AI-Financial-Research-Agent

AI Financial Research Agent is a professional-grade AI & Machine Learning–powered financial intelligence platform that delivers real-time market analysis, predictive insights, and institutional-quality reporting. The platform uses Machine Learning, Generative AI, and automated analytics to convert raw financial data into actionable investment intelligence.

Problem Statement

Challenges

• Information overload with no clear insights

• Expensive and inaccessible professional tools

• Fragmented financial data across multiple platforms

• Manual analysis that is slow and biased

• Lack of professional, decision-ready reports

Market Need

• Institutional-quality financial analysis

• Real-time crypto and stock monitoring

• Machine Learning–driven insights

• Risk-aware investment decisions

• Automated professional reporting

Solution

A unified AI-powered financial intelligence platform that provides:

• Real-time multi-source market data

• Machine Learning models for prediction and risk scoring

• Generative AI–based financial insights

• Interactive visual analytics

• Automated PDF reporting

AI & Machine Learning

• This project actively uses Machine Learning for:

• Risk scoring and volatility modeling

• Trend and pattern detection

• Technical indicator analysis (RSI, MACD, Moving Averages)

• News sentiment analysis using NLP

• Buy / Hold / Sell recommendation engine

• Risk-based position sizing

Core Features

Real-Time Market Intelligence

Dashboard Preview

• Live cryptocurrency prices (100+ assets)

• Stock market quotes and fundamentals

• Market news aggregation with sentiment analysis

• Smart asset detection (Crypto vs Stock)

• Price discrepancy alerts

AI-Powered Analysis

AI-Analysis Preview

• Machine learning–based risk assessment (1–10 scale)

• Technical and trend analysis

• Sentiment scoring from financial news

• Trading recommendations with confidence levels

• Risk-aware position sizing

Visualization

• Interactive candlestick and volume charts

• Multi-timeframe technical analysis

• Market sentiment dashboards

• Portfolio performance analytics

Professional Reporting

Report Preview 1

Report Preview 2

Report Preview 3

• One-click PDF report generation

• Executive and investment memo templates

• Risk matrices and action plans

• Confidential watermarks

Enterprise Capabilities

• Multi-source data verification

• Smart caching system

• Graceful error handling

• API-first architecture

• Scalable and modular design

Architecture

Frontend

• Streamlit-based user interface

• Plotly interactive visualizations

• Professional custom CSS

Backend Services

• ai_engine.py – Machine learning and AI analysis engine

• data_fetch.py – Multi-source data aggregation

• news_fetch.py – News collection and sentiment analysis

• report_gen.py – Automated PDF reporting

Data Flow

User Input

↓

Data Fetching

↓

Multi-Source Verification

↓

Machine Learning Analysis

↓

AI Insight Generation

↓

Visualization

↓

PDF Report

Technology Stack

Frontend & Visualization

• Streamlit

• Plotly

• Custom CSS

• FPDF

Data Processing

• Pandas

• NumPy

• yFinance

AI & Machine Learning

• Scikit-learn

• Llama 3 (Ollama)

• OpenAI GPT-4

• TextBlob (NLP)

APIs

• CoinGecko

• CoinCap

• Yahoo Finance

• Google News RSS

Use Cases

• Retail investors seeking AI-driven insights

• Financial analysts and advisors

• Market research and education

• Institutional monitoring and reporting

Getting Started

1️⃣ Create a virtual environment

Windows:

python -m venv myenv

Linux/macOS:

python3 -m venv myenv

Here, myenv is the name of your virtual environment. You can choose any name.

2️⃣ Activate the virtual environment

Windows (Command Prompt):

myenv\Scripts\activate

Windows (PowerShell):

.\myenv\Scripts\Activate.ps1

Linux/macOS:

source myenv/bin/activate

You should now see (myenv) at the start of your terminal prompt.

3️⃣ Install required packages

Make sure Streamlit is installed:

pip install streamlit

If your project has a requirements.txt, install dependencies using:

pip install -r requirements.txt

4️⃣ Run the Streamlit app

streamlit run app.py

After running this, Streamlit will open your app in the default web browser.