test for inf/nan returns (fixes #440)

pypfopt/expected_returns.py

@@ -25,6 +25,18 @@ import pandas as pd
 import numpy as np
 
 
+def _check_returns(returns):
+    # Check NaNs excluding leading NaNs
+    if np.any(np.isnan(returns.mask(returns.ffill().isnull(), 0))):
+        warnings.warn(
+            "Some returns are NaN. Please check your price data.", UserWarning
+        )
+    if np.any(np.isinf(returns)):
+        warnings.warn(
+            "Some returns are infinite. Please check your price data.", UserWarning
+        )
+
+
 def returns_from_prices(prices, log_returns=False):
     """
     Calculate the returns given prices.
@@ -38,9 +50,10 @@ def returns_from_prices(prices, log_returns=False):
     :rtype: pd.DataFrame
     """
     if log_returns:
-        return np.log(1 + prices.pct_change()).dropna(how="all")
+        returns = np.log(1 + prices.pct_change()).dropna(how="all")
     else:
-        return prices.pct_change().dropna(how="all")
+        returns = prices.pct_change().dropna(how="all")
+    return returns
 
 
 def prices_from_returns(returns, log_returns=False):
@@ -126,6 +139,8 @@ def mean_historical_return(
         returns = prices
     else:
         returns = returns_from_prices(prices, log_returns)
+
+    _check_returns(returns)
     if compounding:
         return (1 + returns).prod() ** (frequency / returns.count()) - 1
     else:
@@ -172,6 +187,7 @@ def ema_historical_return(
     else:
         returns = returns_from_prices(prices, log_returns)
 
+    _check_returns(returns)
     if compounding:
         return (1 + returns.ewm(span=span).mean().iloc[-1]) ** frequency - 1
     else:
@@ -241,6 +257,8 @@ def capm_return(
         market_returns.columns = ["mkt"]
         returns = returns.join(market_returns, how="left")
 
+    _check_returns(returns)
+
     # Compute covariance matrix for the new dataframe (including markets)
     cov = returns.cov()
     # The far-right column of the cov matrix is covariances to market

tests/test_expected_returns.py

@@ -50,6 +50,13 @@ def test_returns_from_prices():
     pd.testing.assert_series_equal(returns_df.iloc[-1], df.pct_change().iloc[-1])
 
 
+def test_returns_warning():
+    df = get_data()
+    df.iloc[3, :] = 0  # make some prices zero
+    with pytest.warns(UserWarning):
+        expected_returns.mean_historical_return(df)
+
+
 def test_log_returns_from_prices():
     df = get_data()
     old_nan = df.isnull().sum(axis=1).sum()