Implement 6-axis peer comparison and refined metrics

backend/agents/peer_comparator.py

@@ -5,6 +5,7 @@ import logging
 from backend.config import GEMINI_API_KEY
 from backend.models.schemas import PeerComparison, FundamentalMetrics
 from backend.utils.ai_helper import generate_content_with_retry
+from backend.utils.peer_comparison import calculate_normalized_scores_v2
 
 logger = logging.getLogger(__name__)
 genai.configure(api_key=GEMINI_API_KEY)
@@ -19,6 +20,25 @@ class PeerComparator:
         from backend.utils.ticker_db import get_ticker_db
         db = get_ticker_db()
         
+        # --- 0. Calculate Scores for TARGET Company ---
+        # We need to construct a raw dict for the utility
+        try:
+            target_raw = {
+                'roe': target_metrics.roe,
+                'roce': target_metrics.roce,
+                'pe_ratio': target_metrics.pe_ratio,
+                'industry_pe': target_metrics.industry_pe,
+                'dividend_yield': target_metrics.dividend_yield, # If already calculated
+                'returns_5y': target_metrics.returns_5y,
+                'returns_1y': target_metrics.returns_1y,
+                # 'current_price' might not be in target_metrics, but maybe we don't need it if div yield is there
+                'current_price': 0.0 # Placeholder
+            }
+            target_metrics.normalized_scores = calculate_normalized_scores_v2(target_raw)
+            print(f"[Peer Comparator] Calculated Target Scores: {target_metrics.normalized_scores}")
+        except Exception as e:
+            print(f"Error calculating target scores: {e}")
+
         peer_data_list = []
         peer_names = []
         
@@ -64,25 +84,46 @@ class PeerComparator:
             p_name = p_data.get('Name')
             peer_names.append(p_name)
             
+            # Helper to safely get float
+            def sf(k): return float(p_data.get(k, 0.0) or 0.0)
+
             # Convert CSV dict to FundamentalMetrics object
             # Note: We only fill quantitative, qualitative will be defaults
             p_metrics = FundamentalMetrics(
-                market_cap=p_data.get('Market Cap (Cr.)', 0.0),
-                pe_ratio=p_data.get('PE Ratio', 0.0),
-                industry_pe=p_data.get('Industry PE', 0.0),
-                roe=p_data.get('ROE', 0.0),
-                roce=p_data.get('ROCE', 0.0),
-                eps=p_data.get('EPS', 0.0),
-                pb_ratio=p_data.get('PB Ratio', 0.0),
-                dividend_yield=p_data.get('Dividend', 0.0),
+                market_cap=sf('Market Cap (Cr.)'),
+                pe_ratio=sf('PE Ratio'),
+                industry_pe=sf('Industry PE'),
+                roe=sf('ROE'),
+                roce=sf('ROCE'),
+                eps=sf('EPS'),
+                pb_ratio=sf('PB Ratio'),
+                dividend_yield=sf('Dividend'), # CSV often has Dividend (Rs) or Yield? Assuming Yield or use logic
                 
-                returns_1y=p_data.get('1 Yr Returns', 0.0),
-                # Add other returns if needed for comparison view
+                returns_1y=sf('1 Yr Returns'),
+                returns_5y=sf('5 Yr Returns'),
                 
                 health_score=5, # Default since we don't analyze peers deeply
                 strengths=[],
                 concerns=[]
             )
+            
+            # Calculate Normalized Scores for Peer
+            try:
+                # Map CSV keys to expected format for calc
+                peer_raw = {
+                    'roe': sf('ROE'),
+                    'roce': sf('ROCE'),
+                    'pe_ratio': sf('PE Ratio'),
+                    'industry_pe': sf('Industry PE'),
+                    'dividend': sf('Dividend'),
+                    'current_price': sf('LTP'),
+                    'returns_1y': sf('1 Yr Returns'),
+                    'returns_5y': sf('5 Yr Returns')
+                }
+                p_metrics.normalized_scores = calculate_normalized_scores_v2(peer_raw)
+            except Exception as e:
+                print(f"Error calculating scores for {p_name}: {e}")
+            
             peer_metrics_map[p_name] = p_metrics
 
         print(f"[Peer Comparator] Found peers: {peer_names}")

backend/models/schemas.py

@@ -56,6 +56,9 @@ class FundamentalMetrics(BaseModel):
     revenue_growth: float = 0.0
     profit_margin: float = 0.0
     
