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

The Problem

If you’ve ever tried to scrape sentiment data for stock analysis, you know how daunting the task can be. Web scraping can be brittle, requiring constant maintenance as sites change their structures. You might end up with half-baked data or, worse, completely outdated insights. With sentiment in the stock market shifting rapidly, you need a reliable, real-time solution.

The Solution

Enter the Pulsebit API. This API offers a straightforward endpoint that allows you to fetch sentiment data efficiently without the hassle of DIY scraping. Specifically, the /news_semantic endpoint provides a wealth of information with just a single request.

The Code

Here’s a quick example of how to use the Pulsebit API to get sentiment data for the stock market in Python:

import requests

# Define the API endpoint
url = "https://pulsebit.lojenterprise.com/api/news_semantic?topic=stock%20market"

# Send the GET request
response = requests.get(url)

# Check if the request was successful
if response.status_code == 200:
    data = response.json()
else:
    raise Exception("API Request Failed")

# Print the data
print(data)

This snippet sends a GET request to the Pulsebit API and retrieves sentiment data for the stock market.

Reading the Response

Let’s break down the fields that you’ll receive in the response:

• TOPIC: The subject area of the sentiment, here it’s stock market.
• momentum_24h: This shows a momentum score of +0.230. It indicates a positive shift in sentiment over the past 24 hours.
• sentiment_score: The score is +0.000, which means the sentiment is neutral at this moment.
• confidence: A confidence level of 0.870 indicates a high degree of certainty about the sentiment data.
• sentiment_index_0_100: The sentiment index is 62.08, which suggests a positive sentiment overall.
• direction: The sentiment direction is rising, which indicates that sentiment is improving.
• semantic_clusters: There are 15 clusters, showing a diverse range of opinions within the sentiment data.
• region: This data is focused on kenya, giving you regional specificity.
• semantic_similarity_avg: An average similarity score of 0.342 indicates how closely related the news articles are in terms of sentiment and topic.

Three Use Cases

1. Algo Alert: You can set up an algorithmic alert that triggers when the momentum score exceeds a certain threshold. For instance, if momentum hits +0.250, notify your trading system to take action.

2. Slack Bot: Create a Slack bot that posts daily sentiment updates. The bot could summarize the sentiment score and direction, so your team gets a quick snapshot of market sentiment without needing to dig through data manually.

3. Dashboard: Build a real-time dashboard that visualizes sentiment shifts. You can plot the sentiment index over time and overlay the momentum score to quickly see correlations between sentiment and market movements.

Get Started

To dive deeper into the Pulsebit API and explore additional endpoints, head over to Pulsebit Documentation. You’ll find everything you need to integrate sentiment data smoothly into your projects.

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What’s noteworthy about the current data is the blend of a neutral sentiment score (+0.000) and a positive momentum (+0.230). This combination indicates that while current sentiment is stable, there's an upward trend in momentum, suggesting an impending shift that you can capitalize on. This is precisely where the Pulsebit API shines, giving you the tools to detect these subtle yet crucial shifts in sentiment.