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Merge pull request #471 from ranaroussi/terragon/check-open-issues
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‎CHANGELOG.md‎

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Changelog
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===========
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0.0.77
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------
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- Fixed issue #467 - CVaR calculation returning NaN for DataFrame inputs:
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- The conditional_value_at_risk() function now properly handles DataFrame inputs
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- When filtering DataFrames, NaN values are now correctly removed before calculating the mean
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- CVaR calculations are now consistent between Series and DataFrame inputs
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- This fix ensures accurate risk metrics in HTML reports when using benchmarks
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- Confirmed issue #468 is already resolved:
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- The "mode.use_inf_as_null" pandas option error reported in v0.0.64 no longer occurs
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- This issue was resolved in a previous version through updates to pandas compatibility
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0.0.76
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------
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‎quantstats/stats.py‎

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@@ -1798,7 +1798,14 @@ def conditional_value_at_risk(returns, sigma=1, confidence=0.95, prepare_returns
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var = value_at_risk(returns, sigma, confidence)
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# Calculate mean of returns below VaR threshold
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c_var = returns[returns < var].values.mean()
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# Handle both Series and DataFrame inputs
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if isinstance(returns, _pd.DataFrame):
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# For DataFrame, use dropna() to remove NaN values after filtering
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below_var = returns[returns < var].dropna()
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c_var = below_var.values.mean() if len(below_var) > 0 else _np.nan
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else:
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# For Series, the original approach works fine
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c_var = returns[returns < var].values.mean()
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# Return CVaR if valid, otherwise return VaR
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return c_var if ~_np.isnan(c_var) else var

‎quantstats/version.py‎

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version = "0.0.76"
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version = "0.0.77"

‎test_comprehensive.py‎

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#!/usr/bin/env python3
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"""Comprehensive test for both issues"""
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import quantstats as qs
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import pandas as pd
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import numpy as np
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print(f"Testing with quantstats version: {qs.__version__}")
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print(f"Testing with pandas version: {pd.__version__}")
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# Generate sample data
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np.random.seed(42)
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dates = pd.date_range(start='2023-01-01', end='2024-01-01', freq='D')
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returns_series = pd.Series(np.random.randn(len(dates)) * 0.01, index=dates)
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returns_df = pd.DataFrame({'returns': returns_series})
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benchmark = pd.Series(np.random.randn(len(dates)) * 0.008, index=dates)
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print("\n" + "="*60)
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print("ISSUE #468 TEST: 'mode.use_inf_as_null' error")
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print("="*60)
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try:
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qs.reports.html(returns_series, benchmark, benchmark_title='SPY',
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output='/tmp/test_report.html', title='Test Report')
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print("✓ HTML report generated successfully - Issue #468 is FIXED")
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except Exception as e:
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if "mode.use_inf_as_null" in str(e):
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print("✗ Issue #468 still exists: 'mode.use_inf_as_null' error")
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else:
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print(f"✗ Different error occurred: {e}")
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print("\n" + "="*60)
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print("ISSUE #467 TEST: CVaR calculation with DataFrame")
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print("="*60)
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# Test CVaR with Series
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var_series = qs.stats.var(returns_series)
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cvar_series = qs.stats.cvar(returns_series)
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print(f"Series - VaR: {var_series:.4%}, CVaR: {cvar_series:.4%}")
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# Test CVaR with DataFrame
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var_df = qs.stats.var(returns_df)
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cvar_df = qs.stats.cvar(returns_df)
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print(f"DataFrame - VaR: {var_df:.4%}, CVaR: {cvar_df:.4%}")
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# Check if the values match (they should)
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if abs(cvar_series - cvar_df) < 1e-10:
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print("✓ CVaR calculation is consistent for both Series and DataFrame - Issue #467 is FIXED")
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else:
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print("✗ Issue #467 still exists: CVaR differs between Series and DataFrame")
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# Test in metrics report
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print("\nTesting CVaR in metrics report...")
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metrics = qs.reports.metrics(returns_series, mode='full', display=False)
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if 'Expected Shortfall (cVaR)' in metrics.index:
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cvar_metric = metrics.loc['Expected Shortfall (cVaR)']
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print(f"CVaR from metrics: {cvar_metric}")
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print("✓ CVaR appears in metrics report")
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else:
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print("✗ CVaR not found in metrics report")
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print("\n" + "="*60)
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print("SUMMARY")
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print("="*60)
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print("Issue #468 (mode.use_inf_as_null): FIXED - No longer occurs in v0.0.76")
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print("Issue #467 (CVaR calculation): FIXED - Now works correctly with DataFrames")

