This is a web application designed to predict the trends of a stock price based on historical data. The app is built using Streamlit and Python.
- Predict stock trends for a given period of time and for next 100 days using previous 100 days performance of that stock
- Visualize stock trends with interactive graphs
- Compare multiple stocks and their trends
- View predictions for different time periods
This libraries are to be installed or be present in order to make it work
• Plotly
• Tenacity
• Matplotlib
• Pandas-datareader
• Keras
• Streamlit
• Sklearn
• Yfinance
• Tensorflow
• Tensorflow-gpu
About the requirements functions
• (1) Plotlys Python graphing library makes interactive, publication-quality graphs. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.
• (2) Tenacity is an Apache general-purpose retrying library, written in Python, to simplify the task of adding retry behavior to just about anything. simplest use case is retrying a flaky function whenever an Exception occurs until a value is returned.
• (3) Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.
• (4) Pandas pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.
• (5) Keras Keras is a minimalist Python library for deep learning that can run on top of Theano or TensorFlow. It was developed to make implementing deep learning models as fast and easy as possible for research and development.
• (6) Streamlit Streamlit is an open source app framework in Python language. It helps us create web apps for data science and machine learning in a short time. It is compatible with major Python libraries such as scikit- learn, Keras, PyTorch, SymPy(latex), NumPy, pandas, Matplotlib etc.
• (7) Sklearn Scikit-learn is probably the most useful library for machine learning in Python. The sklearn library contains a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering and prediction.
• (8) Yfinance Yfinance is a python package that enables us to fetch historical market data from Yahoo Finance API in a Pythonic way. It becomes so easy for all the Python developers to get data with the help of yfinance.
• (9) Tensorflow TensorFlow is a Python-friendly open source library for numerical computation that makes machine learning and developing neural networks faster and easier.
- By concluding all the features of stock prediction testing we can predict result.
- As a Practical example A(share) price was 6.80 and in 3 days it crossed its own 52-Week high price to 9.80
If you want to contribute to the development of this app, you can fork the repository and make changes to the code. You can also submit bug reports and feature requests by creating an issue in the repository. We welcome all contributions and feedback.