{"id":7860,"date":"2025-02-05T13:41:49","date_gmt":"2025-02-05T17:41:49","guid":{"rendered":"https:\/\/www.benzinga.com\/apis\/blog\/\/"},"modified":"2025-02-09T00:42:40","modified_gmt":"2025-02-09T04:42:40","slug":"a-quick-guide-to-sentiment-powered-stock-predictions-with-benzingas-news-and-bars-endpoints","status":"publish","type":"post","link":"https:\/\/www.benzinga.com\/apis\/blog\/a-quick-guide-to-sentiment-powered-stock-predictions-with-benzingas-news-and-bars-endpoints\/","title":{"rendered":"A Quick Guide To Sentiment-Powered Stock Predictions With Benzinga\u2019s News And Bars Endpoints"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

Introduction<\/strong><\/h2>\n\n\n\n

Context is as critical as data. Relying solely on price movements is like evaluating a novel by its cover\u2014you risk overlooking the deeper narrative behind market shifts. Benzinga\u2019s <\/strong>Newsfeed <\/strong><\/a>& <\/strong>Bars<\/strong><\/a> APIs<\/strong> bridge this gap by combining market-moving headlines with detailed historical price data, which gives a better view of market behavior.<\/p>\n\n\n\n

In this guide, we will walk you through integrating these robust data sources into your trading strategy. You\u2019ll learn how to extract full-length articles using Benzinga\u2019s Why Is It Moving v2<\/em> endpoint and obtain precise price bars from the Bars endpoint. By merging these insights into a sentiment-based model, you can uncover actionable intelligence that enhances your market predictions. This straightforward yet powerful approach showcases how real-time news sentiment can elevate your decision-making in a dynamic market environment.<\/p>\n\n\n\n

<\/p>\n\n\n\n

1. The APIs at a Glance<\/strong><\/h2>\n\n\n\n

Newsfeed (Why Is It Moving v2)<\/strong><\/h3>\n\n\n\n
    \n
  • Endpoint<\/strong>: GET \/news<\/li>\n\n\n\n
  • Purpose<\/strong>: Pulls news items by ticker, date, and other filters.<\/li>\n\n\n\n
  • Notable Parameters<\/strong>:\n
      \n
    • token (your API key)<\/li>\n\n\n\n
    • tickers (comma-separated list of symbols)<\/li>\n\n\n\n
    • displayOutput (e.g., full to see the entire article body)<\/li>\n\n\n\n
    • dateFrom, dateTo or page and pageSize to organize how many articles you get.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
    • Why Use It?<\/strong>
      <\/strong>Up-to-date headlines and deeper stories are essential for gauging sentiment and discovering why a stock is in the spotlight.<\/li>\n<\/ul>\n\n\n\n

      Bars Endpoint<\/strong><\/h3>\n\n\n\n
        \n
      • Endpoint<\/strong>: GET \/bars<\/li>\n\n\n\n
      • Purpose<\/strong>: Returns historical candlestick (bar) data to measure how a stock\u2019s price changes over time.<\/li>\n\n\n\n
      • Key Parameters<\/strong>:\n
          \n
        • symbols (one or more comma-separated tickers)<\/li>\n\n\n\n
        • from, to (date range)<\/li>\n\n\n\n
        • interval (1D, 5M, 1W, etc.)<\/li>\n<\/ul>\n<\/li>\n\n\n\n
        • Why Use It?
          <\/strong>Comparing sentiment against price movements can help uncover patterns in how news influences stock returns.<\/li>\n<\/ul>\n\n\n\n

          <\/p>\n\n\n\n

          2. Let\u2019s Build a Sentiment-Fueled Predictor<\/strong><\/h2>\n\n\n\n

          Below, we\u2019ll walk through the code in manageable parts to show how we fetch data, analyze it, and make a simple prediction about tomorrow\u2019s price movement.<\/p>\n\n\n\n

          <\/p>\n\n\n\n

          2.1 Import Libraries & Set Up Endpoints<\/strong><\/h3>\n\n\n\n

          First, we need our usual suspects for making requests, handling data, performing sentiment analysis, and running a model.<\/p>\n\n\n

