Update src/charts.py

src/charts.py

@@ -6,42 +6,35 @@ Dark theme, consistent palette, interactive.
 import numpy as np
 import pandas as pd
 import plotly.graph_objects as go
-import plotly.express as px
 from plotly.subplots import make_subplots
 
 # ---------------------------------------------------------------------------
 # Design tokens
 # ---------------------------------------------------------------------------
-BG = "#0d0f14"
-SURFACE = "#14171f"
+BG       = "#0d0f14"
+SURFACE  = "#14171f"
 SURFACE2 = "#1c202c"
-BORDER = "#2a2f3d"
-TEXT = "#e4e6ef"
+BORDER   = "#2a2f3d"
+TEXT     = "#e4e6ef"
 TEXT_MUTED = "#7a7f94"
-TEAL = "#00d4aa"
+TEAL     = "#00d4aa"
 TEAL_DIM = "#007a63"
-RED = "#ff4d6a"
-AMBER = "#f5a623"
-PURPLE = "#a78bfa"
-BLUE = "#60a5fa"
-GREEN = "#34d399"
+RED      = "#ff4d6a"
+AMBER    = "#f5a623"
+PURPLE   = "#a78bfa"
+BLUE     = "#60a5fa"
+GREEN    = "#34d399"
 
-FONT = "JetBrains Mono, monospace"
+FONT      = "JetBrains Mono, monospace"
 FONT_BODY = "DM Sans, sans-serif"
 
+# Base layout WITHOUT xaxis/yaxis — those are always passed per-chart
+# to avoid "multiple values for keyword argument" when callers also pass them.
 BASE_LAYOUT = dict(
     paper_bgcolor=BG,
     plot_bgcolor=SURFACE,
     font=dict(family=FONT_BODY, color=TEXT, size=12),
     margin=dict(l=48, r=24, t=48, b=40),
-    xaxis=dict(
-        gridcolor=BORDER, zerolinecolor=BORDER,
-        tickfont=dict(family=FONT, size=10, color=TEXT_MUTED),
-    ),
-    yaxis=dict(
-        gridcolor=BORDER, zerolinecolor=BORDER,
-        tickfont=dict(family=FONT, size=10, color=TEXT_MUTED),
-    ),
     legend=dict(
         bgcolor=SURFACE2, bordercolor=BORDER, borderwidth=1,
         font=dict(family=FONT_BODY, size=11),
@@ -52,9 +45,41 @@ BASE_LAYOUT = dict(
     ),
 )
 
+# Reusable axis style helpers
+_XAXIS = dict(gridcolor=BORDER, zerolinecolor=BORDER,
+              tickfont=dict(family=FONT, size=10, color=TEXT_MUTED))
+_YAXIS = dict(gridcolor=BORDER, zerolinecolor=BORDER,
+              tickfont=dict(family=FONT, size=10, color=TEXT_MUTED))
+
+
+def _ax(**kwargs):
+    """Merged x-axis style dict."""
+    return {**_XAXIS, **kwargs}
+
+
+def _ay(**kwargs):
+    """Merged y-axis style dict."""
+    return {**_YAXIS, **kwargs}
+
+
+def _apply_base(fig, **extra):
+    """Apply BASE_LAYOUT + any per-chart overrides."""
+    fig.update_layout(**BASE_LAYOUT, **extra)
+    return fig
+
 
-def _apply_base(fig):
-    fig.update_layout(**BASE_LAYOUT)
+def _empty_fig(msg="No data"):
+    fig = go.Figure()
+    fig.update_layout(
+        **BASE_LAYOUT,
+        xaxis=_ax(), yaxis=_ay(),
+        annotations=[dict(
+            text=msg, xref="paper", yref="paper",
+            x=0.5, y=0.5, showarrow=False,
+            font=dict(size=13, color=TEXT_MUTED),
+        )],
+        height=300,
+    )
     return fig
 
 
@@ -71,37 +96,32 @@ def nav_chart(nav_df: pd.DataFrame, benchmark_df: pd.DataFrame, initial_cash: fl
         subplot_titles=["Portfolio NAV", "Drawdown"],
     )
 
-    dates = nav_df["Date"].astype(str)
-    nav = nav_df["NAV"].values
+    dates   = nav_df["Date"].astype(str)
+    nav     = nav_df["NAV"].values
     nav_norm = nav / initial_cash * 100
 
