Bots Lab Backtesting And Connector

Open Source Backtesting Tool. With this tool you can test your bot with historical data to see how it performs. The results will be compared against the benchmark. See bots/bot_template.py and config_test.py for more information.

This project also allows you to create a bot that runs "live", e.g. a bot that sends signals to buy and sell based on actual data. To run a bot in live mode, run it on a (virtual) server. A Linux-based server is usually the cheapest and most efficient option.

The following guidelines apply for a Linux-based server:

The server should have a recent 64-bit version of Ubuntu Linux as its operating system. Using version 18.04 or 20.04 (when it becomes available) is recommended.
The server should have at least 1 GB of memory and 20 GB of disk space.
You need to have administrative access (via root or sudo) to the server via a terminal (e.g SSH).

If you want to run the bot on a server your own infrastructure contact your systems or network administrator.
You can order suitable virtual machines from the following companies:

DigitalOcean: https://www.digitalocean.com/products/droplets/
Microsoft: https://azure.microsoft.com/en-us/services/virtual-machines/

Once you have a server available, follow the instructions in the first section to allow it to run this project.

This README contains the following:

Instructions to get a development and live environment up and running. See Preparing your development or live environment.
Instructions to create your own bot. See Create and run your own bot.

Preparing your development or live environment

To run an existing bot or to create your own, you will need to prepare your computer to allow this project to run. To do this, follow the instructions in this section.

For instructions to create your own bot, see Create and run your own bot.

Recommended: running the bot using Docker

The easiest way to get up and running is by using Docker. When using Docker, you won't have to install any platform-specific dependencies (e.g. Python). Only Docker itself is required.

If necessary, install Docker.

On Windows and macOS: install Docker Desktop from https://www.docker.com/get-started.

On Ubuntu Linux: install Docker and Docker Compose using the instructions on the following pages:
- https://www.digitalocean.com/community/tutorials/how-to-install-and-use-docker-on-ubuntu-18-04
- https://www.digitalocean.com/community/tutorials/how-to-install-docker-compose-on-ubuntu-18-04
These instructions should also apply for servers from other suppliers. A Docker Hub account is not required.
Clone the Bot-Backtesting project to your computer using Git if you haven't already done so.
Open a command prompt, cd to the project directory and run one of the following commands:

Platform Testing mode Live mode

macOS and Linux ./run_bot.sh test ./run_bot.sh live

Windows run_bot.cmd test run_bot.cmd live

Platform	Testing mode	Live mode
macOS and Linux	`./run_bot.sh test`	`./run_bot.sh live`
Windows	`run_bot.cmd test`	`run_bot.cmd live`

Remarks:

When running one of these commands for the first time, a Docker image will be built for the bot. This may take a few minutes and produce a lot of output. Next runs will be faster and quieter.
In testing mode, the bot runs once and exits automatically. Output is shown immediately.
In live mode, the bot runs periodically in the background. By default the bot runs every 30 minutes. Also, the bot will start automatically when Docker starts. To show output from the bot, run docker logs docker_bot_1.
To stop the bot in live mode, run ./run_bot.sh stop_live or run_bot.cmd stop_live.
You can add extra dependecies by adding them to the requirements.txt file. See the remark below.
After changing any of the files (see Create and run your own bot below), run the test command from the table above again to test your changes. To update the live version with your changes, run the stop_live command and start the live version again.

Alternative: running the bot without Docker

If Docker is unavailable for your platform, or if you prefer not to use it, follow the instructions below to manually install the dependencies required to run a bot.

Download and install Python 3

For this project Python 3 is needed. Mac users take note: Python is already installed as part of your operating system, but this will not suffice. For downloading Python 3 see Download Python.

Install library TA-Lib

TA-Lib is widely used by trading software developers for perform technical analysis of financial market data. Before you can use TA-Lib from Python you must first install the underlying TA-Lib library.

To install the underlying dependency TA-lib:

On Mac OS: install TA-lib using Brew: brew install ta-lib
On Linux, follow the instructions on installing TA-Lib from source.
On Windows, download prebuilt Python packages (wheels) of TA-Lib for your Python version from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib and place them in the wheels folder.

For more information:

Install Python dependencies

On Linux and macOS: run bash create_venv.sh from a terminal. On Windows run create_venv.cmd.

These scripts will create a virtual Python environment in the directory venv below the project directory. This virtualenv contains all required Python dependencies. They are listed in requirements.txt. After adding extra dependencies to this file you must run bash create_venv.sh or create_venv.cmd again.

Running the bot in test mode

On macOS and Linux: venv/bin/python3 run_test.py

On Windows: venv\scripts\python3 run_test.py

Running the bot in live mode

To run the bot in live mode, you will need to schedule a task that periodically runs your bot. This can be done using cron on macOS and Linux:

Run the following command to start editing the cron file of the current user: crontab -e

Add the following lines, replacing /path/to/project with the path to the project:

# Run bot every 30 minutes:
*/30 * * * *   /path/to/project/venv/bin/python3 /path/to/project/run_live.py

Save the file and exit the editor.

For a basic introduction to use the most common editors available on macOS and Linux, see this article: https://www.linux.com/topic/desktop/introduction-text-editors-get-know-nano-and-vim/

Create and run your own bot

This project uses by default a simple bot based on the RSI (Relative Strength Index) algorithm. You can find this bot in bots/rsi.py. This bots folder also contains ema.py and sma.py, two bots that use the Ta-Lib library. To create your own bot, follow the instructions in this section of the README.

Writing your first bot

See bots/bot_template.py for an explanation about writing your first bot. This means you have to implement the function get_buy_or_sell_signal and store that function in a python file (extension .py) in the bots folder.

Configuration Backtesting tool

See config_test.py for the simulation parameters of the Backtesting tool and the data settings for the historical data.

Run your bot against the backtesting engine

Steps:

In config_test.py give the parameter bot_name the right value: bot_name = 'bots.[file_name_of_bot]'
Execute the bot in testing mode using the instructions above, e.g. with the recommended Docker command: run_bot.sh test (or run_bot.cmd test on Windows).

Run your bot live