+    # Normalized Scores for Radar Chart (Growth, Profitability, Efficiency, Valuation, Dividend, Momentum)
+    normalized_scores: Optional[Dict[str, float]] = None
+
     # Raw math fields (Hidden)
     revenue_current: float = 0.0
     revenue_prior: float = 0.0

backend/utils/peer_comparison.py

@@ -0,0 +1,107 @@
+import pandas as pd
+from typing import Dict, Any
+
+def safe_float(val):
+    if val is None or val == '':
+        return 0.0
+    try:
+        return float(val)
+    except:
+        return 0.0
+
+def normalize_linear(value: float, min_val: float, max_val: float) -> float:
+    """
+    Linear normalization to 0-100 scale
+    """
+    if value is None or pd.isna(value):
+        return 50.0  # Neutral if missing
+    
+    if value <= min_val:
+        return 0.0
+    elif value >= max_val:
+        return 100.0
+    else:
+        return ((value - min_val) / (max_val - min_val)) * 100.0
+
+def calculate_normalized_scores_v2(raw_data: Dict[str, Any]) -> Dict[str, float]:
+    """
+    Enhanced normalization with Dividend Yield
+    """
+    
+    # 1. GROWTH (5Y Returns)
+    # Scale: -20% = 0, 0% = 40, 100% = 100
+    returns_5y = raw_data.get('returns_5y')
+    if returns_5y is not None:
+        growth_score = normalize_linear(returns_5y, min_val=-20, max_val=100)
+    else:
+        growth_score = 50.0
+    
+    # 2. PROFITABILITY (ROE)
+    # Scale: 0% = 0, 15% = 50, 30%+ = 100
+    roe = raw_data.get('roe')
+    if roe is not None:
+        profitability_score = normalize_linear(roe, min_val=0, max_val=30)
+    else:
+        profitability_score = 50.0
+    
+    # 3. EFFICIENCY (ROCE)
+    # Scale: 0% = 0, 15% = 50, 30%+ = 100
+    roce = raw_data.get('roce')
+    if roce is not None:
+        efficiency_score = normalize_linear(roce, min_val=0, max_val=30)
+    else:
+        efficiency_score = 50.0
+    
+    # 4. VALUATION (P/E vs Industry P/E)
+    # Closer to industry P/E = better
+    pe_ratio = raw_data.get('pe_ratio')
+    industry_pe = raw_data.get('industry_pe')
+    
+    if pe_ratio and industry_pe and industry_pe > 0:
+        # Calculate deviation percentage
+        deviation = abs(pe_ratio - industry_pe) / industry_pe * 100
+        
+        # 0% deviation = 100 score
+        # 50%+ deviation = 0 score
+        valuation_score = max(0, 100 - (deviation * 2))
+        
+        # Bonus: Slight preference for undervalued (P/E < Industry)
+        if pe_ratio < industry_pe:
+            valuation_score = min(100, valuation_score * 1.1)
+    else:
+        # If P/E is valid but industry PE is missing, maybe assume fair?
+        # Or if PE is 0 (loss making), low score?
+        if pe_ratio and pe_ratio < 0: valuation_score = 20 # Loss making
+        else: valuation_score = 50.0
+    
+    # 5. DIVIDEND YIELD (NEW)
+    # Use pre-calculated yield if present, else calculate
+    dividend_yield = raw_data.get('dividend_yield')
+    if dividend_yield is None:
+        dividend = raw_data.get('dividend', 0.0)
+        current_price = raw_data.get('current_price', 0.0)
+        
+        if dividend and current_price and current_price > 0:
+            dividend_yield = (dividend / current_price) * 100
+        else:
+            dividend_yield = 0.0
+            
+    # Scale: 0% = 0, 2% = 50, 5%+ = 100
+    dividend_score = normalize_linear(dividend_yield, min_val=0, max_val=5)
+    
+    # 6. MOMENTUM (1Y Returns)
+    # Scale: -50% = 0, 0% = 50, 100% = 100
+    returns_1y = raw_data.get('returns_1y')
+    if returns_1y is not None:
+        momentum_score = normalize_linear(returns_1y, min_val=-50, max_val=100)
+    else:
+        momentum_score = 50.0
+    
+    return {
+        "Growth": round(growth_score, 1),
+        "Profitability": round(profitability_score, 1),
+        "Efficiency": round(efficiency_score, 1),
+        "Valuation": round(valuation_score, 1),
+        "Dividend Yield": round(dividend_score, 1),
+        "Momentum": round(momentum_score, 1)
+    }

