Python Stock Prediction Tool

A desktop stock prediction and tracking app built with PyQt6 and SQLite3. The app provides technical analysis-based predictions using Bollinger Bands, RSI, Moving Averages, Volume indicators, and live data & news. It helps users make informed decisions on buying, selling, or holding stocks.

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Features

• PyQt6 GUI for interactive desktop experience
• SQLite3 database to store stock data
• Fetches live stock data using yfinance
• Fetches live business news from NewsData API
• Calculates key technical indicators:
  • RSI (Relative Strength Index)
  • Bollinger Bands
  • Moving Averages (SMA 20, 50, 200)
  • Volume analysis
• Generates buy/sell/hold recommendations with confidence scores
• Computes target prices based on volatility and moving averages

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Stock Prediction Logic

1. Data Collection

• Historical stock data (1 year of daily closing prices)
• Trading volume
• Simple Moving Averages (20-day, 50-day, 200-day)
• Live stock data using yfinance

2. Technical Indicators

Indicator	Description	Usage
RSI	Momentum oscillator (0–100)	RSI < 30 → bullish, RSI > 70 → bearish
Bollinger Bands	Volatility bands around SMA 20	Price near upper band → overbought, lower band → oversold
Moving Averages	SMA 20, 50, 200	Golden Cross (SMA 50 > SMA 200) → bullish, Death Cross → bearish
Volume	Trading volume analysis	Unusually high volume → potentially bullish

3. Score Calculation System

Component	Condition	Score
Bollinger Bands	Price ≤ lower band	+0.3
	Price ≥ upper band	-0.3
RSI	<30	+0.25
	>70	-0.25
Moving Average Cross	Golden Cross	+0.25
	Death Cross	-0.25
Volume	High volume (>1.5× avg)	+0.2

• Total score ranges -0.8 (extremely bearish) to +0.8 (extremely bullish)

4. Prediction Categories

Score	Recommendation
≥ 0.5	Strong Buy
0.15 – 0.5	Buy
-0.15 – 0.15	Hold
-0.5 – -0.15	Sell
≤ -0.5	Strong Sell

5. Target Price Calculation

• Uses 20-day SMA as base and price standard deviation for volatility.
• Strong Buy: SMA 20 + 70% of SD
• Buy: SMA 20 + 30% of SD
• Hold: Current price
• Sell: SMA 20 - 30% of SD
• Strong Sell: SMA 20 - 70% of SD

Two types of standard deviation used:

• Rolling SD (Bollinger Bands): short-term, adapts to recent trends
• Global SD (Target Price): long-term, stable measure of overall volatility

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Live News Feature

• Fetches business news from NewsData API
• Categorizes news into Market Updates, Investing, Company News, Regulations, Economy, Crypto, Banking, Others
• Allows searching news by keywords
• Displayed in a scrollable PyQt6 GUI

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Installation

# Clone the repo
git clone git@github.com:runt1meerr0r07/Python_Stock_Prediction_Tool.git
cd Python_Stock_Prediction_Tool

# Install dependencies
pip install PyQt6 pandas numpy matplotlib yfinance requests