Liquidity Rewards

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Overview

By posting resting limit orders, liquidity providers (makers) are automatically eligible for Polymarket’s incentive program. The overall goal of this program is to catalyze a healthy, liquid marketplace. We can further define this as creating incentives that:

• Catalyze liquidity across all markets
• Encourage liquidity throughout a market’s entire lifecycle
• Motivate passive, balanced quoting tight to a market’s mid-point
• Encourages trading activity
• Discourages blatantly exploitative behaviors

This program is heavily inspired by dYdX’s liquidity provider rewards which you can read more about here. In fact, the incentive methodology is essentially a copy of dYdX’s successful methodology but with some adjustments including specific adaptations for binary contract markets with distinct books, no staking mechanic a slightly modified order utility-relative depth function and reward amounts isolated per market. Rewards are distributed directly to the maker’s addresses daily at midnight UTC.

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Methodology

Polymarket liquidity providers will be rewarded based on a formula that rewards participation in markets (complementary consideration!), boosts two-sided depth (single-sided orders still score), and spread (vs. mid-market, adjusted for the size cutoff!). Each market still configure a max spread and min size cutoff within which orders are considered the average of rewards earned is determined by the relative share of each participant’s Q_n in market m.

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Equations

Equation 1:

$S(v,s)= (\frac{v-s}{v})^2 \cdot b$

Equation 2:

$Q_{one}= S(v,Spread_{m_1}) \cdot BidSize_{m_1} + S(v,Spread_{m_2}) \cdot BidSize_{m_2} + \dots$ $+ S(v, Spread_{m^\prime_1}) \cdot AskSize_{m^\prime_1} + S(v, Spread_{m^\prime_2}) \cdot AskSize_{m^\prime_2}$

Equation 3:

$Q_{two}= S(v,Spread_{m_1}) \cdot AskSize_{m_1} + S(v,Spread_{m_2}) \cdot AskSize_{m_2} + \dots$ $+ S(v, Spread_{m^\prime_1}) \cdot BidSize_{m^\prime_1} + S(v, Spread_{m^\prime_2}) \cdot BidSize_{m^\prime_2}$

Equation 4:

Equation 4a:

If midpoint is in range [0.10,0.90] allow single sided liq to score:

$Q_{\min} = \max(\min({Q_{one}, Q_{two}}), \max(Q_{one}/c, Q_{two}/c))$

Equation 4b:

If midpoint is in either range [0,0.10) or (.90,1.0] require liq to be double sided to score:

$Q_{\min} = \min({Q_{one}, Q_{two}})$

Equation 5:

$Q_{normal} = \frac{Q_{min}}{\sum_{n=1}^{N}{(Q_{min})_n}}$

Equation 6:

$Q_{epoch} = \sum_{u=1}^{10,080}{(Q_{normal})_u}$

Equation 7:

$Q_{final}=\frac{Q_{epoch}}{\sum_{n=1}^{N}{(Q_{epoch})_n}}$

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Steps

1. Quadratic scoring rule for an order based on position between the adjusted midpoint and the minimum qualifying spread

2. Calculate first market side score. Assume a trader has the following open orders:

   • 100Q bid on m @0.49 (adjusted midpoint is 0.50 then spread of this order is 0.01 or 1c)
   • 200Q bid on m @0.48
   • 100Q ask on m’ @0.51

   and assume an adjusted market midpoint of 0.50 and maxSpread config of 3c for both m and m’. Then the trader’s score is:

   $$Q_{ne} = \left( \frac{(3-1)}{3} \right)^2 \cdot 100 + \left( \frac{(3-2)}{3} \right)^2 \cdot 200 + \left( \frac{(3-1)}{3} \right)^2 \cdot 100$$

   $Q_{ne}$ is calculated every minute using random sampling

3. Calculate second market side score. Assume a trader has the following open orders:

   • 100Q bid on m @0.485
   • 100Q bid on m’ @0.48
   • 200Q ask on m’ @0.505

   and assume an adjusted market midpoint of 0.50 and maxSpread config of 3c for both m and m’. Then the trader’s score is:

   $$Q_{no} = \left( \frac{(3-1.5)}{3} \right)^2 \cdot 100 + \left( \frac{(3-2)}{3} \right)^2 \cdot 100 + \left( \frac{(3-.5)}{3} \right)^2 \cdot 200$$

   $Q_{no}$ is calculated every minute using random sampling

4. Boosts 2-sided liquidity by taking the minimum of $Q_{ne}$ and $Q_{no}$, and rewards 1-side liquidity at a reduced rate (divided by c)

   Calculated every minute

5. $Q_{normal}$ is the $Q_{min}$ of a market maker divided by the sum of all the $Q_{min}$ of other market makers in a given sample

6. $Q_{epoch}$ is the sum of all $Q_{normal}$ for a trader in a given epoch

7. $Q_{final}$ normalizes $Q_{epoch}$ by dividing it by the sum of all other market maker’s $Q_{epoch}$ in a given epoch this value is multiplied by the rewards available for the market to get a trader’s reward