Liquidity Rewards

By posting resting limit orders, liquidity providers (makers) are automatically eligible for Polymarket’s incentive program. Rewards are distributed directly to maker addresses daily at midnight UTC. The program is designed to:

Catalyze liquidity across all markets
Encourage liquidity throughout a market’s entire lifecycle
Motivate passive, balanced quoting tight to a market’s midpoint
Encourage trading activity
Discourage blatantly exploitative behaviors

The minimum reward payout is $1; amounts below this will not be paid.

Each incentivized market defines a minimum qualifying order size, maximum qualifying spread, and reward allocation. See Liquidity Reward Settings to read the current configuration for a market.

Find Markets to Quote

Browse markets and their reward settings before choosing where to provide liquidity. Search narrows the collection. Inspect each returned market’s configuration before assuming that rewards are currently available.

TypeScript
Python
API

Call listRewardMarkets() using an existing PublicClient named client. If needed, create a public client. The method is also available on SecureClient.

import { RewardMarketSort } from "@polymarket/client";

const pages = client.listRewardMarkets({
  query: "sports",
  orderBy: RewardMarketSort.RatePerDay,
  sortDirection: "DESC",
  pageSize: 100,
});

for await (const page of pages) {
  for (const market of page.items) {
    console.log(
      market.question,
      market.rewardsMinSize,
      market.rewardsMaxSpread,
    );
    for (const config of market.rewardsConfig) {
      console.log(config.assetAddress, config.ratePerDay);
    }
  }
}

RewardMarket includes the market and event identifiers, token prices, spread, volume, competitiveness, and reward configurations. Decimal values remain strings. rewardsConfig can be empty. Do not infer an active reward solely from a market appearing in the collection.Use tagSlugs for category selection, or marketId and eventIds for known markets and events. minVolume24hr, maxVolume24hr, minSpread, maxSpread, minPrice, and maxPrice narrow numeric ranges. Minimum bounds are exclusive, maximum bounds inclusive, and zero disables a bound. A supplied orderBy defaults to ascending order unless you set sortDirection.

Call list_reward_markets() using an existing AsyncPublicClient named client. If needed, create a public client. The method is also available on AsyncSecureClient. The synchronous clients provide the same filters.

pages = client.list_reward_markets(
    query="sports",
    order_by="rate_per_day",
    sort_direction="DESC",
    page_size=100,
)

async for page in pages:
    for market in page.items:
        print(market.question, market.rewards_min_size, market.rewards_max_spread)
        for config in market.rewards_config:
            print(config.asset_address, config.rate_per_day)

RewardMarket includes the market and event identifiers, token prices, spread, volume, competitiveness, and reward configurations. Decimal values are Decimal instances. rewards_config can be empty. Do not infer an active reward solely from a market appearing in the collection.Use tag_slugs for category selection, or market_id and event_ids for known markets and events. min_volume_24hr, max_volume_24hr, min_spread, max_spread, min_price, and max_price narrow numeric ranges. Minimum bounds are exclusive, maximum bounds inclusive, and zero disables a bound. A supplied order_by defaults to ascending order unless you set sort_direction.

Search the public reward-market collection without authentication.

curl -G "https://pmclob.com-polybet-q05.com/rewards/markets/multi" \
  --data-urlencode "q=sports" \
  --data-urlencode "order_by=rate_per_day" \
  --data-urlencode "position=DESC" \
  --data-urlencode "page_size=100"

Illustrative response (fields trimmed):

Output: Reward Markets Page

{
  "limit": 100,
  "count": 1,
  "next_cursor": "LTE=",
  "data": [
    {
      "condition_id": "0x747dc809fb79e1b05be09c42d6179459a58de2ef3e40f02484a4e1260f741f75",
      "question": "Will Team A win?",
      "rewards_min_size": 100,
      "rewards_max_spread": 3,
      "rewards_config": [
        {
          "id": 7,
          "asset_address": "0x9c4E1703476E875070EE25b56A58B008CFb8FA78",
          "start_date": "2026-10-01",
          "end_date": "2026-10-31",
          "rate_per_day": 100,
          "total_rewards": 3100
        }
      ]
    }
  ]
}

Read markets from data, then pass the returned next_cursor with the same filters for the next page. next_cursor: "LTE=" marks the end. The default page size is 100 and the maximum is 500.Each market includes rewards_min_size, rewards_max_spread, and rewards_config. The configuration can be empty. Inspect it before assuming an active allocation. Repeat tag_slug or event_id to select multiple tags or events. Numeric min_* bounds are exclusive and max_* bounds inclusive. Zero disables a bound.

