DeFAI Trading Agent

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Prerequisites

• Python >=3.10
• Git
• Familiarity with blockchain concepts and Solana development (optional)

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Setup

1. Clone the Galadriel repository:
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   git clone <repository_url>
   cd galadriel
   

   Replace <repository_url> with the actual URL of the Galadriel repository.
2. Create a virtual environment:
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   python3 -m venv venv
   source venv/bin/activate
   

3. Install the Galadriel framework:
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   pip install galadriel
   

4. Configure environment variables:
   • Rename template.env to .env and add your OpenAI API and Coingecko API keys:
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     OPENAI_API_KEY=<YOUR_OPENAI_API_KEY>
     COINGECKO_API_KEY=<YOUR_COINGECKO_API_KEY>
     SOLANA_NETWORK="devnet" # devnet, mainnet
     

   • Rename template.agents.env to agents.env and add your Solana keypair path:
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     AGENT_NAME="trading_agent"
     SOLANA_KEY_PATH=<YOUR_SOLANA_KEYPAIR_PATH>
     

     If you don’t have a Solana keypair, you can create one and get funded using the Galadriel CLI (see Wallet Setup):
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     galadriel wallet create --path <YOUR_SOLANA_KEYPAIR_PATH>
     

     And then:
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     galadriel wallet airdrop
     

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Agent Implementation

1. Create the agent.py file: Create a new file named agent.py in the examples/trading directory and add the following code:
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   import asyncio
   import os
   from pathlib import Path
   
   from dotenv import load_dotenv
   
   from galadriel import AgentRuntime
   from galadriel.agent import CodeAgent
   from galadriel.core_agent import LiteLLMModel
   from galadriel.clients import Cron
   from galadriel.tools.web3.market_data import market_data_devnet
   from galadriel.tools.web3.onchain.solana import raydium_cpmm
   from galadriel.tools.web3.onchain.solana import common as solana_common
   
   TRADING_INTERVAL_SECONDS = 300
   
   # Set up a complex trading prompt which explains the trading strategy
   TRADING_PROMPT = """
           You are an expert crypto trading advisor. Based on the user's portfolio, current market data, and trading patterns, your task is to suggest one of three actions for each token: Buy, Sell, or Hold, and execute your decision using the available tools.
           """
   
   load_dotenv(dotenv_path=Path(".") / ".env", override=True)
   load_dotenv(dotenv_path=Path(".") / ".agents.env", override=True)
   
   model = LiteLLMModel(
       model_id="openai/gpt-4o",
       api_key=os.getenv("OPENAI_API_KEY"),
   )
   
   # Prepare a Web3 specific toolkit, relevant for the trading agent
   tools = [
       market_data_devnet.fetch_mock_market_data,
       raydium_cpmm.BuyTokenWithSolTool(),
       solana_common.GetAdminWalletAddressTool(),
       solana_common.GetUserBalanceTool(),
   ]
   
   # Create a trading agent
   trading_agent = CodeAgent(
       prompt_template=TRADING_PROMPT,
       model=model,
       tools=tools,
       add_base_tools=True,
       additional_authorized_imports=["json"],
       max_steps=8,  # Make the trading agent more reliable by increasing the number of steps he can take to complete the task
   )
   
   # Set up the runtime
   runtime = AgentRuntime(
       inputs=[Cron(TRADING_INTERVAL_SECONDS)],
       outputs=[],
       agent=trading_agent,
   )
   
   # Run the agent
   asyncio.run(runtime.run())
   

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Code Explanation

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Imports

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Configuration

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Tools

• market_data_devnet.fetch_mock_market_data: Fetches mock market data for development purposes.
• raydium_cpmm.BuyTokenWithSolTool(): Tool for buying tokens with SOL using Raydium CPMM.
• solana_common.GetAdminWalletAddressTool(): Tool to get the admin wallet address.
• solana_common.GetUserBalanceTool(): Tool to get the user’s balance for a specific token.

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Agent Initialization

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Runtime Configuration

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Start the Agent

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Running the Agent

1. Make sure the solana keypair defined in .agents.env has some funds.
2. Run the agent:
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   python agent.py