def output_results(self):
# Closes off the Backteste.csv file so it can be read via Pandas
# without the problems
self.backtest_file.close()
in_filename = 'backtest.csv'
out_filename = 'equity.csv'
in_file = os.path.join(OUTPUT_RESULT_DIR, in_filename)
out_file = os.path.join(OUTPUT_RESULT_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) # what value are we summing from backtest.csv?
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"]) # input needs to be pnl
df['Drawdown'] = drawdown
df.to_csv(out_file, index=True)
print("Simulation compelte 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, periods=252 * 60 * 6.5)
#import time
#time0 = time.time()
max_dd, dd_duration = create_drawdowns(pnl)
#time1 = time.time()
#print('timing create drawdown: {}'.format(time1 - time0))
stats = dict()
stats['Total Return'] = (total_return - 1.0)
stats['Annualized Return'] = (
total_return ** (365 / len(self.equity_curve.index))) - 1
stats['Length of Series'] = len(self.equity_curve.index)
stats['Sharpe Ratio'] = sharpe_ratio
stats['Max Drawdown'] = max_dd
stats['Drawdown Duration'] = floor(dd_duration)
self.equity_curve.to_csv('equity.csv')
return stats
def test_create_drowdown1(self):
"""
Test1: non random serie with 3 inflection points
"""
rows = 100
step = 1
values = [100]
inflection1 = 0.15
inflection2 = 0.50
inflection3 = 0.90
for each_row in range(rows - 1):
if each_row < (rows * inflection1):
values.append(values[-1] - step)
elif (each_row >= rows) * inflection1 and \
(each_row < rows) * inflection2:
values.append(values[-1] + step)
elif (each_row >= rows * inflection2) and \
(each_row < rows) * inflection3:
values.append(values[-1] - step)
elif each_row >= rows * inflection3:
values.append(values[-1] + step)
pnl = pd.Series(values, index=np.arange(rows))
result = performance.create_drawdowns(pnl)
expected = (40.0, 49.0)
self.assertEqual(result, expected, 'Drawdown error calculation')
def output_summary_stats(self):
"""
Crea un elenco di statistiche di riepilogo per il portafoglio
come lo Sharpe Ratio e le informazioni sul drowdown.
"""
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 * 6.5 * 60)
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_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_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)
with performance calculation, that avoids having to re-run
the full backtest.
In this case it simply works off the "backtest.csv" file that
is produced from a backtest.py run.
"""
import os
import pandas as pd
from performance.performance import create_drawdowns
from settings import OUTPUT_RESULTS_DIR
if __name__ == "__main__":
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)