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System Architecture
Inspiration
Our project is inspired by the intricate relationship between news and market behavior. We observed how publicly traded companies react to news events and how those reactions can be visualized in their stock charts. This led us to explore a more structured approach to quantifying these effects through prediction markets.
What it does
The project utilizes prediction markets to quantify the impact of news on real-world events. By linking news stories to market predictions, we aim to provide a clearer understanding of how news influences public perception and market behavior. Our platform allows users to forecast event outcomes based on current news, offering insights into the potential consequences of media narratives.
How we built it
We developed the platform using React, LLMs, Python, Flask, VectorDB, Cosine Similarity, Semantics, NLP. We integrated news APIs to fetch real-time news stories and used data analysis tools to evaluate the relationship between news events and market movements. The user interface was designed to be intuitive, allowing users to easily navigate between news articles and their corresponding prediction markets.
Challenges we ran into
Data Integration: Merging data from different news sources with prediction market data posed significant challenges, particularly in ensuring consistency and accuracy. User Engagement: Encouraging users to actively participate in the prediction markets was difficult, especially in a crowded market with many options. Fake News Identification: Developing algorithms to detect and classify fake news without bias was a complex task.
Accomplishments that we're proud of
Successfully binding news stories to prediction markets, providing users with actionable insights. Creating a user-friendly interface that simplifies the process of making predictions based on news events. Establishing a framework for real-time analysis of news impact on market predictions.
What we learned
The importance of accurate data sourcing and verification in developing a reliable platform. User feedback is invaluable for refining features and improving engagement. The dynamic nature of news and its unpredictable effects on markets requires continuous monitoring and adaptation.
What's next for predicTerminal
Expand our data sources to include international news and various market sectors. Implement machine learning algorithms to improve the detection of fake news and its potential impact on predictions. Enhance user engagement through gamification features and community-driven prediction challenges.
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