StockSage AI – AI-Powered Stock Analysis & Prediction Platform

An intelligent stock analysis platform that combines technical analysis, financial news sentiment, machine learning, risk assessment, and multi-horizon forecasting to generate explainable investment insights.

Built using Flask, XGBoost, VADER Sentiment Analysis, and modern web technologies, StockSage AI enables users to analyze publicly traded stocks and receive data-driven BUY, HOLD, or SELL recommendations.

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Features

Technical Analysis

• Simple Moving Averages (SMA 20, SMA 50)
• Exponential Moving Averages (EMA 12, EMA 26)
• Relative Strength Index (RSI)
• MACD and Signal Line
• Bollinger Bands
• Volume Analysis
• Volatility Metrics
• Trend Strength Indicators

News Sentiment Analysis

• Live company news via Yahoo Finance
• Sector-wide news via Google News RSS
• VADER-based sentiment scoring
• Positive, Neutral, and Negative sentiment classification
• Weighted company and sector sentiment aggregation

Machine Learning Predictions

• XGBoost classifier for next-day price direction prediction
• Probability-based bullish/bearish forecasting
• Feature importance analysis
• Confidence scoring

Multi-Horizon Forecasting

Predicts expected price movement across:

• 1 Week
• 1 Month
• 3 Months
• 6 Months
• 1 Year

Implemented using Ridge Regression with fallback forecasting logic for limited datasets.

Risk Assessment

• Annualized Volatility
• Maximum Drawdown
• Sharpe Ratio Analysis
• Composite Risk Score (0–100)
• Risk Categorization (Low, Medium, High)

Recommendation Engine

Combines:

• Machine Learning Signal
• Sentiment Signal
• Technical Indicators
• Risk Adjustment

Generates:

• BUY Recommendation
• HOLD Recommendation
• SELL Recommendation

with confidence scores and explainable reasoning.

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Technology Stack

Layer	Technologies
Backend	Flask, Flask-CORS
Data Processing	Pandas, NumPy
Machine Learning	XGBoost, Scikit-Learn
Sentiment Analysis	NLTK VADER
Market Data	Yahoo Finance (yfinance)
News Sources	Yahoo Finance News, Google News RSS
Frontend	HTML5, CSS3, JavaScript
Visualisation	Chart.js

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Project Architecture

Market Data + News Sources ↓ Data Acquisition Layer ↓ Technical Indicator Engine ↓ Sentiment Analysis Engine ↓ Machine Learning Models ↓ Risk Assessment Module ↓ Recommendation Engine ↓ Interactive Dashboard

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Installation

1. Clone Repository

git clone https://github.com/yourusername/StockSage-AI.git
cd StockSage-AI

2. Create Virtual Environment

python -m venv venv

Activate the environment:

Windows

venv\Scripts\activate

Linux / macOS

source venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Optional Historical News Dataset

Place the dataset at:

data/combined_news.csv

This dataset is loaded only for offline experimentation and future training. Live analysis does not depend on this file.

5. Start Backend Server

python app.py

Backend URL:

http://localhost:5000

6. Launch Frontend

Open:

index.html

in any modern web browser.

No build tools or frontend frameworks are required.

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Usage

1. Enter a stock ticker symbol.

Examples:

AAPL
MSFT
GOOGL
RELIANCE.NS
TCS.NS
INFY.NS

2. Select a historical analysis period.

Options:

• 3 Months
• 1 Year
• 2 Years
• 5 Years

3. Click Analyze

The dashboard will display:

• Price and Trend Analysis
• Technical Indicators
• Live News Sentiment
• Machine Learning Predictions
• Feature Importance Rankings
• Risk Assessment
• Multi-Horizon Forecasts
• AI Recommendation

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API Endpoints

Search Stock

GET /api/search

Resolves company names and ticker symbols.

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Analyze Stock

GET /api/analyze

Returns:

• Historical price data
• Technical indicators
• Sentiment analysis
• Machine learning predictions
• Risk metrics
• Forecasts
• Final recommendation

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Health Check

GET /health

Verifies backend availability.

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Project Structure

StockSage-AI/
│
├── app.py
├── index.html
├── requirements.txt
├── test_forecast.py
│
├── data/
│   └── combined_news.csv
│
└── README.md

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Machine Learning Models

XGBoost Classifier

Purpose:

• Predict next-day price movement

Inputs:

• Technical indicators
• Trend metrics
• Volatility metrics
• Volume metrics
• Sentiment metrics

Outputs:

• Bullish probability
• Bearish probability
• Confidence score

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Ridge Regression

Purpose:

• Multi-horizon price forecasting

Forecast Periods:

• 1 Week
• 1 Month
• 3 Months
• 6 Months
• 1 Year

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Future Enhancements

• LSTM-based forecasting
• Transformer-based financial models
• Prediction tracking database
• Portfolio analytics
• Backtesting framework
• Social media sentiment integration
• Earnings calendar integration
• Docker deployment
• Cloud hosting support

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Disclaimer

This project is intended for educational, research, and demonstration purposes only.

The predictions and recommendations generated by StockSage AI should not be considered financial advice. Stock markets involve risk, and past performance does not guarantee future results.

Always conduct independent research and consult a qualified financial advisor before making investment decisions.

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Acknowledgements

• Yahoo Finance (yfinance)
• VADER Sentiment Analysis
• XGBoost
• Scikit-Learn
• Pandas
• NumPy
• Chart.js
• Flask