johba/harb

johba 400ab325ed refactor: Complete project renaming from HARB/Harberger to KRAIKEN

- Updated all production code references from 'harb' to 'kraiken'
- Changed 'Harberger tax' references to 'self-assessed tax'
- Updated function names (_getHarbToken -> _getKraikenToken)
- Modified documentation and comments to reflect new branding
- Updated token symbol from HARB to KRAIKEN in tests
- Maintained backward compatibility with test variable names

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

2025-08-19 11:05:08 +02:00

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The hAIrberger Protocol

Problem / Current Situation:

A static liquidity provider strategy in a dynamic market leads to:

impermanent loss
reduced earnings

Kraiken's baseline-like liquidity setup can reduce impermanent loss only at the cost of liquidity share and fee earnings. Token-printing-priviliges gives unfair advantage, but not forever.

Succesfull/Dynamic LP strategies use indicators:

onchain:

current and historic price
current and historic volume
current and historic liquidity distribution

offchain:

TA
Macro
Sentiment

Making the Kraiken LP strategy dynamic

Oracle Problem #1: The offchain indicators can not be used by decentralized communities at all, having a unsolved oracle trust issue.
- use staking, an egoistic marketplace, as data source for sentiment
  - % staked
  - average tax rate => egoistic oracle marketplace
Using historic data: The onchain indicators need to be digested, e.g. TA to get signals.
- algorithmic trading / backtesting => model => deep learning
- fold historic data into AI model training
Oracle Problem #2: Any algorithm running offchain can not be trusted by community. AI models can not run onchain. Trained models can't react to real-time data (see LMM knowledge cutoff).
- use on-chain algorithm that works with real-time data, historic knowledge and limited gas => follow-up problem: "write an algorithm that uses limited gas to maximize fees earned, starting with no training data."
Metaheuristic: is a higher-level procedure designed to find, generate, and tune partial search algorithm that may provide a sufficiently good solution to a machine learning problem, especially with incomplete or imperfect information or limited computation capacity.
- Genetic algorithms are used to generate solutions to optimization problems via biologically inspired operators such as selection, crossover, and mutation.
- selection can be applied on:
  - gas usage
  - fees earned after running scenarios
    - scenarios can be replaced by historic trading data after launch of project
- result of evolution (latest generation) can be deployed onchain and integrated into recenter
  - recenter() => getDynamicLiqParams() => _scrapePositions() => _setPositions()
  - function getDynamicLiqParams(inputs) returns (params) is an EA living in upgradeable contract => follow-up problem: EVM op-codes can not be used in Evolution, too much grammar => follow-up problem: upgradeable contract needs governance
Evolving Stack Machines instead of reverting, invalid op-codes should be ignored.
- Push3 by Lee Spector => implement Push3 runtime in solidity
Governance tbd

Conclusion

a dynamic strategy is possible, first using human-generated solutions (sentiment function), and later upgrading into GA.
basic governance to be solved before GA.

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