diff --git a/TLD%/AppData/Local/Temp/system-commandline-sentinel-files/dotnet-suggest-registration-git-credential-manager-core, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null b/TLD%/AppData/Local/Temp/system-commandline-sentinel-files/dotnet-suggest-registration-git-credential-manager-core, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null
new file mode 100644
index 0000000..4e9fcae
--- /dev/null
+++ b/TLD%/AppData/Local/Temp/system-commandline-sentinel-files/dotnet-suggest-registration-git-credential-manager-core, Version=2.0.0.0, Culture=neutral, PublicKeyToken=null	
@@ -0,0 +1,6 @@
+Exception during registration:
+System.ComponentModel.Win32Exception (0x80004005): The system cannot find the file specified
+   at System.Diagnostics.Process.StartWithCreateProcess(ProcessStartInfo startInfo)
+   at System.Diagnostics.Process.Start()
+   at System.CommandLine.Invocation.Process.StartProcess(String command, String args, String workingDir, Action`1 stdOut, Action`1 stdErr, ValueTuple`2[] environmentVariables)
+   at System.CommandLine.Builder.CommandLineBuilderExtensions.<>c.<<RegisterWithDotnetSuggest>b__10_1>d.MoveNext()
\ No newline at end of file
diff --git a/black-scholes.py b/black-scholes.py
new file mode 100644
index 0000000..a6d7305
--- /dev/null
+++ b/black-scholes.py
@@ -0,0 +1,38 @@
+import requests
+from scipy.stats import norm
+from math import log, sqrt, exp
+
+# Function to fetch real-time stock price from Alpha Vantage API
+def get_stock_price(symbol):
+    url = f"https://www.alphavantage.co/query?function=GLOBAL_QUOTE&symbol={symbol}"
+    response = requests.get(url)
+    if response.status_code == 200:
+        data = response.json()
+        return float(data['Global Quote']['05. price'])
+    else:
+        print(f"Failed to fetch stock price for {symbol}.")
+        return None
+
+# Function to calculate the Black-Scholes call option price
+def black_scholes_call(S, K, r, sigma, T):
+    d1 = (log(S / K) + (r + 0.5 * sigma**2) * T) / (sigma * sqrt(T))
+    d2 = d1 - sigma * sqrt(T)
+    return S * norm.cdf(d1) - K * exp(-r * T) * norm.cdf(d2)
+
+# Function to calculate the Black-Scholes put option price
+def black_scholes_put(S, K, r, sigma, T):
+    d1 = (log(S / K) + (r + 0.5 * sigma**2) * T) / (sigma * sqrt(T))
+    d2 = d1 - sigma * sqrt(T)
+    return K * exp(-r * T) * norm.cdf(-d2) - S * norm.cdf(-d1)
+
+# Example usage
+if __name__ == "__main__":
+    symbol = "AAPL"     # Stock symbol
+    K = 200             # Strike price
+    r = 0.05            # Risk-free interest rate
+    sigma = 0.2         # Volatility
+    T = 1               # Time to expiration (in years)
+
+
+    S = get_stock_price(symbol)
+   
diff --git a/qualAnalysis.py b/qualAnalysis.py
new file mode 100644
index 0000000..04459dc
--- /dev/null
+++ b/qualAnalysis.py
@@ -0,0 +1,88 @@
+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))
diff --git a/testing.py b/testing.py
new file mode 100644
index 0000000..ea27647
--- /dev/null
+++ b/testing.py
@@ -0,0 +1,99 @@
+import unittest
+from your_module import black_scholes_call, black_scholes_put, get_sentiment_score, get_stock_price
+
+class TestBlackScholes(unittest.TestCase):
+    def test_black_scholes_call(self):
+        # Test case for call option pricing
+        S = 100  # Stock price
+        K = 100  # Strike price
+        r = 0.05  # Risk-free interest rate
+        sigma = 0.2  # Volatility
+        T = 1  # Time to expiration
+        call_price = black_scholes_call(S, K, r, sigma, T)
+        self.assertIsNotNone(call_price)
+
+    def test_black_scholes_put(self):
+        # Test case for put option pricing
+        S = 100  # Stock price
+        K = 100  # Strike price
+        r = 0.05  # Risk-free interest rate
+        sigma = 0.2  # Volatility
+        T = 1  # Time to expiration
+        put_price = black_scholes_put(S, K, r, sigma, T)
+        self.assertIsNotNone(put_price)
+
+class TestSentimentAnalysis(unittest.TestCase):
+    def test_get_sentiment_score(self):
+        # Test case for sentiment analysis
+        article = "This is a positive article about the company."
+        sentiment_score = get_sentiment_score(article)
+        print("Sentiment Score:", sentiment_score)
+        self.assertIsNotNone(sentiment_score)
+
+class TestLiveness(unittest.TestCase):
+    def test_get_stock_price(self):
+        # Test case for liveness of get_stock_price function
+        symbol = "AAPL"  # Stock symbol
+
+        stock_price = get_stock_price(symbol)
+        self.assertIsNotNone(stock_price)
+
+    def test_get_financial_sentiment(self):
+        # Test case for liveness of get_financial_sentiment function
+        company = 'AAPL'  # Specify the company symbol
+        news_sources = 'bbc-news, bloomberg, cnn, reuters'  # Specify news sources
+        overall_strength = get_financial_sentiment(company, news_sources)
+        self.assertIsNotNone(overall_strength)
+
+if __name__ == '__main__':
+    unittest.main()
+import unittest
+from your_module import black_scholes_call, black_scholes_put, get_sentiment_score, get_stock_price
+
+class TestBlackScholes(unittest.TestCase):
+    def test_black_scholes_call(self):
+        # Test case for call option pricing
+        S = 100  # Stock price
+        K = 100  # Strike price
+        r = 0.05  # Risk-free interest rate
+        sigma = 0.2  # Volatility
+        T = 1  # Time to expiration
+        call_price = black_scholes_call(S, K, r, sigma, T)
+        self.assertIsNotNone(call_price)
+
+    def test_black_scholes_put(self):
+        # Test case for put option pricing
+        S = 100  # Stock price
+        K = 100  # Strike price
+        r = 0.05  # Risk-free interest rate
+        sigma = 0.2  # Volatility
+        T = 1  # Time to expiration
+        put_price = black_scholes_put(S, K, r, sigma, T)
+        self.assertIsNotNone(put_price)
+
+class TestSentimentAnalysis(unittest.TestCase):
+    def test_get_sentiment_score(self):
+        # Test case for sentiment analysis
+        article = "This is a positive article about the company."
+        sentiment_score = get_sentiment_score(article)
+        print("Sentiment Score:", sentiment_score)
+        self.assertIsNotNone(sentiment_score)
+
+class TestLiveness(unittest.TestCase):
+    def test_get_stock_price(self):
+        # Test case for liveness of get_stock_price function
+        symbol = "AAPL"  # Stock symbol
+
+        stock_price = get_stock_price(symbol)
+        self.assertIsNotNone(stock_price)
+
+    def test_get_financial_sentiment(self):
+        # Test case for liveness of get_financial_sentiment function
+        company = 'AAPL'  # Specify the company symbol
+        news_sources = 'bbc-news, bloomberg, cnn, reuters'  # Specify news sources
+
+        overall_strength = get_financial_sentiment(company, news_sources)
+        self.assertIsNotNone(overall_strength)
+
+if __name__ == '__main__':
+    unittest.main()