release: v0.12.3

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "manifoldbt"
-version = "0.12.2"
+version = "0.12.3"
 description = "Rust-powered backtesting engine for quantitative research"
 requires-python = ">=3.9"
 license = { file = "LICENSE" }

python/manifoldbt/plot/_decimate.py

@@ -1,4 +1,4 @@
-"""Min/max time-series decimation for plotting — pure numpy.
+"""Min/max time-series decimation for plotting - pure numpy.
 
 A chart is ~1000-2500 px wide, so plotting 10^5-10^6 samples draws hundreds of
 sub-pixel points per column and bloats saved HTML. Per pixel column we keep the

python/manifoldbt/plot/_theme.py

@@ -1,16 +1,16 @@
-"""Clean dark theme — modern, readable, quant-oriented (plotly template)."""
+"""Clean dark theme - modern, readable, quant-oriented (plotly template)."""
 from __future__ import annotations
 
 from contextlib import contextmanager
 
 # ---------------------------------------------------------------------------
-# Color palette — neutral dark, no decorative colors
+# Color palette - neutral dark, no decorative colors
 # ---------------------------------------------------------------------------
 WHITE = "#e8e6e3"
 GRAY = "#8a8a8a"
 DARK_GRAY = "#555555"
-ACCENT = "#60a5fa"       # Neutral blue — primary data line
+ACCENT = "#60a5fa"       # Neutral blue - primary data line
-ACCENT_ALT = "#a78bfa"   # Subtle purple — secondary series
+ACCENT_ALT = "#a78bfa"   # Subtle purple - secondary series
 GREEN = "#22c55e"        # Positive only
 RED = "#ef4444"          # Negative only
 ORANGE = "#f59e0b"       # OOS / warning
@@ -27,7 +27,7 @@ FONT_FAMILY = "Inter, system-ui, Segoe UI, Arial, sans-serif"
 MONO_FAMILY = "SF Mono, Fira Code, Cascadia Code, Consolas, monospace"
 
 # ---------------------------------------------------------------------------
-# Colorscales (plotly format) — same stops as the old matplotlib colormaps
+# Colorscales (plotly format) - same stops as the old matplotlib colormaps
 # ---------------------------------------------------------------------------
 CS_DIVERGING = [[0.0, "#b91c1c"], [0.5, "#262626"], [1.0, "#15803d"]]
 CS_SEQUENTIAL = [[0.0, "#b91c1c"], [0.5, "#d97706"], [1.0, "#15803d"]]

python/manifoldbt/plot/_utils.py

@@ -67,7 +67,7 @@ def finalize(
                 raise RuntimeError(
                     f"Static image export to {ext} is optional and needs kaleido. "
                     "Install it with: pip install manifoldbt[png]  "
-                    "(the default is the interactive chart — save to .html)"
+                    "(the default is the interactive chart - save to .html)"
                 ) from exc
         else:
             write_responsive_html(fig, path)

python/manifoldbt/plot/_window.py

@@ -124,7 +124,7 @@ def show() -> None:
     _pending.clear()
 
     try:
-        import webview  # noqa: F401 — only to detect the backend
+        import webview  # noqa: F401 - only to detect the backend
     except ImportError:
         for div, title, _ in pending:
             _open_browser(div, title)

python/manifoldbt/plot/research.py

@@ -1,4 +1,4 @@
-"""Charts for research analysis results (sweep, walk-forward, stability) — plotly."""
+"""Charts for research analysis results (sweep, walk-forward, stability) - plotly."""
 from __future__ import annotations
 
 from pathlib import Path
@@ -47,17 +47,46 @@ def _grid_window_size(nx: int, ny: int, plot: int = 720, cbar: int = 160,
     return (int(pw + cbar), int(ph + top))
 
 
-def _plateau_best(grid: np.ndarray):
+def _moving_average_1d(a: np.ndarray, radius: int, axis: int) -> np.ndarray:
-    """Plateau-optimal cell: Gaussian blur finds the center of the best stable
+    """Edge-replicated moving average of window 2*radius+1 along axis (numpy)."""
-    region, not a lucky spike (overfit-resistant). sigma = ~5% of each axis."""
+    if radius < 1:
-    from scipy.ndimage import gaussian_filter
+        return a
+    pad = [(radius, radius) if ax == axis else (0, 0) for ax in range(a.ndim)]
+    padded = np.pad(a, pad, mode="edge")
+    cumsum = np.cumsum(padded, axis=axis)
+    zero = np.zeros_like(np.take(cumsum, [0], axis=axis))
+    cumsum = np.concatenate([zero, cumsum], axis=axis)
+    n = a.shape[axis]
+    width = 2 * radius + 1
+    upper = np.take(cumsum, np.arange(width, width + n), axis=axis)
+    lower = np.take(cumsum, np.arange(0, n), axis=axis)
+    return (upper - lower) / width
 