frontend/static/js/results.js

@@ -172,19 +172,22 @@ document.addEventListener('DOMContentLoaded', async () => {
 
         } catch (e) { console.error("Error rendering News:", e); }
 
-        // --- Peer Comparison (Radar SVG) ---
+        // --- Peer Comparison (Radar SVG - 6 Axes) ---
         try {
-            // Draw a polygon based on the 5 metrics: 
-            // 1. Growth, 2. Margin, 3. ROE, 4. Debt (inverted), 5. Strength
+            // Axes: Growth(0), Profitability(60), Efficiency(120), Valuation(180), Dividend(240), Momentum(300)
+            const scores = fundamentals.normalized_scores || {
+                "Growth": 50, "Profitability": 50, "Efficiency": 50, "Valuation": 50, "Dividend Yield": 0, "Momentum": 50
+            };
 
             // Normalize values 0-100 for the chart radius (max 80px)
-            const p1 = Math.min(Math.max(fundamentals.revenue_growth * 2, 20), 100); // Growth
-            const p2 = Math.min(Math.max(fundamentals.profit_margin * 2, 20), 100); // Margin
-            const p3 = Math.min(Math.max(fundamentals.roe * 2, 20), 100); // ROE
-            const p4 = 100 - Math.min(Math.max(parseFloat(fundamentals.debt_to_equity) * 20, 0), 100); // Low Debt = High Score
-            const p5 = fundamentals.health_score * 10; // Strength
-
-            // Angles: 0(Top), 72, 144, 216, 288
+            const p1 = Math.min(Math.max(scores["Growth"] || 0, 10), 100);
+            const p2 = Math.min(Math.max(scores["Profitability"] || 0, 10), 100);
+            const p3 = Math.min(Math.max(scores["Efficiency"] || 0, 10), 100);
+            const p4 = Math.min(Math.max(scores["Valuation"] || 0, 10), 100);
+            const p5 = Math.min(Math.max(scores["Dividend Yield"] || 0, 10), 100);
+            const p6 = Math.min(Math.max(scores["Momentum"] || 0, 10), 100);
+
+            // Angles: 0, 60, 120, 180, 240, 300
             // Center (100, 100), Max Radius 80.
             function getPoint(val, angleDeg) {
                 const angleRad = (angleDeg - 90) * Math.PI / 180;
@@ -196,10 +199,11 @@ document.addEventListener('DOMContentLoaded', async () => {
 
             const points = [
                 getPoint(p1, 0),
-                getPoint(p2, 72),
-                getPoint(p3, 144),
-                getPoint(p4, 216),
-                getPoint(p5, 288)
+                getPoint(p2, 60),
+                getPoint(p3, 120),
+                getPoint(p4, 180),
+                getPoint(p5, 240),
+                getPoint(p6, 300)
             ].join(' ');
 
             const poly = document.getElementById('peerRadarPolygon');
@@ -210,37 +214,53 @@ document.addEventListener('DOMContentLoaded', async () => {
         } catch (e) { console.error("Error rendering Peer Radar:", e); }
 