          \nimport requests\nimport pandas as pd\nfrom textblob import TextBlob\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score\nimport datetime, sys\n\nAPI_KEY = "YOUR_API_KEY"\nNEWS_ENDPOINT = "https:\/\/api.benzinga.com\/api\/v2\/news"\nBARS_ENDPOINT = "https:\/\/api.benzinga.com\/api\/v2\/bars"\n<\/pre><\/div>\n\n\n
            \n
          • requests helps us call the Benzinga APIs.<\/li>\n\n\n\n
          • pandas structures our data in easy-to-use data frames.<\/li>\n\n\n\n
          • TextBlob quickly calculates text sentiment.<\/li>\n\n\n\n
          • sklearn libraries let us build and evaluate a simple Logistic Regression model.<\/li>\n<\/ul>\n\n\n\n
            <\/p>\n\n\n\n
            2.2 Fetching News Data<\/strong><\/h3>\n\n\n\n
            Next, we define a helper function to retrieve articles from the Newsfeed endpoint. We pass parameters like tickers, dateFrom, dateTo, and\u2014most importantly\u2014our API key.<\/p>\n\n\n
            \ndef fetch_news_data(ticker="AAPL", date_from="2023-01-01", date_to="2023-12-01"):\n    params = {\n        "token": API_KEY,\n        "tickers": ticker,\n        "displayOutput": "full",\n        "dateFrom": date_from,\n        "dateTo": date_to,\n        "pageSize": 20\n    }\n    headers = {"accept": "application\/json"}\n    \n    resp = requests.get(NEWS_ENDPOINT, params=params, headers=headers)\n    if resp.status_code != 200:\n        print("Error:", resp.text)\n\n<\/pre><\/div>\n\n\n
              \n
            • displayOutput=full ensures we get the article\u2019s full text.<\/li>\n\n\n\n
            • pageSize=20 controls how many articles come back in a single query.<\/li>\n<\/ul>\n\n\n\n
              <\/p>\n\n\n\n
              2.3 Fetching Price Bars<\/strong><\/h3>\n\n\n\n
              Similarly, our fetch_bar_data function grabs daily candlestick data. We can adjust the interval to 1D, 5M, or other increments.<\/p>\n\n\n
              \ndef fetch_bar_data(ticker="AAPL", from_date="2023-01-01", to_date="2023-12-01"):\n\u00a0 \u00a0 params = {\n\u00a0 \u00a0 \u00a0 \u00a0 "token": API_KEY,\n\u00a0 \u00a0 \u00a0 \u00a0 "symbols": ticker,\n\u00a0 \u00a0 \u00a0 \u00a0 "interval": "1D",\n\u00a0 \u00a0 \u00a0 \u00a0 "from": from_date,\n\u00a0 \u00a0 \u00a0 \u00a0 "to": to_date\n\u00a0 \u00a0 }\n\u00a0 \u00a0 headers = {"accept": "application\/json"}\n\u00a0 \u00a0\n\u00a0 \u00a0 resp = requests.get(BARS_ENDPOINT, params=params, headers=headers)\n\u00a0 \u00a0 if resp.status_code != 200:\n\u00a0 \u00a0 \u00a0 \u00a0 print("Error:", resp.text)\n\u00a0 \u00a0 \u00a0 \u00a0 sys.exit(1)\n\u00a0 \u00a0 return resp.json()\n<\/pre><\/div>\n\n\n
                \n
              • symbols can be multiple tickers, comma-separated.<\/li>\n\n\n\n
              • interval=1D suits our plan to look at daily moves.<\/li>\n<\/ul>\n\n\n\n
                <\/p>\n\n\n\n
                2.4 Combining News & Bars<\/strong><\/h3>\n\n\n\n
                Now it\u2019s time to call our functions, parse the returned data, and build a pandas DataFrame. Notice how we handle the date in articles, then merge everything by date.<\/p>\n\n\n
                \n# 1. Fetch news\nnews_data = fetch_news_data("AAPL", "2023-01-01", "2023-12-01")\nnews_records = []\nfor article in news_data:\n\u00a0 \u00a0 raw_date = article.get("created", "")\n\u00a0 \u00a0 try:\n\u00a0 \u00a0 \u00a0 \u00a0 date_parsed = datetime.datetime.strptime(raw_date[:25], "%a, %d %b %Y %H:%M:%S")\n\u00a0 \u00a0 except:\n\u00a0 \u00a0 \u00a0 \u00a0 date_parsed = None\n\u00a0 \u00a0\n\u00a0 \u00a0 combined_text = (article.get("title", "") + " " + article.get("body", "")).strip()\n\u00a0 \u00a0 if date_parsed:\n\u00a0 \u00a0 \u00a0 \u00a0 news_records.append({"date": date_parsed.date(), "text": combined_text})\n\ndf_news = pd.DataFrame(news_records)\n<\/pre><\/div>\n\n\n
