How to Detect Stock Market Sentiment Shifts with the Pulsebit API (Python)

The Problem

If you've ever tried to gauge stock market sentiment on your own, you know the pain of DIY scraping. Collecting data from various news sources, parsing articles, and analyzing sentiment can be time-consuming and error-prone. You might end up with a mountain of raw data but no clear insights. Not to mention, maintaining the scrapers when sites change their structures is a never-ending headache.

You need a reliable, efficient way to get sentiment data without reinventing the wheel. Enter the Pulsebit API.

The Solution

The Pulsebit API provides a straightforward way to get stock market sentiment with just one endpoint. Specifically, the /news_semantic endpoint delivers sentiment data, momentum, and other useful metrics all in one JSON response. This means less time coding scrapers and more time interpreting data.

In our example, the current sentiment data looks like this:

• Stock Market Sentiment: +0.00
• Momentum: +0.29
• Clusters: 0
• Confidence: 0.87

The Code

To fetch this data, you'll need to set up a simple Python script. Here's how you can do it using the requests library:

import requests

def fetch_sentiment(api_key):
    url = "https://api.pulsebit.co/news_semantic"
    headers = {
        "Authorization": f"Bearer {api_key}"
    }

    response = requests.get(url, headers=headers)

    if response.status_code == 200:
        return response.json()
    else:
        raise Exception(f"Error fetching data: {response.status_code}")

if __name__ == "__main__":
    API_KEY = "your_api_key_here"
    sentiment_data = fetch_sentiment(API_KEY)
    print(sentiment_data)

Make sure to replace "your_api_key_here" with your actual Pulsebit API key.

Reading the Response

The response from the /news_semantic endpoint will provide you with several fields. Here's what each of them means:

• stock_market_sentiment: The overall sentiment score, where a positive value indicates positive sentiment, and a negative value indicates negative sentiment. For example, +0.00 means neutral sentiment.

• momentum: This value indicates the strength of the sentiment shift. A momentum of +0.29 suggests a slight positive trend in recent sentiment.

• clusters: This field shows the number of distinct sentiment clusters identified in the data. In our case, 0 means that no significant clusters were found.

• confidence: This is a measure of the API's confidence in the sentiment analysis, ranging from 0 to 1. A confidence of 0.87 suggests a high level of certainty in the sentiment assessment.

Three Use Cases

1. Algo Alert: You can set up an algorithmic trading alert that triggers whenever sentiment shifts above a certain threshold. For example, if momentum exceeds 0.5, you could buy stocks in a bullish market.

2. Slack Bot: Integrate the API into a Slack bot to keep your team updated on market sentiment. You can schedule the bot to fetch data at regular intervals and send alerts when significant sentiment changes occur.

3. Dashboard: Build a simple dashboard that visualizes sentiment trends over time. You could plot sentiment, momentum, and confidence against stock prices to identify correlations.

Get Started

To dive deeper into the Pulsebit API, visit pulsebit.co/docs. You'll find comprehensive documentation, including authentication methods and additional endpoints that can enhance your sentiment analysis capabilities.

In summary, the Pulsebit API simplifies the task of detecting stock market sentiment shifts. By leveraging its capabilities, you can focus on what matters most—interpreting data and making informed decisions. Happy coding!