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https://github.com/bonelesswater/tradingbot
This project is a web application for a trading bot that displays financial data and indicators. It includes functionality for researching financial data, displaying market indicators, and more.
https://github.com/bonelesswater/tradingbot
ai azure css d3 data-analysis django html javascript jquery materializecss python stock-market
Last synced: 15 days ago
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This project is a web application for a trading bot that displays financial data and indicators. It includes functionality for researching financial data, displaying market indicators, and more.
- Host: GitHub
- URL: https://github.com/bonelesswater/tradingbot
- Owner: BonelessWater
- Created: 2024-06-19T03:09:52.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-09-05T01:11:06.000Z (4 months ago)
- Last Synced: 2024-10-26T18:44:25.095Z (2 months ago)
- Topics: ai, azure, css, d3, data-analysis, django, html, javascript, jquery, materializecss, python, stock-market
- Language: Python
- Homepage: https://econsurge.azurewebsites.net
- Size: 89.1 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Django Trading Bot
This project is a web application for a trading bot that displays financial data and indicators. It includes functionality for researching financial data, displaying market indicators, and more.
## Table of Contents
- [Project Overview](#project-overview)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Configuration](#configuration)
- [Running Tests](#running-tests)
- [Contributing](#contributing)
- [License](#license)
## Project Overview
The Django Trading Bot is designed to help users analyze financial data and market indicators. It provides a user-friendly interface to view financial metrics, perform technical analysis, and offers additional functionalities like the efficient portfolio with the most recent stock market data and AI news sentiment analysis.
## Features
- Display financial data for selected stocks
- Show technical indicators like SMA, EMA, Bollinger Bands, RSI, MACD, and Stochastic
- Interactive research page to view stock data and charts
- Generate an efficient portfolio with the latest stock market data
- AI-powered news sentiment analysis for informed decision-making
- Admin panel for managing data and settings
## Installation
Follow these steps to set up the project on your local machine.
### Prerequisites
- Python 3.7+
- Django 3.0+
- Virtualenv (optional but recommended)
### Steps
1. **Clone the repository**
```sh
git clone https://github.com/yourusername/django-trading-bot.git
cd django-trading-bot
```
2. **Create and activate a virtual environment**
```sh
python -m venv env
source env/bin/activate # On Windows use `env\Scripts\activate`
```
3. **Install the required packages**
```sh
pip install -r requirements.txt
```
4. **Apply migrations**
```sh
python manage.py migrate
```
5. **Run the development server**
```sh
python manage.py runserver
```
6. **Access the application**
Open your browser and go to `http://127.0.0.1:8000/`.
## Usage
### Admin Panel
- Access the admin panel at `http://127.0.0.1:8000/admin/`.
- Create a superuser to access the admin panel:
```sh
python manage.py createsuperuser
```
### Research Page
- Visit `http://127.0.0.1:8000/research/` to use the research functionalities.
- Click on the stock tickers to view detailed financial data and indicators.
### Efficient Portfolio
- Visit `http://127.0.0.1:8000/portfolio/` to view the efficient portfolio generated using the most recent stock market data.
### AI News Sentiment Analysis
- Visit `http://127.0.0.1:8000/news-sentiment/` to see AI-powered news sentiment analysis, helping you make informed trading decisions.
## Configuration
- Update settings in `settings.py` as per your environment and requirements.
- Configure database settings, email settings, and other environment-specific variables.
## Running Tests
- Run tests using the following command:
```sh
python manage.py test
```
## Contributing
We welcome contributions! Please read `CONTRIBUTING.md` for details on our code of conduct and the process for submitting pull requests.
## License
This project is licensed under the MIT License. See the `LICENSE` file for more details.