] = rolling_sharpe_b
statistics["cum_returns_b"] = cum_returns_b
return statistics
def get_results(self):
"""
Return a dict with all important results & stats.
"""
# Equity
equity_s = pd.Series(self.equity).sort_index()
# Returns
returns_s = equity_s.pct_change().fillna(0.0)
# Rolling Annualised Sharpe
rolling = returns_s.rolling(window=self.periods)
rolling_sharpe_s = np.sqrt(self.periods) * (
rolling.mean() / rolling.std()
)
# Cummulative Returns
cum_returns_s = np.exp(np.log(1 + returns_s).cumsum())
# Drawdown, max drawdown, max drawdown duration
dd_s, max_dd, dd_dur = perf.create_drawdowns(cum_returns_s)
statistics = {}
# Equity statistics
statistics["sharpe"] = perf.create_sharpe_ratio(
returns_s, self.periods
)
statistics["drawdowns"] = dd_s
# TODO: need to have max_drawdown so it can be printed at end of test
statistics["max_drawdown"] = max_dd
statistics["max_drawdown_pct"] = max_dd
statistics["max_drawdown_duration"] = dd_dur
statistics["equity"] = equity_s
statistics["returns"] = returns_s
statistics["rolling_sharpe"] = rolling_sharpe_s
statistics["cum_returns"] = cum_returns_s
statistics["positions"] = self._get_positions()
# Benchmark statistics if benchmark ticker specified
if self.benchmark is not None:
equity_b = pd.Series(self.equity_benchmark).sort_index()
returns_b = equity_b.pct_change().fillna(0.0)
rolling_b = returns_b.rolling(window=self.periods)
rolling_sharpe_b = np.sqrt(self.periods) * (
rolling_b.mean() / rolling_b.std()
)
cum_returns_b = np.exp(np.log(1 + returns_b).cumsum())
dd_b, max_dd_b, dd_dur_b = perf.create_drawdowns(cum_returns_b)
statistics["sharpe_b"] = perf.create_sharpe_ratio(returns_b)
statistics["drawdowns_b"] = dd_b
statistics["max_drawdown_pct_b"] = max_dd_b
statistics["max_drawdown_duration_b"] = dd_dur_b
statistics["equity_b"] = equity_b
statistics["returns_b"] = returns_b
statistics["rolling_sharpe_b"] = rolling_sharpe_b
statistics["cum_returns_b"] = cum_returns_b
return statistics
def get_results(self, equity_df):
"""
Return a dict with all important results & stats.
"""
# Returns
equity_df["returns"] = equity_df["Equity"].pct_change().fillna(0.0)
# Cummulative Returns
equity_df["cum_returns"] = np.exp(np.log(1 + equity_df["returns"]).cumsum())
# Drawdown, max drawdown, max drawdown duration
dd_s, max_dd, dd_dur = perf.create_drawdowns(equity_df["cum_returns"])
# Equity statistics
statistics = {}
statistics["sharpe"] = perf.create_sharpe_ratio(
equity_df["returns"], self.periods
)
statistics["drawdowns"] = dd_s
statistics["max_drawdown"] = max_dd
statistics["max_drawdown_pct"] = max_dd
statistics["max_drawdown_duration"] = dd_dur
statistics["equity"] = equity_df["Equity"]
statistics["returns"] = equity_df["returns"]
statistics["cum_returns"] = equity_df["cum_returns"]
return statistics
def get_results(self):
"""
Return a dict with all important results & stats.
"""
# Equity
equity_s = pd.Series(self.equity).sort_index()
# Returns
returns_s = equity_s.pct_change().fillna(0.0)
# Cummulative Returns
cum_returns_s = np.exp(np.log(1 + returns_s).cumsum())
# Drawdown, max drawdown, max drawdown duration
dd_s, max_dd, dd_dur = perf.create_drawdowns(cum_returns_s)
statistics = {}
# Equity statistics
statistics["sharpe"] = perf.create_sharpe_ratio(
returns_s, self.periods)
statistics["drawdowns"] = dd_s
# TODO: need to have max_drawdown so it can be printed at end of test
statistics["max_drawdown"] = max_dd
statistics["max_drawdown_pct"] = max_dd
statistics["max_drawdown_duration"] = dd_dur
statistics["equity"] = equity_s
statistics["returns"] = returns_s
statistics["cum_returns"] = cum_returns_s
statistics["positions"] = self._get_positions()
# Benchmark statistics if benchmark ticker specified
if self.benchmark is not None:
equity_b = pd.Series(self.equity_benchmark).sort_index()
returns_b = equity_b.pct_change().fillna(0.0)
cum_returns_b = np.exp(np.log(1 + returns_b).cumsum())
dd_b, max_dd_b, dd_dur_b = perf.create_drawdowns(cum_returns_b)
statistics["sharpe_b"] = perf.create_sharpe_ratio(returns_b)
statistics["drawdowns_b"] = dd_b
statistics["max_drawdown_pct_b"] = max_dd_b
statistics["max_drawdown_duration_b"] = dd_dur_b
statistics["equity_b"] = equity_b
statistics["returns_b"] = returns_b
statistics["cum_returns_b"] = cum_returns_b
return statistics
def _calculate_statistics(self, curve):
"""
Creates a dictionary of various statistics associated with
the backtest of a trading strategy via a supplied equity curve.
