Cryptocurrency March 9, 2026

Walbi Unveils No-Code AI Trading Agents to Bring Autonomous Crypto Trading to Retail Users

Platform enables traders to describe strategies in plain language, offering 24/7 autonomous execution, a strategy marketplace, and built-in risk limits

By Sofia Navarro
Walbi Unveils No-Code AI Trading Agents to Bring Autonomous Crypto Trading to Retail Users

Walbi has launched a no-code environment that lets retail crypto traders create, test, and deploy AI-driven trading agents without programming. Agents draw on portfolio data, technical indicators, macro signals and liquidation metrics, and were trialed in a 14-week closed beta with over 1,000 participants who created more than 9,500 agents and generated 187,000 autonomous trades.

Key Points

  • No-code AI agents allow retail traders to create autonomous trading strategies via plain-language instructions, lowering the barrier to strategy automation - impacts retail crypto trading and fintech infrastructure.
  • A 14-week closed beta (Oct 2025 - Jan 2026) tested agent stability and risk controls with over 1,000 participants, more than 9,500 agents created, and 187,000 autonomous trades - impacts trading platform operations and risk management frameworks.
  • Walbi launched a live strategy marketplace with transparent performance and risk metrics so users can review return history before investing - impacts capital allocation and strategy distribution within crypto markets.

Dubai, UAE - March 9th, 2026

Walbi, a trading platform focused on blockchain markets, has rolled out no-code AI trading agents designed for retail crypto traders. The new feature allows users to build, test, and activate autonomous trading agents by describing their strategy in natural language rather than writing scripts or configuring rigid rule sets.


How the no-code AI agents work

Users working in Walbi's environment can specify strategy elements - including timeframes, risk parameters, and entry or exit logic - through plain-language instructions. Once defined, an AI agent operates inside Walbi's ecosystem and bases its execution on a blend of inputs: portfolio holdings, standard technical indicators, a published economic calendar, the Fear & Greed Index, and liquidation insights. Interaction and adjustments occur through chat, enabling traders to keep direction and oversight over agent behavior without managing code or infrastructure.

Walbi positions this workflow as a way for retail traders to formalize and deploy structured, autonomous strategies without relying on developer teams or manual automation tools. The company says the agents run continuously, responding to changing conditions in real time, and aim to provide quicker execution than manual trading and broader contextual awareness than traditional rule-based bots.

"Retail traders are familiar with automation, but most existing tools require coding or rigid rule configuration," said Anthony Cerullo, CCO at Walbi. "Our goal with AI agents is to make strategy automation accessible through dialogue, while maintaining transparency and user control."


Closed beta results and testing approach

Before the public launch, Walbi ran a 14-week closed beta from October 2025 through January 2026. The stated objective of the trial was not to beat the market, but to observe how agents performed under live crypto futures conditions - specifically assessing stability, execution logic, and the effectiveness of risk controls.

During the beta, more than 1,000 participants created over 9,500 distinct agents, which together executed 187,000 autonomous trades. The company reports that most users experimented with multiple configurations before finding stable strategy logic.

Performance across agents varied notably with market regimes and individual risk settings. While a majority of agents ended the period with positive results, outcomes were uneven and closely tied to prevailing volatility. Walbi supplemented live testing with backtesting across historical datasets that included both trending markets and high-volatility episodes to further stress-test agent logic beyond the 14-week window.

Walbi observed that momentum-focused configurations which incorporated Fear & Greed Index signals and liquidation information tended to show the most consistent behavior during volatile stretches. The company also cautioned that, as with any leveraged futures trading, drawdowns occurred and capital risk remained material.


How these AI agents differ from rule-based bots

Many existing algorithmic tools in the market rely on fixed scripts or pre-programmed rule sets. Walbi's agents, by contrast, integrate multiple data streams into their decision-making process - not only technical indicators but also news signals and macroeconomic events - allowing continuous 24/7 operation and real-time responses to changing market conditions.

Walbi frames this as a meaningful distinction for volatile crypto markets: faster execution than manual approaches and a broader contextual understanding than typical rule-based bots, enabled by the agents' multi-source inputs and conversational control.


Strategy marketplace and transparency

Alongside the no-code agent builder, Walbi has opened an AI agent marketplace where experienced traders can publish strategies and others can allocate capital to them. Each listing provides transparent performance data, including return history and risk metrics, so prospective users can evaluate strategies before investing.

The marketplace is live, and Walbi says it is actively seeking collaboration with strategy creators while gathering feedback from retail users on practical performance and usability.


Company overview and contact

Walbi describes itself as a platform for building AI trading agents: users describe a strategy, and the system converts that description into an autonomous agent that monitors markets in real time and trades within predefined risk limits. Agents can be constructed independently or by plugging into strategies from the marketplace. Walbi reports 2.9M registered users and growing.

Contact: Walbi Marketing - [email protected]

Risks

  • Performance was uneven and highly dependent on market volatility regimes; while most agents ended the beta period positively, results varied across configurations - this affects retail traders and leveraged futures markets.
  • Agents trading in leveraged futures experienced drawdowns and material capital risk, underscoring exposure to loss in volatile conditions - this is a risk for individual investors and platform risk oversight.
  • Reliance on multiple data streams and automated decision-making means execution and model behavior can shift with changing inputs; effectiveness of risk controls was a central focus of the beta but remains an operational uncertainty - relevant to trading infrastructure and market stability.

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