 
-
         // --- NEW: Detailed Metrics Grid ---
         try {
-            const f = fundamentals;
-            const setTxt = (id, val, suffix = '') => {
-                const el = document.getElementById(id);
-                if (el) el.textContent = val !== undefined && val !== null ? val + suffix : '--';
-            };
-            const setCol = (id, val) => {
-                const el = document.getElementById(id);
-                if (el && typeof val === 'number') {
-                    if (val > 0) el.className = "font-mono font-bold text-lg text-primary";
-                    else if (val < 0) el.className = "font-mono font-bold text-lg text-danger";
-                    else el.className = "font-mono font-bold text-lg text-white";
-                }
-            };
-
-            setTxt('d_marketCap', f.market_cap ? f.market_cap.toLocaleString() : '--', ' Cr');
-            setTxt('d_pe', f.pe_ratio);
-            setTxt('d_indPe', f.industry_pe);
-            setTxt('d_pb', f.pb_ratio);
-            setTxt('d_div', f.dividend_yield, '%');
-            setTxt('d_eps', f.eps);
-            setTxt('d_roe', f.roe, '%');
-            setTxt('d_roce', f.roce, '%');
-
-            setTxt('d_ret1y', f.returns_1y, '%'); setCol('d_ret1y', f.returns_1y);
-            setTxt('d_ret3y', f.returns_3y, '%'); setCol('d_ret3y', f.returns_3y);
-            setTxt('d_ret5y', f.returns_5y, '%'); setCol('d_ret5y', f.returns_5y);
-
+            const grid = document.getElementById('metricsGrid');
+            if (grid) {
+                grid.innerHTML = ''; // Clear loading state
+
+                const createCard = (title, value, sub = '') => {
+                    const card = document.createElement('div');
+                    card.className = "metric-card";
+
+                    let valColor = "text-white";
+                    if (title.includes('Return') || title.includes('Growth')) {
+                        const num = parseFloat(value);
+                        if (num > 0) valColor = "text-primary";
+                        else if (num < 0) valColor = "text-danger";
+                    }
+
+                    card.innerHTML = `
+                        <span class="metric-title">${title}</span>
+                        <div class="flex items-baseline gap-1">
+                            <span class="metric-value ${valColor}">${value !== undefined && value !== null ? value : '--'}</span>
+                            <span class="metric-sub">${sub}</span>
+                        </div>
+                    `;
+                    return card;
+                };
 
+                const f = fundamentals;
+
+                // Row 1: Valuation
+                grid.appendChild(createCard("Market Cap", f.market_cap ? f.market_cap.toLocaleString() : '--', " Cr"));
+                grid.appendChild(createCard("P/E Ratio", f.pe_ratio));
+                grid.appendChild(createCard("Ind. P/E", f.industry_pe));
+                grid.appendChild(createCard("P/B Ratio", f.pb_ratio));
+
+                // Row 2: Profitability
+                grid.appendChild(createCard("Div. Yield", f.dividend_yield, "%"));
+                grid.appendChild(createCard("EPS", f.eps));
+                grid.appendChild(createCard("ROE", f.roe, "%"));
+                grid.appendChild(createCard("ROCE", f.roce, "%"));
+
+                // Row 3: Returns
+                grid.appendChild(createCard("1Y Return", f.returns_1y, "%"));
+                grid.appendChild(createCard("3Y Return", f.returns_3y, "%"));
+                grid.appendChild(createCard("5Y Return", f.returns_5y, "%"));
+                // Debt/Eq removed per user request
+            }
 