All Pandas Series indexed by date-time are converted into
milliseconds since epoch representation.
Parameters
----------
curve : `pd.DataFrame`
The equity curve DataFrame.
Returns
-------
`dict`
The statistics dictionary.
"""
stats = {}
# Drawdown, max drawdown, max drawdown duration
dd_s, max_dd, dd_dur = perf.create_drawdowns(curve['CumReturns'])
# Equity curve and returns
stats['equity_curve'] = JSONStatistics._series_to_tuple_list(curve['Equity'])
stats['returns'] = JSONStatistics._series_to_tuple_list(curve['Returns'])
stats['cum_returns'] = JSONStatistics._series_to_tuple_list(curve['CumReturns'])
# Month/year aggregated returns
stats['monthly_agg_returns'] = self._calculate_monthly_aggregated_returns(curve['Returns'])
stats['monthly_agg_returns_hc'] = self._calculate_monthly_aggregated_returns_hc(curve['Returns'])
stats['yearly_agg_returns'] = self._calculate_yearly_aggregated_returns(curve['Returns'])
stats['yearly_agg_returns_hc'] = self._calculate_yearly_aggregated_returns_hc(curve['Returns'])
# Returns quantiles
stats['returns_quantiles'] = self._calculate_returns_quantiles(curve['Returns'])
stats['returns_quantiles_hc'] = self._calculate_returns_quantiles_hc(stats['returns_quantiles'])
# Drawdown statistics
stats['drawdowns'] = JSONStatistics._series_to_tuple_list(dd_s)
stats['max_drawdown'] = max_dd
stats['max_drawdown_duration'] = dd_dur
# Performance
stats['mean_returns'] = np.mean(curve['Returns'])
stats['stdev_returns'] = np.std(curve['Returns'])
stats['cagr'] = perf.create_cagr(curve['CumReturns'], self.periods)
stats['annualised_vol'] = np.std(curve['Returns']) * np.sqrt(self.periods)
stats['sharpe'] = perf.create_sharpe_ratio(curve['Returns'], self.periods)
stats['sortino'] = perf.create_sortino_ratio(curve['Returns'], self.periods)
return stats
def _plot_txt_curve(self, stats):
"""
Output the statistics for the equity curve
"""
returns = stats["returns"]
cum_returns = stats['cum_returns']
if 'positions' not in stats:
trd_yr = 0
else:
positions = stats['positions']
trd_yr = positions.shape[0] / (
(returns.index[-1] - returns.index[0]).days / 365.0)
tot_ret = cum_returns[-1] - 1.0
cagr = perf.create_cagr(cum_returns, self.periods)
sharpe = perf.create_sharpe_ratio(returns, self.periods)
sortino = perf.create_sortino_ratio(returns, self.periods)
rsq = perf.rsquared(range(cum_returns.shape[0]), cum_returns)
dd, dd_max, dd_dur = perf.create_drawdowns(cum_returns)
header = ["Performance", "Value"]
rows = [["Total Return", "{:.0%}".format(tot_ret)],
["CAGR", "{:.2%}".format(cagr)],
["Sharpe Ratio", "{:.2f}".format(sharpe)],
["Sortino Ratio", "{:.2f}".format(sortino)],
[
"Annual Volatility",
"{:.2%}".format(returns.std() * np.sqrt(252))
], ["R-Squared", '{:.2f}'.format(rsq)],
["Max Daily Drawdown", '{:.2%}'.format(dd_max)],
["Max Drawdown Duration", '{:.0f}'.format(dd_dur)],
["Trades per Year", '{:.1f}'.format(trd_yr)]]
table = (Table().add(header, rows).set_global_opts(
title_opts=opts.ComponentTitleOpts(title="Curve")))
return table
def _plot_txt_curve(self, stats, ax=None, **kwargs):
"""
Outputs the statistics for the equity curve.
