Update README.md to reflect project name and enhance description

README.md

@@ -1,16 +1,32 @@
-# capstone_project
+# Augor
 
-A new Flutter project.
+> Drilling beneath surface sentiment to extract trading signals from financial news.
 
-## Getting Started
+## What is this?
 
-This project is a starting point for a Flutter application.
+Augor uses Large Language Models to interpret business events and generate trading signals. Instead of just counting positive/negative words, it understands what events *mean* for markets.
 
-A few resources to get you started if this is your first Flutter project:
+Traditional sentiment analysis: "This news mentions 'partnership' → positive"  
+Augor: "This partnership expands market access but dilutes margins → mixed signal"
 
-- [Lab: Write your first Flutter app](https://docs.flutter.dev/get-started/codelab)
-- [Cookbook: Useful Flutter samples](https://docs.flutter.dev/cookbook)
+## Why?
 
-For help getting started with Flutter development, view the
-[online documentation](https://docs.flutter.dev/), which offers tutorials,
-samples, guidance on mobile development, and a full API reference.
+Traditional NLP methods are limited:
+- Can't understand context or nuance
+- Miss complex implications of business events
+- Struggle with negation and sarcasm
+
+LLMs can reason about *why* news matters, not just classify it as positive/negative.
+
+## What it does
+
+1. Aggregates news from multiple sources
+2. Interprets business events (mergers, partnerships, product launches)
+3. Generates trading signals based on reasoned analysis
+4. Backtests performance against traditional approaches
+
+## Author
+
+Benjamin Watt  
+Supervisor: Panagiotis Kanellopoulos  
+University of Essex