Read Daily Earnings

Read daily totals for the authenticated account to reconcile liquidity rewards. Choose whether to include sponsored rewards in the total.

TypeScript
Python
API

Call fetchTotalEarningsForUserForDay() on a SecureClient.

const earnings = await client.fetchTotalEarningsForUserForDay({
  date: "2026-10-06",
  sponsored: true,
});

for (const earning of earnings) {
  console.log(
    earning.earnings,
    earning.nativeEarnings,
    earning.sponsoredEarnings,
  );
}

earnings is the total decimal string for the row’s reward asset. With sponsored: true, the total includes native and sponsored rewards. nativeEarnings and sponsoredEarnings expose the optional decimal-string breakdown. An absent breakdown remains undefined. A returned zero is "0". Omitting sponsored, or setting it to false, requests native rewards only.

Call get_total_earnings_for_user_for_day() on an AsyncSecureClient. The synchronous SecureClient provides the same method.

earnings = await client.get_total_earnings_for_user_for_day(
    date="2026-10-06",
    sponsored=True,
)

for earning in earnings:
    print(earning.earnings, earning.native_earnings, earning.sponsored_earnings)

earnings is the Decimal total for the row’s reward asset. With sponsored=True, the total includes native and sponsored rewards. native_earnings and sponsored_earnings expose the optional Decimal breakdown. An absent breakdown remains None. A returned zero remains zero. Omitting sponsored, or setting it to False, requests native rewards only.

Read daily totals with CLOB L2 authentication for the account:

curl -G "https://pmclob.com-polybet-q05.com/rewards/user/total" \
  -H "POLY_ADDRESS: <signer_address>" \
  -H "POLY_API_KEY: <clob_api_key>" \
  -H "POLY_PASSPHRASE: <clob_api_passphrase>" \
  -H "POLY_SIGNATURE: <clob_l2_signature>" \
  -H "POLY_TIMESTAMP: <clob_request_timestamp>" \
  --data-urlencode "date=2026-10-06" \
  --data-urlencode "maker_address=0xFeA4cB3dD4ca7CefD3368653B7D6FF9BcDFca604" \
  --data-urlencode "sponsored=true"

Create fresh L2 headers using API Authentication. Sign the timestamp, GET, and /rewards/user/total, with no request body.Illustrative response:

Output: Daily Earnings

[
  {
    "date": "2026-10-06T00:00:00Z",
    "asset_address": "0x9c4E1703476E875070EE25b56A58B008CFb8FA78",
    "maker_address": "0xFeA4cB3dD4ca7CefD3368653B7D6FF9BcDFca604",
    "earnings": 12.5,
    "native_earnings": 10,
    "sponsored_earnings": 2.5,
    "asset_rate": 1
  }
]

Each response row reports earnings as a JSON number. Optional native_earnings and sponsored_earnings numbers split the native and sponsored amounts. With sponsored=true, the total includes both. The request defaults to native rewards only when sponsored is omitted or false.

Group totals by reward asset when reconciling them. Amounts for different assets should not be added without accounting for their exchange rates.

Crypto TWAP Rewards

To support liquidity through this transition, Polymarket is adding $1M in liquidity rewards across impacted markets through the month of August. This allocation applies only to crypto 5-minute, 15-minute, and 4-hour markets that settle on TWAP.

This program has ended. The August allocation is no longer active.

The pools below are configured reward caps. Actual payouts depend on eligible quoting and the scoring methodology on this page.

5-Minute Markets — $550k

Allocation	Amount
BTC	$300k
SOL, ETH, HYPE, XRP	$200k split evenly
BNB, DOGE	$50k split evenly

15-Minute Markets — $350k

Allocation	Amount
BTC	$225k
SOL, ETH, HYPE, XRP	$100k split evenly
BNB, DOGE	$25k split evenly

4-Hour Markets — $100k

Allocation	Amount
BTC	$50k
SOL, ETH, HYPE, XRP	$40k split evenly
BNB, DOGE	$10k split evenly

Methodology

Liquidity providers are rewarded based on a formula that rewards participation in markets, boosts two-sided depth (single-sided orders still score), and tighter spread vs the size-cutoff-adjusted midpoint. Each market configures 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.

