def get_results(self):
for ticker in self.portfolio.positions:
daily_return = pd.DataFrame(self.portfolio.positions[ticker].log)["price"].astype(float).pct_change().fillna(0.0)
cum_return = np.exp(np.log(1+daily_return).cumsum())
drawdown,max_drawdown,drawdown_duration = perf.create_drawdowns(cum_return)
statistics = {}
statistics["sharpe"] = perf.create_sharpe_ratio(daily_return,self.periods)
statistics["drawdowns"] = drawdown
statistics["max_drawdown"] = max_drawdown
statistics["max_drawdown_pct"] = max_drawdown
statistics["max_drawdown_duration"] = drawdown_duration
statistics["daily_returns"] = daily_return
statistics["cum_returns"] = cum_return
statistics["date"] = pd.DataFrame(self.portfolio.positions[ticker].log)["date"]
self.constituents[ticker] = statistics
if self.benchmark is not None:
"Need to change the benchamrk here"
daily_return_b = pd.DataFrame(self.portfolio.positions[ticker].log)["price"].astype(float).pct_change().fillna(0.0)
cum_returns_b = np.exp(np.log(1 + daily_return_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["security_b"] = security_b
statistics["returns_b"] = returns_b
statistics["rolling_sharpe_b"] = rolling_sharpe_b
statistics["cum_returns_b"] = cum_returns_b
self.constituents[ticker] = statistics
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio
Outputs:
equity.csv
Can be loaded into a Matplotlib script, or spreadsheet software for
analysis
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns, period=252*60*6.5)
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve['drawdown'] = drawdown
stats = [("Total Return", "%0.2f%%" % \
((total_return - 1.0) * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration)]
self.equity_curve.to_csv('equity.csv')
return stats
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio.
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
# Days, hours, minutes or seconds
if self.periods == "D":
periods = 252
elif self.periods == "H":
periods = 252 * 6.5
elif self.periods == "M":
periods = 252 * 6.5 * 60
elif self.periods == "S":
periods = 252 * 6.5 * 60 * 60
cagr = create_cagr(pnl, periods=periods)
sharpe_ratio = create_sharpe_ratio(returns, periods=periods)
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
stats = [("Total Return", "%0.2f%%" % ((total_return - 1.0) * 100.0)),
("CAGR", "%0.2f%%" % (cagr * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration)]
self.equity_curve["drawdown"] = drawdown
# Output equity curve statistics
self.equity_curve.to_csv("equity.csv")
return stats
def output_summary_stats(self):
"""
Calulate states(total_return, sharpe_ratio, max_drawdown, max_duration).
:return: list; summary data.
"""
total_return=self.equity_curve['equity_curve'][-1]
returns=self.equity_curve['returns']
pnl=self.equity_curve['equity_curve']
sharpe_ratio=create_sharpe_ratio(returns,periods=252*60*6.5)
drawdown,max_dd,max_duration=create_drawdowns(pnl)
self.equity_curve['drawdown']=drawdown
stats=[("Total Return","%0.2f%%"%((total_return-1.0)*100.0)),
("Sharpe Ratio","%0.2f%%"%sharpe_ratio),
("Max Drawdown","%0.2f%%"%(max_dd*100.0)),
("Drawdown Duration","%d"%max_duration)]
self.equity_curve.to_csv('equity.csv')
return stats
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio
Outputs:
equity.csv
Can be loaded into a Matplotlib script, or spreadsheet software for
analysis
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns, period=252*60*6.5)
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve['drawdown'] = drawdown
stats = [("Total Return", "%0.2f%%" % \
((total_return - 1.0) * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration)]
self.equity_curve.to_csv('equity.csv')
return stats
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio.
