📈 Stock Price Forecasting and Trading Strategy Optimization using ARIMA

This project is focused on forecasting stock prices using the ARIMA (AutoRegressive Integrated Moving Average) model and generating Buy/Sell trading signals based on predicted price movements. We use historical stock data fetched via the yfinance API and visualize both the forecast and trading signals using matplotlib.

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🧠 Project Summary

• 📦 Model Used: ARIMA (5,1,0)
• 🗃️ Data Source: Yahoo Finance (yfinance)
• 🧮 Target Variable: Closing price of selected stock (example: Apple Inc. - AAPL)
• 📅 Timeframe: Past 1 Year (from today's date)
• 💹 Forecast Window: Next 10 business days
• 📍 Signals Generated:
  • 🔼 Buy when next predicted price is higher than the previous.
  • 🔽 Sell when next predicted price is lower than the previous.

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🧰 Tools and Libraries

• yfinance – fetch stock data
• statsmodels – ARIMA modeling
• pandas – data manipulation
• matplotlib – data visualization
• datetime – date range generation
• warnings – suppress warnings for clean output

🖼️ Example Screenshots

• 1. 📊 ARIMA Forecasted Stock Prices with Buy/Sell Signals
  
• 2. 📈 AAPL Stock Forecast
  

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🔧 Installation

Install the required Python libraries using:

pip install yfinance statsmodels matplotlib pandas