Update aggregator.py

aggregator.py

@@ -45,28 +45,6 @@ def get_articles_sentiment(ticker, model):
         print(e)
         vantage_results = []
 
-    try:
-        marketaux_list = get_marketaux(ticker)
-        marketaux_results = pipe(marketaux_list)
-    except Exception as e:
-        print(e)
-        marketaux_results = []
-    
-    
-
-    # finhub_list = get_finhub(ticker)
-    # marketaux_list = get_marketaux(ticker)
-    # newsapi_list = get_newsapi(ticker)
-    # newsdata_list = get_newsdata(ticker)
-    # vantage_list = get_vantage(ticker)
-
-    
-    # calling ai model on each list
-    # finhub_results = pipe(finhub_list)
-    # marketaux_results = pipe(marketaux_list)
-    # newsapi_results = pipe(newsapi_list)
-    # newsdata_results = pipe(newsdata_list)
-    # vantage_results = pipe(vantage_list)
     
     # replacing values for calculations and doing the sentiment for each source
     def replace_values(result):
@@ -78,7 +56,7 @@ def get_articles_sentiment(ticker, model):
                 dict["label"] = 1
 
     
-    total_articles = len(bezinga_results) + len(finhub_results) + len(marketaux_results) + len(newsapi_results) + len(newsdata_results) + len(vantage_results)
+    total_articles = len(bezinga_results) + len(finhub_results) + len(newsapi_results) + len(newsdata_results) + len(vantage_results)
 
     bezinga_positives = []
     bezinga_negatives = []
@@ -86,9 +64,6 @@ def get_articles_sentiment(ticker, model):
     finhub_positives = []
     finhub_negatives = []
 
-    marketaux_positives = []
-    marketaux_negatives = []
-
     newsapi_positives = []
     newsapi_negatives = []
 
@@ -138,24 +113,6 @@ def get_articles_sentiment(ticker, model):
             
         if len(finhub_negatives) > 0:
             finhub_negative_score_mean = float(sum(d['score'] for d in finhub_negatives)) / len(finhub_negatives)
-
-    # marketaux
-    if len(marketaux_results) > 0:
-        replace_values(marketaux_results)
-
-        marketaux_label_mean = float(sum(d['label'] for d in marketaux_results)) / len(marketaux_results)
-
-        for dict in marketaux_results:
-            if dict["label"] == 2:
-                marketaux_positives.append(dict)
-            else:
-                marketaux_negatives.append(dict)
-
-        if len(marketaux_positives) > 0:
-            marketaux_positive_score_mean = float(sum(d['score'] for d in marketaux_positives)) / len(marketaux_positives)
-            
-        if len(marketaux_negatives) > 0:
-            marketaux_negative_score_mean = float(sum(d['score'] for d in marketaux_negatives)) / len(marketaux_negatives)
     
     # newsapi
     if len(newsapi_results) > 0:
@@ -232,14 +189,6 @@ def get_articles_sentiment(ticker, model):
             "finhub_positive_score_mean": finhub_positive_score_mean if len(finhub_results) > 0 else 0,
             "finhub_negative_score_mean": finhub_negative_score_mean if len(finhub_results) > 0 else 0
         },
-        "marketaux": {
-            "marketaux_articles": len(marketaux_results),
-            "marketaux_positives": len(marketaux_positives),
-            "marketaux_negatives": len(marketaux_negatives),
-            "marketaux_sentiment_mean": marketaux_label_mean if len(marketaux_results) > 0 else 0,
-            "marketaux_positive_score_mean": marketaux_positive_score_mean if len(marketaux_results) > 0 else 0,
-            "marketaux_negative_score_mean": marketaux_negative_score_mean if len(marketaux_results) > 0 else 0
-        },
         "newsapi": {
             "newsapi_articles": len(newsapi_results),
             "newsapi_positives": len(newsapi_positives),