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['return']
pnl = self.equity_curve['equity_curve']
# periods = 252 * 60 * 6.5
earn_rate_year = (1 + np.mean(returns))**252 - 1
print("平均年化收益率：", earn_rate_year)
sharpe_ratio = create_sharpe_ratio(returns, periods=252)
max_dd, dd_duration = create_drawdowns(pnl)
# drawdown, max_dd, dd_duration = create_drawdowns(pnl)
# # self.equity_curve['drawdown'] = drawdown
stats = [("Total Return", "%0.2f%%" % ((total_return - 1.0) * 100.0)),
("Avg Year Return", "%0.2f%%" % (earn_rate_year * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f" % (max_dd)),
("Drawdown Duration", "%d" % dd_duration)]
filesavepath = os.path.join(self.savepath, 'equity.csv')
self.equity_curve.to_csv(filesavepath, index=False)
# 绘制累计日收益率曲线
self.plot_equity_curve()
return stats
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio.
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
# Days, hours, minutes or seconds
if self.periods == "D":
periods = 252
elif self.periods == "H":
periods = 252*6.5
elif self.periods == "M":
periods = 252*6.5*60
elif self.periods == "S":
periods = 252*6.5*60*60
cagr = create_cagr(pnl, periods=periods)
sharpe_ratio = create_sharpe_ratio(returns, periods=periods)
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
stats = [("Total Return", "%0.2f%%" % ((total_return - 1.0) * 100.0)),
("CAGR", "%0.2f%%" % (cagr * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration)]
self.equity_curve["drawdown"] = drawdown
# Output equity curve statistics
self.equity_curve.to_csv("equity.csv")
return stats
def output_summary_stats(self):
total_return = self.equity_curve['equity_curve'][-1]
drawdown, duration = create_drawdowns(
self.equity_curve['equity_curve'])
return [('Total return', (total_return - 1) * 100),
('Sharpe ratio',
create_sharpe_ratio(self.equity_curve['returns'])),
('Max drawdown', drawdown * 100),
('Drawdown duration', duration)]
def output_summary_stats(self):
total_return=self.equity_curve['equity_curve'][-1]
returns=self.equity_curve['returns']
pnl=self.equity_curve['equity_curve']
sharpe_ratio=create_sharpe_ratio(returns)
max_dd,dd_duration=create_drawdowns(pnl)
stats=[("Total Return","%0.2f%%" % ((total_return-1.0)*1000.0)),("Sharpe Ratio", "%0.2f" % sharpe_ratio),("Max Drawdown", "%0.2f%%" % (max_dd*100.0)),("Drawdown Duration", "%d" % dd_duration)]
plt.clf()
plt.plot(self.equity_curve.index,pnl)
plt.savefig('cumulative_return')
return stats
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns)
max_dd, dd_duration = create_drawdowns(pnl)
stats = [("Total Return", "%0.2f%%" % ((total_return - 1)*100)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd*100.0)),
("Drawdown Duration", "%d" %dd_duration)]
self.equity_curve.to_csv('equity.csv')
return stats
def output_summary_stats(self):
total_return=self.equity_curve['equity_curve'][-1]
returns=self.equity_curve['returns']
pnl=self.equity_curve['equity_curve']
sharpe_ratio=create_sharpe_ratio(returns)
max_dd,dd_duration=create_drawdowns(pnl)
stats=[("total return","%0.2f%%"%((total_return-1.0)*100.0)),
("sharpe ratio","%0.2f"%sharpe_ratio),
("Max Drawdown","%0.2f%%"%(max_dd*100)),
("Drawdown duration","%d"%dd_duration)]
return stats
def output_summary_stats(self):
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns)
max_dd, dd_duration = create_drawdowns(pnl)
stats = [("Total Return", "%0.2f%%" % ((total_return - 1.0) * 1000.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration)]
plt.clf()
plt.plot(self.equity_curve.index, pnl)
plt.savefig('cumulative_return')
return stats
def output_summary_stats(self):
# creates a list of summary statistics for the portfolio (ie. sharpie and markdown info)
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns)
max_dd, dd_duration = create_drawdowns(pnl)
stats = [("Total Return", "%0.2f%%" % ((total_return - 1.0) * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration)]
return stats
def output_summary_stats(self):
equity_curve = self.create_equity_curve_df()
total_return = equity_curve['equity_curve'][-1]
returns = equity_curve['returns']
pnl = equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns)
max_dd, dd_duration = create_drawdowns(pnl)
stats = [('Total Return', '%0.2f%%' % ((total_return - 1.0) * 100.0)),
('sharpe Ratio', '%0.2f' % sharpe_ratio),
('Max Drawdown', '%0.2f%%' % (max_dd * 100.0)),
('Drawdown Duration', '%s' % dd_duration)]
return stats
def output_summary_stats(self):
"""
Создает список статистических показателей для портфолио — коэффициент Шарпа и данные по просадке.
