fix: misc bugfixes for 0.0.78 release (#499, #501, #502)

- Fixed circular import errors that broke import quantstats
- Fixed NameError in reports.full()
- Fixed reports.html() to work without output file (opens in browser)
- Fixed profit_ratio() DataFrame handling
- Removed dark mode CSS from HTML report
- Improved HTML report header (Compounded/matched dates conditionals)
- Added new metrics to full report (UPI, RAR, Risk-Return Ratio, etc.)
- Added terminal output parameters table
- Added comprehensive test suite (125 tests)

Author: ranaroussi

.gitignore

@@ -11,7 +11,6 @@ dist
 quantstats.egg-info
 QuantStats.egg-info
 Icon
-/tests
 .vscode
 Icon
 CLAUDE.md

CHANGELOG.md

@@ -4,29 +4,28 @@ Changelog
 0.0.81
 ------
 
-**Hotfix Release**
+**Bugfixes for 0.0.78 release**
 
+- Fixed circular import errors that broke `import quantstats` (#499, #501)
 - Fixed `NameError: name 'dd_get_stats' is not defined` in `reports.full()` (#502)
-  - Root cause: Accidental find-and-replace during v0.0.80 lazy import refactoring changed `dd_stats` to `dd_get_stats()`
-  - Fix: Restored correct variable name `dd_stats` in `_calc_dd()` function
+- Fixed `reports.html()` to work without specifying output file (opens in browser)
+- Fixed `profit_ratio()` to handle DataFrame inputs properly
+- Removed dark mode CSS from HTML report template
+- Suppressed pandas FutureWarning for callable resampler.apply()
 
-0.0.80
-------
-
-**Hotfix Release**
+- Improved HTML report header:
+  - Title shows "(Compounded)" only when compounded=True
+  - Date range shows "(matched dates)" only when match_dates=True with benchmark
+  - Parameters now always show Benchmark, Periods/Year, and RF rate
 
-- Fixed circular import errors that broke `import quantstats` (#499, #501):
-  - Root cause: Multiple modules imported `stats` and `utils` at module level during package initialization
-  - Fix: Implemented lazy imports in `utils.py`, `reports.py`, `_plotting/core.py`, `_plotting/wrappers.py`
-  - Removed erroneous `quantstats/stats.py` entry from `.gitignore` that excluded it from wheel builds
-  - Import now works correctly on fresh installations
-
-0.0.79
-------
+- Added new metrics to full HTML report:
+  - Ulcer Performance Index, Risk-Adjusted Return, Risk-Return Ratio
+  - Avg. Return, Avg. Win, Avg. Loss, Win/Loss Ratio, Profit Ratio
 
-**Hotfix Release (Incomplete)**
+- Added terminal output parameters table for `reports.full()` and `reports.basic()`
 
-- Attempted fix for circular import (#499), but additional circular imports remained
+- Added comprehensive test suite (125 tests):
+  - Tests for stats, reports, utils, plots, compat, extend_pandas, and Monte Carlo
 
 0.0.78
 ------

quantstats/_compat.py

@@ -161,7 +161,11 @@ def safe_resample(data: Union[pd.Series, pd.DataFrame],
                 result = resampler.apply(func_name, **kwargs)
     else:
         # For callable functions, use apply method
-        result = resampler.apply(func_name, **kwargs)
+        # Suppress FutureWarning about callable usage - our use is intentional
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=FutureWarning, 
+                                    message=".*callable.*")
+            result = resampler.apply(func_name, **kwargs)
 
