Skip to content

BUG: Fix CAGR and annualized return calculations #1346

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
blue-int wants to merge 1 commit into
base: master
Choose a base branch
from
+8 −8
Open

BUG: Fix CAGR and annualized return calculations #1346

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions backtesting/_stats.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -129,7 +129,7 @@ def _round_timedelta(value, _period=_data_period(index)):
1 if freq_days == 365 else
(365 if have_weekends else 252))
freq = {7: 'W', 31: 'ME', 365: 'YE'}.get(freq_days, 'D')
day_returns = equity_df['Equity'].resample(freq).last().dropna().pct_change()
day_returns = equity_df['Equity'].resample(freq).last().dropna().pct_change().dropna()
gmean_day_return = geometric_mean(day_returns)

# Annualized return and risk metrics are computed based on the (mostly correct)
Expand All @@ -142,7 +142,7 @@ def _round_timedelta(value, _period=_data_period(index)):
# s.loc['Return (Ann.) [%]'] = gmean_day_return * annual_trading_days * 100
# s.loc['Risk (Ann.) [%]'] = day_returns.std(ddof=1) * np.sqrt(annual_trading_days) * 100
if is_datetime_index:
time_in_years = (s.loc['Duration'].days + s.loc['Duration'].seconds / 86400) / annual_trading_days
time_in_years = (s.loc['Duration'].days + s.loc['Duration'].seconds / 86400) / 365.25
s.loc['CAGR [%]'] = ((s.loc['Equity Final [$]'] / equity[0])**(1 / time_in_years) - 1) * 100 if time_in_years else np.nan # noqa: E501

# Our Sharpe mismatches `empyrical.sharpe_ratio()` because they use arithmetic mean return
Expand Down
12 changes: 6 additions & 6 deletions backtesting/test/_test.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -319,7 +319,7 @@ def test_compute_stats(self):
'Avg. Trade [%]': 2.531715975158555,
'Best Trade [%]': 53.59595229490424,
'Buy & Hold Return [%]': 522.0601851851852,
'Calmar Ratio': 0.4414380935608377,
'Calmar Ratio': 0.44166409202994605,
'Duration': pd.Timedelta('3116 days 00:00:00'),
'End': pd.Timestamp('2013-03-01 00:00:00'),
'Equity Final [$]': 51422.98999999996,
Expand All @@ -330,14 +330,14 @@ def test_compute_stats(self):
'Max. Drawdown [%]': -47.98012705007589,
'Max. Trade Duration': pd.Timedelta('183 days 00:00:00'),
'Profit Factor': 2.167945974262033,
'Return (Ann.) [%]': 21.180255813792282,
'Return (Ann.) [%]': 21.191099249053224,
'Return [%]': 414.2298999999996,
'Volatility (Ann.) [%]': 36.49390889140787,
'CAGR [%]': 14.159843619607383,
'Volatility (Ann.) [%]': 36.49716090702389,
'CAGR [%]': 21.160245705962623,
'SQN': 1.0766187356697705,
'Kelly Criterion': 0.1518705127029717,
'Sharpe Ratio': 0.5803778344714113,
'Sortino Ratio': 1.0847880675854096,
'Sharpe Ratio': 0.5806232244485349,
'Sortino Ratio': 1.0853434352488662,
'Start': pd.Timestamp('2004-08-19 00:00:00'),
'Win Rate [%]': 46.96969696969697,
'Worst Trade [%]': -18.39887353835481,
Expand Down