                Key Points<\/strong><\/p>\n\n\n\n
                  \n
                • We parse out the \u201ccreated\u201d field, trimming the string to avoid time-zone quirks.<\/li>\n\n\n\n
                • We combine the title and body to get the entire text for sentiment analysis.<\/li>\n<\/ul>\n\n\n\n
                  <\/p>\n\n\n\n
                  2.5 Calculating Sentiment<\/strong><\/h3>\n\n\n\n
                  Here\u2019s where TextBlob comes in. We average the polarity (sentiment) of all articles that appear on the same day. Values range from -1 (very negative) to 1 (very positive).<\/p>\n\n\n
                  \ndf_news["sentiment"] = df_news["text"].apply(lambda x: TextBlob(x).sentiment.polarity)\ndf_daily_sent = df_news.groupby("date")["sentiment"].mean().reset_index()\ndf_daily_sent.columns = ["date", "avg_sentiment"]\n<\/pre><\/div>\n\n\n
                  <\/p>\n\n\n\n
                  2.6 Merging with Bar Data<\/strong><\/h3>\n\n\n\n
                  Let\u2019s grab the bar data, convert it to a DataFrame, and then merge the sentiment we just computed. We fill missing sentiment data with 0.<\/p>\n\n\n
                  \nbars_data = fetch_bar_data("AAPL", "2023-01-01", "2023-12-01")\nif isinstance(bars_data, list) and len(bars_data) > 0 and "candles" in bars_data[0]:\n\u00a0 \u00a0 candle_list = bars_data[0]["candles"]\n\u00a0 \u00a0 df_bars = pd.DataFrame(candle_list)\n\u00a0 \u00a0 df_bars["time"] = pd.to_numeric(df_bars["time"], errors="coerce")\n\u00a0 \u00a0 df_bars["date"] = pd.to_datetime(df_bars["time"], unit='ms').dt.date\n\u00a0 \u00a0 df_bars = df_bars[["date", "open", "high", "low", "close"]]\nelse:\n\u00a0 \u00a0 df_bars = pd.DataFrame(columns=["date", "open", "high", "low", "close"])\n\ndf_merged = pd.merge(df_bars, df_daily_sent, on="date", how="left").fillna(0)\n<\/pre><\/div>\n\n\n
                  <\/p>\n\n\n\n
                  2.7 Creating a Label and Training the Model<\/strong><\/h3>\n\n\n\n
                  Finally, we shift the closing price by one day to see if tomorrow\u2019s close is higher than today\u2019s.<\/p>\n\n\n
                  \ndf_merged["next_close"] = df_merged["close"].shift(-1)\ndf_merged.dropna(inplace=True)\ndf_merged["label"] = (df_merged["next_close"] > df_merged["close"]).astype(int)\n\nX = df_merged[["close", "avg_sentiment"]]\ny = df_merged["label"]\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)\n\nmodel = LogisticRegression()\nmodel.fit(X_train, y_train)\n<\/pre><\/div>\n\n\n
                    \n
                  • label is 1 if the stock price goes up, 0 otherwise.<\/li>\n\n\n\n
                  • We\u2019re using an 80\/20 split for training and testing.<\/li>\n<\/ul>\n\n\n\n
                    <\/p>\n\n\n\n
                    2.8 Evaluating & Making a Prediction<\/strong><\/h3>\n\n\n\n
                    We check accuracy on the test set, then predict tomorrow\u2019s movement.<\/p>\n\n\n
                    \ny_pred = model.predict(X_test)\nacc = accuracy_score(y_test, y_pred)\nprint(f"Validation Accuracy: {acc:.2f}")\n\nlatest_row = df_merged.iloc[-1][["close", "avg_sentiment"]]\nprediction = model.predict([latest_row])\nsignal = "UP" if prediction[0] == 1 else "DOWN"\nprint(f"Tomorrow's predicted movement: {signal}")\n<\/pre><\/div>\n\n\n