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns)
max_dd, dd_duration = create_drawdowns(pnl)
stats = [("Total Return", "%0.2f%%" % ((total_return - 1.0) * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration)]
return stats
def output_summary_stats(self):
"""
统计夏普率， 回测等信息。
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns)
max_dd, dd_duration = create_drawdowns(pnl)
stats = [("Total Return", "%0.2f%%" % ((total_return - 1.0) * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration)]
return stats
def output_summary_stats(self):
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns)
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve['drawdown'] = drawdown
stats = [("Total Return", "%0.2f%%" % ((total_return - 1.0) * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100)),
("Drawdown Duration", "%d" % dd_duration)]
self.equity_curve.to_csv('equity.csv')
return stats
def output_summary_stats(self, frequency = 252):
"""
Creates a list of summary statistics for the portfolio.
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns, periods=frequency)
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve['drawdown'] = drawdown
self.equity_curve['drawdown'][0] = 0.0
stats = [("Total Return", "%0.2f%%" % ((total_return - 1.0) * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio), ("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)), ("Drawdown Duration", "%d" % dd_duration)]
self.equity_curve.to_csv('EquityCurve.csv')
return stats
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio such
as Sharpe Ratio and drawdown information.
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_cure['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns)
max_dd, dd_duration = create_drawdowns(pnl)
stats = [("Total Return: %0.2f%%" % ((total_return - 1.0) * 100.0)),
("Sharpe Ratio: %0.2f" % sharpe_ratio),
("Max Drawdown: %0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration: %d" % dd_duration)]
return stats
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio.
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns, periods=252)
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve['drawdown'] = drawdown
stats = [("Total Return: {:.2f}%".format((total_return - 1) * 100)),
("Sharpe Ratio: {:.2f} ".format(sharpe_ratio)),
("Max Drawdown: {:.2f}%".format(max_dd * 100)),
("Drawdown Duration: {:.2f}".format(dd_duration))]
# self.equity_curve.to_csv("equity.csv")
return stats
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio.
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns, periods=252)
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve['drawdown'] = drawdown
stats = [("Total Return: {:.2f}%".format((total_return - 1) * 100)),
("Sharpe Ratio: {:.2f} ".format(sharpe_ratio)),
("Max Drawdown: {:.2f}%".format(max_dd * 100)),
("Drawdown Duration: {:.2f}".format(dd_duration))]
# self.equity_curve.to_csv("equity.csv")
return stats
def output_summary_stats(self):
#Returns a list of tuples
self.equity_curve = self.create_equity_curve()
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
equity_curve = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns)
max_drawdown, duration = create_drawdowns(self.equity_curve)
stats = [('Total Return', '{}'.format((total_return - 1) * 100)),
('Sharpe Ratio', '{}'.format(sharpe_ratio)),
('Max Drawdown', '{}'.format(max_drawdown)),
('Drawdown Duration', '{}'.format(duration))]
return stats
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio such
as Sharpe Ratio and drawdown information.
