Introduction

This trader bot automatizes orders on all kind of financial products, and is not targetting crypto. Objective is that it can run in autonomy. It is primarily a personal project, it must be seen as an example, not as a general framework.

Features implemented:

• Backstaging of complex strategies (including 101 Formulaic Alphas) using vectorbtpro
• Performing live automatically orders using the tested strategies and interactive brockers, thanks to ib_insync
• Send Telegram messages when performing an order
• Send Telegram alerts when the action price variation exceeds a certain threshold
• Writting at regular interval reports on the market, using Django and Celery with Redis

Structure

• core contains the strategies and all backstaging logic supported by vectorbt. Its indicator factory is extensively used (for more information read https://vectorbt.pro/tutorials/superfast-supertrend/).
• saved_cours contains some pre-saved data to perform backtesting. The jupyter notebooks in the root are there to perform this backtesting
• orders contains the Django models relative to orders and financial products. IB communication is handled also there
• reporting contains the Django models relative to reports
• trading_bot contains Django configuration

Get started

Configuration:

• Go in trading_bot/settings.py, set IB settings relative to port (don't forget to open your Api in this software), useIB_for_data to the correct value depending if you use Interactive brokers or not. Look at the settings, for instance for Telegram.
• In trading_bot/etc/ put your Django key (whatever you want), your telegram token and your database credentials. I used postgresQL but it does not matter (read https://docs.djangoproject.com/en/4.0/ref/databases/)
• (optional) reimport the dump file using "python manage.py loaddata dump.rdb" to fill your database with some financial products: CAC40, DAX and Nasdaq100

When you are done:

• Click on start_bot.sh

Deployment

Deployment of the bot on external machine has not been achieved yet for several reasons:

• If you use Interactive brokers, your trader workstation needs to be open on a machine which can communicate with the bot. As the login requires MFA, you need to be able to display the desktop of this machine. It requires a minimum of 4Gb ram, which exclude use of Raspberry pi. It is a challenge also for the security.
• Talib library, which is coded in C, need to be installed. In proved to be challenging on heroku for instance
• Pandas is very heavy. It excludes a deployment on Amazon lambda for instance
• In its present version, you need to start a Redis server, Celery, Django and Interactive Brokers at the same time. So it is a bit complex.

Open points

• Tests

Disclaimer

Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.