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Challenge

Autonomous Crypto Trading Agent

Develop an autonomous agent system for trading cryptocurrencies using advanced AI models and agent communication protocols. The system will leverage real-time market data and predictive analytics to make informed trading decisions. This requires integrating with multiple cryptocurrency exchanges, leveraging sophisticated risk management strategies, and ensuring regulatory compliance. It aims to build a system capable of consistent, profitable, and adaptable trading strategies within the complex cryptocurrency market.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build a multi-agent system using A2A protocol and GPT-5 for crypto trading.

Delivery guide

How work is evaluated

Evaluation

Evaluate the trading agent's performance in a simulated market environment.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Learning goals

What you should walk away with

  • Develop a multi-agent system for crypto trading

  • Implement risk management strategies in the agent system.

  • Perform continuous learning and adaptation to changing market conditions

  • Integrate with multiple cryptocurrency exchanges API via MCP.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 1 tasks

Challenge setup

Evaluate the trading agent's performance in a simulated market environment.

Challenge input

Market data (historical and real-time)

Agent execution

No agent tool is listed for this challenge.

Evaluated output

Trading decisions (buy/sell orders)

Evaluation checks
  • Trading agent respects regulatory compliance guidelines
Configured evaluation evidence
  • Profitability target: 80
Configured runtime
  • Python execution harness

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

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Configured tools

No tool records are attached.

Evaluation contract

  • Trading agent respects regulatory compliance guidelines

Metrics

  • Profitability ยท Target 80

Workflow tasks

  1. Task 1

    Input: Market data (historical and real-time)

    Output: Trading decisions (buy/sell orders)

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

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