DXAPEnter app

AI trading, from idea to operation.

DXAP is a hosted AI trading agent platform for Hyperliquid traders who want to describe a strategy in plain language, set its limits and inspect its decisions.

Published by DXAP, a product of DX Research Group. Updated September 13, 2026.

Choose an operating approach, turn a thesis into instructions, or understand what to inspect before and after a trade. Strategy examples are prompts to adapt, not preconfigured agents.

Choosing a platform

  • Hosted vs self-hosted trading agents

    Use a hosted platform when your priority is directing a strategy rather than maintaining trading infrastructure. Self-hosting gives you responsibility for the software, credentials, uptime and upgrades. DXAP is a hosted Hyperliquid agent platform: you manage the strategy and account permissions.

  • AI trading agent vs rule-based bot

    A rule-based bot executes conditions you specify. An AI trading agent can interpret context before proposing an action. DXAP combines model reasoning with a separate policy engine, so flexible strategy interpretation and execution limits have different roles.

  • Build a trading strategy in plain English

    In DXAP, describe what the agent should consider, when it should stay out and how you want decisions explained. Configure enforceable account and risk limits separately. A useful natural-language strategy gives the agent a decision process rather than a profit target alone.

  • AI trading without a monthly subscription

    DXAP has no monthly subscription or separate model bill. It charges 0.025% of volume executed by your agent on Hyperliquid. That makes the software bill depend on trading activity, not merely keeping an account open.

  • Choose an AI trading platform, not just a model

    The model is one part of a trading agent. Data access, instructions, execution permissions, policy checks and decision records determine what the complete system can do. DXAP combines these in a hosted Hyperliquid platform.

Operating an agent

  • Move from manual trading to a Hyperliquid agent

    Start by translating your existing decision process into instructions, then review the agent against that process. DXAP lets you direct a hosted Hyperliquid agent and inspect its decisions. The goal is a controlled handoff of specific tasks, not an unexplained change of strategy.

  • How to review why an AI trading agent traded

    Review the instruction, the information available at the time, the proposed action and the execution result. DXAP records agent turns, including no-trade turns, and supports debriefs so you can ask why the agent acted or stayed out.

  • Pause an agent, revoke access, or close a position?

    Pausing an agent, revoking its trading authorization and closing a position are different operations. In DXAP, review the agent state and the Hyperliquid account separately when stopping automation. Existing exposure needs an explicit check.

  • Risk limits versus strategy prompts in AI trading

    A strategy prompt tells the model how you want it to reason. A policy setting defines a boundary that execution must respect. DXAP separates these roles: the model proposes actions and the policy engine checks them outside the model.

  • Why an AI trading agent decides not to trade

    A no-trade decision can be a valid result of the strategy. In DXAP, inspect the recorded turn to distinguish weak evidence, conflicting context and a configured constraint from an operational problem.

  • Trading-agent costs: fees, funding and turnover

    Budget for platform fees, venue execution fees, perpetual funding and the difference between intended and actual execution prices. DXAP’s 2.5 bps charge is one component of the cost of running an agent on Hyperliquid.

Strategy examples

  • Design a news-aware Hyperliquid trading agent

    Use news as context for a market thesis, then ask the agent to consider conflicting evidence and the current market. DXAP provides news and research inputs alongside Hyperliquid data. A headline should not automatically become an order.

  • Use social sentiment as trading context

    Social discussion can suggest a question to investigate. It should not substitute for a market thesis. DXAP includes Crypto Twitter Alpha, a lab-curated and tagged X stream, among its documented inputs for agent decisions.

  • Use prediction-market odds as trading context

    DXAP can use Polymarket odds as context while trading Hyperliquid perpetuals. Reading a prediction market and placing a trade in that market are different capabilities. This workflow concerns research inputs for a Hyperliquid agent.

  • Give an AI trading agent order-book context

    Order-book data helps describe current liquidity and execution conditions. DXAP includes Hyperliquid order-book access among its documented tools. Use it to inform a decision, while recognizing that visible liquidity can change before an order executes.

  • Write trend-following instructions for an AI agent

    A useful trend-following instruction defines what evidence supports continuation, what contradicts it and when the agent should wait. DXAP can interpret a plain-language thesis using available market context, subject to configured limits.

  • Write mean-reversion instructions for an AI agent

    Mean reversion requires a reason to expect a move to reverse, not just an observation that price has fallen or risen. DXAP lets you express that distinction in a strategy and inspect how the agent applied it.

Comparisons

  • Autonomous AI agents vs copy trading

    DXAP is for directing your own Hyperliquid agent with a strategy and configured limits. Copy trading follows another trader’s activity. Choose according to whose decision process you want to run and what you need to inspect.

  • AI strategy builders vs autonomous trading agents

    AI can help construct a strategy without making its later trading decisions. DXAP uses a model during agent turns, then checks proposed actions in a separate policy engine. Choose between AI-assisted authoring and ongoing interpretation of market context.

  • Visual trading workflows vs managed AI agents

    A visual workflow gives you blocks to connect. DXAP gives you an integrated Hyperliquid agent to direct and inspect. Choose whether you want to assemble the automation path or express a strategy within a managed trading system.