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Installation

This page explains how to prepare your environment for running the bot.

To understand how to set up the bot please read the Bot Configuration page.

Table of Contents

Easy Installation - Linux Script

If you are on Debian, Ubuntu or MacOS a freqtrade provides a script to Install, Update, Configure, and Reset your bot.

$ ./setup.sh
usage:
	-i,--install    Install freqtrade from scratch
	-u,--update     Command git pull to update.
	-r,--reset      Hard reset your develop/master branch.
	-c,--config     Easy config generator (Will override your existing file).

--install

This script will install everything you need to run the bot:

  • Mandatory software as: Python3, ta-lib, wget
  • Setup your virtualenv
  • Configure your config.json file

This script is a combination of install script --reset, --config

--update

Update parameter will pull the last version of your current branch and update your virtualenv.

--reset

Reset parameter will hard reset your branch (only if you are on master or develop) and recreate your virtualenv.

--config

Config parameter is a config.json configurator. This script will ask you questions to setup your bot and create your config.json.

Automatic Installation - Docker

Start by downloading Docker for your platform:

Once you have Docker installed, simply create the config file (e.g. config.json) and then create a Docker image for freqtrade using the Dockerfile in this repo.

1. Prepare the Bot

1.1. Clone the git repository

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

1.2. (Optional) Checkout the develop branch

git checkout develop

1.3. Go into the new directory

cd freqtrade

1.4. Copy config.json.example to config.json

cp -n config.json.example config.json

To edit the config please refer to the Bot Configuration page.

1.5. Create your database file (optional - the bot will create it if it is missing)

Production

touch tradesv3.sqlite

Dry-Run

touch tradesv3.dryrun.sqlite

2. Build the Docker image

cd freqtrade
docker build -t freqtrade .

For security reasons, your configuration file will not be included in the image, you will need to bind mount it. It is also advised to bind mount an SQLite database file (see the "5. Run a restartable docker image" section) to keep it between updates.

3. Verify the Docker image

After the build process you can verify that the image was created with:

docker images

4. Run the Docker image

You can run a one-off container that is immediately deleted upon exiting with the following command (config.json must be in the current working directory):

docker run --rm -v /etc/localtime:/etc/localtime:ro -v `pwd`/config.json:/freqtrade/config.json -it freqtrade

There is known issue in OSX Docker versions after 17.09.1, whereby /etc/localtime cannot be shared causing Docker to not start. A work-around for this is to start with the following cmd.

docker run --rm -e TZ=`ls -la /etc/localtime | cut -d/ -f8-9` -v `pwd`/config.json:/freqtrade/config.json -it freqtrade

More information on this docker issue and work-around can be read here

In this example, the database will be created inside the docker instance and will be lost when you will refresh your image.

5. Run a restartable docker image

To run a restartable instance in the background (feel free to place your configuration and database files wherever it feels comfortable on your filesystem).

5.1. Move your config file and database

mkdir ~/.freqtrade
mv config.json ~/.freqtrade
mv tradesv3.sqlite ~/.freqtrade

5.2. Run the docker image

docker run -d \
  --name freqtrade \
  -v /etc/localtime:/etc/localtime:ro \
  -v ~/.freqtrade/config.json:/freqtrade/config.json \
  -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
  freqtrade --db-url sqlite:///tradesv3.sqlite

Note: db-url defaults to sqlite:///tradesv3.sqlite but it defaults to sqlite:// if dry_run=True is being used. To override this behaviour use a custom db-url value: i.e.: --db-url sqlite:///tradesv3.dryrun.sqlite

6. Monitor your Docker instance

You can then use the following commands to monitor and manage your container:

docker logs freqtrade
docker logs -f freqtrade
docker restart freqtrade
docker stop freqtrade
docker start freqtrade

For more information on how to operate Docker, please refer to the official Docker documentation.

Note: You do not need to rebuild the image for configuration changes, it will suffice to edit config.json and restart the container.

7. Backtest with docker

The following assumes that the above steps (1-4) have been completed successfully. Also, backtest-data should be available at ~/.freqtrade/user_data/.

docker run -d \
  --name freqtrade \
  -v /etc/localtime:/etc/localtime:ro \
  -v ~/.freqtrade/config.json:/freqtrade/config.json \
  -v ~/.freqtrade/tradesv3.sqlite:/freqtrade/tradesv3.sqlite \
  -v ~/.freqtrade/user_data/:/freqtrade/user_data/ \
  freqtrade --strategy AwsomelyProfitableStrategy backtesting

Head over to the Backtesting Documentation for more details.

