DeFi Oracle - The Poets' Corner

Inspired by the Shrike in Hyperion, this DeFi oracle monitors and analyzes textual data from various sources (news articles, social media, forum posts) to extract nuanced sentiment about DeFi assets. Imagine a city like Metropolis, where the heart is constantly influenced by the workers – in this case, online communities. This project will use AI to interpret this 'collective consciousness' of DeFi. The AI workflow, inspired by the 'AI Workflow for Companies' scraper project, will consist of: 1. Data Scraping: Scrape text from relevant sources using Python and libraries like Beautiful Soup or Scrapy. 2. Language Model Integration: Use a pre-trained language model (e.g., BERT, RoBERTa, or a specifically fine-tuned model for financial sentiment analysis) to evaluate the sentiment expressed in the scraped text. 3. Sentiment Scoring: Develop a scoring system to quantify the overall 'narrative sentiment' (e.g., bullish, bearish, neutral). The score would consider multiple factors, such as the emotional tone of the text, the frequency of positive/negative keywords, and the credibility of the source. 4. Oracle Implementation: Implement a smart contract that allows DeFi protocols to query the 'Poets' Corner' for its sentiment score on a specific asset. This can be integrated with existing oracle systems like Chainlink or Band Protocol to provide an additional layer of information. Potential use cases include: - Predictive DeFi: Using sentiment data to predict price movements and inform trading strategies. - Risk Management: Identifying assets with negative sentiment and mitigating potential losses. - Decentralized Lending: Adjusting lending rates based on the sentiment score of the underlying asset. The low-cost nature comes from leveraging pre-trained language models, readily available scraping libraries, and potentially deploying on a low-cost blockchain. High earning potential stems from providing a unique and valuable data feed to DeFi protocols, who are constantly seeking new and innovative sources of information to improve their algorithms and services.