Merge pull request #51 from shivam096/shivam-new-excpetions

Shivam new excpetions

dinero/backend/main.py

@@ -1,50 +1,50 @@
-from pprint import pprint
-from backend.sentiment_analysis import get_sentiment_value
-from backend.req import get_news_articles
-from backend.transform import get_filter_dates
-from backend.kpi_manager import get_technical_indicator
-import pandas as pd
+# from pprint import pprint
+# from backend.sentiment_analysis import get_sentiment_value
+# from backend.req import get_news_articles
+# from backend.transform import get_filter_dates
+# from backend.kpi_manager import get_technical_indicator
+# import pandas as pd
 
-from backend.processing import get_sentiments
+# from backend.processing import get_sentiments
 
-sentiment_date = {}
-percent_change = 5
-stock_symbol = "AAPL"
+# sentiment_date = {}
+# percent_change = 5
+# stock_symbol = "AAPL"
 
 
-print(get_sentiments(stock_symbol,percent_change))
+# print(get_sentiments(stock_symbol,percent_change))
 
 
-# data = {
-#     'Date': [],
-#     'Title': [],
-#     'Link': [],
-#     'Compound Sentiment Score': [],
-#     'Positive Sentiment Score': [],
-#     'Negative Sentiment Score': [],
-#     'Neutral Sentiment Score': []
-# }
+# # data = {
+# #     'Date': [],
+# #     'Title': [],
+# #     'Link': [],
+# #     'Compound Sentiment Score': [],
+# #     'Positive Sentiment Score': [],
+# #     'Negative Sentiment Score': [],
+# #     'Neutral Sentiment Score': []
+# # }
 
-# for date, articles in sentiment_date.items():
-#     for title, details in articles.items():
-#         data['Date'].append(date)
-#         data['Title'].append(title)
-#         data['Link'].append(details['link'])
-#         data['Compound Sentiment Score'].append(details['sentiment_score']['compound'])
-#         data['Positive Sentiment Score'].append(details['sentiment_score']['pos'])
-#         data['Negative Sentiment Score'].append(details['sentiment_score']['neg'])
-#         data['Neutral Sentiment Score'].append(details['sentiment_score']['neu'])
+# # for date, articles in sentiment_date.items():
+# #     for title, details in articles.items():
+# #         data['Date'].append(date)
+# #         data['Title'].append(title)
+# #         data['Link'].append(details['link'])
+# #         data['Compound Sentiment Score'].append(details['sentiment_score']['compound'])
+# #         data['Positive Sentiment Score'].append(details['sentiment_score']['pos'])
+# #         data['Negative Sentiment Score'].append(details['sentiment_score']['neg'])
+# #         data['Neutral Sentiment Score'].append(details['sentiment_score']['neu'])
 
-# df = pd.DataFrame(data)
-# print(df)
+# # df = pd.DataFrame(data)
+# # print(df)
 
 
 
 
-# # usage of KPI
-# ticker_symbol = 'AAPL'
-# length = 50
-# indicator = 'MA'
+# # # usage of KPI
+# # ticker_symbol = 'AAPL'
+# # length = 50
+# # indicator = 'MA'
 
-# indicator_data = get_technical_indicator(ticker_symbol, length, indicator)
-# print(indicator_data)
+# # indicator_data = get_technical_indicator(ticker_symbol, length, indicator)
+# # print(indicator_data)

