diff --git a/qualAnalysis.py b/qualAnalysis.py
new file mode 100644
index 0000000..04459dc
--- /dev/null
+++ b/qualAnalysis.py
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+import nltk
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+import requests
+import logging
+import json
+from functools import lru_cache
+
+# Initialize logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+@lru_cache(maxsize=128)
+def get_sentiment_score(article):
+    """
+    Function to get sentiment score of a news article using NLTK Vader sentiment analysis.
+    """
+    try:
+        sid = SentimentIntensityAnalyzer()
+        sentiment_score = sid.polarity_scores(article)['compound']
+        return sentiment_score
+    except Exception as e:
+        logger.error("Error getting sentiment score: %s", str(e))
+        return 0
+
+@lru_cache(maxsize=128)
+def get_news_articles(symbol, news_sources):
+    """
+    Function to get news articles related to a given symbol from News API.
+    """
+    try:
+        url = f'https://newsapi.org/v2/everything?q={symbol}&sources={news_sources}'
+        response = requests.get(url)
+        data = response.json()
+        if 'articles' in data:
+            articles = [article['description'] for article in data['articles']]
+            return articles
+        else:
+            return []
+    except Exception as e:
+        logger.error("Error getting news articles: %s", str(e))
+        return []
+
+@lru_cache(maxsize=128)
+def get_analyst_recommendations(symbol):
+    """
+    Function to get analyst recommendations for a given symbol from Alpha Vantage.
+    """
+    try:
+        url = f'https://www.alphavantage.co/query?function=OVERVIEW&symbol={symbol}'
+        response = requests.get(url)
+        data = response.json()
+        if 'AnalystRating' in data:
+            return int(data['AnalystRating'])  # Convert to int
+        else:
+            return 0
+    except Exception as e:
+        logger.error("Error getting analyst recommendations: %s", str(e))
+        return 0
+
+def get_financial_sentiment(symbol, news_sources):
+    """
+    Function to compute the overall strength value based on sentiment analysis of news articles and analyst recommendations.
+    """
+    try:
+        articles = get_news_articles(symbol, news_sources)
+        if not articles:
+            logger.warning("No news articles found for symbol %s", symbol)
+            return 0
+
+        total_sentiment_score = sum(get_sentiment_score(article) for article in articles) / len(articles)
+        analyst_recommendations = get_analyst_recommendations(symbol)
+        overall_strength = total_sentiment_score + analyst_recommendations
+        return overall_strength
+    except Exception as e:
+        logger.error("Error computing overall strength: %s", str(e))
+        return 0
+
+if __name__ == "__main__":
+    try:
+        # Configuration
+        company = 'AAPL'  # Specify the company symbol
+        news_sources = 'bbc-news, bloomberg, cnn, reuters'  # Specify news sources
+
+        # Get overall strength
+        strength = get_financial_sentiment(company, news_sources)
+        print("Overall Strength:", strength)
+    except Exception as e:
+        logger.error("Error in main: %s", str(e))