Token Engineering Commons Upgrade Dashboard Backend

This repository contains the API for the four models of the Token Engineering Commons Upgrade Dashboard:

1. Token Freeze and Token Thaw
2. Augmented Bonding Curve (ABC)
3. Tao Voting
4. Conviction Voting

Models

1. Token Freeze and Token Thaw

The model inputs are:

• openingPrice (the initial price floor for the token)
• tokenFreeze (number of weeks that the opening price will be kept the same)
• tokenThaw (number of weeks in which the price floor will go from the opening price to zero)

The model output is a linechart data of the price floor over time and a table with the price floor and % of tokens unlocked in specific weeks.

To do an API call with the model input and receive the model outputs, it uses a POST request through the route /token-lockup/ with the following body:

{
  "openingPrice": 5,
  "tokenFreeze": 20,
  "tokenThaw": 15
}

2. Augmented Bonding Curve

The model inputs are:

• commonsPercentage (Percentage of funds that get substracted from the total funding to go to the commons pool. Between 0 and 95)
• ragequitPercentage (Percentage of supply burned before the bonding curve gets initialized. Between 0 and 20)
• initialPrice (Initial token prive. No real limit but, expected to be between 1 and 4)
• entryTribute (Percentage of funds substracted on buy (mint) operations before interacting with the bonding curve. Between 0 and 99)
• exitTribute (Percentage of funds substracted on sell (burn) operations after interacting with the boding curve. Between 0 and 99)
• stepList Set of buy/sell operations applied to the bonding curve. AMOUNT IN THOUSANDS. List with format [[AMOUNT, "TOKEN"],[AMOUNT, "TOKEN"]]
• zoomGraph optional, value 0 or 1. Used to specify if the draw function should show the whole curve(0) or "zoom in" into the area where operations are happening (1)

The model output is a linechart data of the price plotted over the wxDai balance and a table showing how price evolves when the steps are applied and the resulting tribute/slippage.

To do an API call with the model input and receive the model outputs, it uses a POST request through the route /augmented-bonding-curve/ with the following body:

{
  "commonsTribute": 0.5,
  "ragequitAmount": 60,
  "openingPrice": 1.65,
  "entryTribute": 0.02,
  "exitTribute": 0.15,
  "reserveBalance": 1571.22357,
  "initialBuy": 0,
  "stepList": [[100, "wxDai"]],
  "zoomGraph": 0
}

3. Tao Voting

The model inputs are:

• supportRequired (Minimum percentage of "yes" votes in relation to the total votes needed to a proposal pass)
• minimumQuorum (Minimum percentage of quorum needed to a proposal pass)
• voteDuration (Vote duration in days)
• delegatedVotingPeriod (Delegated voting period in days)
• quietEndingPeriod (Quiet ending period in days)
• quietEndingExtension (Quiet ending extension in days)
• executionDelay (Execution delay in days)

The model output is a bar chart plot of the voting timeline and a pie chart of the division of periods within the Tao voting.

To do an API call with the model input and receive the model outputs, it uses a POST request through the route /disputable-voting/ (the old name for Tao Voting) with the following body:

{
  "supportRequired": 0.4,
  "minimumQuorum": 0.1,
  "voteDuration": 7,
  "delegatedVotingPeriod": 3,
  "quietEndingPeriod": 2,
  "quietEndingExtension": 1,
  "executionDelay": 1
}

4. Conviction Voting

The model inputs are:

• convictionGrowth (Number of days to a staked vote to acquire 50% of the maximum conviction)
• convictionVotingPeriodDays (Number of days that a vote is staked and acquiring conviction)
• minimumConviction (Minimum conviction to pass the smallest proposal possible)
• spendingLimit (Maximum percentage of the Commons Pool requested by a proposal)

The model output is a line chart plot of the percentage of effective supply voting on a proposal over the percentage of the commons pool funds being requested and a table showing different scenarios of the amount in the Commons Pool.

To do an API call with the model input and receive the model outputs, it uses a POST request through the route /conviction-voting/ with the following body:

{
  "convictionGrowth": 2,
  "convictionVotingPeriodDays": 7,
  "minimumConviction": 0.05,
  "spendingLimit": 0.2
}

5. Output Generator

This endpoint takes as input all the previous model inputs and generate a github issue with all the selected parameters and outputs.

To do an API call with the model input and receive the model outputs, it uses a POST request through the route /issue-generator/ with the following body:

{
  "title": "TEC Dashboard Parameters Proposal",
  "overallStrategy": "",
  "tokenLockup": {
    "strategy": "",
    "openingPrice": 5,
    "tokenFreeze": 20,
    "tokenThaw": 15
  },
  "augmentedBondingCurve": {
    "strategy": "",
    "commonsTribute": 0.5,
    "ragequitAmount": 60,
    "openingPrice": 1.65,
    "entryTribute": 0.02,
    "exitTribute": 0.15,
    "reserveBalance": 1571.22357,
    "initialBuy": 0,
    "stepList": [[5000, "wxDai"], [100000, "wxDai"], [3000, "TEC"]],
    "zoomGraph": 0
  },
  "taoVoting": {
    "strategy": "",
    "supportRequired": 40,
    "minimumQuorum": 10,
    "voteDuration": 7,
    "delegatedVotingPeriod": 3,
    "quietEndingPeriod": 2,
    "quietEndingExtension": 1,
    "executionDelay": 1
  },
  "convictionVoting": {
    "strategy": "",
    "convictionGrowth": 2,
    "minimumConviction": 0.01,
    "votingPeriodDays": 7,
    "spendingLimit": 0.2
  },
  "advancedSettings": {
    "minimumEffectiveSupply": 4,
    "hatchersRageQuit": 3,
    "virtualBalance": 3000000
  }
}

6. Import Parameters

This endpoint takes as input the output issue number and return all of its parameters into a JSON format. To do an API call with the model input and receive the model outputs, it uses a GET request through the route /import-parameters/ with the following body:

{
	"issueNumber": 177
}

Install

For setting up the Python3 virtual environment

python3 -m venv venv
source venv/bin/activate

To install the requirements

pip install -r requirements.txt

Usage

To run the development server locally

python main.py 

It can be reached at http://127.0.0.1:5000/.