Quantitative Token Model radCAD Integration

! This repository is a work in progress !

Disclaimer

MUST READ: The Quantitative Token Model (“QTM”) and accompanying information provided on this document has been prepared by Outlier Ventures (“OV”) for educational and general information purposes only. No undertaking, warrant or other assurance is given, and none should be implied, as to, and no reliance should be placed on the accuracy, adequacy, validity, reliability, fairness or completeness of any information in the QTM or the document. The QTM and information should not be considered a recommendation by OV or any of its directors, officers, employees, agents or advisers in connection with your token model. The information contained in the QTM has been prepared purely for informational purposes. In all cases persons should conduct their own investigation and analysis of the data in the QTM. Under no circumstances shall OV have any liability for any loss or damage of any kind incurred as a result of the use of, or reliance on, the QTM. The use of the information contained in the QTM is solely at the user’s own risk.

Background

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

QTM Structure

Quantitative Token Model

Motivation for the radCAD Extension

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.

Development Roadmap

V.1 (Static Base Model)

Initialize the project, create the development roadmap & README.md
Implement interface to the QTM spreadsheet parameters
Update the postprocessing in the post_processing.py with respect to the new QTM parameters and conventions
Update the plot functionallities in the plots.py with respect to the new parameter conventions
Build and test the vesting policies
Build and test the incentivisation module
Build and test the airdrop module
Build and test the static agent behavior
Build and test the utility policies
Build and test the liquidity pool interactions
Build and test the user adoption policies
Build and test protocol bucket allocations
Build and test the rest of token ecosystem KPIs / metrics
Update the postprocessing w.r.t. the new implemented policies and corresponding state variables
Web based UI for result output plots
Improve function & overall code documentation
Improve the robustness of all functions
Improve the robustness of all model input parameter
Staging tests of the whole model
Case studies & publishing first results in an article
Write the documentation for the QTM and radCAD integration

V.2 (Sophisticated Model)

Build a web-based UI to create another input option
Implement user authentication and data set shareability between users
Build custom user plot capabilities
Account for different revenue receiver buckets and track them
Add external rewards for stakers in diverse assets to mimic bribe markets (can be dependent on revenue received by a certain revenue bucket)
Add input inconsistency icons to respective input section expander text
Update fundraising module to more complex scenarios, including SAFTs and SAFT+Ts
Develop risk analysis procedures
Add more dynamic agent (behavior) policies
- Intelligent agents I: Hard coded logics
- Agents tend to stake tokens if reward APR is above target APR and remove tokens if it is below the target APR
- Add DAO voting caused staking demand based on revenue and business funds
- Add market buy behavior based on intrinsic protocol value, which is proportional to business funds and revenue projection
- Add market buy behavior based on market sentiment (Brownian Motion) and protocol reputation
- Define individual agent optimization goals (agents perform KPI based decision making)
- Intelligent agents II: AI driven decision making
Add alternative token price prediction models
Add product to token holder growth correlation
Improve UX -> Reduce confusion on parameters and streamline the user experience (potentially reduce complexity)
Add Metcalfe's law to product user growth model
Parameter Optimization
- Implement and test parameter sweep capabilities
- Add advanced optimization procedures
LLM support agents

Installation

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

Usage

Make sure you followed the previous installation section.
Navigate with your terminal to the main project directory.
Run streamlit run .\Welcome.py within 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

New Module Implementation Procedure

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

OutlierVentures / QTM-Interface

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