spider plot enhancement

src/app_utils.py

@@ -192,7 +192,7 @@ def spider_plot(df: pd.DataFrame) -> None:
 
 
 # Create a new function in app_utils.py that returns the figure instead of showing it
-def get_spider_plot_fig(df: pd.DataFrame):
+def get_spider_plot_fig_v0(df: pd.DataFrame):
     spider_plot_cols = ['Beta norm.', 'Debt to Equity norm.', '1-year Return norm.', 'Revenue Growth norm.', 'Volatility norm.']
     plot_data = df[df['Field'].isin(spider_plot_cols)].set_index('Field')
     values = plot_data.loc[spider_plot_cols, 'Value'].fillna(0.5).astype(float).tolist()
@@ -252,4 +252,83 @@ def get_spider_plot_fig(df: pd.DataFrame):
         height=500
     )
 
-    return fig  
+    return fig  
+
+
+def get_spider_plot_fig(df: pd.DataFrame):
+    spider_plot_cols = ['Beta norm.', 'Debt to Equity norm.', '1-year Return norm.', 'Revenue Growth norm.', 'Volatility norm.']
+    plot_data = df[df['Field'].isin(spider_plot_cols)].set_index('Field')
+    values = plot_data.loc[spider_plot_cols, 'Value'].fillna(0.5).astype(float).tolist()
+    metrics_to_invert = ['Debt to Equity norm.', 'Beta norm.', 'Volatility norm.']
+    values = [1 - v if col in metrics_to_invert else v for v, col in zip(values, spider_plot_cols)]
+    
+    # Calculate average strength score
+    avg_strength = round(np.mean(values) * 100)
+    
+    # Determine color based on score
+    if avg_strength < 30:
+        profile_color = 'red'
+    elif avg_strength < 50:
+        profile_color = 'gold'
+    elif avg_strength < 70:
+        profile_color = 'blue'
+    else:
+        profile_color = 'green'
+    
+    categories = [s.replace(' norm.', '').replace('1-year', '1yr').replace('Debt to Equity', 'D/E') for s in spider_plot_cols]
+    company_name = df.loc[df['Field'] == 'Name', 'Value'].values[0]
+    fig = go.Figure()
+
+    fig.add_trace(go.Scatterpolar(
+        r=values + [values[0]],
+        theta=categories + [categories[0]],
+        fill='toself',
+        name='Company Profile',
+        line=dict(color=profile_color),
+        fillcolor=f'rgba({",".join(["255,0,0,0.2" if profile_color == "red" else "255,215,0,0.2" if profile_color == "gold" else "0,0,255,0.2" if profile_color == "blue" else "0,128,0,0.2"])})'
+    ))
+
+    fig.add_trace(go.Scatterpolar(
+        r=[0.5] * len(categories) + [0.5],
+        theta=categories + [categories[0]],
+        mode='lines',
+        line=dict(dash='dot', color='grey'),
+        fill='toself',
+        fillcolor='rgba(0,0,0,0)',
+        name='Median (0.5)'
+    ))
+
+    legend_text = (
+        f"<b>Avg. strength: {avg_strength}</b><br>"
+        "<b>Quantile Scale: 0 to 1</b><br>"
+        "D/E, Beta, and Volatility:<br>"
+        "0 is highest, 1 is lowest<br>"
+        "Rev. growth and 1yr return:<br>"
+        "0 is lowest, 1 is highest<br>"
+    )
+
+    fig.update_layout(
+    polar=dict(
+        radialaxis=dict(
+        visible=True,
+        range=[0, 1]
+        )),
+    showlegend=True,
+    title=f'{company_name} - Normalized Metrics',
+    annotations=[
+            go.layout.Annotation(
+                text=legend_text,
+                align='right',
+                showarrow=False,
+                xref='paper',
+                yref='paper',
+                x=1.41, 
+                y=-0.1 
+            )
+        ],
+        margin=dict(b=120),
+        width=600,
+        height=500
+    )
+
+    return fig