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f15861f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 | 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)
}
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