400ab325ed
- 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>
58 lines
3 KiB
Markdown
58 lines
3 KiB
Markdown
# The hAIrberger Protocol
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## Problem / Current Situation:
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A static liquidity provider strategy in a dynamic market leads to:
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- impermanent loss
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- reduced earnings
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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.
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## Succesfull/Dynamic LP strategies use indicators:
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onchain:
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- current and historic price
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- current and historic volume
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- current and historic liquidity distribution
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offchain:
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- TA
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- Macro
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- Sentiment
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## Making the Kraiken LP strategy dynamic
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- **Oracle Problem \#1:** The offchain indicators can not be used by decentralized communities at all, having a unsolved oracle trust issue.
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- use staking, an egoistic marketplace, as data source for sentiment
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- % staked
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- average tax rate
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=> egoistic oracle marketplace
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- **Using historic data:** The onchain indicators need to be digested, e.g. TA to get signals.
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- algorithmic trading / backtesting => model => deep learning
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- fold historic data into AI model training
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- **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).
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- use on-chain algorithm that works with real-time data, historic knowledge and limited gas
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=> **follow-up problem:** "write an algorithm that uses limited gas to maximize fees earned, starting with no training data."
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- **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.
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- **Genetic algorithms** are used to generate solutions to optimization problems via biologically inspired operators such as selection, crossover, and mutation.
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- selection can be applied on:
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- gas usage
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- fees earned after running scenarios
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- scenarios can be replaced by historic trading data after launch of project
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- result of evolution (latest generation) can be deployed onchain and integrated into recenter
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- recenter() => getDynamicLiqParams() => _scrapePositions() => _setPositions()
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- function getDynamicLiqParams(inputs) returns (params) is an EA living in upgradeable contract
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=> **follow-up problem:** EVM op-codes can not be used in Evolution, too much grammar
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=> **follow-up problem:** upgradeable contract needs governance
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- **Evolving Stack Machines** instead of reverting, invalid op-codes should be ignored.
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- [Push3 by Lee Spector](https://www.youtube.com/watch?v=ryW9w5cAwaI)
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=> implement Push3 runtime in solidity
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- **Governance** tbd
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## Conclusion
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- a dynamic strategy is possible, first using human-generated solutions (sentiment function), and later upgrading into GA.
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- basic governance to be solved before GA.
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