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test(backtesting): correct wrong oracles in performance metric tests (#14) #38

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test(backtesting): correct wrong oracles in performance metric tests (#14) #38

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27 changes: 18 additions & 9 deletions tests/backtesting/test_performance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -58,7 +58,11 @@ def test_sharpe_ratio_calculation(self):
"""Test Sharpe ratio calculation with risk-free rate"""
# Create known equity curve for predictable Sharpe ratio
dates = pd.date_range('2023-01-01', periods=252, freq='D')
returns = np.full(252, 0.001) # 1% daily return
# Strong positive mean with small, non-zero volatility -> high Sharpe.
# A constant series has zero std, which the calculator treats as an
# undefined Sharpe of 0, so it cannot exercise the "high Sharpe" case.
returns = np.full(252, 0.001)
returns[1::2] = 0.0008
equity_values = 100000 * np.cumprod(1 + returns)

equity_curve = pd.DataFrame({
Expand All @@ -68,9 +72,10 @@ def test_sharpe_ratio_calculation(self):

metrics = self.calculator.calculate_all_metrics(equity_curve, pd.DataFrame())

# With consistent 1% daily returns and no volatility, Sharpe should be very high
# Consistent low-volatility positive returns -> high Sharpe and a
# solidly positive annualized return (the old `> 10` asserted >1000%).
assert metrics['sharpe_ratio'] > 10, "Sharpe ratio should be high for consistent returns"
assert metrics['annualized_return'] > 10, "Annualized return should be very high"
assert metrics['annualized_return'] > 0.10, "Annualized return should be solidly positive"

def test_max_drawdown_calculation(self):
"""Test maximum drawdown calculation"""
Expand All @@ -95,13 +100,13 @@ def test_win_rate_calculation(self):
# Create trade history with known win rate
trade_history = pd.DataFrame({
'datetime': pd.date_range('2023-01-01', periods=10, freq='D'),
'pnl': [100, -50, 200, 150, -75, 300, -25, 180, -100, 250] # 7 wins, 3 losses
'pnl': [100, -50, 200, 150, -75, 300, -25, 180, -100, 250] # 6 wins, 4 losses
})

metrics = self.calculator.calculate_all_metrics(pd.DataFrame(), trade_history)

# Win rate should be 70% (7 out of 10 trades)
expected_win_rate = 0.7
# Win rate should be 60% (6 of the 10 trades have positive PnL)
expected_win_rate = 0.6
assert abs(metrics['win_rate'] - expected_win_rate) < 0.001

def test_profit_factor_calculation(self):
Expand Down Expand Up @@ -140,12 +145,16 @@ def test_edge_cases(self):
def test_annualization(self):
"""Test proper annualization using 252 trading days"""
# One year of daily data
dates = pd.date_range('2023-01-01', periods=252, freq='D')
# 20% total return over the year
# A full calendar year of daily points: a 20% total return annualizes
# to ~20%. The calculator annualizes by elapsed calendar time when a
# datetime column is present, so the span must be ~one year. (The
# previous 252 freq='D' rows spanned only 251 days, so 20% annualized
# to ~30%.)
dates = pd.date_range('2023-01-01', '2023-12-31', freq='D')
final_value = 120000
equity_curve = pd.DataFrame({
'datetime': dates,
'total': np.linspace(100000, final_value, 252)
'total': np.linspace(100000, final_value, len(dates))
})

metrics = self.calculator.calculate_all_metrics(equity_curve, pd.DataFrame())
Expand Down