"""
def format_perc(x, pos):
return '%.0f%%' % x
returns = stats["returns"]
cum_returns = stats['cum_returns']
if 'positions' not in stats:
trd_yr = 0
else:
positions = stats['positions']
trd_yr = positions.shape[0] / (
(returns.index[-1] - returns.index[0]).days / 365.0
)
if ax is None:
ax = plt.gca()
y_axis_formatter = FuncFormatter(format_perc)
ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
tot_ret = cum_returns[-1] - 1.0
cagr = perf.create_cagr(cum_returns, self.periods)
sharpe = perf.create_sharpe_ratio(returns, self.periods)
sortino = perf.create_sortino_ratio(returns, self.periods)
rsq = perf.rsquared(range(cum_returns.shape[0]), cum_returns)
dd, dd_max, dd_dur = perf.create_drawdowns(cum_returns)
ax.text(0.25, 8.9, 'Total Return', fontsize=8)
ax.text(7.50, 8.9, '{:.0%}'.format(tot_ret), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 7.9, 'CAGR', fontsize=8)
ax.text(7.50, 7.9, '{:.2%}'.format(cagr), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 6.9, 'Sharpe Ratio', fontsize=8)
ax.text(7.50, 6.9, '{:.2f}'.format(sharpe), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 5.9, 'Sortino Ratio', fontsize=8)
ax.text(7.50, 5.9, '{:.2f}'.format(sortino), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 4.9, 'Annual Volatility', fontsize=8)
ax.text(7.50, 4.9, '{:.2%}'.format(returns.std() * np.sqrt(252)), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 3.9, 'R-Squared', fontsize=8)
ax.text(7.50, 3.9, '{:.2f}'.format(rsq), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 2.9, 'Max Daily Drawdown', fontsize=8)
ax.text(7.50, 2.9, '{:.2%}'.format(dd_max), color='red', fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 1.9, 'Max Drawdown Duration', fontsize=8)
ax.text(7.50, 1.9, '{:.0f}'.format(dd_dur), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 0.9, 'Trades per Year', fontsize=8)
ax.text(7.50, 0.9, '{:.1f}'.format(trd_yr), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.set_title('Curve', fontweight='bold')
if self.benchmark is not None:
returns_b = stats['returns_b']
equity_b = stats['cum_returns_b']
tot_ret_b = equity_b[-1] - 1.0
cagr_b = perf.create_cagr(equity_b)
sharpe_b = perf.create_sharpe_ratio(returns_b)
sortino_b = perf.create_sortino_ratio(returns_b)
rsq_b = perf.rsquared(range(equity_b.shape[0]), equity_b)
dd_b, dd_max_b, dd_dur_b = perf.create_drawdowns(equity_b)
ax.text(9.75, 8.9, '{:.0%}'.format(tot_ret_b), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 7.9, '{:.2%}'.format(cagr_b), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 6.9, '{:.2f}'.format(sharpe_b), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 5.9, '{:.2f}'.format(sortino_b), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 4.9, '{:.2%}'.format(returns_b.std() * np.sqrt(252)), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 3.9, '{:.2f}'.format(rsq_b), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 2.9, '{:.2%}'.format(dd_max_b), color='red', fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 1.9, '{:.0f}'.format(dd_dur_b), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.set_title('Curve vs. Benchmark', fontweight='bold')
ax.grid(False)
ax.spines['top'].set_linewidth(2.0)
ax.spines['bottom'].set_linewidth(2.0)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.get_yaxis().set_visible(False)
ax.get_xaxis().set_visible(False)
ax.set_ylabel('')
ax.set_xlabel('')
ax.axis([0, 10, 0, 10])
return ax
def _plot_txt_curve(self, stats, bench_stats=None, ax=None, **kwargs):
"""
Outputs the statistics for the equity curve.
