Advisor

Advisor is an anonymous financial network all about improving the financial well-being of its users through peer-benchmarking, storytelling, and collaboration.

Think Bravado mixed with the personal finance subreddit, but totally anonymized.

It consists of three main features:

• A self-onboarding experience to set up your profile, offering insight into your current financial and demographic information
• A network search page where you can filter other users based on financial and demographic settings
• A basic chat application to message other users (currently not functional)

Unfortunately, this project never got further than an MVP running on a laptop. It was a pet project between two old college friends. It was a lot of fun working through the idea and writing the code.

If we were to come back to this project, we would:

• Refine the signup & self-onboarding experience
• Get chat working
• Add forums
• Build integrations with personal finance management apps like rocket money

Stack

• Docker
• React JS frontend
• Django python backend
• Meant to be deployed using AWS (AWS connection not required to run locally)

How to get things running

• Clone this repository
• Run git submodule init && git submodule update
  • This repo points to commits of webservices and frontend
• Run the following command to set up environment variables for backend
  • cp webservices/.env.example webservices/.env
• Make sure Docker is installed on your machine
• Run docker-compose up --build -d
  • This may take a few minutes the first time you run it
• Build a simulated dataset do make the network search meaningful
  • Get into an interactive shell using docker-compose exec backend bash
  • Run the simulate_dataset management command using python webservices/manage.py simulate_dataset
• Now create a superuser by running python webservices/manage.py createsuperuser
• Now navigate to the frontend directory
• Run the following command to set up environment variables for frontend
  • cp .env.example .env
• Run yarn start
  • This will take you to a login screen at localhost:3000
• After logging in, you will be walked through the self-onboarding experience
• Following the completion of setting up your profile, you are good to go!

FAQ

• How can I reset the database?
  • Go to the postgres folder and delete its contents, then rebuild the postgres container.
  • If you wish to rebuild the simulation data, do that using python webservices/manage.py simulate_dataset