dinero/backend/processing.py

@@ -1,52 +1,80 @@
-"""
-Module: dinero_analysis
-
-This module provides functions for analyzing financial data related to stocks.
-
-Functions:
-    - process_dict_to_df
-    - get_sentiments
-"""
-
-import os
-import pandas as pd
-from pprint import pprint
-from backend.transform import get_filter_dates
-from backend.sentiment_analysis import get_sentiment_value
-
-
-def process_dict_to_df(sentiment_data):
-    data = {
-        'Date': [],
-        'Title': [],
-        'Link': [],
-        'Compound Sentiment Score': [],
-        'Positive Sentiment Score': [],
-        'Negative Sentiment Score': [],
-        'Neutral Sentiment Score': []
-    }
-
-    for date, articles in sentiment_data.items():
-        for title, details in articles.items():
-            data['Date'].append(date)
-            data['Title'].append(title)
-            data['Link'].append(details['link'])
-            data['Compound Sentiment Score'].append(details['sentiment_score']['compound'])
-            data['Positive Sentiment Score'].append(details['sentiment_score']['pos'])
-            data['Negative Sentiment Score'].append(details['sentiment_score']['neg'])
-            data['Neutral Sentiment Score'].append(details['sentiment_score']['neu'])
-
-    formatted_df = pd.DataFrame(data)
-    return formatted_df
-
-
-def get_sentiments(stock_symbol,percent_change):
-    sentiment_data ={}
-    file_path = os.path.join("data",f"{stock_symbol}.csv")
-    dates_dictionary = get_filter_dates(file_path, percent_change, stock_symbol)
-
-    for key,value in dates_dictionary.items():
-        sentiment_data[key] = get_sentiment_value(value)
-
-    data_frame = process_dict_to_df(sentiment_data=sentiment_data)
-    return data_frame
+"""
+Module: dinero_analysis
+
+This module provides functions for analyzing financial data related to stocks.
+
+Functions:
+    - process_dict_to_df: Processes sentiment data from a dictionary to a pandas DataFrame.
+    - get_sentiments: Retrieves sentiment analysis for a given stock symbol and percent change.
+"""
+import os
+import pandas as pd
+from backend.transform import get_filter_dates
+from backend.sentiment_analysis import get_sentiment_value
+
+
+def process_dict_to_df(sentiment_data):
+    """
+    Processes sentiment data from a dictionary to a pandas DataFrame.
+
+    Args:
+        sentiment_data (dict): A dictionary containing sentiment data for each date.
+
+    Returns:
+        pd.DataFrame: A DataFrame containing processed sentiment data.
+    """
+    data = {
+        'Date': [],
+        'Title': [],
+        'Link': [],
+        'Compound Sentiment Score': [],
+        'Positive Sentiment Score': [],
+        'Negative Sentiment Score': [],
+        'Neutral Sentiment Score': []
+    }
+
+    for date, articles in sentiment_data.items():
+        for title, details in articles.items():
+            data['Date'].append(date)
+            data['Title'].append(title)
+            data['Link'].append(details.get('link', 'N/A'))
+            sentiment_score = details.get('sentiment_score', {})
+            data['Compound Sentiment Score'].append(sentiment_score.get('compound', 0))
+            data['Positive Sentiment Score'].append(sentiment_score.get('pos', 0))
+            data['Negative Sentiment Score'].append(sentiment_score.get('neg', 0))
+            data['Neutral Sentiment Score'].append(sentiment_score.get('neu', 0))
+
+    formatted_df = pd.DataFrame(data)
+    return formatted_df
+
+
+def get_sentiments(stock_symbol, percent_change):
+    """
+    Retrieves sentiment analysis for a given stock symbol and percent change.
+
+    Args:
+        stock_symbol (str): The stock symbol for which sentiment analysis is to be retrieved.
+        percent_change (float): The percent change threshold for filtering dates.
+
+    Returns:
+        pd.DataFrame or None: A DataFrame containing sentiment analysis data, 
+                            or None if an error occurs.
+    """
+    sentiment_data = {}
+    try:
+        if percent_change is None:
+            raise ValueError("percent_change argument is None")
+        if stock_symbol is None:
+            raise ValueError("stock_symbol argument is None")
+        file_path = os.path.join("data", f"{stock_symbol}.csv")
+        dates_dictionary = get_filter_dates(file_path, percent_change, stock_symbol)
+
+        for key, value in dates_dictionary.items():
+            sentiment_data[key] = get_sentiment_value(value)
+
+        data_frame = process_dict_to_df(sentiment_data=sentiment_data)
+        return data_frame
+
+    except ValueError as e:
+        print(f"Value Error raised: {e}")
+        return None

