README.md

credmark-risk-and-research

This respository is used for resources for Credmark's Research and Risk Analysis Team

Developing with Poetry

Poetry is a very useful and modern package and dependency management tool. Installation instructions can be found here. Once poetry is installed, to install this project locally, run the following:

poetry install

This will install all the package dependencies as defined in pyproject.toml. If you need to add a new package, instead of using pip as you normally would you would run the following:

poetry add numpy

*Note: this is the equivalent of running pip install numpy but the package dependencies will be added to pyproject.toml and any conflicts with other packages will be identified and/or resolved automatically for you.

Virtual environment

poetry will create a separate environment per project. If you want to run the code locally using the packages installed in the project, you need to activate the environment by running:

poetry shell

This will activate the environment where all the project dependencies are installed. To exit the environment, just run exit in the terminal.

requirements.txt

Finally, the Dockerfile that builds the image used for the lambdas requires a requirements.txt file (for now; will look to make this a smoother workflow in the future). If new project dependencies are added to the pyproject.toml file by running poetry add <PACKAGE> then you will need to update the requirements.txt file by running:

poetry export -f requirements.txt > requirements.txt --without-hashes

Using Serverless to Deploy

In order to use serverless and deploy a new lambda, you will need both serverless and docker installed.

Installation

The easiest way to install serverless is by running the following command:

npm install -g serverless

For other methods of installing serverless please refer to the docs.

Next, you will need to ensure you have docker installed. Instructions on how to install docker can be found here.

Deploying

To deploy all the lambdas defined in serverless.yml, simply run:

serverless deploy --aws-profile PROFILE_NAME

where "PROFILE_NAME" is the name of a profile defined in ~/.aws/credentials. For help setting up a new profile, take a look here.

By default, serverless will try to deploy the lambdas to "us-west-2". The region can be overriden by passing in the --region option like so:

serverless deploy --aws-profile PROFILE_NAME --region REGION_NAME

Adding a new lambda

Adding a new lambda for deployment via serverless is simple. First, you will need to add a new handler to the handlers.py file by importing the lambda handler from the correct path. For example, if you add a new VAR model for Cream Finance, you would add a new file called src/protocol/cream/var.py which could then be imported like this:

from protocol.cream.var import lambda_handler as cream_var

And then simply define a new handler that executes cream_var:

def cream_var_handler(event, context):
    cream_var(event, context)

Finally, you just need to add the new function in serverless.yml under the "functions" key like so:

functions:
  cream-var:
    image:
      name: "baseRiskMetrics"
      command: ["handlers.cream_var_handler"]  # Change the command to use the name of the handler defined in handlers.py
    events:
      - http:
          path: /lcr/cream  # Update the path to reflect the correct protocol and metric being used
          method: GET

Note: adding the events key will create a new API gateway endpoint where the lambda can be triggered. In the case above, this would create a new endpoint with the path some-url.com/dev/lcr/cream

Finally, run the serverless deploy command shown in the "Deploying" section.

Reserach Development

This contains code and research resources currently at development stage.