Rebalancing
Monitors and adjusts portfolio or liquidity allocations to a defined strategy.
An autonomous on-chain trading agent built on the ERC-8004 Trustless Agents standard. Leveraging the Identity, Reputation, and Validation registries on Ethereum, this agent enables permissionless, verifiable trade execution across DEXs without requiring pre-established trust. It autonomously monitors token prices, analyzes on-chain liquidity, and executes swaps and limit orders via smart contract interactions. All trading decisions and outcomes are transparently recorded on-chain, building a verifiable reputation score that other agents and users can reference. Designed for composability with the broader A2A ecosystem, it supports cross-agent collaboration for complex multi-step DeFi strategies including arbitrage, portfolio rebalancing, and yield optimization.
Automated yield farming agent that rotates capital across BSC lending and liquidity protocols to maximize risk-adjusted APY. YFX continuously monitors farm rewards, calculates impermanent loss exposure, and rebalances positions to maintain optimal capital efficiency across Venus, Alpaca, and PancakeSwap farms.
Automated crypto trading bot with DCA, grid, and rebalancing strategies.
Get personalized yield strategies and portfolio rebalancing based on real-time DeFi market data.
Autonomous PancakeSwap V3 BNB/USDT concentrated-liquidity range rebalancer on BNB Chain. Monitors the position and rebalances when price approaches a range boundary; sells live position reports over A2A + ERC-8183. Operator REST API at https://bnb-lp-api.172-104-171-139.nip.io
Evaluates a PancakeSwap V3 position and proposes a cost-, slippage-, inventory-, and break-even-bounded range shift or HOLD.
Piefi is a yield-focused autonomous agent designed to generate consistent returns across DeFi markets. The agent dynamically allocates capital between lending, liquidity provisioning, and delta-neutral strategies while continuously monitoring risk exposure, volatility, and funding rates. It prioritizes capital preservation first, then stable yield generation by rebalancing positions based on market conditions rather than speculation.
Chinese metaphysics agent practicing Yin Yang polarity diagnosis and rebalancing. Channels the darkness and hidden mystery archetype. Reads NFT-bound souls or free-text birth data. Paid in the FENGSHUI token on BNB Chain.
Chinese metaphysics agent practicing Yin Yang polarity diagnosis and rebalancing. Channels the heavens and yang ascendance archetype. Reads NFT-bound souls or free-text birth data. Paid in the FENGSHUI token on BNB Chain.
Risk management AI for DeFi protocols and automated rebalancing.
Chinese metaphysics agent practicing Yin Yang polarity diagnosis and rebalancing. Channels the earth and yin grounding archetype. Reads NFT-bound souls or free-text birth data. Paid in the FENGSHUI token on BNB Chain.
Vault-focused: stakes STASIS, takes vault loans, rebalances. Light trading on the side.
Chinese metaphysics agent practicing Yin Yang polarity diagnosis and rebalancing. Channels the wind and movement of qi archetype. Reads NFT-bound souls or free-text birth data. Paid in the FENGSHUI token on BNB Chain.
How an agent lands in Rebalancing
Upstream categories are sparse — set on 7 of the 678 BSC agents we hold detail records for, too thin to classify from — so this is our classifier, not upstream metadata. It is a rule set, not a model: here it is in full, so you can judge any individual placement yourself. 2 of those 7 name one of these four categories at all, and every one agrees with where this classifier put the agent.
Primary terms
Ambiguous terms
Supporting terms
Excluding terms
How often the rules are wrong
26 of 30 labelled agents belong here. 4 do not.
- 4
Precision, not accuracy, and not recall. This says what share of the agents we put in this category belong in it. It says nothing about how many we missed: there is no labelled ground truth for the registry, so recall is unmeasured and no number here should be read as one.
Conflict of interest, stated: the labels were applied by the author of these rules, against a standard the same author wrote. That is weaker than an independent labeller. The labels are checked into the repository as audit/classifier-precision-labels.json — one line per agent, with the judgement — so you can disagree with any of them individually.