Stock-prediction-based-on-real-time-data

This project originated from a personal challenge when my brother asked for assistance in making better stock market decisions. To address this, I developed a Python-based tool that leverages real-time data from publicly traded companies using the yfinance library. The tool is designed to help users make informed stock purchases by analyzing several key parameters.
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The core components of the model include:
‍Historical Data Analysis: By examining the historical performance of stocks, the model identifies long-term trends and patterns that can inform future price movements
.Fibonacci Sequence: The model uses the Fibonacci sequence, a widely recognized tool in technical analysis, to predict potential reversal points in the market. This sequence helps in determining key support and resistance levels based on retracement and extension patterns.
‍Resistance Levels: The tool calculates resistance levels, providing insight into potential price points where stocks may struggle to break through. These levels are crucial for traders looking to identify selling or holding opportunities.
‍Moving Averages: The model incorporates short-term and long-term moving averages to smooth out price data and provide a clearer picture of stock momentum. The crossover between different moving averages can signal buying or selling opportunities.
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By combining these techniques, the model offers users a comprehensive view of stock performance, allowing them to make data-driven decisions when selecting which stocks to buy. This project not only reflects my ability to implement real-time data solutions but also showcases my practical understanding of stock market mechanics and technical analysis.