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_cure['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns)
max_dd, dd_duration = create_drawdowns(pnl)
stats = [("Total Return: %0.2f%%" % ((total_return - 1.0) * 100.0)),
("Sharpe Ratio: %0.2f" % sharpe_ratio),
("Max Drawdown: %0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration: %d" % dd_duration)]
return stats
def output_summary_stats(self):
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
print(total_return, returns, pnl)
sharpe_ratio = create_sharpe_ratio(returns, periods=252 * 60 * 6.5)
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve['drawdown'] = drawdown
stats = [('Total Return', '%0.2f%%' % ((total_return - 1.0) * 100.0)),
('Sharpe Ratio', '%0.2f' % sharpe_ratio),
('Max Drawdown', '%0.2f%%' % (max_dd * 100.0)),
('Drawdown Duration', '%d' % dd_duration)]
self.equity_curve.to_csv('equity.csv')
return stats
def output_summary_stats(self, filename):
"""
Statistiche create per il portfolio
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns, periods=252)
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve['drawdown'] = drawdown
stats = [("Total Return", "%0.2f%%" % \
((total_return - 1.0) * 100.0)),
("Sharpe Ratio", "%0.2f%%" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%f" % dd_duration)]
self.equity_curve.to_csv(filename)
return stats
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio.
"""
total_return = self.equity_curve["equity_curve"][-1]
returns = self.equity_curve["returns"]
pnl = self.equity_curve["equity_curve"]
sharpe_ratio = create_sharpe_ratio(returns, periods=252) #*60*6.5)
# здесь похоже не верно считал, так как все же мы статистику ведём пока по дням, а не по часам
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve["drawdown"] = drawdown
stats = [("Total Return", "%0.2f%%" % \
((total_return - 1.0) * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration)]
self.equity_curve.to_csv("equity.csv")
draw(self.equity_curve['equity_curve'])
# TODO Здесь рисует график. В идеале перенести его нужно по результатам выполнения бэктестинга
return stats
def output_summary_stats(self):
#用performance的两个方程算sharp ratio/drawdown
"""
Creates a list of summary statistics for the portfolio.
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns)
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve['drawdown'] = drawdown
stats = [("Total Return", "%0.2f%%" % ((total_return - 1.0) * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration)]
self.equity_curve.to_csv('equity.csv')
return stats
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio.
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns, periods=252 * 60 * 6.5)
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve['drawdown'] = drawdown
trades_avg_duration = floor(
self.trades['duration'].mean().total_seconds() / 60)
trades_avg_return = self.trades['returns'].mean() * 100
trades_total_win = self.trades['win_trades'].sum()
trades_total_loss = self.trades['loss_trades'].sum()
trades_win_loss_ratio = trades_total_win / trades_total_loss
total_trades = len(self.trades)
trades_win_pct = trades_total_win / total_trades * 100
trades_loss_pct = trades_total_loss / total_trades * 100
stats = [("Total Return", "%0.2f%%" % ((total_return - 1.0) * 100.0)),
("Avg Return (%)", trades_avg_return),
("Avg Trade Duration (min)", trades_avg_duration),
("Total win trades", trades_total_win),
("Total loss trades", trades_total_loss),
("Win/Loss Ratio", trades_win_loss_ratio),
("% Wins", trades_win_pct), ("% Losses", trades_loss_pct),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration)]
dir_path = f'{Path().absolute()}/backtest_results'
self.equity_curve.to_csv(f'{dir_path}/equity.csv')
self.trades.to_csv(f'{dir_path}/trades.csv')
return stats
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio
This method outputs the equity curve and varioys performance
statistics related to the strategy.
The final line ouputs a file, equity.csv, to the same directory
as the code, whcih can be loaded into a Matplotlib Python script
for subsequent analysis.
Note:
The drawdown duration is given in terms of the absolute
number of "bars" that teh drawdown carried on for, as
opposed to a particular timeframe.