         } catch (e) { console.error("Error rendering Detailed Metrics:", e); }
 
@@ -250,9 +270,6 @@ document.addEventListener('DOMContentLoaded', async () => {
             tbody.innerHTML = '';
 
             const peerMap = peers.peer_metrics || {};
-            // Add Target Company first for comparison? Or just peers. Let's add peers.
-            // Be nice to show Target vs Peers. 
-            // Let's add Target as the first row highlighted
             const rows = [];
 
             // Target Row
@@ -263,6 +280,7 @@ document.addEventListener('DOMContentLoaded', async () => {
                 roe: fundamentals.roe,
                 roce: fundamentals.roce,
                 ret: fundamentals.returns_1y,
+                dy: fundamentals.dividend_yield,
                 isTarget: true
             });
 
@@ -275,6 +293,7 @@ document.addEventListener('DOMContentLoaded', async () => {
                     roe: pM.roe,
                     roce: pM.roce,
                     ret: pM.returns_1y,
+                    dy: pM.dividend_yield,
                     isTarget: false
                 });
             }

frontend/templates/results.html

@@ -50,6 +50,42 @@
             color: #ffffff;
         }
 
+        .metric-card {
+            background: rgba(255, 255, 255, 0.03);
+            border: 1px solid rgba(255, 255, 255, 0.08);
+            border-radius: 12px;
+            padding: 16px;
+            display: flex;
+            flex-direction: column;
+            gap: 4px;
+            transition: all 0.2s ease;
+        }
+
+        .metric-card:hover {
+            background: rgba(255, 255, 255, 0.05);
+            border-color: rgba(51, 242, 13, 0.2);
+            transform: translateY(-2px);
+        }
+
+        .metric-title {
+            font-size: 0.75rem;
+            text-transform: uppercase;
+            letter-spacing: 0.05em;
+            color: #94a3b8;
+        }
+
+        .metric-value {
+            font-family: 'JetBrains Mono', monospace;
+            font-size: 1.25rem;
+            font-weight: 700;
+            color: #ffffff;
+        }
+
+        .metric-sub {
+            font-size: 0.75rem;
+            color: #64748b;
+        }
+
         .bento-card {
             background: linear-gradient(145deg, #0a0f1c 0%, #0d1221 100%);
             border: 1px solid rgba(255, 255, 255, 0.08);
@@ -237,7 +273,7 @@
                                     class="absolute top-0 left-0 h-full bg-gradient-to-r from-green-900 to-primary w-[0%] transition-all duration-1000 group-hover:brightness-110">
                                 </div>
                                 <span id="newsPosText"
-                                    class="absolute top-1/2 left-4 -translate-y-1/2 text-xs font-bold text-black z-10 mix-blend-screen">0%</span>
+                                    class="absolute top-1/2 left-4 -translate-y-1/2 text-xs font-bold text-white z-10 mix-blend-screen">0%</span>
                             </div>
                         </div>
                         <div class="flex items-center gap-4">
@@ -311,9 +347,7 @@
                             <div class="flex items-center gap-1.5">
                                 <div class="w-2 h-2 rounded-full bg-primary shadow-glow-sm"></div> TARGET
                             </div>
-                            <div class="flex items-center gap-1.5">
-                                <div class="w-2 h-2 rounded-full bg-gray-600"></div> AVG
-                            </div>
+                            <!-- Removed AVG logic for now as we focus on raw peers but chart still compares? -->
                         </div>
                     </div>
                     <div class="flex-1 flex items-center justify-center relative z-10">
@@ -325,24 +359,26 @@
                                 <circle cx="100" cy="100" r="40"></circle>
                                 <circle cx="100" cy="100" r="60"></circle>
                                 <circle cx="100" cy="100" r="80"></circle>
-                                <!-- Axis Lines -->
-                                <line x1="100" x2="100" y1="100" y2="10"></line>
-                                <line x1="100" x2="185" y1="100" y2="38"></line>
-                                <line x1="100" x2="158" y1="100" y2="180"></line>
-                                <line x1="100" x2="42" y1="100" y2="180"></line>
-                                <line x1="100" x2="15" y1="100" y2="38"></line>
+                                <!-- Axis Lines (6 Axis) -->
+                                <line x1="100" y1="100" x2="100" y2="10"></line> <!-- 0 deg (Top) -->
+                                <line x1="100" y1="100" x2="178" y2="55"></line> <!-- 60 deg -->
+                                <line x1="100" y1="100" x2="178" y2="145"></line> <!-- 120 deg -->
+                                <line x1="100" y1="100" x2="100" y2="190"></line> <!-- 180 deg -->
+                                <line x1="100" y1="100" x2="22" y2="145"></line> <!-- 240 deg -->
+                                <line x1="100" y1="100" x2="22" y2="55"></line> <!-- 300 deg -->
