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

The Problem

As developers working in the finance and trading space, we often need to extract sentiment data about commodities to inform trading strategies. However, scraping news articles and parsing sentiment from unstructured data is a tedious and error-prone task. You can spend hours writing scrapers, dealing with rate limits, and cleaning data, only to find that the sentiment analysis isn’t reliable or needs constant updating.

We need a streamlined solution that offers real-time sentiment data without the headaches. Enter the Pulsebit API.

The Solution

The Pulsebit API provides an easy way to access sentiment data for various assets, including commodities, through a single endpoint. This means you don’t have to worry about data sourcing, parsing, or analysis. You simply make a GET request and get structured sentiment data back.

For this example, we’ll focus on the /news_semantic endpoint to retrieve commodity sentiment data.

The Code

Here’s a simple Python script that demonstrates how to fetch commodity sentiment using the Pulsebit API.

import requests

def get_commodities_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} {response.text}")

# Example usage
if __name__ == "__main__":
    API_KEY = "your_api_key_here"  # Replace with your actual API key
    sentiment_data = get_commodities_sentiment(API_KEY)
    print(sentiment_data)

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

Reading the Response

The response from the /news_semantic endpoint provides various fields that give insights into commodities sentiment. Here's a breakdown of the fields you might encounter:

• commodities_sentiment: This indicates the overall sentiment towards commodities. For instance, you might see a value of +0.00, suggesting neutral sentiment.

• momentum: This field indicates the sentiment momentum, which in our case is +0.67. A positive value suggests increasing positive sentiment over time.

• clusters: This tells you the number of sentiment clusters detected in the recent news. A value of 0 might suggest that there hasn't been enough recent news to form any clusters.

• confidence: This reflects the API's confidence level in the sentiment analysis, which is 0.87 here. A higher value indicates greater reliability in the sentiment data.

Three Use Cases

1. Algorithmic Alert: You can set up an alert system that triggers trades based on sentiment thresholds. For example, if the momentum exceeds +0.5, you could trigger a buy order for related commodities.

2. Slack Bot: Integrate the sentiment data into a Slack bot that provides daily updates on commodities sentiment. This could help teams stay informed without needing to check multiple sources.

3. Dashboard: Visualize the sentiment data in a dashboard using libraries like Dash or Flask. You can create real-time visualizations that show sentiment trends over time, helping traders make informed decisions.

Get Started

If you’re ready to dive in and start using the Pulsebit API, check out the official documentation. It provides detailed information on how to authenticate, make requests, and interpret responses.

In summary, the Pulsebit API simplifies the process of obtaining commodities sentiment data, allowing you to focus on building intelligent trading strategies rather than dealing with data collection headaches.