Applicant Name or Alias: Numa

Project Name: Logarithm Finance

Project Description:

Logarithm builds an on-chain public derivative crossing infrastructure and delivering asymmetric yields by optimizing leveraged trades.

Logarithm Thesis

The market is transitioning into a new phase where on-chain leverage is set to be one of the major growth areas. On-chain basis trading yields higher returns than basis trading on centralized exchanges due to the limited number of professional trades on-chain. This results in more favorable funding rates and more opportunities for lucrative yields. Furthermore, assets are evolving into productive assets. On-chain basis trading offers the opportunity to earn extra profit from these products and build on-chain public derivative crossing infrastructure.

Team Members and Qualifications:

Our roots are in traditional finance and cryptocurrency markets, with extensive experience in MEV bots, venture capital, asset management, and financial data & trading infrastructure. Logarithm team is a hub of mathematical talent, boasting educational backgrounds from leading technical universities.

  • Numa - CEO & SmC Architect
  • Boring - CTO & Backend
  • Alex - DevOps & Fullstack
  • Tal - Solidity Dev
  • Gamma - Quant Dev
  • Peter - Data Engineer
  • Light - Research Analyst

Project Links:

Contact Information:


Requested Grant Size:

Requested Grant Size: 75,000 $ARB

Grant Breakdown:

  • 60% of funds will be be used to incentivize depositors in the new types of vaults

    • 40% will be assigned to the token-based vaults (WETH, WBTC notional).
    • 20% will be assigned to the GMX meta vault.
  • 30% of funds will be used to cover internal costs for building, delivering, and supporting backtesting engine and analytics tools including specialized GMX V2 dashboards, data loaders, etc.

  • 10% of funds will be allocated to encourage the developers community and asset manager protocols to integrate their entities & strategies in the python library.

Here’s how we plan to distribute ARB incentives to Liquidity Providers:

  • Every epoch, a predetermined quantity of ARB is allocated for each market.

  • We calculate proportional rewards using a time-weighted average of users’ vaults balances over the week.

  • After each epoch ends, ARB incentives are directly airdropped to LPs.

  • The number of ARB allocated in each phase of this program can occasionally vary. This helps improve the effectiveness of GMX’s grant.

Funding Address:


Funding Address Characteristics:

Gnosis Safe Multisig (2/3)



  • Conduct research, analytics, and tooling for the GMX V2 funding market structure.

  • Priority release of new types of strategies on GMX V2.

    • There is a significant liquidity difference between USDC-based and token-based pools. By using Logarithm Basis vaults with token-based notionals like ETH and WBTC, we aim to significantly increase the liquidity of token-based vaults.

Source: GMX | Decentralized Perpetual Exchange (13 Jun 2024)

  • For now, Logarithm Finance offers isolated basis markets for each GMX V2 market. The next goal is to equip GMX V2 users with a meta strategy. This strategy will include a funding yield optimization algorithm that aims to outperform funding rate market benchmarks.

  • Overall boost trading volume & TVL on GMX by encouraging deposits and active participation in GMX V2 Vaults.

Key Performance Indicators (KPIs):

  • Conduct research, analytics, and tooling for the GMX V2 funding market structure.

    • Publish at least two research pieces on the GMX V2 funding rate structure and market overview to assist users in developing their strategies for GMX V2.

    • Release community python backtesting library equipped with an advanced toolkit for creating trading strategies on GMX V2, and incentivize at least 5 community contributors who add GMX related strategies, entities and data loaders to the public repository.

    • Publish at least two dashboards:

      • Live GMX V2 data focused on funding rates analysis (which will be the advanced version of the GMX internal dashboard).
      • Live meta and vaults performance and execution dashboard.
  • Release new types of strategies on GMX V2.

    • Increase liquidity by at least 30% in GMX V2 GM token-based liquidity pools using a Logarithm basis notional vaults.

    • Incentivize up to $1,000,000 worth of Open Interest across all supported assets through basis strategies as a part of a grant.

    • Launch the meta funding yield strategy, ensuring that backtest results exceed the market benchmark (WFRI - OI weighted funding rate index per exchange).

    • Track the increase in the number of users, specifically targeting those interested in low-volatility investment opportunities.

    • Note: Logarithm will continue to grow the OI and liquidity of GMX V2 GM tokens through basis trading strategies even after grant incentives.

How will receiving a grant enable you to foster growth or innovation within the GMX ecosystem?

Our grant primarily focuses on two main objectives.

  1. Our team possesses unique and advanced expertise in trading, asset management, and analytics. We aim to share this expertise with the GMX ecosystem to enrich it with research, analytics, and tooling.

  2. Our strategy enhances the GMX ecosystem by providing additional liquidity & volume to existing GMX markets and balancing open interest in these markets. This added liquidity is expected to reduce volatility in funding rates across GMX markets.

Execution Strategy:

  • Conduct and publish a comprehensive research aboutGMX V2 funding rate market and publish a GMX V2 funding focused dashboard.

  • Launch WETH, WBTC notional vaults and initiate the first wave of the incentive program.

  • Launch a meta vault with USDC-based deposits and start the second wave of the incentive.

  • Release a backtesting tool along with an article on backtest results. Open incentivized issues for incorporating new strategies based on GMX.

Grant Timeline:

  • Grant Approval - 10%

  • Milestone 1: GMX V2 market dashboard & funding research - 15%

  • Milestone 2: Release backtesting tool with GMX V2 entities, strategies examples, and data loaders - 15%

  • Since the vaults run grant streams linearly for the duration of your grant proposal

    • Milestone 3: WETH, WBTC notional vaults - 40%

    • Milestone 4: USDC meta vault - 20%


  • What date did you build on GMX?

    Since April 2023, the team has been executing on-chain strategies on GMX, even starting with GMX V1.

  • Protocol perfomance

    We are currently in public beta on the Arbitrum Mainnet, and we have capped vaults to minimize community risk. After completing the audit, we plan to execute the mainnet launch.

    Here you can find strategies perfomance since inception: Basis Vaults Dashboard

  • Protocol Roadmap

    You can find our roadmap here.

  • Audit History (if any)

    Our core contracts are currently under audit by the first-tier audit company Hexens.

SECTION 5: Data and Reporting

  • Is your team prepared to create Dune Spells and/or Dashboards for your incentive program?

    Yes, we already have an infrastructure of Grafana dashboards with detailed data on protocols and vaults and we will create Dune dashboards too.

  • Does your team agree to provide bi-weekly program updates on the GMX Forum thread?


  • Does your team acknowledge that failure to comply with any of the above requests can result in the halting of the program’s funding stream?


1 Like

Hey @logarithm, Thanks for submitting the application.


The proposal mentions “pre-made GMX V2 entities” within the backtesting library. Can Logarithm Finance elaborate on what these entities represent and how they interact with GMX V2 contracts?

Our backtesting library consists of 3 main modules: loaders, entities, and strategies. Loaders serve as ad-hoc ETLs, loading market data for backtesting. Entities are replicated in Python ocnhain objects. The strategy module is the core, with each strategy functioning as a class object. These objects possess a set of registered entities and can manipulate them to craft policy based on observations. By “GMX V2 entities”, we offer ad-hoc loaders for GMX data such as prices, funding rates, and borrowing rates. In addition, we provide Python classes with replicated GMX V2 trading logic, along with strategy examples from the Logarithm Finance protocol.

it doesn’t elaborate on how accurate this replication is. Can you quantify the level of accuracy in replicating GMX V2 trading for backtesting purposes?