Maro AI Financial Assistant Development
Abang Tah
Overview
Maro AI Financial Assistant is an intelligent, AI-powered chatbot designed for investors and financial professionals. It leverages real-time stock data, news sentiment analysis, and advanced large language models (LLMs) to provide market insights, predictive analytics, and actionable advice, including sell recommendations.
Intelligent, AI-powered chatbot designed for investors and financial professionals.
Leverages real-time stock data to keep users updated on market movements.
Analyzes news sentiment to understand how headlines affect stock performance.
Utilizes advanced large language models (LLMs) for deep reasoning and contextual analysis.
Provides market insights to simplify complex financial trends.
Delivers predictive analytics for short-term and long-term market forecasting.
Offers actionable advice, including sell recommendations, to support decision-making.
Purpose & Value Proposition
Empowers Investors: Reduces information overload by extracting and summarizing stock trends, news sentiment, and portfolio impacts.
Informed Decision-Making: Enhances buy/sell decisions with data-driven analysis grounded in both quantitative metrics and qualitative sentiment.
Accessible & Conversational: Simplifies market complexity through chat-based interaction—just ask questions like “Should I sell XYZ today?” and receive a structured response.
Technical Stack
While your actual stack may vary, a typical architecture includes:
Frontend Interface: Web app (React, Vue.js, Next.js) deployed (e.g., Vercel).
Backend & APIs:
LLM: Integration with GPT-4 (or higher) via OpenAI API for natural language understanding and analysis.
Stock Data: Real-time and historical financial data (e.g., via Yahoo Finance, Alpha Vantage, IEX Cloud).
News Feeds: Aggregators or APIs (e.g., NewsAPI, Google News, Bloomberg) for latest market headlines and sentiment.
Sentiment Analysis: NLP modules to process news sentiment (positive/negative/neutral).
Orchestration Layer: A controller coordinating data retrieval, analyses, and passing prompts to the LLM.
Hosting & Infrastructure: Cloud deployment (Vercel), secure API endpoints, data caching, and rate-limiting controls.
Security & Privacy: Secure transmission (HTTPS), data protection, optional logging or anonymization.
How It Works — Workflow
Here’s a typical flow when a user asks about selling:
User Input: “Should I sell TSLA right now?”
Data Aggregation:
Retrieve TSLA’s current and historical stock price.
Fetch recent news articles about TSLA.
Sentiment Processing:
Run sentiment analysis to classify recent news tone (e.g., negative due to executive changes or supply issues).
LLM Prompting:
Construct a prompt that includes:
“Current price: $… Historical volatility: … News sentiment: Negative …”
“Based on these, advise whether to SELL, HOLD, or BUY, with reasoning.”
LLM Response:
The model generates a structured recommendation:
SELL—“Short term indicators negative; recent news suggests supply disruptions causing volatility; advising sell unless long-term position.”
Delivery:
Display answer to user: recommendation, reasoning, supporting data, and cautionary note.
Core Features
Real-Time Market Monitoring
Fetches current stock prices, volume, trends.
Offers customizable watchlists and real-time alerts.
News & Sentiment Analysis
Aggregates and cleans recent news.
Classifies overall sentiment and flags impacts on stock movement.
LLM-Powered Insights
Contextual responses: "Why did ABC stock drop?"
Generates sell recommendations based on combined price movement and sentiment.
Prediction & Scenario Modeling
Offers short-term forecasts (e.g., “Price may fall 2–3% tomorrow”) based on historical and sentiment inputs.
Conversational Interface
Natural language queries: “Should I sell Apple today?”
Detailed yet accessible answers with numerical support and narrative context.
Benefits
Speed: Instantly synthesizes large data—no manual research required.
Holistic View: Integrates both market metrics and sentiment into recommendations.
Transparency: LLM outputs come with context on what drove the decision.
Conversation-Driven: Users get clarity through natural language rather than dashboards.
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Posted Aug 26, 2025
Developed AI chatbot for financial insights and advice.with LLM integration chart analysis and detailed fundamental anaylysis from linear regression on replit
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Timeline
Aug 7, 2025 - Aug 16, 2025