-    # NAV line
     fig.add_trace(go.Scatter(
         x=dates, y=nav_norm,
         name="Sniper Portfolio",
         line=dict(color=TEAL, width=2),
         fill="tozeroy",
-        fillcolor=f"rgba(0,212,170,0.06)",
+        fillcolor="rgba(0,212,170,0.06)",
         hovertemplate="<b>%{x}</b><br>NAV: %{y:.1f}<extra></extra>",
     ), row=1, col=1)
 
-    # Benchmark
     if benchmark_df is not None and not benchmark_df.empty and "Benchmark NAV" in benchmark_df.columns:
         b_norm = benchmark_df["Benchmark NAV"].values / initial_cash * 100
-        b_label = "Benchmark"
         fig.add_trace(go.Scatter(
             x=benchmark_df["Date"].astype(str), y=b_norm,
-            name=b_label,
+            name="Benchmark",
             line=dict(color=TEXT_MUTED, width=1.5, dash="dash"),
             hovertemplate="<b>%{x}</b><br>Benchmark: %{y:.1f}<extra></extra>",
         ), row=1, col=1)
 
-    # Cash baseline
     fig.add_hline(y=100, line=dict(color=BORDER, width=1, dash="dot"), row=1, col=1)
 
-    # Drawdown
     peak = pd.Series(nav).cummax()
-    dd = (pd.Series(nav) - peak) / peak * 100
+    dd   = (pd.Series(nav) - peak) / peak * 100
     colors = [RED if v < -5 else AMBER if v < -2 else TEXT_MUTED for v in dd.values]
 
     fig.add_trace(go.Bar(
@@ -115,9 +135,10 @@ def nav_chart(nav_df: pd.DataFrame, benchmark_df: pd.DataFrame, initial_cash: fl
     fig.update_layout(
         **BASE_LAYOUT,
         title=dict(text="Portfolio Performance", font=dict(size=16, family=FONT_BODY, color=TEXT)),
-        xaxis2=dict(gridcolor=BORDER, tickfont=dict(family=FONT, size=10, color=TEXT_MUTED)),
-        yaxis=dict(ticksuffix="", gridcolor=BORDER),
-        yaxis2=dict(ticksuffix="%", gridcolor=BORDER),
+        xaxis=_ax(),
+        yaxis=_ay(),
+        xaxis2=_ax(),
+        yaxis2=_ay(ticksuffix="%"),
         height=520,
     )
     return fig
@@ -128,26 +149,19 @@ def monthly_returns_heatmap(nav_df: pd.DataFrame, initial_cash: float) -> go.Fig
     nav_df["Date"] = pd.to_datetime(nav_df["Date"])
     nav_df = nav_df.set_index("Date").sort_index()
 
-    monthly = nav_df["NAV"].resample("ME").last()
+    monthly     = nav_df["NAV"].resample("ME").last()
     monthly_ret = monthly.pct_change() * 100
 
     if monthly_ret.empty:
-        fig = go.Figure()
-        _apply_base(fig)
-        fig.add_annotation(text="Insufficient data for monthly heatmap",
-                           xref="paper", yref="paper", x=0.5, y=0.5, showarrow=False,
-                           font=dict(color=TEXT_MUTED))
-        return fig
-
-    years = sorted(monthly_ret.index.year.unique())
-    months = list(range(1, 13))
+        return _empty_fig("Insufficient data for monthly heatmap")
+
+    years        = sorted(monthly_ret.index.year.unique())
+    months       = list(range(1, 13))
     month_labels = ["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"]
 
-    z = []
-    text = []
+    z, text = [], []
     for yr in years:
-        row_z = []
-        row_t = []
+        row_z, row_t = [], []
         for mo in months:
             mask = (monthly_ret.index.year == yr) & (monthly_ret.index.month == mo)
             if mask.sum() > 0:
@@ -168,33 +182,24 @@ def monthly_returns_heatmap(nav_df: pd.DataFrame, initial_cash: float) -> go.Fig
             [0.52, "#374151"], [0.65, "#065f46"], [1.0, "#064e3b"],
         ],
         zmid=0,
-        colorbar=dict(
-            tickfont=dict(family=FONT, size=10, color=TEXT_MUTED),
-            ticksuffix="%",
-        ),
+        colorbar=dict(tickfont=dict(family=FONT, size=10, color=TEXT_MUTED), ticksuffix="%"),
         hovertemplate="<b>%{y} %{x}</b><br>Return: %{text}<extra></extra>",
     ))
     fig.update_layout(
         **BASE_LAYOUT,
         title=dict(text="Monthly Returns", font=dict(size=15, family=FONT_BODY, color=TEXT)),
         height=max(200, 60 + len(years) * 38),
-        yaxis=dict(autorange="reversed", gridcolor=BORDER,
-                   tickfont=dict(family=FONT, size=11, color=TEXT_MUTED)),
-        xaxis=dict(side="top", gridcolor=BORDER,
-                   tickfont=dict(family=FONT_BODY, size=11, color=TEXT_MUTED)),
+        xaxis=_ax(side="top"),
+        yaxis=_ay(autorange="reversed"),
     )
     return fig
 
