sentiment logging

app.py

@@ -128,12 +128,12 @@ class NewsSentiment:
     def __init__(self, API_KEY):
         self.newsapi = NewsApiClient(api_key=API_KEY)
         self.sia = SentimentIntensityAnalyzer()
-        self.alpha_vantage_api_key = st.secrets.get("ALPHA_VANTAGE_API_KEY")  # Add your Alpha Vantage key to Streamlit secrets
+        self.alpha_vantage_api_key = st.secrets.get("ALPHA_VANTAGE_API_KEY")
 
     def get_sentiment_and_headlines(self, symbol):
         """
         Try NewsAPI first, fallback to Alpha Vantage if needed.
-        Returns (sentiment, headlines).
+        Returns (sentiment, headlines, source).
         """
         # Try NewsAPI
         try:
@@ -141,7 +141,8 @@ class NewsSentiment:
             headlines = [a['title'] for a in articles.get('articles', [])[:5]]
             if headlines:
                 sentiment = self._calculate_sentiment(headlines)
-                return sentiment, headlines
+                logger.info(f"NewsAPI sentiment for {symbol}: {sentiment} | Headlines: {headlines}")
+                return sentiment, headlines, "NewsAPI"
             else:
                 logger.warning(f"NewsAPI returned no headlines for {symbol}.")
         except Exception as e:
@@ -152,7 +153,7 @@ class NewsSentiment:
         try:
             if not self.alpha_vantage_api_key:
                 logger.error("Alpha Vantage API key not found in Streamlit secrets.")
-                return None, []
+                return None, [], "AlphaVantage"
             import requests
             url = (
                 f"https://www.alphavantage.co/query?function=NEWS_SENTIMENT&tickers={symbol}"
@@ -160,12 +161,11 @@ class NewsSentiment:
             )
             resp = requests.get(url)
             data = resp.json()
-            # Alpha Vantage returns a list of news items under "feed"
             headlines = [item.get("title") for item in data.get("feed", [])[:5] if item.get("title")]
             if headlines:
-                logger.info(f"Using Alpha Vantage headlines for {symbol}: {headlines}")
                 sentiment = self._calculate_sentiment(headlines)
-                return sentiment, headlines
+                logger.info(f"Alpha Vantage sentiment for {symbol}: {sentiment} | Headlines: {headlines}")
+                return sentiment, headlines, "AlphaVantage"
             else:
                 logger.warning(f"Alpha Vantage returned no headlines for {symbol}.")
         except Exception as e:
@@ -174,7 +174,7 @@ class NewsSentiment:
         logger.info(
             f"No sentiment/headlines available for {symbol} from either NewsAPI or Alpha Vantage."
         )
-        return None, []
+        return None, [], None
 
     def _calculate_sentiment(self, headlines):
         if not headlines:
@@ -191,17 +191,26 @@ class NewsSentiment:
     def get_sentiment_bulk(self, symbols):
         """
         Bulk sentiment for a list of symbols using NewsAPI only (for auto-trade).
-        Returns dict: symbol -> sentiment.
+        Returns dict: symbol -> (sentiment, source).
         """
         sentiment = {}
         for symbol in symbols:
             try:
                 articles = self.newsapi.get_everything(q=symbol, language='en', sort_by='publishedAt', page=1)
                 headlines = [a['title'] for a in articles.get('articles', [])[:5]]
-                sentiment[symbol] = self._calculate_sentiment(headlines) if headlines else 'Neutral'
+                if headlines:
+                    s = self._calculate_sentiment(headlines)
+                    logger.info(f"NewsAPI sentiment for {symbol}: {s} | Headlines: {headlines}")
+                    sentiment[symbol] = (s, "NewsAPI")
+                else:
+                    # fallback to Alpha Vantage
+                    s, _, src = self.get_sentiment_and_headlines(symbol)
+                    sentiment[symbol] = (s, src)
             except Exception as e:
                 logger.error(f"Error getting news for {symbol}: {e}")
-                sentiment[symbol] = 'Neutral'
+                # fallback to Alpha Vantage
+                s, _, src = self.get_sentiment_and_headlines(symbol)
+                sentiment[symbol] = (s, src)
         return sentiment
 
