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Commit dc10b30

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kernckernc
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ENH: Add stat 'CAGR [%]' (compound annual growth rate)
1 parent aa2f0f1 commit dc10b30
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4 files changed

+28-21Lines changed: 28 additions & 21 deletions

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‎README.md‎

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+1Lines changed: 1 addition & 0 deletions
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Original file line numberDiff line numberDiff line change
@@ -62,6 +62,7 @@ Return [%] 589.35
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Buy & Hold Return [%] 703.46
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Return (Ann.) [%] 25.42
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Volatility (Ann.) [%] 38.43
65+
CAGR [%] 16.80
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Sharpe Ratio 0.66
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Sortino Ratio 1.30
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Calmar Ratio 0.77
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‎backtesting/_stats.py‎

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+5-1Lines changed: 5 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -99,7 +99,8 @@ def _round_timedelta(value, _period=_data_period(index)):
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gmean_day_return: float = 0
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day_returns = np.array(np.nan)
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annual_trading_days = np.nan
102-
if isinstance(index, pd.DatetimeIndex):
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is_datetime_index = isinstance(index, pd.DatetimeIndex)
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if is_datetime_index:
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day_returns = equity_df['Equity'].resample('D').last().dropna().pct_change()
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gmean_day_return = geometric_mean(day_returns)
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annual_trading_days = float(
@@ -115,6 +116,9 @@ def _round_timedelta(value, _period=_data_period(index)):
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s.loc['Volatility (Ann.) [%]'] = np.sqrt((day_returns.var(ddof=int(bool(day_returns.shape))) + (1 + gmean_day_return)**2)**annual_trading_days - (1 + gmean_day_return)**(2*annual_trading_days)) * 100 # noqa: E501
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# s.loc['Return (Ann.) [%]'] = gmean_day_return * annual_trading_days * 100
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# s.loc['Risk (Ann.) [%]'] = day_returns.std(ddof=1) * np.sqrt(annual_trading_days) * 100
119+
if is_datetime_index:
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time_in_years = (s.loc['Duration'].days + s.loc['Duration'].seconds / 86400) / annual_trading_days
121+
s.loc['CAGR [%]'] = ((s.loc['Equity Final [$]'] / equity[0])**(1/time_in_years) - 1) * 100 if time_in_years else np.nan # noqa: E501
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# Our Sharpe mismatches `empyrical.sharpe_ratio()` because they use arithmetic mean return
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# and simple standard deviation
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‎backtesting/backtesting.py‎

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+21-20Lines changed: 21 additions & 20 deletions
Original file line numberDiff line numberDiff line change
@@ -1145,31 +1145,32 @@ def run(self, **kwargs) -> pd.Series:
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Start 2004-08-19 00:00:00
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End 2013-03-01 00:00:00
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Duration 3116 days 00:00:00
1148-
Exposure Time [%] 93.9944
1149-
Equity Final [$] 51959.9
1150-
Equity Peak [$] 75787.4
1151-
Return [%] 419.599
1152-
Buy & Hold Return [%] 703.458
1153-
Return (Ann.) [%] 21.328
1154-
Volatility (Ann.) [%] 36.5383
1155-
Sharpe Ratio 0.583718
1156-
Sortino Ratio 1.09239
1157-
Calmar Ratio 0.444518
1158-
Max. Drawdown [%] -47.9801
1148+
Exposure Time [%] 96.74115
1149+
Equity Final [$] 51422.99
1150+
Equity Peak [$] 75787.44
1151+
Return [%] 414.2299
1152+
Buy & Hold Return [%] 703.45824
1153+
Return (Ann.) [%] 21.18026
1154+
Volatility (Ann.) [%] 36.49391
1155+
CAGR [%] 14.15984
1156+
Sharpe Ratio 0.58038
1157+
Sortino Ratio 1.08479
1158+
Calmar Ratio 0.44144
1159+
Max. Drawdown [%] -47.98013
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Avg. Drawdown [%] -5.92585
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Max. Drawdown Duration 584 days 00:00:00
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Avg. Drawdown Duration 41 days 00:00:00
1162-
# Trades 65
1163-
Win Rate [%] 46.1538
1164-
Best Trade [%] 53.596
1165-
Worst Trade [%] -18.3989
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Avg. Trade [%] 2.35371
1163+
# Trades 66
1164+
Win Rate [%] 46.9697
1165+
Best Trade [%] 53.59595
1166+
Worst Trade [%] -18.39887
1167+
Avg. Trade [%] 2.53172
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Max. Trade Duration 183 days 00:00:00
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Avg. Trade Duration 46 days 00:00:00
1169-
Profit Factor 2.08802
1170-
Expectancy [%] 8.79171
1171-
SQN 0.916893
1172-
Kelly Criterion 0.6134
1170+
Profit Factor 2.16795
1171+
Expectancy [%] 3.27481
1172+
SQN 1.07662
1173+
Kelly Criterion 0.15187
11731174
_strategy SmaCross
11741175
_equity_curve Eq...
11751176
_trades Size EntryB...
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‎backtesting/test/_test.py‎

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+1Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -275,6 +275,7 @@ def test_compute_stats(self):
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'Return (Ann.) [%]': 21.180255813792282,
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'Return [%]': 414.2298999999996,
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'Volatility (Ann.) [%]': 36.49390889140787,
278+
'CAGR [%]': 14.159843619607383,
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'SQN': 1.0766187356697705,
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'Kelly Criterion': 0.1518705127029717,
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'Sharpe Ratio': 0.5803778344714113,

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