 
 def exit_reasons_chart(trades_df: pd.DataFrame) -> go.Figure:
     if trades_df.empty or "Exit Reason" not in trades_df.columns:
-        fig = go.Figure()
-        _apply_base(fig)
-        fig.add_annotation(text="No trades to display", xref="paper", yref="paper",
-                           x=0.5, y=0.5, showarrow=False, font=dict(color=TEXT_MUTED))
-        return fig
+        return _empty_fig("No trades to display")
 
-    counts = trades_df["Exit Reason"].value_counts()
+    counts    = trades_df["Exit Reason"].value_counts()
     color_map = {"Stop Loss": RED, "Take Profit": GREEN, "Time Horizon": AMBER}
 
     fig = go.Figure(go.Bar(
@@ -211,42 +216,39 @@ def exit_reasons_chart(trades_df: pd.DataFrame) -> go.Figure:
         title=dict(text="Exit Breakdown", font=dict(size=15, family=FONT_BODY, color=TEXT)),
         height=300,
         showlegend=False,
-        yaxis=dict(gridcolor=BORDER),
-        xaxis=dict(gridcolor="rgba(0,0,0,0)"),
+        xaxis=_ax(gridcolor="rgba(0,0,0,0)"),
+        yaxis=_ay(),
     )
     return fig
 
 
 def trade_return_distribution(trades_df: pd.DataFrame) -> go.Figure:
     if trades_df.empty or "Return %" not in trades_df.columns:
-        fig = go.Figure()
-        _apply_base(fig)
-        return fig
+        return _empty_fig("No trades to display")
 
     returns = trades_df["Return %"].dropna()
-    wins = returns[returns > 0]
-    losses = returns[returns <= 0]
+    wins    = returns[returns > 0]
+    losses  = returns[returns <= 0]
 
     fig = go.Figure()
     if len(losses) > 0:
         fig.add_trace(go.Histogram(
-            x=losses, name="Losses", marker_color=RED,
-            opacity=0.7, nbinsx=20,
+            x=losses, name="Losses", marker_color=RED, opacity=0.7, nbinsx=20,
             hovertemplate="Return: %{x:.1f}%<br>Count: %{y}<extra></extra>",
         ))
     if len(wins) > 0:
         fig.add_trace(go.Histogram(
-            x=wins, name="Wins", marker_color=GREEN,
-            opacity=0.7, nbinsx=20,
+            x=wins, name="Wins", marker_color=GREEN, opacity=0.7, nbinsx=20,
             hovertemplate="Return: %{x:.1f}%<br>Count: %{y}<extra></extra>",
         ))
     fig.add_vline(x=0, line=dict(color=BORDER, width=1.5, dash="dash"))
     fig.update_layout(
         **BASE_LAYOUT,
         title=dict(text="Return Distribution", font=dict(size=15, family=FONT_BODY, color=TEXT)),
-        barmode="overlay", height=300,
-        xaxis=dict(title="Return %", gridcolor=BORDER),
-        yaxis=dict(title="Trades", gridcolor=BORDER),
+        barmode="overlay",
+        height=300,
+        xaxis=_ax(title="Return %"),
+        yaxis=_ay(title="Trades"),
     )
     return fig
 
@@ -256,18 +258,17 @@ def trade_return_distribution(trades_df: pd.DataFrame) -> go.Figure:
 # ---------------------------------------------------------------------------
 
 def radar_chart(dimension_results: list) -> go.Figure:
-    names = [d.name.replace("_", " ").title() for d in dimension_results]
+    names  = [d.name.replace("_", " ").title() for d in dimension_results]
     scores = [d.score for d in dimension_results]
 
