high-frequency-trading

A small devops project can be used as a template for organizing a python-kafka-clickhouse performance testing system. In this case, high-frequency trading systems.

Python script generates data at 1ms interval, Kafka and Clickhouse try to broadcast, collect and process this data.

Since the system depends on many factors, a CI/CD process is built that constantly tests the performance of the components. Build-test-deploy cycle. Testing is built around a docker-compose file as the simplest basis.

Data path

Data path: Python generator -> Kafka -> Clickhouse -> Web -> Triggers.

Python generator

• When debugging, the script outputs data at a reduced frequency so that generation performance does not suffer.
• The time_ns function was used for generation.
• Container with python 3.11 - they say that sleep in 3.11 is implemented better.
• Kafka producer receives json.dump.
• If the generator failed to sleep 100 cycles in a row - there is a logging.critical("overload") alarm.

Kafka

Kafka is configured by default and requires Zookieper to work.

Clickhouse

Data is transferred from the queue to the database using MATERIALIZED VIEW On the clickhouse side. Monitoring is based on the still experimental Clickhouse LIVE VIEW and curl feature.

Web

echo 'WATCH hft.monitoring' | curl 'http://localhost:8123/?allow_experimental_live_view=1' --data-binary @-

We believe that when receiving 2 events, the testing was successful. echo 'WATCH hft.monitoring LIMIT 2' | curl -s 'http://localhost:8123/?allow_experimental_live_view=1' --data-binary @-

Triggers

Not yet included in the project.

CI/CD without Github Actions and Jenkins

To develop on a local computer in the style of CI / CD without Github Actions and Jenkins, you need to install ubuntu-22.04 (also on github) in a virtual machine, after which you can use the docker-compose profiles:

• git clone git@github.com:skosachiov/high-frequency-trading.git
• cd high-frequency-trading
• sudo docker-compose --profile build up. Need to get "Ran 2 tests in 0.584s OK".
• sudo docker-compose --profile test up. You can start monitoring in another session:
  user@ubuntu:~$ echo 'WATCH hft.monitoring' | curl 'http://localhost:8123/?allow_experimental_live_view=1' --data-binary @-
• If everything is OK, deploy: sudo docker-compose --profile deploy up -d

Useful commands for local development with docker-compose

git commit -a -m "some fix"; git push

git pull; sudo docker-compose --profile test up [-d]

DEBUG="" python -u hft_producer.py 6000 0.01 10