Note: Additional parameters can be appended after the image name (freqtrade in the above example).

Custom Installation

We've included/collected install instructions for Ubuntu 16.04, MacOS, and Windows. These are guidelines and your success may vary with other distros. OS Specific steps are listed first, the common section below is necessary for all systems.

Requirements

Click each one for install guide:

Linux - Ubuntu 16.04

Install Python 3.6, Git, and wget

sudo add-apt-repository ppa:jonathonf/python-3.6
sudo apt-get update
sudo apt-get install python3.6 python3.6-venv python3.6-dev build-essential autoconf libtool pkg-config make wget git

Raspberry Pi / Raspbian

Before installing FreqTrade on a Raspberry Pi running the official Raspbian Image, make sure you have at least Python 3.6 installed. The default image only provides Python 3.5. Probably the easiest way to get a recent version of python is miniconda.

The following assumes that miniconda3 is installed and available in your environment, and is installed. It's recommended to use (mini)conda for this as installation/compilation of scipy and pandas takes a long time.

conda config --add channels rpi
conda install python=3.6
conda create -n freqtrade python=3.6
conda install scipy pandas

python3 -m pip install -r requirements.txt
python3 -m pip install -e .

MacOS

Install Python 3.6, git, wget and ta-lib

brew install python3 git wget

common

1. Install TA-Lib

Official webpage: https://mrjbq7.github.io/ta-lib/install.html

wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz
tar xvzf ta-lib-0.4.0-src.tar.gz
cd ta-lib
sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h
./configure --prefix=/usr
make
make install
cd ..
rm -rf ./ta-lib*

Note: An already downloaded version of ta-lib is included in the repository, as the sourceforge.net source seems to have problems frequently.

2. Setup your Python virtual environment (virtualenv)

Note: This step is optional but strongly recommended to keep your system organized

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

3. Install FreqTrade

Clone the git repository:

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

Optionally checkout the stable/master branch:

git checkout master

4. Initialize the configuration

cd freqtrade
cp config.json.example config.json

To edit the config please refer to Bot Configuration.

5. Install python dependencies

pip3 install --upgrade pip
pip3 install -r requirements.txt
pip3 install -e .

6. Run the Bot

If this is the first time you run the bot, ensure you are running it in Dry-run "dry_run": true, otherwise it will start to buy and sell coins.

python3.6 ./freqtrade/main.py -c config.json

Note: If you run the bot on a server, you should consider using Docker a terminal multiplexer like screen or tmux to avoid that the bot is stopped on logout.

7. [Optional] Configure freqtrade as a systemd service

From the freqtrade repo... copy freqtrade.service to your systemd user directory (usually ~/.config/systemd/user) and update WorkingDirectory and ExecStart to match your setup.

After that you can start the daemon with:

systemctl --user start freqtrade

For this to be persistent (run when user is logged out) you'll need to enable linger for your freqtrade user.

sudo loginctl enable-linger "$USER"

Windows

We recommend that Windows users use Docker as this will work much easier and smoother (also more secure).

If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work. If that is not available on your system, feel free to try the instructions below, which led to success for some.

Install freqtrade manually

Clone the git repository

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

copy paste config.json to ``\path\freqtrade-develop\freqtrade`

Install ta-lib

Install ta-lib according to the ta-lib documentation.

As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of inofficial precompiled windows Wheels here, which needs to be downloaded and installed using pip install TA_Lib‑0.4.17‑cp36‑cp36m‑win32.whl (make sure to use the version matching your python version)

>cd \path\freqtrade-develop
>python -m venv .env
>cd .env\Scripts
>activate.bat
>cd \path\freqtrade-develop
REM optionally install ta-lib from wheel
REM >pip install TA_Lib‑0.4.17‑cp36‑cp36m‑win32.whl
>pip install -r requirements.txt
>pip install -e .
>python freqtrade\main.py

Thanks Owdr for the commands. Source: Issue #222

Error during installation under Windows

error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools

Unfortunately, many packages requiring compilation don't provide a pre-build wheel. It is therefore mandatory to have a C/C++ compiler installed and available for your python environment to use.

The easiest way is to download install Microsoft Visual Studio Community here and make sure to install "Common Tools for Visual C++" to enable building c code on Windows. Unfortunately, this is a heavy download / dependency (~4Gb) so you might want to consider WSL or docker first.

Now you have an environment ready, the next step is Bot Configuration...