     # Normalize timezone to ensure consistent comparisons
     return normalize_timezone(result)

quantstats/report.html

@@ -8,11 +8,10 @@
     <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
     <title>Tearsheet (generated by QuantStats)</title>
     <meta name="robots" content="noindex, nofollow">
-    <meta name="color-scheme" content="light dark">
     <link rel="shortcut icon" href="https://qtpylib.io/favicon.ico" type="image/x-icon">
     <style>
     body{-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale;margin:30px;background:#fff;color:#000}body,p,table,td,th{font:13px/1.4 Arial,sans-serif}.container{max-width:960px;margin:auto}img,svg{width:100%}h1,h2,h3,h4{font-weight:400;margin:0}h1 dt{display:inline;margin-left:10px;font-size:14px}h3{margin-bottom:10px;font-weight:700}h4{color:grey}h4 a{color:#09c;text-decoration:none}h4 a:hover{color:#069;text-decoration:underline}hr{margin:25px 0 40px;height:0;border:0;border-top:1px solid #ccc}#left{width:620px;margin-right:18px;margin-top:-1.2rem;float:left}#right{width:320px;float:right}#left svg{margin:-1.5rem 0}#monthly_heatmap{overflow:hidden}#monthly_heatmap svg{margin:-1.5rem 0}table{margin:0 0 40px;border:0;border-spacing:0;width:100%}table td,table th{text-align:right;padding:4px 5px 3px 5px}table th{text-align:right;padding:6px 5px 5px 5px}table td:first-of-type,table th:first-of-type{text-align:left;padding-left:2px}table td:last-of-type,table th:last-of-type{text-align:right;padding-right:2px}td hr{margin:5px 0}table th{font-weight:400}table thead th{font-weight:700;background:#eee}#eoy table td:after{content:"%"}#eoy table td:first-of-type:after,#eoy table td:last-of-type:after,#eoy table td:nth-of-type(4):after{content:""}#eoy table th{text-align:right}#eoy table th:first-of-type{text-align:left}#eoy table td:after{content:"%"}#eoy table td:first-of-type:after,#eoy table td:last-of-type:after{content:""}#ddinfo table td:nth-of-type(3):after{content:"%"}#ddinfo table th{text-align:right}#ddinfo table td:first-of-type,#ddinfo table td:nth-of-type(2),#ddinfo table th:first-of-type,#ddinfo table th:nth-of-type(2){text-align:left}#ddinfo table td:nth-of-type(3):after{content:"%"}
-    @media (prefers-color-scheme:dark){body{background:#1a1a2e;color:#e6e6e6}h4{color:#b0b0b0}h4 a{color:#5dade2}h4 a:hover{color:#85c1e9}hr{border-top-color:#444}table thead th{background:#333}img,svg{background:#fff;border-radius:4px;padding:8px}}
+
     @media print{hr{margin:25px 0}body{margin:0}.container{max-width:100%;margin:0}#left{width:55%;margin:0}#left svg{margin:0 0 -10%}#left svg:first-of-type{margin-top:-30%}#right{margin:0;width:45%}}
     </style>
 </head>

quantstats/reports.py

@@ -57,6 +57,8 @@ def _get_plots():
 from dateutil.relativedelta import relativedelta
 from io import StringIO
 from pathlib import Path
+import tempfile
+import webbrowser
 
 try:
     from IPython.display import display as iDisplay, HTML as iHTML
@@ -95,6 +97,44 @@ def _get_trading_periods(periods_per_year=252):
     return periods_per_year, half_year
 
 
+def _print_parameters_table(
+    benchmark_title=None,
+    periods_per_year=252,
+    rf=0.0,
+    compounded=True,
+    match_dates=True,
+):
+    """
+    Print a formatted parameters table for terminal/console output.
+
+    Parameters
+    ----------
+    benchmark_title : str or None
+        Benchmark name/ticker
+    periods_per_year : int
+        Number of trading periods per year
+    rf : float
+        Risk-free rate
+    compounded : bool
+        Whether returns are compounded
+    match_dates : bool
+        Whether dates are matched with benchmark
+    """
+    width = 40
+    print("=" * width)
+    print("                 Parameters")
+    print("-" * width)
+    if benchmark_title:
+        print(f"{'Benchmark':<25}{benchmark_title.upper():>15}")
+    print(f"{'Periods/Year':<25}{periods_per_year:>15}")
+    print(f"{'Risk-Free Rate':<25}{rf:>14.1%}")
+    print(f"{'Compounded':<25}{'Yes' if compounded else 'No':>15}")
+    if benchmark_title:
+        print(f"{'Match Dates':<25}{'Yes' if match_dates else 'No':>15}")
+    print("=" * width)
+    print()
+
+
 def _match_dates(returns, benchmark):
     """
     Align returns and benchmark data to start from the same date.
@@ -203,15 +243,9 @@ def html(
 
