On-chain rule-based trading bot that executes predefined actions based on price-movement triggers — allowing users to automatically enter, rebalance, and exit positions. Features intelligent liquidity routing, dynamic slippage tolerance, and MEV-aware execution — optimized for the Base network.
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Liquid Flow is a non-custodial, rule-based liquidity automation engine operating on Base. Instead of swapping one token for another, the system automates transitions between holding a main stable asset (such as USDC, USDT, or other stablecoins) and providing liquidity in pools. Users configure the logic once, and the system executes it consistently based on real market conditions.
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Users define conditions such as percentage drop or rise thresholds, and associate them with actions to perform. When an exit condition is triggered, Liquid Flow removes liquidity from the pool, receives both pool tokens, and converts them into the main stable asset. When an entry condition is triggered, the system converts the main stable asset into both pool tokens in the required ratio and adds liquidity.
This enables automatic rotation between a stable defensive position and active LP exposure, synchronized with market direction.
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Liquid Flow works with both standard AMM pools and concentrated-liquidity pools. On entry, the system splits the main stable asset into the token pair required by the pool and adds liquidity. On exit, it removes liquidity, obtains both pool tokens, and consolidates back into the chosen stable asset. To the user this is a single action, even though multiple on-chain calls may be required behind the scenes.
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The core execution engine is implemented in Python. It continuously monitors price feeds and liquidity states, evaluates trigger conditions, and initiates the required on-chain transaction sequences for LP entry or exit. The engine runs independently of user presence, executing purely according to predefined rules.
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The interface is built using React and TypeScript, with Viem and Wagmi providing blockchain interaction. Thanks to Wagmi’s optimized data fetching, the system reduces redundant RPC calls and avoids multiple parallel queries, improving performance and responsiveness of the UI even during high-frequency state monitoring.
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Because LP actions typically involve different contracts (liquidity removal and token swaps), the system executes the flow as multiple transactions when required. Where operations are confined to a single contract, we can reduce the number of user confirmations using EIP-5792-compatible batching. The goal is to reduce friction while maintaining correct execution semantics and clarity for the user.
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Slippage settings are configured per operation type, particularly for LP exits to stable assets and LP entries from stable assets. Liquid Flow enforces user-defined slippage bounds during execution. Additionally, the system avoids routing paths known to be vulnerable to sandwiching or aggressive MEV extraction, improving execution quality and reducing adverse price impact during volatile periods.
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Liquid Flow integrates with existing audited AMM and liquidity pool contracts on Base, rather than introducing new core contracts. All operations — liquidity adds/removals, conversions between stable assets and pool tokens, state reads — are executed directly through established protocols. This ensures reliability and minimizes additional contract risk.
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Every key action — trigger activation, pool exit into a stable asset, re-entry into a pool — is reflected on-chain. Users can inspect transactions via standard block explorers and verify the entire lifecycle of the strategy. This preserves transparency while benefiting from automated execution.
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Liquid Flow was fully designed and engineered by 5Hz specifically for our client. We created the architecture, the automation engine, the on-chain execution model, the LP interaction flows and the user-facing dashboard. The delivered system enables automated LP positioning, reduced operational overhead and disciplined execution based on clearly defined market-triggered rules.