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

In the world of quantitative finance and algorithmic trading, understanding market sentiment can give you an edge. When it comes to hardware sentiment, however, gathering relevant data can be a real hassle. Let’s dive into how you can leverage the Pulsebit API to streamline this process.

The Problem (DIY Scraping Pain)

If you've ever tried to scrape sentiment data from multiple news sources or social media platforms, you know it can be tedious and error-prone. The challenges include:

• Data inconsistencies: Different sites might use varying terminologies and formats.
• Rate limits: Scraping too aggressively can result in IP bans.
• Maintenance: Frequent changes to site structures require constant updates to your scraper.

Instead of dealing with these headaches, why not use an API that gives you structured data in one request? Enter the Pulsebit API.

The Solution (Pulsebit API — One Endpoint)

Pulsebit offers a single endpoint that aggregates sentiment data across various sources. This means you can get a holistic view of the current hardware sentiment without wrestling with multiple data feeds.

The endpoint we’ll focus on is /news_semantic, which provides sentiment scores, confidence levels, and more.

The Code (Python GET /news_semantic)

Here’s a quick setup to fetch hardware sentiment data using Python. Make sure you have the requests library installed. If not, you can install it via pip:

pip install requests

Now, here’s how you can call the Pulsebit API:

import requests

def get_hardware_sentiment():
    url = "https://api.pulsebit.co/news_semantic"
    params = {
        "query": "hardware",
        "language": "en"
    }

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

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

if __name__ == "__main__":
    sentiment_data = get_hardware_sentiment()
    print(sentiment_data)

Reading the Response

When you call the /news_semantic endpoint, the response will include several important fields. Here’s a breakdown of what you can expect:

• sentiment: The overall sentiment score for hardware, which might be +0.00 in this case. It indicates a neutral sentiment.
• momentum: This is a measure of how quickly sentiment is changing. A value of +0.85 suggests a strong upward momentum.
• clusters: This will show how many distinct sentiment clusters were identified. In our example, it's 0, meaning no distinct sentiment groups were found.
• confidence: The confidence level of the sentiment measurement, which is 0.87, indicating a high degree of certainty in the collected data.

Three Use Cases

1. Algo Alert: Integrate the sentiment data into your trading algorithms. Set up a threshold; for example, if momentum exceeds +0.80, trigger an alert to buy hardware stocks.

2. Slack Bot: Create a Slack bot that posts daily sentiment updates. Use the sentiment data to inform team decisions or discussions around hardware investments.

3. Dashboard: Build a dashboard that visualizes sentiment trends over time. You can plot the sentiment and momentum scores to monitor shifts in market sentiment.

Get Started

To dive deeper into the Pulsebit API and explore additional endpoints, check out their documentation. It’s well-structured and provides various use cases that can enhance your data-driven decision-making process.

By utilizing the Pulsebit API, you can eliminate the hassle of DIY scraping and focus on what really matters: making informed trading decisions based on accurate sentiment data. Happy coding!