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import pandas as pd
import numpy as np
from utils.indicators import add_indicators
from utils.signals import generate_signals, get_signal_summary
def calculate_technical_score(df):
"""Calculate technical analysis score based on multiple indicators."""
score = 0
latest = df.iloc[-1]
# RSI Analysis (0-100)
rsi = latest['RSI']
if rsi < 30: # Oversold
score += 20
elif rsi > 70: # Overbought
score -= 20
else: # Neutral
score += 10
# MACD Analysis
if latest['MACD'] > latest['MACD_Signal']: # Bullish crossover
score += 20
elif latest['MACD'] < latest['MACD_Signal']: # Bearish crossover
score -= 20
# Moving Average Analysis
if latest['Close'] > latest['SMA_20']: # Above MA
score += 15
else: # Below MA
score -= 15
# Bollinger Bands Analysis
if latest['Close'] < latest['BB_Lower']: # Oversold
score += 15
elif latest['Close'] > latest['BB_Upper']: # Overbought
score -= 15
# Volume Analysis
vol_sma = df['Volume'].rolling(window=20).mean().iloc[-1]
if latest['Volume'] > vol_sma * 1.5: # High volume
score += 10
return max(min(score, 100), -100) # Normalize between -100 and 100
def get_recommendation(score):
"""Convert technical score to recommendation."""
if score >= 50:
return 'Strong Buy'
elif score >= 20:
return 'Buy'
elif score <= -50:
return 'Strong Sell'
elif score <= -20:
return 'Sell'
else:
return 'Hold'
def get_confidence_level(score):
"""Calculate confidence level based on absolute score."""
abs_score = abs(score)
if abs_score >= 70:
return 'High'
elif abs_score >= 40:
return 'Medium'
else:
return 'Low'
def analyze_stock(symbol, df):
"""Generate comprehensive stock analysis and recommendation."""
try:
# Add technical indicators
df = add_indicators(df)
# Generate signals
signals = generate_signals(df)
signal_summary = get_signal_summary(signals)
# Calculate technical score
tech_score = calculate_technical_score(df)
# Generate recommendation
recommendation = get_recommendation(tech_score)
confidence = get_confidence_level(tech_score)
return {
'symbol': symbol,
'recommendation': recommendation,
'technical_score': tech_score,
'confidence': confidence,
'signal_summary': signal_summary,
'last_price': df['Close'].iloc[-1],
'price_change': ((df['Close'].iloc[-1] / df['Close'].iloc[-2]) - 1) * 100
}
except Exception as e:
return None
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