Sling Academy
Home/Python/Deploying freqtrade on a Cloud Server or Docker Environment

Deploying freqtrade on a Cloud Server or Docker Environment

Last updated: December 22, 2024

Deploying freqtrade, an open-source crypto trading bot, on a cloud server or a Docker environment allows for seamless algorithmic trading execution. In this guide, we'll outline detailed steps for deploying freqtrade in both environments.

Deploying Freqtrade on a Cloud Server

Deploying freqtrade on a cloud server is an efficient choice for reducing local resource usage and ensuring continuous trading. Here's how you can achieve this:

1. Setting Up the Cloud Server

First, select a cloud provider such as AWS, Google Cloud, or DigitalOcean. For demonstration purposes, we will use DigitalOcean:

  1. Create a new project on DigitalOcean.
  2. Deploy a Droplet with your preferred operating system (preferably Ubuntu for simplicity).
  3. SSH into your newly created server:
ssh root@your_server_ip

2. Installing Dependencies

Start by updating your package list and installing the required dependencies:

sudo apt update && sudo apt upgrade -y
sudo apt install python3-pip python3-dev libssl-dev libffi-dev git -y

3. Cloning and Configuring Freqtrade

Next, clone the freqtrade repository and enter the directory:

git clone https://github.com/freqtrade/freqtrade.git
cd freqtrade

Create a virtual environment to manage dependencies and activate it:

python3 -m venv .env
source .env/bin/activate

Install freqtrade dependencies:

pip install -r requirements.txt

Generate the default configuration:

freqtrade new-config

4. Running Freqtrade

After setting up the configuration, start freqtrade:

freqtrade trade

freqtrade is now deployed on your cloud server and is ready to execute trading strategies defined in the configuration.

Deploying Freqtrade with Docker

Deploying on Docker is another efficient and simpler method given Docker’s containerization advantage:

1. Installing Docker

Ensure Docker is installed and running on your system. You can follow these commands for Ubuntu:

sudo apt update
sudo apt install docker.io -y
sudo systemctl start docker
sudo systemctl enable docker

2. Pulling the Freqtrade Docker Image

Get the latest freqtrade image from Docker Hub:

docker pull freqtradeorg/freqtrade:stable

3. Initializing Freqtrade with Docker

Create a directory for your freqtrade configuration files:

mkdir freqtrade
cd freqtrade

Initialize your container, mapping it to the local config directory:

docker run -it --rm -v $(pwd)/user_data:/freqtrade/user_data freqtradeorg/freqtrade:stable create-userdir --userdir /freqtrade/user_data

4. Configuring the Trading Bot

Edit the generated config.json within the user_data/config/mystrategy/config.json file, ensuring it reflects your chosen strategy and exchange details.

5. Running the Trading Bot with Docker

Run your configured bot:

docker-compose up -d

Ensure Docker Compose is installed; if not, it can be added with the command:

sudo apt install docker-compose

With these steps completed, your freqtrade bot is actively running on a Docker container, enabling easy updates and scalability.

Conclusion

By deploying freqtrade on a cloud server or using Docker, you gain an edge with persistent market presence and operational efficiency. Ensure consistent monitoring and adjustments to your chosen strategies to keep up with market dynamics.

Next Article: Setting Up a freqtrade Dashboard for Real-Time Monitoring

Previous Article: Optimizing Strategy Parameters with freqtrade’s Hyperopt

Series: Algorithmic trading with Python

Python

You May Also Like

  • Introduction to yfinance: Fetching Historical Stock Data in Python
  • Monitoring Volatility and Daily Averages Using cryptocompare
  • Advanced DOM Interactions: XPath and CSS Selectors in Playwright (Python)
  • Automating Strategy Updates and Version Control in freqtrade
  • Setting Up a freqtrade Dashboard for Real-Time Monitoring
  • Optimizing Strategy Parameters with freqtrade’s Hyperopt
  • Risk Management: Setting Stop Loss, Trailing Stops, and ROI in freqtrade
  • Integrating freqtrade with TA-Lib and pandas-ta Indicators
  • Handling Multiple Pairs and Portfolios with freqtrade
  • Using freqtrade’s Backtesting and Hyperopt Modules
  • Developing Custom Trading Strategies for freqtrade
  • Debugging Common freqtrade Errors: Exchange Connectivity and More
  • Configuring freqtrade Bot Settings and Strategy Parameters
  • Installing freqtrade for Automated Crypto Trading in Python
  • Scaling cryptofeed for High-Frequency Trading Environments
  • Building a Real-Time Market Dashboard Using cryptofeed in Python
  • Customizing cryptofeed Callbacks for Advanced Market Insights
  • Integrating cryptofeed into Automated Trading Bots
  • Monitoring Order Book Imbalances for Trading Signals via cryptofeed