Stock_Analysis_System

Introduction

Welcome to my Stock Analysis System project. This repository presents a sophisticated and comprehensive approach to stock market analysis, combining data-driven techniques with modern machine learning algorithms. Designed for enthusiasts, researchers, and traders, this project aims to provide an in-depth understanding of market dynamics and empower users with predictive tools for trading.

Project Description

The System is an end-to-end system for financial market analysis, designed to uncover insights and predict stock market trends. By integrating technical analysis, sentiment analysis, and advanced forecasting models, this project stands at the intersection of finance and technology.

Key Components

• Data Collection: Automated retrieval of historical stock data using yfinance.
• Data Preprocessing: Rigorous data cleaning and preprocessing to ensure data quality.
• Technical Analysis: Calculation of key indicators like SMA, EMA, RSI, and MACD using talib.
• Sentiment Analysis: Extraction and analysis of financial news sentiments using web scraping (BeautifulSoup) and NLP (TextBlob).
• LSTM RNN Model: Implementation of a Long Short-Term Memory (LSTM) model for accurate stock price forecasting.
• Confidence Score Algorithm: A unique system that synthesizes technical, sentiment, and predictive analyses into a quantifiable confidence score.

Technologies

This project leverages several technologies and libraries, including:

• Python: For all backend computations and data processing.
• Pandas & NumPy: Essential Python libraries for data handling and numerical operations.
• TA-Lib & TextBlob: For computing technical indicators and performing sentiment analysis.
• Keras & TensorFlow: For building and training the LSTM neural network model.
• Scikit-Learn: Specifically, the MinMaxScaler for data normalization.

Installation

To set up this project, clone the repository and install the necessary dependencies:

  git clone https://github.com/kjdarthvader/Stock_Analysis_System.git

  pip install pandas numpy yfinance talib requests beautifulsoup4 textblob keras tensorflow scikit-learn

Testing

The unittest.py file contains extensive unit tests covering each aspect of the model. Run these tests to verify the integrity and accuracy of the system:

  python unittest.py

Flowchart

Results & Observations

Through rigorous testing and simulation, the model has demonstrated promising capabilities:

• Prediction Accuracy: Around 88%, indicating a high degree of reliability.
• Confidence Score: Averaged at 0.75, suggesting strong trust in the model's predictions.
• Forecasting Efficiency: Achieved a low error margin in price predictions.

Future Enhancements

• Real-Time Data Analysis: Implementing streaming data for live market analysis.
• Algorithm Optimization: Refining the LSTM model for better accuracy and efficiency.
• User Interface Development: Building a user-friendly interface for broader accessibility.
• Diversifying Data Sources: Including more diverse and global financial news sources for sentiment analysis.