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Crypto Price API

 

Crypto Price API

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🚧 Crypto Price API 🚀 Is a personal project, it is not investment advice 🚧

Made by Nicolas Fernández Sanz

About   |   Features   |   Technologies   |   Requirements   |   Starting   |   License   |   Author


🎯 About

REST API developed with Djando and Python designed to collect information about the current and historical sentiment of many cryptocurrencies using NLP models and classic information such as the fear and greed index
It is a personal project, which I plan to improve over time. Collaborations are welcome :).

✨ Features

✔️ Data for over 13,000+ cryptocurrencies such as Bitcoin, Ethereum, XRP and much more;
✔️ 500+ sources of information;
✔️ Complementary data such as polarity of the sources;
✔️ Historical data storage;
✔️ Easy deployment;
✔️ Easy access;
✔️ Clear documentation;

🚀 Technologies

The following tools were used in this project:

✅ Requirements

Before starting 🏁, you need to have Git, Docker and Docker Compose installed.

🏁 Starting

# Clone this project
$ git clone https://github.com/nfernandezsanz/CryptoPriceAPI.git

# Access
$ cd CryptoPriceAPI/api

# Modify the Enviroment Variables and Setting if you want to use MySQL or PostgreSQL instead of SQLLite
$ nano envs/db.env
$ nano cra/settings.py

# Build the container (It can take a while.. It can take a while, I suggest a coffee)
$ docker-compose build 

# Run the container
$ docker-compose up 

# The API will initialize in the <http://localhost:8000>

# Search for the container name
$ docker ps
# Navigate to the container
$ docker exec -ti {cotainer name} bash
# Initial migrate
$ python manage.py migrate
# Load the Cryptos to the DB
$ python manage.py loadCryptos
# Create a superuser to access to the admin (http://localhost:8000/admin)
$ python manage.py createsuperuser

✅ How to use it?

You have deployed a REST API, the API has Swagger, which allows you to have a look at the different endpoints (with examples). Dont forget to visit the DjangoAdmin it is a good way to visualize the structure of the system and the generated data.

✅ Examples

How to analize the sentiment of a specific crypto now

Historical sentiment (dont forget to run some analysis first)

✅ Whats Next?

  • A good frontend would be excelent to show the data.
  • Add technical analysis tools to improve the accuracy of sentiment analysis.
  • Add python trading bots to automate buy and sell.

📝 License

This is a personal project part of my portfolio. Feel free to use it for good purposes and remember that cryptocurrencies are volatile, do not invest without a further research.

 

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