Returns
-------
'list'
Returns a list of stats relating to portfolio performance
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(
returns, periods=252) # For Minute level bars: periods=252*6.5*60
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve['drawdown'] = drawdown
stats = [("Total Return", "%0.2f%%" % ((total_return - 1) * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration)]
self.equity_curve.to_csv('equity.csv')
return stats
def output_results(self):
# Chiusura del file csv del backtest.csv cosiì da poter
# essere caricato con Pandas senza errori
self.backtest_file.close()
in_filename = "backtest.csv"
out_filename = "equity.csv"
in_file = os.path.join(OUTPUT_RESULTS_DIR, in_filename)
out_file = os.path.join(OUTPUT_RESULTS_DIR, out_filename)
# Crea il dataframe della curva di equity
df = pd.read_csv(in_file, index_col=0)
df.dropna(inplace=True)
df["Total"] = df.sum(axis=1)
df["Returns"] = df["Total"].pct_change()
df["Equity"] = (1.0 + df["Returns"]).cumprod()
# Crea le statistiche di drawdown
drawdown, max_dd, dd_duration = create_drawdowns(df["Equity"])
df["Drawdown"] = drawdown
df.to_csv(out_file, index=True)
print("Simulation complete and results exported to %s" % out_filename)
def output_results(self):
# Closes off the Backtest.csv file so it can be
# read via Pandas without problems
self.backtest_file.close()
in_filename = "backtest.csv"
out_filename = "equity.csv"
in_file = os.path.join(OUTPUT_RESULTS_DIR, in_filename)
out_file = os.path.join(OUTPUT_RESULTS_DIR, out_filename)
# Create equity curve dataframe
df = pd.read_csv(in_file, index_col=0)
df.dropna(inplace=True)
df["Total"] = df.sum(axis=1)
df["Returns"] = df["Total"].pct_change()
df["Equity"] = (1.0+df["Returns"]).cumprod()
# Create drawdown statistics
drawdown, max_dd, dd_duration = create_drawdowns(df["Equity"])
df["Drawdown"] = drawdown
df.to_csv(out_file, index=True)
print("Simulation complete and results exported to %s" % out_filename)
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio.
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns)
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve['drawdown'] = drawdown
if len(dd_duration) == 1:
dd_duration = dd_duration[0]
stats = [("Total Return", "%0.2f%%" % ((total_return - 1.0) * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%s" % dd_duration)]
self.equity_curve.to_csv('equity.csv')
self.positions.to_csv('positions.csv')
self.prices.to_csv('prices.csv')
return stats
def output_summary_stats(self, strategy_title):
'''
Creates a list of summary statistics for the portfolio.
'''
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(
returns, periods=252) # i guess this is for minute resolution
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve['drawdown'] = drawdown
stats = [
'Total Return',
'%0.2f%%' % ((total_return - 1.0) * 100.0),
('Sharpe Ratio', '%0.2f' % sharpe_ratio),
('Max Drawdown', '%0.2f%%' % (max_dd * 100.0)),
('Drawdown Duration', '%d' % dd_duration)
]
self.equity_curve.to_csv('equity.csv')
self.print_chart(max_dd, dd_duration, total_return, sharpe_ratio,
strategy_title)
return stats
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio.
:return:
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
# self.equity_curve.to_csv("prac_equity_curve.csv")
# pd.DataFrame(self.all_positions).to_csv("prac_position.csv")
sharpe_ratio = create_sharpe_ratio(returns, periods='minutely')
drawdown, max_dd, max_dd_duration = create_drawdowns(pnl)
self.equity_curve['drawdown'] = drawdown
stats = [('Total_Return', "%0.2f%%" % ((total_return - 1.0) * 100.0)),
('Sharpe_Ratio', "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Max Drawdown Dur.", "%d" % max_dd_duration)]
self.equity_curve.to_csv('equity_curve.csv')
# pnl.plot()
# plt.show()
return stats
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio.