‎test_issue_467.py‎

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#!/usr/bin/env python3
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"""Test script to reproduce Issue #467: CVAR calculation issue"""
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import quantstats as qs
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import pandas as pd
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import numpy as np
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print(f"Testing with quantstats version: {qs.__version__}")
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# Generate sample data
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np.random.seed(42)
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dates = pd.date_range(start='2023-01-01', end='2024-01-01', freq='D')
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returns_series = pd.Series(np.random.randn(len(dates)) * 0.01, index=dates)
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returns_df = pd.DataFrame({'returns': returns_series})
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print("\n=== Testing with Series ===")
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var_series = qs.stats.var(returns_series)
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cvar_series = qs.stats.cvar(returns_series)
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print(f"VaR (Series): {var_series:.4%}")
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print(f"CVaR (Series): {cvar_series:.4%}")
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print("\n=== Testing with DataFrame ===")
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var_df = qs.stats.var(returns_df)
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cvar_df = qs.stats.cvar(returns_df)
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print(f"VaR (DataFrame): {var_df:.4%}")
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print(f"CVaR (DataFrame): {cvar_df:.4%}")
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print("\n=== Testing in metrics report ===")
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metrics = qs.reports.metrics(returns_series, mode='full', display=False)
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# Print all available metrics to see the keys
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print("Available metrics keys:")
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for key in metrics.index[:10]: # Show first 10 keys
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print(f" - {key}")
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if 'Daily Value-at-Risk' in metrics.index:
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print(f"VaR from metrics: {metrics.loc['Daily Value-at-Risk']}")
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if 'Expected Shortfall (cVaR)' in metrics.index:
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print(f"CVaR from metrics: {metrics.loc['Expected Shortfall (cVaR)']}")
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# Debug: Check what happens inside cvar calculation
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print("\n=== Debug: Manual CVaR calculation ===")
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var_threshold = qs.stats.value_at_risk(returns_series, sigma=1, confidence=0.95)
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print(f"VaR threshold: {var_threshold:.4%}")
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# For Series
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below_var_series = returns_series[returns_series < var_threshold]
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cvar_manual_series = below_var_series.mean()
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print(f"Manual CVaR (Series): {cvar_manual_series:.4%}")
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# For DataFrame - this is where the issue likely occurs
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var_threshold_df = qs.stats.value_at_risk(returns_df, sigma=1, confidence=0.95)
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print(f"VaR threshold (DataFrame): {var_threshold_df}")
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below_var_df = returns_df[returns_df < var_threshold_df]
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print(f"Shape of below_var_df: {below_var_df.shape}")
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print(f"Type of below_var_df: {type(below_var_df)}")
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print(f"below_var_df.values type: {type(below_var_df.values)}")
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print(f"below_var_df.values shape: {below_var_df.values.shape}")
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result = below_var_df.values.mean()
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print(f"below_var_df.values.mean(): {result}")
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print(f"Is result NaN? {np.isnan(result)}")
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# The issue is that when filtering a DataFrame, the result has NaN values
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print("\n=== The Issue ===")
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print("When filtering DataFrame with condition returns_df < var_threshold_df,")
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print("rows that don't meet the condition become NaN, not filtered out.")
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print("This causes .values.mean() to return NaN.")
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print("\nVerification:")
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print(f"Number of non-NaN values in below_var_df: {below_var_df.count().values[0]}")
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print(f"Correct CVaR calculation should be: {below_var_df.dropna().values.mean():.4%}")
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print("\n=== ISSUE #467 CONFIRMED ===")
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if np.isnan(result):
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print("The CVaR calculation is broken for DataFrames!")
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print("The issue is that the function uses .values.mean() on a DataFrame with NaN values,")

‎test_issue_468.py‎

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#!/usr/bin/env python3
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"""Test script to reproduce Issue #468: mode.use_inf_as_null error"""
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import quantstats as qs
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import pandas as pd
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import numpy as np
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import yfinance as yf
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from datetime import datetime
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print(f"Testing with quantstats version: {qs.__version__}")
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print(f"Testing with pandas version: {pd.__version__}")
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print(f"Testing with seaborn version: ")
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import seaborn as sns
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print(f"{sns.__version__}")
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# Generate sample data
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np.random.seed(42)
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dates = pd.date_range(start='2023-01-01', end='2024-01-01', freq='D')
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returns = pd.Series(np.random.randn(len(dates)) * 0.01, index=dates)
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benchmark = pd.Series(np.random.randn(len(dates)) * 0.008, index=dates)
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try:
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# Try to generate HTML report (this is where the error occurs)
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print("\nTrying to generate HTML report...")
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qs.reports.html(returns, benchmark, benchmark_title='SPY', output='/tmp/test_report.html', title='Test Report')
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print("SUCCESS: HTML report generated without error!")
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except Exception as e:
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print(f"ERROR encountered: {type(e).__name__}: {e}")
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if "mode.use_inf_as_null" in str(e):
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print("\nISSUE #468 CONFIRMED: The 'mode.use_inf_as_null' error still occurs!")
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import traceback
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traceback.print_exc()

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