dinero/backend/req.py

@@ -1,25 +1,31 @@
-"""
-Module for retrieving news articles related to a specific stock ticker.
-
-    Function :
-    -   get_news_articles
-
-"""
-import requests
-
-def get_news_articles(ticker : str, date: str) :
-    """
-    Retrieve news articles related to a specific stock ticker on a given date.
-
-    Args:
-        ticker (str): The stock ticker symbol.
-        date (str): The date for which news articles are to be retrieved.
-                    Should be in the format 'YYYY-MM-DD'.
-    Returns:
-        dict: A dictionary containing the news articles data.
-
-    
-    """
-    url = f'https://eodhd.com/api/news?s={ticker}&offset=0&api_token=demo&fmt=json&from={date}&to={date}'
-    data = requests.get(url, timeout= 100).json()
-    return data
+"""
+Module for retrieving news articles related to a specific stock ticker.
+
+    Function :
+    -   get_news_articles
+
+"""
+import requests
+
+def get_news_articles(ticker: str, date: str):
+    """
+    Retrieve news articles related to a specific stock ticker on a given date.
+
+    Args:
+        ticker (str): The stock ticker symbol.
+        date (str): The date for which news articles are to be retrieved.
+                    Should be in the format 'YYYY-MM-DD'.
+    Returns:
+        dict: A dictionary containing the news articles data.
+    """
+    try:
+        url = f'https://eodhd.com/api/news?s={ticker}\
+            &offset=0&api_token=demo&fmt=json&from={date}&to={date}'
+        response = requests.get(url, timeout=100)
+        response.raise_for_status()  # Raise an exception for HTTP errors (status codes >= 400)
+        data = response.json()
+        return data
+    except requests.RequestException as e:
+        print(f"Error retrieving news articles: {e}")
+        return None
+

dinero/backend/sentiment_analysis.py

@@ -1,33 +1,37 @@
-"""
-Module: sentiment_analysis
-This module provides functions for sentiment analysis of text data.
-
-Function:
-    - get_sentiment_value
-"""
-
-from nltk.sentiment.vader import SentimentIntensityAnalyzer
-import nltk
-nltk.download('vader_lexicon')
-
-def get_sentiment_value(title_list: list) -> dict:
-    """
-    Analyzes the sentiment of each title in the given list using 
-    VADER (Valence Aware Dictionary and sEntiment Reasoner).
-
-    Args:
-    - title_list (list): A list of strings representing titles or sentences to analyze.
-
-    Returns:
-    - dict: A dictionary where keys are the titles/sentences and 
-            values are dictionaries containing sentiment scores.
-            The sentiment scores include 'neg' (negative), 
-            'neu' (neutral), 'pos' (positive), and 'compound' (overall sentiment).
-    """
-    senti_dict = {}
-    analyzer = SentimentIntensityAnalyzer()
-    for sentence in title_list:
-        vs = analyzer.polarity_scores(sentence.get('content'))
-        senti_dict[sentence.get('title')] = {'sentiment_score':vs, 'link':sentence.get('link')}
-
-    return senti_dict
+"""
+Module: sentiment_analysis
+This module provides functions for sentiment analysis of text data.
+
+Function:
+    - get_sentiment_value
+"""
+
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+import nltk
+nltk.download('vader_lexicon')
+
+def get_sentiment_value(title_list: list) -> dict:
+    """
+    Analyzes the sentiment of each title in the given list using 
+    VADER (Valence Aware Dictionary and sEntiment Reasoner).
+
+    Args:
+    - title_list (list): A list of dictionaries representing titles or sentences to analyze.
+                         Each dictionary should have 'title', 'content', and 'link' keys.
+
+    Returns:
+    - dict: A dictionary where keys are the titles/sentences and 
+            values are dictionaries containing sentiment scores.
+            The sentiment scores include 'neg' (negative), 
+            'neu' (neutral), 'pos' (positive), and 'compound' (overall sentiment).
+    """
+    senti_dict = {}
+    analyzer = SentimentIntensityAnalyzer()
+    for sentence in title_list:
+        content = sentence['content']
+        title = sentence['title']
+        link = sentence['link']
+        vs = analyzer.polarity_scores(content)
+        senti_dict[title] = {'sentiment_score': vs, 'link': link}
+
+    return senti_dict