"""
total_return = self.equity_curve['equity_curve'][-1]
returns = self.equity_curve['returns']
pnl = self.equity_curve['equity_curve']
sharpe_ratio = create_sharpe_ratio(returns, periods=252*60*6.5)
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve['drawdown'] = drawdown
stats = [("Total Return", "%0.2f%%" % ((total_return - 1.0) * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration)]
output = {'Total Return': "%0.2f%%" % ((total_return - 1.0) * 100.0), 'Sharpe Ratio': "%0.2f" % sharpe_ratio, 'Max Drawdown': "%0.2f%%" % (max_dd * 100.0), 'Drawdown Duration': dd_duration}
df = pd.Series(output).to_frame()
df.columns = ['value']
df.to_csv('output.csv', index_label='parameter')
self.equity_curve.to_csv('../equity.csv')
return stats
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio.
"""
total_return = self.equity_curve[’equity_curve’][-1]
returns = self.equity_curve[’returns’]
pnl = self.equity_curve[’equity_curve’]
sharpe_ratio = create_sharpe_ratio(returns, periods=252*60*6.5) drawdown, max_dd, dd_duration = create_drawdowns(pnl)
self.equity_curve[’drawdown’] = drawdown
stats = [("Total Return", "%0.2f%%" % \
((total_return - 1.0) * 100.0)),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration)]
self.equity_curve.to_csv('eqiuity.csv')
return stats
periods = 252
# Adjust lookback period from 50 to 200 in increments
# of 10 in order to produce sensitivities
for lb in lookbacks:
print "Calculating lookback=%s..." % lb
pairs = create_pairs_dataframe(datadir, symbols)
pairs = calculate_spread_zscore(pairs, symbols, lookback=lb)
pairs = create_long_short_market_signals(pairs, symbols,
z_entry_threshold=2.0,
z_exit_threshold=1.0)
portfolio = create_portfolio_returns(pairs, symbols)
returns.append(portfolio.ix[-1]['cum_returns'])
sharps.append(create_sharpe_ratio(portfolio['returns']))
drawdown, max_dd, dd_duration = create_drawdowns(portfolio['cum_returns'])
draw_downs.append(max_dd)
df = pd.DataFrame(lookbacks)
df.columns = ['LookBack']
df['Returns'] = returns
df['Sharps'] = sharps
df['Max Draw Down'] = draw_downs
print df
print "Plot the lookback-performance scatterchart..."
plt.plot(lookbacks, returns, '-o')
plt.show()
def output_summary_stats(self):
"""
Creates a list of summary statistics for the portfolio.
"""
total_return = self.equity_curve["equity_curve"][-1]
returns = self.equity_curve["returns"]
pnl = self.equity_curve["equity_curve"]
periods = self.equity_curve.shape[0]
start_date = self.equity_curve.index[
1] #注意，一般來說應該要是[0]，選擇[1]是因為資料不足一年以上
end_date = self.equity_curve.index[-1]
time_delta_days = (end_date - start_date).days
cmd_list = ["TXFC2", "TXFD1", "TXFE1", "TXFL1"] #先寫死，之後再改成讀入
win = 0 #賺錢次數
lose = 0 #賠錢次數
entry_amount = 0 #進場金額
out_amount = 0 #出場金額
record_amount = {}
record_amount["entry_amount"] = []
record_amount["out_amount"] = []
record_amount["profit"] = []
record_amount["loss"] = []
for commodity in cmd_list:
for j in range(0, len(self.equity_curve[commodity]) - 1):
if ((self.equity_curve[commodity][j + 1] >
self.equity_curve[commodity][j])
and self.equity_curve[commodity][j] == 0):
entry_amount = self.equity_curve[commodity][j + 1]
record_amount["entry_amount"].append(entry_amount)
if (self.equity_curve[commodity][j] != 0
and self.equity_curve[commodity][j + 1] == 0):