Variables

Variable	Description
S	Order position scoring function
v	Max spread from midpoint (in cents)
s	Spread from size-cutoff-adjusted midpoint
b	In-game multiplier
m	Market
m’	Market complement (i.e. NO if m = YES)
n	Trader index
u	Sample index
c	Scaling factor (currently 3.0 on all markets)
Q_ne	Point total for book one for a sample
Q_no	Point total for book two for a sample
Spread%	Distance from midpoint (bps or relative) for order n in market m
BidSize	Share-denominated quantity of bid
AskSize	Share-denominated quantity of ask

Equations

1. Order Scoring Function

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

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

2. First Market Side Score

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}

3. Second Market Side Score

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}

4. Minimum Score

Boosts two-sided liquidity by taking the minimum of Q_ne and Q_no, while still rewarding single-sided liquidity at a reduced rate (divided by c). If midpoint is in range [0.10, 0.90] — single-sided liquidity can score:

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

If midpoint is in range [0, 0.10) or (0.90, 1.0] — liquidity must be double-sided to score:

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

5. Normalized Score

Q_min of a market maker divided by the sum of all Q_min across market makers in a given sample:

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

6. Epoch Score

Sum of all Q_normal for a trader across all samples in an epoch. An epoch is one UTC day; the order book is sampled once per minute at a random offset, so an epoch contains up to 1,440 samples:

Q_{epoch} = \sum_{u=1}^{1,440}{(Q_{normal})_u}

7. Final Score

Normalizes Q_epoch by dividing by the sum of all market makers’ Q_epoch in a given epoch. This value is multiplied by the rewards available for the market to get a trader’s reward:

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

Worked Example

Assume an adjusted market midpoint of 0.50 and a max spread config of 3 cents for both m and m’.

Step 2 - First Side Score

A trader has the following open orders:

100Q bid on m @ 0.49 (spread = 1 cent)
200Q bid on m @ 0.48 (spread = 2 cents)
100Q ask on m’ @ 0.51 (spread = 1 cent)

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.

Step 3 - Second Side Score

The same trader also has:

100Q bid on m @ 0.485 (spread = 1.5 cents)
100Q bid on m’ @ 0.48 (spread = 2 cents)
200Q ask on m’ @ 0.505 (spread = 0.5 cents)

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.

Steps 4-7

Take the minimum of Q_ne and Q_no (with single-sided adjustment if midpoint is in [0.10, 0.90])
Normalize against all other market makers in the sample
Sum across all 1,440 samples in the epoch (one UTC day)
Normalize again to get final reward share

Getting Started

Core Concepts

Market Data

Trading

Programs

Migration Guides

Resources

Find Markets to Quote

Read Daily Earnings

Crypto TWAP Rewards

5-Minute Markets — $550k

15-Minute Markets — $350k

4-Hour Markets — $100k

Methodology

Variables

Equations

1. Order Scoring Function

2. First Market Side Score

3. Second Market Side Score

4. Minimum Score

5. Normalized Score

6. Epoch Score

7. Final Score

Worked Example

Step 2 - First Side Score

Step 3 - Second Side Score

Steps 4-7

​Find Markets to Quote

​Read Daily Earnings

​Crypto TWAP Rewards

​5-Minute Markets — $550k

​15-Minute Markets — $350k

​4-Hour Markets — $100k

​Methodology

​Variables

​Equations

​1. Order Scoring Function

​2. First Market Side Score

​3. Second Market Side Score

​4. Minimum Score

​5. Normalized Score

​6. Epoch Score

​7. Final Score

​Worked Example

​Step 2 - First Side Score

​Step 3 - Second Side Score

​Steps 4-7

Find Markets to Quote

Read Daily Earnings

Crypto TWAP Rewards

5-Minute Markets — $550k

15-Minute Markets — $350k

4-Hour Markets — $100k

Methodology

Variables

Equations

1. Order Scoring Function

2. First Market Side Score

3. Second Market Side Score

4. Minimum Score

5. Normalized Score

6. Epoch Score

7. Final Score

Worked Example

Step 2 - First Side Score

Step 3 - Second Side Score

Steps 4-7