-    # Close the polygon
-    names_closed = names + [names[0]]
+    names_closed  = names  + [names[0]]
     scores_closed = scores + [scores[0]]
 
     fig = go.Figure()
     fig.add_trace(go.Scatterpolar(
         r=scores_closed, theta=names_closed,
         fill="toself",
-        fillcolor=f"rgba(0,212,170,0.15)",
+        fillcolor="rgba(0,212,170,0.15)",
         line=dict(color=TEAL, width=2),
         marker=dict(color=TEAL, size=7),
         hovertemplate="<b>%{theta}</b><br>Score: %{r:.1f}<extra></extra>",
@@ -292,23 +293,21 @@ def radar_chart(dimension_results: list) -> go.Figure:
         height=420,
         margin=dict(l=60, r=60, t=60, b=60),
         showlegend=False,
+        hoverlabel=dict(bgcolor=SURFACE2, bordercolor=BORDER, font=dict(family=FONT_BODY, size=12)),
     )
     return fig
 
 
 def reliability_diagram(reliability_bins: dict) -> go.Figure:
-    bins = reliability_bins.get("bin_centers", [])
+    bins   = reliability_bins.get("bin_centers", [])
     actual = reliability_bins.get("actual_freqs", [])
     counts = reliability_bins.get("bin_counts", [])
 
     if not bins:
-        fig = go.Figure()
-        _apply_base(fig)
-        return fig
+        return _empty_fig("No calibration data")
 
     fig = go.Figure()
 
-    # Perfect calibration diagonal
     fig.add_trace(go.Scatter(
         x=[0, 1], y=[0, 1], mode="lines",
         line=dict(color=BORDER, dash="dash", width=1.5),
@@ -316,7 +315,6 @@ def reliability_diagram(reliability_bins: dict) -> go.Figure:
         hoverinfo="skip",
     ))
 
-    # Actual calibration
     valid = [(b, a, c) for b, a, c in zip(bins, actual, counts) if c > 0]
     if valid:
         vb, va, vc = zip(*valid)
@@ -335,59 +333,52 @@ def reliability_diagram(reliability_bins: dict) -> go.Figure:
         **BASE_LAYOUT,
         title=dict(text="Reliability Diagram (Calibration)", font=dict(size=15, family=FONT_BODY, color=TEXT)),
         height=340,
-        xaxis=dict(title="Mean Predicted Probability", range=[-0.02, 1.02], gridcolor=BORDER),
-        yaxis=dict(title="Actual Positive Fraction", range=[-0.02, 1.02], gridcolor=BORDER),
+        xaxis=_ax(title="Mean Predicted Probability", range=[-0.02, 1.02]),
+        yaxis=_ay(title="Actual Positive Fraction",   range=[-0.02, 1.02]),
     )
     return fig
 
 
 def regime_heatmap(regime_scores: dict) -> go.Figure:
-    labels_order = ["Bear / Low VIX", "Bear / High VIX", "Bull / Low VIX", "Bull / High VIX"]
     x_labels = ["Low VIX", "High VIX"]
-    y_labels = ["Bear Market", "Bull Market"]
+    y_labels  = ["Bear Market", "Bull Market"]
 
-    z = [[None, None], [None, None]]
+    z    = [[None, None], [None, None]]
     text = [["", ""], ["", ""]]
 
     for name, data in regime_scores.items():
         auc = data.get("auc")
-        n = data.get("n", 0)
-        if "Bear" in name:
-            row = 0
-        else:
-            row = 1
+        n   = data.get("n", 0)
+        row = 1 if "Bull" in name else 0
         col = 1 if "High VIX" in name else 0
-        z[row][col] = auc
-        text[row][col] = f"AUC: {auc:.3f}<br>n={n:,}" if auc is not None else f"n={n}<br>insufficient"
+        z[row][col]    = auc
+        text[row][col] = (f"AUC: {auc:.3f}<br>n={n:,}" if auc is not None
+                          else f"n={n}<br>insufficient")
 