out_amount = self.equity_curve[commodity][j]
record_amount["out_amount"].append(out_amount)
for i in range(0, len(record_amount["out_amount"])):
profit = record_amount["out_amount"][i] - record_amount[
"entry_amount"][i]
if profit > 0:
win = win + 1
record_amount["profit"].append(profit)
elif profit <= 0:
lose = lose + 1
record_amount["loss"].append(profit)
win_rate = win / (win + lose) #勝率
#賺賠比 = gross profit / gross loss
profit_factor = (-1) * sum(record_amount["profit"]) / sum(
record_amount["loss"])
#CAGR = (初始價值/結束價值)**(1/年化期數)-1
CAGR = (self.equity_curve["total"][-1] /
self.equity_curve["total"][0])**(1 /
(time_delta_days / 252)) - 1
yearly_sharpe_ratio, minutely_sharpe_ratio = create_sharpe_ratio(
returns, periods)
yearly_sortino_ratio, minutely_sortino_ratio = create_sortino_ratio(
returns, periods)
minutely_skewness = create_skewness(returns, periods)
minutely_kurtosis = create_kurtosis(returns, periods)
drawdown, max_dd, dd_duration = create_drawdowns(pnl)
minutely_calmar_ratio = create_calmar_ratio(returns, periods, max_dd)
self.equity_curve["drawdown"] = drawdown
stats = [
"Start Date:" + str(start_date), "End Date:" + str(end_date),
("Total Return", "%0.2f%%" % ((total_return - 1.0) * 100.0)),
("Minutely Sharpe Ratio", "%0.4f" % minutely_sharpe_ratio),
("Minutely Sortino Ratio", "%0.4f" % minutely_sortino_ratio),
("Minutely Skewness", "%0.3f" % minutely_skewness),
("Minutely Kurtosis", "%0.3f" % minutely_kurtosis),
("Minutely Calmar Ratio", "%0.6f" % minutely_calmar_ratio),
("CAGR", "%0.5f%%" % (CAGR * 100.0)),
("Yearly Sharpe Ratio", "%0.2f" % yearly_sharpe_ratio),
("Yearly Sortino Ratio", "%0.2f" % yearly_sortino_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration),
("Win Rate", "%0.2f" % win_rate),
("Profit Factor", "%0.2f" % profit_factor)
]
self.equity_curve.to_csv('equity.csv')
return stats
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)
bench = pd.read_pickle(self.benchmark)
rets = pd.concat([bench.loc[self.equity.index[0]:self.equity.index[-1]],
self.equity.rename("equity")], axis=1).fillna(0.0)
# returns_s=self.equity.sort_index().fillna(0.0)
returns_s = rets['equity']
# 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
# 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)
# returns_b=self.equity_benchmark.sort_index().fillna(0.0)
returns_b = rets['rets']
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
alphas = rets['equity'] - rets['rets']
statistics["IR"] = np.mean(alphas) / np.std(alphas)
return statistics
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']
IR=stats['IR']
# 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, '总收益率', fontsize=30)
ax.text(7.50, 8.9, '{:.0%}'.format(
tot_ret), fontweight='bold', horizontalalignment='right', fontsize=30)
ax.text(0.25, 7.9, '复合年均增长率', fontsize=30)
ax.text(7.50, 7.9, '{:.2%}'.format(
cagr), fontweight='bold', horizontalalignment='right', fontsize=30)
ax.text(0.25, 6.9, '夏普比率', fontsize=30)
ax.text(7.50, 6.9, '{:.2f}'.format(
sharpe), fontweight='bold', horizontalalignment='right', fontsize=30)
ax.text(0.25, 5.9, '索提诺比率', fontsize=30)
ax.text(7.50, 5.9, '{:.2f}'.format(
sortino), fontweight='bold', horizontalalignment='right', fontsize=30)
ax.text(0.25, 4.9, '年化波动率', fontsize=30)
