Financial Sentiment & Forecast Analyzer

A comprehensive stock market analysis tool that combines technical indicators, machine learning forecasts, and sentiment analysis to provide investment recommendations and price predictions.

Project Overview

The Financial Sentiment & Forecast Analyzer is a sophisticated web application built with Gradio that helps investors make informed decisions by analyzing stock market data through multiple lenses:

1. Technical Analysis: Evaluates price movements using SMA, RSI, MACD, and other indicators.
2. Machine Learning Forecasts: Predicts future price movements using Prophet and LSTM models.
3. Sentiment Analysis: Gauges market sentiment using natural language processing techniques.
4. Investment Recommendations: Provides actionable insights based on combined analysis results.

Youtube Overview

https://youtu.be/w5qDHLH_9Ck

Features

• Interactive User Interface: Easy-to-use Gradio interface with tabbed results display.
• Comprehensive Stock Analysis: View technical indicators, forecasts, and sentiment in one dashboard.
• Multi-timeframe Support: Analyze stocks over various timeframes (3 months to 10 years).
• Advanced Visualizations:
  • Price history with 20, 50, and 200-day moving averages.
  • RSI (Relative Strength Index) with overbought/oversold indicators.
  • MACD (Moving Average Convergence Divergence) with signal line and histogram.
  • Visual recommendation display with color-coding.
• Dual Forecast Models:
  • Prophet model for statistical forecasting.
  • LSTM neural network for deep learning-based predictions.
• NLP-Powered Sentiment Analysis: Combines VADER and TextBlob for robust sentiment scoring.

Technologies Used

• Frontend: Gradio (web interface framework).
• Data Analysis: Pandas, NumPy.
• Visualization: Matplotlib.
• Machine Learning: Prophet, LSTM.
• Natural Language Processing: VADER, TextBlob.
• API Integration: GROQ API for enhanced language model capabilities.

Requirements

This project requires the following Python packages:

• gradio
• pandas
• numpy
• matplotlib
• prophet
• tensorflow
• textblob
• vadersentiment
• yfinance
• scikit-learn
• groq

You can install all dependencies using the provided requirements.txt file.

Configuration

The application uses the GROQ API for enhanced language model capabilities. You can provide your own API key through environment variables:

For Windows:

set GROQ_API_KEY=your_api_key_here

For Mac and Linux:

export GROQ_API_KEY="your_api_key_here"

You can get your API key from groq console and then you can also select your model to use for your work from here. Make sure to create an account first.

Installation

# Clone the repository
git clone https://github.com/MuhammadAli7896/AIES-CCP-Project.git
cd AIES-CCP-Project
cd "Source Code"

# Create and activate a virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows, use: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Usage

To run the application:

python app.py

The web interface will automatically open in your default browser at http://localhost:7860.

Using the Interface

1. Enter a valid stock ticker symbol (e.g., AAPL, MSFT, GOOGL).
2. Select your desired analysis timeframe.
3. Click "Analyze Stock".
4. View results in the tabbed interface:
   • Visualization: Technical indicators and price forecasts.
   • Recommendation: Investment advice based on all analysis factors.
   • Sentiment Analysis: Market sentiment metrics.
   • Trend Analysis: Short, medium, and long-term trend indicators.

How It Works

Data Collection and Processing

The application fetches historical stock data and processes it to calculate technical indicators like moving averages, RSI, and MACD.

Machine Learning Models

• Prophet: A time series forecasting model developed by Facebook Research.
• LSTM: A recurrent neural network architecture for sequence prediction.

Sentiment Analysis

The system analyzes news and social media sentiment related to the stock using VADER and TextBlob sentiment analyzers, providing a comprehensive view of market perception.

Recommendation Engine

All data points are combined to generate a final investment recommendation (BUY, HOLD, or SELL) with confidence levels and supporting rationale.