! This repository is a work in progress !
The Quantitative Token Model (QTM) is an open source spreadsheet model developed by Outlier Ventures. It's purpose is to forecast key metrics of different token economies on a higher level by abstracting a set of often leveraged token utilities. It should be used for educational purposes only and not to derive any financial advise. The market making for the token is approximated by a DEX liquidity pool with constant product relationship. To understand the usage of the tool please refer to the User Story Map
The goal of the QTM radCAD integration is to extend and to improve the static high-level approach of the QTM spreadsheet model to a more flexible and dynamic one. With the radCad integration one should be able to perform parameter sweeps and optimizations. Furthermore it opens up the capabilities for more dynamic agent behaviors, Monte Carlo runs, and Markov decision trees, which reflect a more realistic approximation of a highly non-linear web3 token ecosystem. At a later stage there should also be a more accessible (web-based) UI.
Initialize the project, create the development roadmap & README.md
post_processing.pywith respect to the new QTM parameters and conventionsplots.pywith respect to the new parameter conventions
Account for different revenue receiver buckets and track them
Update fundraising module to more complex scenarios, including SAFTs, SAFT+Ts and SAFTs
Python 3.9 is recommended!
- Clone this repository to your local machine by
git clone https://github.com/OutlierVentures/QTM-Interface.git - Create a new Python environment in the projects directory by
python -m venv venv - Activate the new environment by
venv/bin/activate - Install all required packages by
pip install -r requirements.txt
- Make sure you followed the previous installation section.
- Navigate with your terminal to the main project directory.
- Run
streamlit run .\Welcome.pywithin the previously installed and activated environment. - Expand the Sign-Up expander on the Welcome landing page and create a new user account or use the test user credentials
- Expand and login via the Login expander on the Welcome landing page by using your preferred account credentials.
Test user login data:
Username: user
Password: 1234
Create a function that combines all of these into a single file
1. Add parameters to ingest external data
2. Function to initialize values in state variables
3. The policy and state update functions
4. Update state update block file
5. Post-processing and plots to display it