ax.text(7.50, 4.9, '{:.2%}'.format(returns.std() * np.sqrt(252)),
fontweight='bold', horizontalalignment='right', fontsize=30)
ax.text(0.25, 3.9, 'R-Squared', fontsize=30)
ax.text(7.50, 3.9, '{:.2f}'.format(
rsq), fontweight='bold', horizontalalignment='right', fontsize=30)
ax.text(0.25, 2.9, '最大日回撤', fontsize=30)
ax.text(7.50, 2.9, '{:.2%}'.format(dd_max), color='red',
fontweight='bold', horizontalalignment='right', fontsize=30)
ax.text(0.25, 1.9, '最大回撤持续期', fontsize=30)
ax.text(7.50, 1.9, '{:.0f}'.format(
dd_dur), fontweight='bold', horizontalalignment='right', fontsize=30)
ax.text(0.25, 0.9, '信息比率', fontsize=30)
ax.text(7.50, 0.9, '{:.2%}'.format(
IR), fontweight='bold', horizontalalignment='right', fontsize=30)
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=30)
ax.text(9.75, 7.9, '{:.2%}'.format(
cagr_b), fontweight='bold', horizontalalignment='right', fontsize=30)
ax.text(9.75, 6.9, '{:.2f}'.format(
sharpe_b), fontweight='bold', horizontalalignment='right', fontsize=30)
ax.text(9.75, 5.9, '{:.2f}'.format(
sortino_b), fontweight='bold', horizontalalignment='right', fontsize=30)
ax.text(9.75, 4.9, '{:.2%}'.format(returns_b.std(
) * np.sqrt(252)), fontweight='bold', horizontalalignment='right', fontsize=30)
ax.text(9.75, 3.9, '{:.2f}'.format(
rsq_b), fontweight='bold', horizontalalignment='right', fontsize=30)
ax.text(9.75, 2.9, '{:.2%}'.format(dd_max_b), color='red',
fontweight='bold', horizontalalignment='right', fontsize=30)
ax.text(9.75, 1.9, '{:.0f}'.format(
dd_dur_b), fontweight='bold', horizontalalignment='right', fontsize=30)
ax.set_title('策略 vs. 基准', fontweight='bold',size='xx-large')
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 security curve.
"""
def format_perc(x, pos):
return '%.0f%%' % x
stats = pd.DataFrame(stats)
stats['date']=pd.to_datetime(stats['date'])
stats.set_index('date',inplace=True)
returns = stats["daily_returns"]
cum_returns = stats['cum_returns']
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)
rsq = perf.rsuqare(range(cum_returns.shape[0]), cum_returns)
dd, dd_max, dd_dur = perf.create_drawdowns(cum_returns)
ax.text(0.25, 7.9, 'Total Return', fontsize=8)
ax.text(7.50, 7.9, '{:.0%}'.format(tot_ret), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 6.9, 'CAGR', fontsize=8)
ax.text(7.50, 6.9, '{:.2%}'.format(cagr), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(0.25, 5.9, 'Sharpe Ratio', fontsize=8)
ax.text(7.50, 5.9, '{:.2f}'.format(sharpe), 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.set_title('Curve', fontweight='bold')
if self.benchmark is not None:
returns_b = stats['returns_b']
security_b = stats['cum_returns_b']
tot_ret_b = security_b[-1] - 1.0
cagr_b = perf.create_cagr(security_b)
sharpe_b = perf.create_sharpe_ratio(returns_b)
rsq_b = perf.rsquared(range(security_b.shape[0]), security_b)
dd_b, dd_max_b, dd_dur_b = perf.create_drawdowns(security_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, '{:.2%}'.format(returns_b.std() * np.sqrt(252)), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 4.9, '{:.2f}'.format(rsq_b), fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 3.9, '{:.2%}'.format(dd_max_b), color='red', fontweight='bold', horizontalalignment='right', fontsize=8)
ax.text(9.75, 2.9, '{:.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