Pool Creation Helpers

Some of the values that needs to be passed can be a bit occult so here we try to explain them and offer some helpers for the most convenient ways of creating a market.

к сведению

In the example below we are creating a categorical market with two possible outcomes and therefore two asset tokens.

• We use the ZTG helper to correctly calculate the pool value to 300 ZTG
• We use the helper evenWeights(x_number_of_outcomes) to distribute the weights evenly among the outcomes.
• We use the helper swapFeeFromFloat(percent) to set the swap fee to 1%

import {
  ZTG,
  evenWeights,
  swapFeeFromFloat
} from '@zeitgeistpm/sdk'

const params = {
  ...,
  marketType: { Categorical: 2 },
  pool: {
    amount: ZTG.mul(300).toString(),
    weights: evenWeights(2),
    swapFee: swapFeeFromFloat(1).toString(),
  },
}

Initial Prediction (Uneven Asset Weighting)

When you are creating a market and providing liquidity there is a good chance you already have a sense of what the prediction will be and want to position your liquidity across outcome assets in a way that is most beneficial to you.

You can use the weightsFromRelativeRatio to do this by supplying prices.

import {
  weightsFromRelativeRatio
} from '@zeitgeistpm/sdk'

const yesOutcomePricePrediction = 0.8
const noOutcomePricePrediction = 0.2

const params = {
  ...,
  marketType: { Categorical: 2 },
  pool: {
    ...,
    weights: weightsFromRelativeRatio([yesOutcomePricePrediction, noOutcomePricePrediction]),
  },
}

к сведению

In this example we are prediction that the yes outcome has a 80%percent chance of being the outcome and the nooutcome a 20% chance.

Since the total price of all assets add up to 1 ZTG its easier to reason around weighting if you make sure that all the number supplied to the weightsFromRelativeRatio function adds up to 1.

If you are thinking about it as percentage chances its easier if the numbers add upp to 100