+
+def _box_blur_2d(a: np.ndarray, sigma_y: float, sigma_x: float, passes: int = 3) -> np.ndarray:
+    """Separable box blur that approximates a Gaussian (central-limit theorem),
+    pure numpy. A scipy-free fallback for _plateau_best."""
+    out = a.astype(float)
+    ry, rx = max(1, int(round(sigma_y))), max(1, int(round(sigma_x)))
+    for _ in range(passes):
+        out = _moving_average_1d(out, ry, axis=0)
+        out = _moving_average_1d(out, rx, axis=1)
+    return out
+
+
+def _plateau_best(grid: np.ndarray):
+    """Plateau-optimal cell: a blur finds the center of the best stable region,
+    not a lucky spike (overfit-resistant). sigma = ~5% of each axis. Uses
+    scipy's Gaussian filter when installed, else a pure-numpy box blur so the
+    plotting extra needs no scipy."""
+    filled = np.nan_to_num(grid, nan=np.nanmin(grid))
     sigma_y = max(1.0, grid.shape[0] * 0.05)
     sigma_x = max(1.0, grid.shape[1] * 0.05)
-    smoothed = gaussian_filter(
+    try:
-        np.nan_to_num(grid, nan=np.nanmin(grid)),
+        from scipy.ndimage import gaussian_filter
-        sigma=(sigma_y, sigma_x),
+        smoothed = gaussian_filter(filled, sigma=(sigma_y, sigma_x))
-    )
+    except ImportError:
+        smoothed = _box_blur_2d(filled, sigma_y, sigma_x)
     return np.unravel_index(np.argmax(smoothed), smoothed.shape)
 
 
@@ -137,7 +166,7 @@ def heatmap_2d(
             best_label = f"best: {best_val:{fmt}} ({x_param}={best_x:.0f}, {y_param}={best_y:.0f})"
 
         combos = nx * ny
-        main_title = title or f"{metric} — Parameter Sweep ({combos:,} combos)"
+        main_title = title or f"{metric} · Parameter Sweep ({combos:,} combos)"
         if best_label:
             main_title = f"{main_title}<br><span style='font-size:11px;color:{GRAY}'>{best_label}</span>"
         fig.update_layout(title_text=main_title, hovermode="closest")
@@ -224,7 +253,7 @@ def surface_3d(
         )
 
         combos = len(x_vals) * len(y_vals)
-        main_title = title or f"{metric} — Surface ({combos:,} combos)"
+        main_title = title or f"{metric} · Surface ({combos:,} combos)"
         if best_label:
             main_title = f"{main_title}<br><span style='font-size:11px;color:{GRAY}'>{best_label}</span>"
 

python/manifoldbt/plot/tearsheet.py

@@ -1,4 +1,4 @@
-"""Composite tearsheet — HTML strategy report with interactive plotly charts."""
+"""Composite tearsheet - HTML strategy report with interactive plotly charts."""
 from __future__ import annotations
 
 import tempfile
@@ -144,7 +144,7 @@ def tearsheet(
     dpi: int = 150,
     plotlyjs: str = "cdn",
 ) -> str:
-    """Strategy report — self-contained HTML page with interactive charts.
+    """Strategy report - self-contained HTML page with interactive charts.
 
     Returns the HTML string. Opens in browser when ``show=True``,
     writes to disk when ``save`` is given.
@@ -225,7 +225,7 @@ def tearsheet(
 <head>
 <meta charset="utf-8">
 <meta name="viewport" content="width=device-width, initial-scale=1">
-<title>{escape(strategy_name)} — Tearsheet</title>
+<title>{escape(strategy_name)} · Tearsheet</title>
 <style>{_CSS}</style>
 {plotly_js_tag}
 </head>
@@ -296,7 +296,7 @@ def research_report(
     save: Optional[Union[str, Path]] = None,
     dpi: int = 150,
 ) -> List[Any]:
-    """Research report — one figure per analysis (plotly Figures)."""
+    """Research report - one figure per analysis (plotly Figures)."""
     from manifoldbt.plot.research import (
         heatmap_2d,
         stability,
@@ -340,7 +340,7 @@ def research_report(
 def _fmt_hold_time(seconds):
     """Format holding time in human-readable units."""
     if seconds <= 0:
-        return "—"
+        return "-"
     days = seconds / 86400
     if days >= 365:
         return f"{days / 365:.1f}y"