                             </g>
                             <!-- Labels -->
                             <g class="text-[8px] fill-gray-400 font-mono" text-anchor="middle">
                                 <text x="100" y="8">GROWTH</text>
-                                <text x="190" y="40">MARGIN</text>
-                                <text x="160" y="195">ROE</text>
-                                <text x="40" y="195">DEBT</text>
-                                <text x="10" y="40">STRENGTH</text>
+                                <text x="190" y="55">PROFIT</text>
+                                <text x="190" y="150">EFFICIENCY</text>
+                                <text x="100" y="198">VALUATION</text>
+                                <text x="10" y="150">DIVIDEND</text>
+                                <text x="10" y="55">MOMENTUM</text>
                             </g>
                             <!-- Data Polygon: Target (Green) -->
                             <polygon id="peerRadarPolygon" class="drop-shadow-[0_0_8px_rgba(51,242,13,0.4)]"
-                                fill="rgba(51, 242, 13, 0.15)" points="100,100 100,100 100,100 100,100 100,100"
+                                fill="rgba(51, 242, 13, 0.15)" points="100,100 100,100 100,100 100,100 100,100 100,100"
                                 stroke="#33f20d" stroke-width="2"></polygon>
                         </svg>
                     </div>
@@ -378,58 +414,9 @@
                     <h3 class="text-xl font-bold text-white">Fundamental Strength</h3>
                 </div>
                 <!-- Adjusted grid cols for layout -->
-                <div class="grid grid-cols-2 lg:grid-cols-4 gap-x-6 gap-y-8 text-sm h-full content-center">
-
-                    <!-- Line 1 -->
-                    <div class="space-y-1">
-                        <p class="text-gray-500 text-xs uppercase tracking-wider">Market Cap</p>
-                        <p class="font-mono font-bold text-lg text-white" id="d_marketCap">--</p>
-                    </div>
-                    <div class="space-y-1">
-                        <p class="text-gray-500 text-xs uppercase tracking-wider">P/E Ratio</p>
-                        <p class="font-mono font-bold text-lg text-primary" id="d_pe">--</p>
-                    </div>
-                    <div class="space-y-1">
-                        <p class="text-gray-500 text-xs uppercase tracking-wider">Ind. P/E</p>
-                        <p class="font-mono font-bold text-lg text-gray-300" id="d_indPe">--</p>
-                    </div>
-                    <div class="space-y-1">
-                        <p class="text-gray-500 text-xs uppercase tracking-wider">P/B Ratio</p>
-                        <p class="font-mono font-bold text-lg text-white" id="d_pb">--</p>
-                    </div>
-
-                    <!-- Line 2 -->
-                    <div class="space-y-1">
-                        <p class="text-gray-500 text-xs uppercase tracking-wider">Dividend Yield</p>
-                        <p class="font-mono font-bold text-lg text-white" id="d_div">--</p>
-                    </div>
-                    <div class="space-y-1">
-                        <p class="text-gray-500 text-xs uppercase tracking-wider">EPS</p>
-                        <p class="font-mono font-bold text-lg text-white" id="d_eps">--</p>
-                    </div>
-                    <div class="space-y-1">
-                        <p class="text-gray-500 text-xs uppercase tracking-wider">ROE</p>
-                        <p class="font-mono font-bold text-lg text-white" id="d_roe">--</p>
-                    </div>
-                    <div class="space-y-1">
-                        <p class="text-gray-500 text-xs uppercase tracking-wider">ROCE</p>
-                        <p class="font-mono font-bold text-lg text-white" id="d_roce">--</p>
-                    </div>
-
-                    <!-- Line 3 Returns -->
-                    <div class="space-y-1">
-                        <p class="text-gray-500 text-xs uppercase tracking-wider">1Y Return</p>
-                        <p class="font-mono font-bold text-lg" id="d_ret1y">--</p>
-                    </div>
-                    <div class="space-y-1">
-                        <p class="text-gray-500 text-xs uppercase tracking-wider">3Y Return</p>
-                        <p class="font-mono font-bold text-lg" id="d_ret3y">--</p>
-                    </div>
-                    <div class="space-y-1">
-                        <p class="text-gray-500 text-xs uppercase tracking-wider">5Y Return</p>
-                        <p class="font-mono font-bold text-lg" id="d_ret5y">--</p>
-                    </div>
-
+                <div class="grid grid-cols-2 lg:grid-cols-4 gap-4 text-sm h-full content-center" id="metricsGrid">
+                    <!-- Cards will be injected by JS displayDetailedMetrics() -->
+                    <div class="col-span-full text-center text-gray-500 animate-pulse">Loading detailed metrics...</div>
                 </div>
             </div>
 

