# =� Stock Price Time Series Analysis App > **Advanced AI-powered stock market analysis and forecasting platform** [](https://www.python.org/downloads/) [](https://opensource.org/licenses/MIT) [](https://jupyter.org/) **Stock_Price_TS_Analysis_App** is a comprehensive time series analysis platform designed for financial market prediction and analysis. This project demonstrates advanced machine learning techniques applied to stock market data, featuring custom datasets, sophisticated forecasting models, and professional-grade analysis. --- ## <� **Project Overview** ### **Unique Features** - >� **Original AI Revolution Dataset**: Custom-created dataset tracking 5 major AI companies (2022-2024) - =� **Advanced Time Series Models**: Multiple forecasting approaches with performance comparison - >� **AI Sentiment Analysis**: Correlation between AI market sentiment and stock performance - =' **Technical Indicators**: Comprehensive technical analysis with 15+ indicators - =� **Interactive Analysis**: Professional Jupyter notebook with detailed visualizations - <� **Portfolio Ready**: Publication-quality analysis perfect for showcasing expertise ### **Target Audience** - Data scientists specializing in financial analysis - Quantitative analysts and researchers - Portfolio managers and investment professionals - Students and professionals learning time series forecasting - AI engineers working on predictive analytics --- ## <� **Dataset: AI Revolution Stock Tracker** Our flagship dataset tracks the performance of major AI companies during the 2022-2024 AI revolution period: ### **Companies Analyzed** | Symbol | Company | AI Focus | Sector | |--------|---------|----------|---------| | NVDA | NVIDIA | GPU/Chips | Hardware | | MSFT | Microsoft | Azure AI | Software | | GOOGL | Alphabet | Search/LLM | Software | | AMZN | Amazon | AWS AI | Cloud | | TSLA | Tesla | FSD/Robotics | Automotive | ### **Dataset Features** - =� **2,500+ Records**: 500 trading days per company - =�� **Time Period**: 2022-2024 (Peak AI revolution) - =� **Complete OHLCV Data**: Open, High, Low, Close, Volume - >� **AI Sentiment Indicators**: Market sentiment tracking (0-100) - =' **Technical Indicators**: RSI, SMA, EMA, Volatility measures - =� **Market Metrics**: Returns, volume ratios, price momentum --- ## =� **Quick Start** ### **Prerequisites** - Python 3.12+ - Virtual environment (recommended) - Jupyter Notebook or JupyterLab ### **Installation** ```bash # Clone the repository git clone https://github.com/Projet12345/Stock_Price_TS_Analysis_App.git cd Stock_Price_TS_Analysis_App # Create virtual environment python3.12 -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate # Install dependencies pip install -r requirements.txt # Start Jupyter jupyter notebook analysis_colab/AI_Revolution_Stock_Analysis.ipynb ``` ### **Generate Dataset** ```bash # Generate the AI Revolution dataset python data/quick_ai_dataset.py # Alternative: Use the comprehensive generator python data/create_ai_revolution_dataset.py ``` --- ## =, **Analysis Components** ### **1. Data Exploration & Visualization** - Stock price evolution analysis - Volume and volatility patterns - Cross-company correlation studies - Distribution analysis and outlier detection ### **2. Technical Analysis** - Moving Averages (SMA, EMA) - RSI (Relative Strength Index) - Volatility indicators - Volume analysis - Price momentum indicators ### **3. Time Series Forecasting** - Trend-based forecasting - Mean reversion models - Volatility-adjusted predictions - Multi-step ahead forecasting - Model performance evaluation ### **4. AI Sentiment Correlation** - Sentiment-price correlation analysis - Market sentiment distribution - High/low sentiment period analysis - Sentiment-driven trading signals ### **5. Risk Assessment** - Portfolio volatility analysis - Individual stock risk metrics - Correlation-based risk evaluation - Performance attribution analysis --- ## =� **Project Structure** ``` Stock_Price_TS_Analysis_App/ � =� analysis_colab/ � � AI_Revolution_Stock_Analysis.ipynb # Main analysis notebook � =�� data/ � � ai_revolution_stock_data.csv # Generated dataset � � quick_ai_dataset.py # Quick dataset generator � � create_ai_revolution_dataset.py # Comprehensive generator � =' api/ # API development (future) � =� docs/ # Documentation � >� tests/ # Test files � = venv/ # Virtual environment � =� requirements.txt # Dependencies � =� README.md # This file ``` --- ## =� **Sample Results & Insights** ### **Key Findings** - **Best Performer**: NVIDIA with highest volatility and returns - **Most Stable**: Microsoft with consistent performance - **Highest Correlation**: AI sentiment shows strong correlation with tech stocks - **Volatility Leader**: Tesla with significant daily fluctuations - **Volume Leader**: NVIDIA with highest average daily volume ### **Forecasting Performance** - **Trend Accuracy**: Advanced trend analysis with confidence intervals - **Technical Signals**: Multiple indicator convergence analysis - **Risk Metrics**: Comprehensive volatility and correlation assessment --- ## <� **Learning Outcomes** This project demonstrates expertise in: - **Time Series Analysis**: Advanced forecasting techniques - **Financial Modeling**: Professional-grade stock analysis - **Data Engineering**: Custom dataset creation and validation - **Visualization**: Publication-quality charts and dashboards - **Statistical Analysis**: Correlation, volatility, and risk metrics --- ## =� **License** This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. --- ## =h =� **Author** **Anderson Nguetoum** *AI Engineer specialized in Predictive Analytics, Time Series Forecasting, Computer Vision & MLOps* - <� **Website**: [andersonnguetoum.com](https://andersonnguetoum.com) - =� **Email**: anderson@andersonnguetoum.com - =1 **GitHub**: [@Projet12345](https://github.com/Projet12345) ### **Expertise Areas** - Time Series Forecasting & Predictive Analytics - Financial Machine Learning - Computer Vision & Image Processing - MLOps & Production AI Systems --- ## P **Star This Repository** If you find this project helpful for your financial analysis or learning journey, please consider giving it a star! [](https://github.com/Projet12345/Stock_Price_TS_Analysis_App) --- *Last updated: November 2025* *Created as part of AI/ML portfolio development*