Robonet has successfully integrated with Allora’s decentralized machine learning network, now operational in production. This partnership allows Robonet strategies to leverage real-time ML inferences for making critical trading decisions, including entries, exits, and position sizing. The Allora Network enhances this capability by coordinating multiple ML models to synthesize validated on-chain inferences, thus supporting the no-code deployment of AI agents in various trading environments.

Robonet: Robonet is a prompt-to-quant execution engine that lets users build, backtest, and deploy automated trading strategies using natural language without coding. It supports DeFi venues for perpetuals and prediction markets, enabling autonomous AI agents for trading. In this news, Robonet is live in production and integrates Allora Network’s decentralized ML inferences for strategy logic like entries, exits, position sizing, and optimization.
RoboNetHQ: RoboNetHQ is the official X account for Robonet, sharing updates on its AI-native trading platform. It highlights features like prompt-based strategy deployment and integrations. The account detailed the production integration with Allora Network for decentralized ML in trading.
Allora Network: Allora Network is a self-improving decentralized AI network that aggregates predictions from community-built machine learning models for context-aware inferences. It powers applications with secure, decentralized intelligence. Robonet pulls live inferences from Allora’s production ML network to run real trading strategies.

`json
{
“Live Integration”: “Robonet strategies now use decentralized ML inferences from Allora Network for real-time trading decisions.”,
“AI Network Features”: “Allora Network coordinates multiple ML models for on-chain inference synthesis, supporting integrations like Robonet.”,
“Trading Capabilities”: “Robonet enables deployment of AI agents across trading and prediction markets using natural language prompts.”
}
`