dydx Re-launch Rewards Explainers
Table of Contents
Trading Points
Traders earn points by paying taker fees. Taker fees in non top 5 markets will earn twice as many points as fees in the top 5 markets.
At the end of the season, the value of DYDX tokens earned by a trader will be calculated as their share of total trading points multiplied by the $600k trading reward pool.
A 7-day TWAP of the DYDX price will convert the allocated dollar reward amounts into DYDX tokens, which will then be distributed.
Market Maker Points
Market makers earn points by fulfilling taker orders and providing liquidity to the order book. The closer the spreads on this liquidity, the higher its weighting.
At the end of each hour, each maker's total maker volume satisfied, V, and liquidity score, L, will be calculated in each market, i. The number of maker points earned over the hour in all markets will then be computed as: where
is computed as the total dollar value of maker volume in the market i in the hour
is computed as follows:
- The dollar value of each market maker’s order book liquidity in each market is sampled frequently throughout the hour within 25bps, 50bps, and 100bps of the market price.
- The liquidity score is calculated in each market as follows:
Only makers satisfying over 0.25% of total maker volume over a season will earn maker rewards at the end of a season.
Season 7 will run for 1 month from the launch of dYdX Unlimited. There will be four seasons in this iteration of the incentive program.
A 7-day TWAP of the DYDX price will convert the allocated dollar reward amounts into DYDX tokens, which will then be distributed.
AVS Risk Assessment Methodology
This model quantifies maximum slashing risk, referred to here as Value at Risk (VaR), by analyzing slashing behavior across multiple node operators and AVSs. Notably, slashing one operator on a particular AVS does not guarantee that others will be slashed simultaneously, nor does slashing on one AVS imply slashing on all AVSs secured by the same operator.
Trusting Trust in the Age of AI
While resources are heavily invested in improving frontier models and optimizing prompts, the true challenge lies in how AI systems retrieve and rank information. Chaos Labs identifies the greatest risk in AI-generated misinformation infiltrating the data pipeline. If AI agents are unknowingly trained on manipulated or sybilled content, it becomes nearly impossible to trust their outputs. This vulnerability is exacerbated by unreliable document ranking systems, which prioritize popularity and commercial interests over accuracy. The Dead Internet Theory, which warns of a future where human-created content is drowned out by machine-generated noise, serves as a chilling reminder of what’s at stake if these systems are not carefully safeguarded.
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