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MAPE-K Trading bot using Docker and Binance API

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SE4AS-MAPE-K-TRADE

Project developed for the Software Engineering for Autonomous System course - University of L'Aquila

This repository contains the implementation of MAPE-K Trading Bot: Project developed for the Software Engineering for Autonomous System course - University of L'Aquila

Installation Requirements

  1. Install the latest version of Docker - https://docs.docker.com/get-docker/
  2. Install the latest version of Docker Compose (if not already installed) - https://docs.docker.com/compose/install/

Project Description

This project consists in a set of containers running in Docker and automatically configured using the file docker-compose.yml. In the following the description of them:

  • MAPE-K-Loop / Managing System containers
  • Other services containers
    • grafana_container: A dashboard that allows us to graphically monitor the system and check if it is working properly.

Configuration

The configuration of the system is mainly contained in the docker-compose.yml file. Be sure that all the exposed mapped ports are free on your environment:

  • 3005 for the Monitor
  • 3001 for the Executor
  • 5020 for the Planner
  • 8086 for InfluxDB
  • 3000 for the Grafana dashboard

If you are not able to free all the listed ports, you can change the mapping in the compose file.

In order to manage variables needed for the monitor you need to create a file called .env in the root directory of the project.
Ex:

    INFLUXDB_TOKEN=<token>
    INFLUXDB_ORG=<your-org>
    INFLUXDB_BUCKET=<your-bucket>
    INFLUXDB_URL=https://<name-of-your-influxdb-container>:8086

Running

First of all you have to open your terminal on this folder and build all the images running the following command:

    docker-compose build

Then, you can run all the containers with the following command:

    docker-compose up -d

The bot will start by downloading the data of the specified coins and then start trading. If you want to stop all, you can use this:

    docker-compose down

Authors

This project has been realized by:

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  • Python 68.9%
  • Jupyter Notebook 28.3%
  • Dockerfile 2.8%