Skip to content

Latest commit

 

History

History
164 lines (97 loc) · 5.33 KB

README.md

File metadata and controls

164 lines (97 loc) · 5.33 KB
Raw

AI Trading Platform

Published IEEE paper: https://ieeexplore.ieee.org/document/9734127

What is the project?

To build a platform for creating, backtesting and paper trading new automated strategies on real-time, minute scale data with the help of AI models.

Components in the project

Data Feeder

  • Used to source real-time minute scale data from over 5000 stocks in the stock market.

  • Perform very fast, basic calculations on the raw data.

Database

  • We use a database to store the data from the data feeder.

  • We also use it to perform more complex calculations (such as indicators) very quickly at a large scale for all stocks whose data has been sent from the data feeder.

Strategies

This is the heart of the project

  • Strategies read data from the database and can use multiple indicators, machine learning models, or a combination to generate orders according to the rules mentioned in the strategy.

  • There may be multiple strategies created and deployed in this service.

Backtester

  • This service helps in building Strategies. It allows us test how a strategy would have performed over past data over previous weeks, months and years.

  • It takes a parameterised strategy and data over the period of the back test as input. It generates orders over the previous data according to the strategy's rules and outputs a detailed report on the performance of the strategy.

Paper Trader

  • This is used to test strategies in real-time market scenarios after a strategy has performed well in back testing.

  • It tracks and evaluates orders generated by multiple strategies in real-time market conditions and provides various performance metrics for both strategies and orders.

UI

  • This service is a website which provides a GUI for the user to interact with all the other services.

  • It allows the user flexibility to create, back test, paper trade and deploy new strategies on real-time data from the stock market.

To build this project

Development environment

  1. run these commands to initialize the project:

git clone "git@github.com:Varun487/CapstoneProject_AITradingPlatform.git"

cd CapstoneProject_AITradingPlatform

docker-compose -f devops/docker-compose.dev.yml up
  1. To start the backend and frontend development servers + run all containers:

docker-compose -f devops/docker-compose.dev.yml up
  1. To stop and destroy all containers:

docker-compose -f devops/docker-compose.dev.yml down
  1. Run with fresh build:

docker-compose -f devops/docker-compose.dev.yml down

docker-compose -f devops/docker-compose.dev.yml up --build
  1. To create a superuser for the database

docker-compose -f devops/docker-compose.dev.yml run restapi python3 manage.py createsuperuser
  1. To run all restapi tests

docker-compose -f devops/docker-compose.dev.yml run restapi python3 manage.py test
  1. Command to Initialize the database

docker-compose -f devops/docker-compose.dev.yml run restapi python3 manage.py initialize
  1. Command to run all paper trade services

docker-compose -f devops/docker-compose.dev.yml run restapi python3 manage.py papertrade
  1. Command to extract and store all data from DB in a csv format

docker-compose -f devops/docker-compose.dev.yml run restapi python3 manage.py extractDBdata
NOTE: To run in production, the commands are the same, but the file is docker-compose.prod.yml

Project members

All members are from Semester 6 Pes University EC Campus
  1. Varun Seshu - PES2201800074
  2. Hritik Shanbhag - PES2201800082
  3. Disha Venkatesh - PES2201800109
  4. Samrudhi R Rao - PES2201800126

Future prospects

We intend to build a trading system with a broker to place orders generated by the strategies in the live market and generate consistent profits.