     fig = go.Figure(go.Heatmap(
         z=z, x=x_labels, y=y_labels,
         text=text, texttemplate="%{text}",
         colorscale=[[0, "#7f1d1d"], [0.5, SURFACE2], [1, "#064e3b"]],
         zmin=0.4, zmax=0.8, zmid=0.55,
-        colorbar=dict(
-            title="AUC",
-            tickfont=dict(family=FONT, size=10, color=TEXT_MUTED),
-        ),
+        colorbar=dict(title="AUC", tickfont=dict(family=FONT, size=10, color=TEXT_MUTED)),
         hovertemplate="<b>%{y} / %{x}</b><br>%{text}<extra></extra>",
     ))
     fig.update_layout(
         **BASE_LAYOUT,
-        title=dict(text="Regime Robustness (AUC by Market Condition)", font=dict(size=15, family=FONT_BODY, color=TEXT)),
+        title=dict(text="Regime Robustness (AUC by Market Condition)",
+                   font=dict(size=15, family=FONT_BODY, color=TEXT)),
         height=280,
-        yaxis=dict(autorange="reversed", gridcolor=BORDER),
-        xaxis=dict(gridcolor=BORDER),
+        xaxis=_ax(),
+        yaxis=_ay(autorange="reversed"),
     )
     return fig
 
 
 def feature_psi_chart(psi_df: pd.DataFrame, top_n: int = 25) -> go.Figure:
     if psi_df.empty:
-        fig = go.Figure()
-        _apply_base(fig)
-        return fig
+        return _empty_fig("No PSI data available")
 
-    top = psi_df.head(top_n)
+    top    = psi_df.head(top_n)
     colors = []
     for s in top["Status"]:
         if "🔴" in s:
@@ -404,31 +395,30 @@ def feature_psi_chart(psi_df: pd.DataFrame, top_n: int = 25) -> go.Figure:
         marker_color=colors,
         hovertemplate="<b>%{y}</b><br>PSI: %{x:.4f}<extra></extra>",
     ))
-    fig.add_vline(x=0.2, line=dict(color=RED, width=1.5, dash="dot"),
-                  annotation=dict(text="High drift", font=dict(color=RED, size=10), y=1.02))
-    fig.add_vline(x=0.1, line=dict(color=AMBER, width=1, dash="dot"),
-                  annotation=dict(text="Watch", font=dict(color=AMBER, size=10), y=0.95))
+    fig.add_vline(x=0.2, line=dict(color=RED,   width=1.5, dash="dot"),
+                  annotation=dict(text="High drift", font=dict(color=RED,   size=10), y=1.02))
+    fig.add_vline(x=0.1, line=dict(color=AMBER,  width=1,   dash="dot"),
+                  annotation=dict(text="Watch",      font=dict(color=AMBER,  size=10), y=0.95))
     fig.update_layout(
         **BASE_LAYOUT,
-        title=dict(text=f"Feature PSI — Top {top_n} by Drift", font=dict(size=15, family=FONT_BODY, color=TEXT)),
+        title=dict(text=f"Feature PSI — Top {top_n} by Drift",
+                   font=dict(size=15, family=FONT_BODY, color=TEXT)),
         height=max(300, 20 * len(top) + 100),
-        xaxis=dict(title="Population Stability Index", gridcolor=BORDER),
-        yaxis=dict(gridcolor="rgba(0,0,0,0)", autorange="reversed",
-                   tickfont=dict(family=FONT, size=10, color=TEXT_MUTED)),
         showlegend=False,
+        xaxis=_ax(title="Population Stability Index"),
+        yaxis=_ay(autorange="reversed",
+                  tickfont=dict(family=FONT, size=10, color=TEXT_MUTED),
+                  gridcolor="rgba(0,0,0,0)"),
     )
     return fig
 
 
 def multi_model_comparison(results: list, model_labels: list) -> go.Figure:
-    """Bar chart comparing multiple model scores across dimensions."""
     if not results:
-        fig = go.Figure()
-        _apply_base(fig)
-        return fig
+        return _empty_fig("No results to compare")
 
     dim_names = [d.name.replace("_", " ").title() for d in results[0].dimensions]
-    palette = [TEAL, PURPLE, AMBER, BLUE, GREEN, RED]
+    palette   = [TEAL, PURPLE, AMBER, BLUE, GREEN, RED]
 
     fig = go.Figure()
     for i, (result, label) in enumerate(zip(results, model_labels)):
@@ -447,7 +437,7 @@ def multi_model_comparison(results: list, model_labels: list) -> go.Figure:
         title=dict(text="Model Comparison", font=dict(size=16, family=FONT_BODY, color=TEXT)),
         barmode="group",
         height=380,
-        yaxis=dict(range=[0, 105], title="Score (0–100)", gridcolor=BORDER),
-        xaxis=dict(gridcolor="rgba(0,0,0,0)"),
+        xaxis=_ax(gridcolor="rgba(0,0,0,0)"),
+        yaxis=_ay(range=[0, 105], title="Score (0–100)"),
     )
     return fig