"""
def format_perc(x, pos):
return '%.0f%%' % x
if ax is None:
ax = plt.gca()
y_axis_formatter = FuncFormatter(format_perc)
ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
# Strategy statistics
returns = stats["returns"]
cum_returns = stats['cum_returns']
tot_ret = cum_returns[-1] - 1.0
cagr = perf.create_cagr(cum_returns, self.periods)
sharpe = perf.create_sharpe_ratio(returns, self.periods)
sortino = perf.create_sortino_ratio(returns, self.periods)
dd, dd_max, dd_dur = perf.create_drawdowns(cum_returns)
# Benchmark statistics
if bench_stats is not None:
bench_returns = bench_stats["returns"]
bench_cum_returns = bench_stats['cum_returns']
bench_tot_ret = bench_cum_returns[-1] - 1.0
bench_cagr = perf.create_cagr(bench_cum_returns, self.periods)
bench_sharpe = perf.create_sharpe_ratio(bench_returns, self.periods)
bench_sortino = perf.create_sortino_ratio(bench_returns, self.periods)
bench_dd, bench_dd_max, bench_dd_dur = perf.create_drawdowns(bench_cum_returns)
# Strategy Values
ax.text(7.50, 8.2, 'Strategy', fontweight='bold', horizontalalignment='right', fontsize=8, color='green')
ax.text(0.25, 6.9, 'Total Return', fontsize=8)
ax.text(7.50, 6.9, '{:.0%}'.format(tot_ret), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 5.9, 'CAGR', fontsize=8)
ax.text(7.50, 5.9, '{:.2%}'.format(cagr), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 4.9, 'Sharpe Ratio', fontsize=8)
ax.text(7.50, 4.9, '{:.2f}'.format(sharpe), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 3.9, 'Sortino Ratio', fontsize=8)
ax.text(7.50, 3.9, '{:.2f}'.format(sortino), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 2.9, 'Annual Volatility', fontsize=8)
ax.text(7.50, 2.9, '{:.2%}'.format(returns.std() * np.sqrt(252)), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 1.9, 'Max Daily Drawdown', fontsize=8)
ax.text(7.50, 1.9, '{:.2%}'.format(dd_max), color='red', fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 0.9, 'Max Drawdown Duration (Days)', fontsize=8)
ax.text(7.50, 0.9, '{:.0f}'.format(dd_dur), fontweight='bold', horizontalalignment='right', fontsize=8)
# Benchmark Values
if bench_stats is not None:
ax.text(10.0, 8.2, 'Benchmark', fontweight='bold', horizontalalignment='right', fontsize=8, color='gray')
ax.text(10.0, 6.9, '{:.0%}'.format(bench_tot_ret), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(10.0, 5.9, '{:.2%}'.format(bench_cagr), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(10.0, 4.9, '{:.2f}'.format(bench_sharpe), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(10.0, 3.9, '{:.2f}'.format(bench_sortino), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(10.0, 2.9, '{:.2%}'.format(bench_returns.std() * np.sqrt(252)), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(10.0, 1.9, '{:.2%}'.format(bench_dd_max), color='red', fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(10.0, 0.9, '{:.0f}'.format(bench_dd_dur), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.set_title('Equity Curve', fontweight='bold')
ax.grid(False)
ax.spines['top'].set_linewidth(2.0)
ax.spines['bottom'].set_linewidth(2.0)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.get_yaxis().set_visible(False)
ax.get_xaxis().set_visible(False)
ax.set_ylabel('')
ax.set_xlabel('')
ax.axis([0, 10, 0, 10])
return ax
def _plot_txt_curve(self, stats, ax=None, **kwargs):
"""
Outputs the statistics for the equity curve.
"""
def format_perc(x, pos):
return '%.0f%%' % x
returns = stats["returns"]
cum_returns = stats['cum_returns']
if 'positions' not in stats:
trd_yr = 0
else:
positions = stats['positions']
trd_yr = positions.shape[0] / (
(returns.index[-1] - returns.index[0]).days / 365.0
)
if ax is None:
ax = plt.gca()
y_axis_formatter = FuncFormatter(format_perc)
ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
tot_ret = cum_returns[-1] - 1.0
cagr = perf.create_cagr(cum_returns, self.periods)
sharpe = perf.create_sharpe_ratio(returns, self.periods)
sortino = perf.create_sortino_ratio(returns, self.periods)
rsq = perf.rsquared(range(cum_returns.shape[0]), cum_returns)
dd, dd_max, dd_dur = perf.create_drawdowns(cum_returns)
ax.text(0.25, 8.9, 'Total Return', fontsize=8)
ax.text(7.50, 8.9, '{:.0%}'.format(tot_ret), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 7.9, 'CAGR', fontsize=8)
ax.text(7.50, 7.9, '{:.2%}'.format(cagr), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 6.9, 'Sharpe Ratio', fontsize=8)
ax.text(7.50, 6.9, '{:.2f}'.format(sharpe), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 5.9, 'Sortino Ratio', fontsize=8)