verify_peer_logic.py

@@ -0,0 +1,50 @@
+from backend.utils.peer_comparison import calculate_normalized_scores_v2
+import json
+
+def test_normalization():
+    print("Testing Normalization Logic...")
+    
+    # Case 1: High Growth, High Profit
+    sample_1 = {
+        'returns_5y': 150.0, # Expect 100
+        'roe': 35.0,         # Expect 100
+        'roce': 40.0,        # Expect 100
+        'pe_ratio': 20.0,
+        'industry_pe': 25.0, # Undervalued -> Expect high score
+        'dividend_yield': 1.5, # Expect ~37.5 (1.5/4 * 100 ?) No, range 0-5. 1.5/5 * 100 = 30? Wait formula is linear 0-5.
+        'returns_1y': 80.0   # Expect High
+    }
+    
+    scores_1 = calculate_normalized_scores_v2(sample_1)
+    print(f"\nSample 1 (High Performance):\n{json.dumps(scores_1, indent=2)}")
+    
+    # Case 2: Poor Performance
+    sample_2 = {
+        'returns_5y': -30.0, # Expect 0 (min -20)
+        'roe': -5.0,         # Expect 0
+        'roce': 2.0,         # Expect low
+        'pe_ratio': 50.0,
+        'industry_pe': 25.0, # Overvalued -> Expect low score
+        'dividend_yield': 0.0, # Expect 0
+        'returns_1y': -60.0  # Expect 0 (min -50)
+    }
+    
+    scores_2 = calculate_normalized_scores_v2(sample_2)
+    print(f"\nSample 2 (Poor Performance):\n{json.dumps(scores_2, indent=2)}")
+    
+    # Case 3: Average / Missing
+    sample_3 = {
+        'pe_ratio': 25.0,
+        'industry_pe': 25.0 # Fair value
+        # Others missing -> Expect 50
+    }
+    
+    scores_3 = calculate_normalized_scores_v2(sample_3)
+    print(f"\nSample 3 (Average/Missing):\n{json.dumps(scores_3, indent=2)}")
+
+if __name__ == "__main__":
+    try:
+        test_normalization()
+        print("\n✅ Verification Successful: Logic runs without errors.")
+    except Exception as e:
+        print(f"\n❌ Verification Failed: {e}")