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)
print("Generating summary stats.")
sharpe_ratio = self.create_sharpe_ratio(df["Returns"], periods = 1)
stats = [("Total Return", "%0.2f%%" % \
((df["Equity"][-1] - 1.0) * 100.0)),
("cagr", "%0.2f%%" % 0.0),
("Sharpe Ratio", "%0.2f" % sharpe_ratio),
("Max Drawdown", "%0.2f%%" % (max_dd * 100.0)),
("Drawdown Duration", "%d" % dd_duration)]
return stats
def output_results(self):
# Closes off the Backtest.csv file so it can be
# read via Pandas without problems
ts = time.time()
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)
create_perf_plot(df)
elapsed = time.time() - ts
print("Simulation complete and results exported to %s in %3.2fs" % (out_filename, elapsed))
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)
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 qsforex.performance.performance import create_drawdowns
from qsforex.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)
This is a small helper script written to help debug issues
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 qsforex.performance.performance import create_drawdowns
from qsforex.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)
