Trade Predictor Project 🚀

Welcome to the Trade Predictor Project! This repository houses an AI-powered trade prediction system designed to assist traders and investors in making informed decisions. By leveraging machine learning, technical analysis, and time series models, this project aims to deliver accurate predictions for stock market trends.

Table of Contents

• Overview
• Technologies Used
• Features
• Installation
• Usage
• Contributing
• License
• Contact

Overview

The Trade Predictor Project utilizes various advanced techniques to analyze financial data. By integrating models like ARIMA, Random Forest, and Kalman Filters, the system can identify patterns and predict future price movements. The user-friendly interface, built with React and styled using Tailwind CSS, makes it accessible for both novice and experienced traders.

For the latest updates and releases, visit our Releases section.

Technologies Used

This project employs a mix of powerful technologies:

• FastAPI: A modern web framework for building APIs with Python.
• React: A JavaScript library for building user interfaces.
• Tailwind CSS: A utility-first CSS framework for rapid UI development.
• Machine Learning Libraries: Such as scikit-learn and TensorFlow.
• Data Analysis Tools: Including pandas and NumPy.
• Time Series Analysis: Utilizing ARIMA and Wavelet Transform techniques.

Features

• AI-Powered Predictions: Utilize machine learning models to predict stock prices.
• Technical Analysis: Analyze historical data using various indicators.
• User-Friendly Interface: Easy navigation and interaction for users.
• Real-Time Data: Access to up-to-date financial data for accurate predictions.
• Customizable Settings: Users can adjust parameters to suit their trading strategies.

Installation

To set up the Trade Predictor Project on your local machine, follow these steps:

1. Clone the Repository:

   git clone https://github.com/RainyEarth/Trade_Predictor_Project.git
   cd Trade_Predictor_Project

2. Install Dependencies: For the backend, navigate to the FastAPI directory and install the required packages:

   cd backend
   pip install -r requirements.txt

   For the frontend, navigate to the React directory and install the necessary packages:

   cd frontend
   npm install

3. Run the Application: Start the backend server:

   uvicorn main:app --reload

   Then, start the frontend:

   npm start

Your application should now be running on http://localhost:3000.

Usage

Once the application is running, you can access the interface through your web browser. Here’s how to get started:

1. Input Financial Data: Upload your CSV files containing historical stock data.
2. Select Analysis Type: Choose the type of analysis you wish to perform, such as ARIMA or Random Forest.
3. View Predictions: After processing, the system will display predictions based on your selected model.
4. Export Results: You can download the prediction results for further analysis.

For detailed instructions, refer to the user guide included in the repository.

Contributing

We welcome contributions from the community! If you want to contribute to the Trade Predictor Project, please follow these steps:

1. Fork the Repository: Click on the "Fork" button at the top right of the repository page.
2. Create a New Branch:

   git checkout -b feature/YourFeatureName

3. Make Your Changes: Implement your feature or fix a bug.
4. Commit Your Changes:

   git commit -m "Add Your Feature Description"

5. Push to Your Fork:

   git push origin feature/YourFeatureName

6. Open a Pull Request: Go to the original repository and click on "New Pull Request".

We appreciate all contributions and will review your pull request as soon as possible.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or feedback, feel free to reach out:

• Email: rainyearth@example.com
• GitHub: RainyEarth

Thank you for checking out the Trade Predictor Project! For the latest releases, visit our Releases section and stay updated on new features and improvements.

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Topics

• arima
• csv-analysis
• fastapi
• financial-data
• kalman-filter
• machine-learning
• markov-models
• pca
• prediction-system
• random-forest
• react
• stock-market
• tailwindcss
• tda
• technical-indicators
• trade-prediction
• tsne
• vite
• wavelet-transform

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Feel free to explore, use, and contribute to this project. Happy trading! 📈