     Raises
     ------
-    ValueError
-        If output is None and not running in notebook environment
     FileNotFoundError
         If custom template_path doesn't exist
     """
-    # Check if output parameter is required (not in notebook environment)
-    if output is None and not _get_utils()._in_notebook():
-        raise ValueError("`output` must be specified")
-
     # Clean returns data by removing NaN values if date matching is enabled
     if match_dates:
         returns = returns.dropna()
@@ -290,10 +324,13 @@ def html(
     # Format date range for display in template
     date_range = returns.index.strftime("%e %b, %Y")
     tpl = tpl.replace("{{date_range}}", date_range[0] + " - " + date_range[-1])
-    tpl = tpl.replace("{{title}}", title)
+
+    # Build title with compounding indicator (only show if compounded)
+    full_title = f"{title} (Compounded)" if compounded else title
+    tpl = tpl.replace("{{title}}", full_title)
     tpl = tpl.replace("{{v}}", __version__)
 
-    # Build parameters string for subtitle (feature #472)
+    # Build parameters string for subtitle
     params_parts = []
 
     # Add user-provided parameters first if present
@@ -302,15 +339,12 @@ def html(
         for key, value in user_params.items():
             params_parts.append(f"{key}: {value}")
 
-    # Add auto-detected parameters
+    # Add auto-detected parameters (always show key params)
     if benchmark_title:
         params_parts.append(f"Benchmark: {benchmark_title.upper()}")
-    if rf != 0:
-        params_parts.append(f"RF: {rf:.1%}")
-    if periods_per_year != 252:
-        params_parts.append(f"Periods: {periods_per_year}")
-    if not compounded:
-        params_parts.append("Simple Returns")
+    params_parts.append(f"Periods/Year: {periods_per_year}")
+    params_parts.append(f"RF: {rf:.1%}")
+
     params_str = " &bull; ".join(params_parts)
     if params_str:
         params_str += " | "
@@ -725,8 +759,16 @@ def html(
 
     # Handle output - either download in browser or save to file
     if output is None:
-        # _open_html(tpl)
-        _download_html(tpl, download_filename)
+        if _get_utils()._in_notebook():
+            _download_html(tpl, download_filename)
+        else:
+            # Save to temp file and open in browser
+            with tempfile.NamedTemporaryFile(
+                mode="w", suffix=".html", delete=False, encoding="utf-8"
+            ) as f:
+                f.write(tpl)
+                temp_path = f.name
+            webbrowser.open("file://" + temp_path)
         return
 
     # Write HTML content to specified output file
@@ -888,6 +930,13 @@ def full(
         iDisplay(iHTML("<h4>Strategy Visualization</h4>"))
     else:
         # Display in console/terminal environment
+        _print_parameters_table(
+            benchmark_title=benchmark_title,
+            periods_per_year=periods_per_year,
+            rf=rf,
+            compounded=compounded,
+            match_dates=match_dates,
+        )
         print("[Performance Metrics]\n")
         metrics(
             returns=returns,
@@ -1043,6 +1092,13 @@ def basic(
         iDisplay(iHTML("<h4>Strategy Visualization</h4>"))
     else:
         # Display in console/terminal environment
+        _print_parameters_table(
+            benchmark_title=benchmark_title,
+            periods_per_year=periods_per_year,
+            rf=rf,
+            compounded=compounded,
+            match_dates=match_dates,
+        )
         print("[Performance Metrics]\n")
         metrics(
             returns=returns,
@@ -1383,9 +1439,24 @@ def metrics(
         metrics["Skew"] = _get_stats().skew(df, prepare_returns=False)
         metrics["Kurtosis"] = _get_stats().kurtosis(df, prepare_returns=False)
 