                    <\/p>\n\n\n\n
                    3. The Output<\/strong><\/h2>\n\n\n\n
                    Below is a sample output you might see when running the script:<\/p>\n\n\n
                    \nnikhil05@Nikhil:~\/Algo-AI\/client\/python $ python3 bezinga.py\nNews endpoint status: 200\nNews articles retrieved: 20\nBars endpoint status: 200\nBars retrieved: 231\nValidation Accuracy: 0.59\nTomorrow's predicted movement: DOWN\n<\/pre><\/div>\n\n\n
                      \n
                    • Validation Accuracy:<\/strong> This numeric score (e.g., 0.59) tells you how often the model guesses the next day\u2019s movement correctly during back-testing.<\/li>\n\n\n\n
                    • Tomorrow\u2019s Predicted Movement:<\/strong> Printed as either \u201cUP\u201d or \u201cDOWN.\u201d This uses the last known data point\u2019s sentiment and closing price to guess where the market might head tomorrow.<\/li>\n<\/ul>\n\n\n\n
                      <\/p>\n\n\n\n
                      4. Why This Matters<\/strong><\/h2>\n\n\n\n
                      Market headlines can trigger sudden spikes or drops, so ignoring news is like driving with one eye closed. By pairing Benzinga\u2019s robust news feed (including article titles, teasers, and full bodies) with bar data, you can catch subtle shifts in investor sentiment. Even a simple logistic regression reveals how bullish or bearish signals in the news might align with a stock\u2019s next-day performance.<\/p>\n\n\n\n
                      <\/p>\n\n\n\n
                      Final Thoughts<\/strong><\/h2>\n\n\n\n
                      That\u2019s all it takes to build a basic sentiment-based stock predictor\u2014and you can level it up in countless ways. Try analyzing multiple tickers at once, applying advanced natural language processing, or adding technical indicators on top of price bars. <\/p>\n\n\n\n
                      With Benzinga\u2019s APIs, you have the raw ingredients for your own unique \u201crecipe\u201d in market analysis. The beauty is in experimenting, refining, and seeing how each new insight helps you navigate the market more confidently.<\/p>\n","protected":false},"excerpt":{"rendered":"
                      Introduction Context is as critical as data. Relying solely on price movements is like evaluating a novel by its cover\u2014you risk overlooking the deeper narrative behind market shifts. Benzinga\u2019s Newsfeed & Bars APIs bridge this gap by combining market-moving headlines with detailed historical price data, which gives a better view of market behavior. In this<\/p>\n","protected":false},"author":77,"featured_media":7877,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[149,134,137],"acf":[],"yoast_head":"\nA Quick Guide To Sentiment-Powered Stock Predictions With Benzinga\u2019s News And Bars Endpoints - Benzinga API\u2019s<\/title>\n<meta name=\"description\" content=\"Learn to use Benzinga\u2019s News And Bars Endpoints to create Sentiment-Powered Stock Predictions with Python and ML\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.benzinga.com\/apis\/blog\/a-quick-guide-to-sentiment-powered-stock-predictions-with-benzingas-news-and-bars-endpoints\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A Quick Guide To Sentiment-Powered Stock Predictions With Benzinga\u2019s News And Bars Endpoints - 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