ax.text(7.50, 5.9, '{:.2f}'.format(sortino), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 4.9, 'Annual Volatility', fontsize=8)
ax.text(7.50, 4.9, '{:.2%}'.format(returns.std() * np.sqrt(252)), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 3.9, 'R-Squared', fontsize=8)
ax.text(7.50, 3.9, '{:.2f}'.format(rsq), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 2.9, 'Max Daily Drawdown', fontsize=8)
ax.text(7.50, 2.9, '{:.2%}'.format(dd_max), color='red', fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 1.9, 'Max Drawdown Duration', fontsize=8)
ax.text(7.50, 1.9, '{:.0f}'.format(dd_dur), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 0.9, 'Trades per Year', fontsize=8)
ax.text(7.50, 0.9, '{:.1f}'.format(trd_yr), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.set_title('Curve', fontweight='bold')
if self.benchmark is not None:
returns_b = stats['returns_b']
equity_b = stats['cum_returns_b']
tot_ret_b = equity_b[-1] - 1.0
cagr_b = perf.create_cagr(equity_b)
sharpe_b = perf.create_sharpe_ratio(returns_b)
sortino_b = perf.create_sortino_ratio(returns_b)
rsq_b = perf.rsquared(range(equity_b.shape[0]), equity_b)
dd_b, dd_max_b, dd_dur_b = perf.create_drawdowns(equity_b)
ax.text(9.75, 8.9, '{:.0%}'.format(tot_ret_b), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 7.9, '{:.2%}'.format(cagr_b), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 6.9, '{:.2f}'.format(sharpe_b), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 5.9, '{:.2f}'.format(sortino_b), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 4.9, '{:.2%}'.format(returns_b.std() * np.sqrt(252)), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 3.9, '{:.2f}'.format(rsq_b), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 2.9, '{:.2%}'.format(dd_max_b), color='red', fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 1.9, '{:.0f}'.format(dd_dur_b), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.set_title('Curve vs. Benchmark', fontweight='bold')
ax.grid(False)
ax.spines['top'].set_linewidth(2.0)
ax.spines['bottom'].set_linewidth(2.0)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_visible(False)
ax.get_yaxis().set_visible(False)
ax.get_xaxis().set_visible(False)
ax.set_ylabel('')
ax.set_xlabel('')
ax.axis([0, 10, 0, 10])
return ax
def get_results(self):
"""
Return a dict with all important results & stats.
"""
# Equity
equity_s = pd.Series(self.equity).sort_index()
# Returns
returns_s = equity_s.pct_change().fillna(0.0)
# Rolling Annualised Sharpe
rolling = returns_s.rolling(window=self.periods)
rolling_sharpe_s = np.sqrt(
self.periods) * (rolling.mean() / rolling.std())
# Cummulative Returns
cum_returns_s = np.exp(np.log(1 + returns_s).cumsum())
# Drawdown, max drawdown, max drawdown duration
dd_s, max_dd, dd_dur = perf.create_drawdowns(cum_returns_s)
statistics = {}
# Equity statistics
statistics["sharpe"] = perf.create_sharpe_ratio(
returns_s, self.periods)
statistics["drawdowns"] = dd_s
# TODO: need to have max_drawdown so it can be printed at end of test
statistics["max_drawdown"] = max_dd
statistics["max_drawdown_pct"] = max_dd
statistics["max_drawdown_duration"] = dd_dur
statistics["equity"] = equity_s
statistics["returns"] = returns_s
statistics["rolling_sharpe"] = rolling_sharpe_s
statistics["cum_returns"] = cum_returns_s
positions = self._get_positions()
if positions is not None:
statistics["positions"] = positions
durations = (positions['end_timestamp'] -
positions['start_timestamp']) / pd.Timedelta(days=1)
avg_duration = durations.mean()
statistics["max_duration"] = '{:.2f}'.format(durations.max())
statistics["min_duration"] = '{:.2f}'.format(durations.min())
statistics["std_duration"] = '{:.2f}'.format(durations.std())
statistics["avg_duration"] = '{:.2f}'.format(avg_duration)
# Benchmark statistics if benchmark ticker specified
if self.benchmark is not None:
equity_b = pd.Series(self.equity_benchmark).sort_index()
returns_b = equity_b.pct_change().fillna(0.0)
rolling_b = returns_b.rolling(window=self.periods)
rolling_sharpe_b = np.sqrt(
self.periods) * (rolling_b.mean() / rolling_b.std())
cum_returns_b = np.exp(np.log(1 + returns_b).cumsum())
dd_b, max_dd_b, dd_dur_b = perf.create_drawdowns(cum_returns_b)
statistics["sharpe_b"] = perf.create_sharpe_ratio(returns_b)
statistics["drawdowns_b"] = dd_b
statistics["max_drawdown_pct_b"] = max_dd_b
statistics["max_drawdown_duration_b"] = dd_dur_b
statistics["equity_b"] = equity_b
statistics["returns_b"] = returns_b
statistics["rolling_sharpe_b"] = rolling_sharpe_b
statistics["cum_returns_b"] = cum_returns_b
return statistics