Stratis

Stratis is a python-based framework for developing and testing strategies, inspired by the simplicity of tradingview's Pinescript. Currently, stratis is in the early stages of development, and is not yet ready for production use. However, it is encouraged to try it out and provide feedback via GitHub issues.

Stratis is a part of Shenandoah Research's Open source Trading Software Initiative and developed by Polyad Decision Sciences software engineers!

Please Note

Stratis is under active development and is not yet ready for production use.

Basic Example

The following code demonstrates how to create a strategy that prints the timestamp and close price of the OHLC data every hour. The on_step decorator is used to run the function on every step of the OHLC data. You can find more info about how to create strategies here.

Using the Strategy class, along with the on_step decorator, you can create strategies that are as simple or as complex as you want, with the full power of python at your disposal.

class OHLCDemo(Strategy):

    @on_step  # the on_step decorator runs the function on every "step" of the OHLC data
    def print_ohlc(self):

        # strategy shorthands for the OHLC data
        timestamp = self.data.timestamp
        close = self.data.close

        # if the timestamp is a multiple of 3600000 (1 hour), print the timestamp and close price
        if timestamp % 3600000 == 0:
            print(f'{timestamp}: {close}')
                

data = CSVAdapter('data/AAPL.csv')
strategy = OHLCDemo().run(data)

Table of Contents

• Installation
• Features

Installation

It is heavily recommended to use Docker to run stratis. This is because stratis requires a number of dependencies that can be difficult to install.

To install Docker, follow the instructions here.

Once Docker is installed, you can run stratis by running the following commands:

# Clone the repository
git clone https://github.com/robswc/stratis

# Change directory to the repository
cd stratis

# Run the docker-compose file
docker-compose up -d # -d runs the containers in the background

For more advanced usage, you can run app with python directly, as it is a FastAPI app under the hood. I would recommend using a virtual environment for this. You will also have to install the requirements.

cd app  # change directory to the app folder
python python -m uvicorn main:app --reload  # reloads the app on file changes (useful for development)