+        # Additional ratios
+        metrics["Ulcer Performance Index"] = _get_stats().ulcer_performance_index(df, rf)
+        metrics["Risk-Adjusted Return %"] = _get_stats().rar(df, rf) * pct
+        metrics["Risk-Return Ratio"] = _get_stats().risk_return_ratio(df, prepare_returns=False)
+
         # Add separator
         metrics["~~~~~~~~~~"] = blank
 
+        # Average return metrics
+        metrics["Avg. Return %"] = _get_stats().avg_return(df, prepare_returns=False) * pct
+        metrics["Avg. Win %"] = _get_stats().avg_win(df, prepare_returns=False) * pct
+        metrics["Avg. Loss %"] = _get_stats().avg_loss(df, prepare_returns=False) * pct
+        metrics["Win/Loss Ratio"] = _get_stats().win_loss_ratio(df, prepare_returns=False)
+        metrics["Profit Ratio"] = _get_stats().profit_ratio(df, prepare_returns=False)
+
+        # Add separator
+        metrics["~~~~~~~~~~~"] = blank
+
         # Expected returns at different frequencies
         metrics["Expected Daily %%"] = (
             _get_stats().expected_return(df, compounded=compounded, prepare_returns=False)

quantstats/stats.py

@@ -2115,11 +2115,11 @@ def profit_ratio(returns, prepare_returns=True):
     providing insight into the consistency of profitable periods.
 
     Args:
-        returns (pd.Series): Return series to analyze
+        returns (pd.Series or pd.DataFrame): Return series to analyze
         prepare_returns (bool): Whether to prepare returns first (default: True)
 
     Returns:
-        float: Profit ratio
+        float or pd.Series: Profit ratio
 
     Example:
         >>> returns = pd.Series([0.01, -0.02, 0.03, -0.01, 0.02])
@@ -2129,30 +2129,33 @@ def profit_ratio(returns, prepare_returns=True):
     if prepare_returns:
         returns = _utils._prepare_returns(returns)
 
-    # Separate wins and losses
-    wins = returns[returns >= 0]
-    loss = returns[returns < 0]
+    def _profit_ratio(ret):
+        # Separate wins and losses
+        wins = ret[ret >= 0]
+        loss = ret[ret < 0]
 
-    # Handle edge cases
-    if wins.count() == 0:
-        warn("No winning returns found for profit ratio calculation")
-        return 0.0
-    if loss.count() == 0:
-        warn("No losing returns found for profit ratio calculation, returning infinity")
-        return float('inf')
+        # Handle edge cases
+        win_count = len(wins)
+        loss_count = len(loss)
 
-    # Calculate win and loss ratios
-    win_ratio = abs(wins.mean() / wins.count()) if wins.count() > 0 else 0
-    loss_ratio = abs(loss.mean() / loss.count()) if loss.count() > 0 else 0
+        if win_count == 0:
+            return 0.0
+        if loss_count == 0:
+            return _np.nan
+
+        # Calculate win and loss ratios
+        win_ratio = abs(wins.mean() / win_count) if win_count > 0 else 0
+        loss_ratio = abs(loss.mean() / loss_count) if loss_count > 0 else 0
 
-    try:
         if loss_ratio == 0:
-            warn("Loss ratio is zero, returning infinity for profit ratio")
-            return float('inf')
+            return _np.nan
         return win_ratio / loss_ratio
-    except (ValueError, TypeError) as e:
-        warn(f"Error calculating profit ratio: {e}, returning 0.0")
-        return 0.0
+
+    # Handle DataFrame by applying to each column
+    if isinstance(returns, _pd.DataFrame):
+        return returns.apply(_profit_ratio)
+
+    return _profit_ratio(returns)
 
 
 def profit_factor(returns, prepare_returns=True):

tests/__init__.py

@@ -0,0 +1 @@
+# QuantStats test suite