 
@@ -342,7 +351,7 @@ class TradingApp:
         self.sentiment = NewsSentiment(st.secrets['NEWS_API_KEY'])
         self.analyzer = StockAnalyzer(self.alpaca)
         self.data = self.analyzer.get_historical_data(self.analyzer.symbols)
-        self.auto_trade_log = []  # Store automatic trade actions
+        self.auto_trade_log = []
 
     def display_charts(self):
         # Dynamically adjust columns based on number of stocks and available width
@@ -403,24 +412,29 @@ class TradingApp:
                 st.session_state["sentiment_result"] = None
             if "article_headlines" not in st.session_state:
                 st.session_state["article_headlines"] = []
+            if "sentiment_source" not in st.session_state:
+                st.session_state["sentiment_source"] = None
 
             if st.button("Check Sentiment"):
                 if symbol:
-                    sentiment_result, article_headlines = self.sentiment.get_sentiment_and_headlines(symbol)
+                    sentiment_result, article_headlines, sentiment_source = self.sentiment.get_sentiment_and_headlines(symbol)
                     st.session_state["sentiment_result"] = sentiment_result
                     st.session_state["article_headlines"] = article_headlines
                     st.session_state["sentiment_symbol"] = symbol
+                    st.session_state["sentiment_source"] = sentiment_source
                 else:
                     st.session_state["sentiment_result"] = None
                     st.session_state["article_headlines"] = []
                     st.session_state["sentiment_symbol"] = ""
+                    st.session_state["sentiment_source"] = None
 
             sentiment_result = st.session_state.get("sentiment_result")
             article_headlines = st.session_state.get("article_headlines", [])
             sentiment_symbol = st.session_state.get("sentiment_symbol", "")
+            sentiment_source = st.session_state.get("sentiment_source", "")
 
             if symbol and sentiment_symbol == symbol and sentiment_result is not None:
-                st.markdown(f"**Sentiment for {symbol.upper()}:** {sentiment_result if sentiment_result in ['Positive', 'Negative', 'Neutral'] else 'No sentiment available'}")
+                st.markdown(f"**Sentiment for {symbol.upper()} ({sentiment_source}):** {sentiment_result if sentiment_result in ['Positive', 'Negative', 'Neutral'] else 'No sentiment available'}")
             elif symbol and sentiment_symbol == symbol and sentiment_result is None:
                 st.markdown("**Sentiment:** No sentiment available")
 
@@ -518,17 +532,23 @@ class TradingApp:
     def _execute_sentiment_trades(self, sentiment):
         actions = []
         symbol_to_name = self.analyzer.symbol_to_name
-        for symbol, sentiment_value in sentiment.items():
+        for symbol, sentiment_info in sentiment.items():
+            # sentiment_info is now (sentiment, source)
+            if isinstance(sentiment_info, tuple):
+                sentiment_value, sentiment_source = sentiment_info
+            else:
+                sentiment_value, sentiment_source = sentiment_info, None
             # If sentiment is missing or invalid, try to get it using fallback
             if sentiment_value is None or sentiment_value not in ['Positive', 'Negative', 'Neutral']:
-                sentiment_value, _ = self.sentiment.get_sentiment_and_headlines(symbol)
+                sentiment_value, _, sentiment_source = self.sentiment.get_sentiment_and_headlines(symbol)
             action = None
             is_market_open = self.alpaca.get_market_status()
+            logger.info(f"Auto-trade: {symbol} | Sentiment: {sentiment_value} | Source: {sentiment_source}")
             if sentiment_value == 'Positive':
-                order = self.alpaca.buy(symbol, 1, reason="Sentiment: Positive")
+                order = self.alpaca.buy(symbol, 1, reason=f"Sentiment: Positive ({sentiment_source})")
                 action = 'Buy'
             elif sentiment_value == 'Negative':
-                order = self.alpaca.sell(symbol, 1, reason="Sentiment: Negative")
+                order = self.alpaca.sell(symbol, 1, reason=f"Sentiment: Negative ({sentiment_source})")
                 action = 'Sell'
             else:
                 order = None
@@ -547,6 +567,7 @@ class TradingApp:
                 'symbol': symbol,
                 'company_name': symbol_to_name.get(symbol, ''),
                 'sentiment': sentiment_value,
+                'sentiment_source': sentiment_source,
                 'action': action
             })
         return actions
@@ -555,7 +576,7 @@ def background_auto_trade(app):
     """This function runs in a background thread and updates session state with automatic trades."""
     while True:
         start_time = time.time()
-        # Use NewsAPI only for bulk sentiment (to avoid rate limits and speed)
+        # Use NewsAPI and Alpha Vantage for bulk sentiment (with fallback)
         sentiment = app.sentiment.get_sentiment_bulk(app.analyzer.symbols)
         actions = app._execute_sentiment_trades(sentiment)
         log_entry = {
@@ -699,7 +720,9 @@ def main():
         st.write(f"Last checked: {last_entry['timestamp']}")
         df = pd.DataFrame(last_entry["actions"])
         if "company_name" in df.columns:
-            df = df[["symbol", "company_name", "sentiment", "action"]]
+            # Show sentiment source if available
+            display_cols = ["symbol", "company_name", "sentiment", "sentiment_source", "action"] if "sentiment_source" in df.columns else ["symbol", "company_name", "sentiment", "action"]
+            df = df[display_cols]
         st.dataframe(df)
         st.write("Sentiment Analysis (latest):")
         st.write(last_entry["sentiment"])