YFinance Agent

Yahoo Finance data retrieval tools for AI agents.

from praisonaiagents import Agent
from praisonaiagents import get_stock_price, get_stock_info

agent = Agent(
    name="MarketAnalyst",
    tools=[get_stock_price, get_stock_info],
)
agent.start("What is NVDA trading at and what is its market cap?")

The user asks about a ticker; the agent pulls live quotes and fundamentals with yfinance tools.

Prerequisites

• Python 3.10 or higher
• PraisonAI Agents package installed
• PraisonAI Tools package installed
• yfinance package installed

YFinance Tools

Use YFinance Tools to retrieve and analyze financial data with AI agents.

1. Install Dependencies

   First, install the required packages:

   pip install praisonaiagents praisonai-tools yfinance
   
2. Import Components

   Import the necessary components:

   from praisonaiagents import Agent, Task, AgentTeam
   from praisonai_tools import get_stock_price, get_stock_info, get_historical_data
   
3. Create Agent

   Create a financial data agent:

   finance_agent = Agent(
       name="FinanceAnalyst",
       role="Financial Data Specialist",
       goal="Retrieve and analyze financial data efficiently.",
       backstory="Expert in financial data analysis and market research.",
       tools=[get_stock_price, get_stock_info, get_historical_data],
       reflection=False
   )
   
4. Define Task

   Define the financial analysis task:

   finance_task = Task(
       description="Analyze stock performance and market trends.",
       expected_output="Detailed financial analysis with market insights.",
       agent=finance_agent,
       name="market_analysis"
   )
   
5. Run Agent

   Initialize and run the agent:

   agents = AgentTeam(
       agents=[finance_agent],
       tasks=[finance_task],
       process="sequential"
   )
   agents.start()
   

Understanding YFinance Tools

What are YFinance Tools?

YFinance Tools provide financial data capabilities for AI agents:

• Real-time stock prices
• Detailed company information
• Historical market data
• Financial metrics and ratios
• Market performance analysis

Key Components

Finance Agent

Create specialized finance agents:

Agent(tools=[get_stock_price, get_stock_info, get_historical_data])

Finance Task

Define finance tasks:

Task(description="finance_query")

Process Types

Sequential or parallel processing:

process="sequential"

Finance Options

Customize data parameters:

period="1y", interval="1d"

Available Functions

from praisonai_tools import get_stock_price
from praisonai_tools import get_stock_info
from praisonai_tools import get_historical_data

Examples

Basic Financial Data Agent

from praisonaiagents import Agent, Task, AgentTeam
from praisonai_tools import get_stock_price, get_stock_info, get_historical_data

# Create finance agent
finance_agent = Agent(
    name="MarketAnalyst",
    role="Financial Data Specialist",
    goal="Analyze market data efficiently and accurately.",
    backstory="Expert in financial analysis and market research.",
    tools=[get_stock_price, get_stock_info, get_historical_data],
    reflection=False
)

# Define finance task
finance_task = Task(
    description="Analyze tech sector performance and trends.",
    expected_output="Comprehensive market analysis report.",
    agent=finance_agent,
    name="sector_analysis"
)

# Run agent
agents = AgentTeam(
    agents=[finance_agent],
    tasks=[finance_task],
    process="sequential"
)
agents.start()

Advanced Market Analysis with Multiple Agents

from praisonaiagents import Agent, Task, AgentTeam
from praisonai_tools import get_stock_price, get_stock_info, get_historical_data

# Create data retrieval agent
data_agent = Agent(
    name="DataCollector",
    role="Market Data Collector",
    goal="Retrieve financial data systematically.",
    tools=[get_stock_price, get_historical_data],
    reflection=False
)

# Create analysis agent
analysis_agent = Agent(
    name="Analyst",
    role="Market Analyst",
    goal="Analyze market trends and patterns.",
    backstory="Expert in financial market analysis.",
    tools=[get_stock_info],
    reflection=False
)

# Define tasks
data_task = Task(
    description="Collect historical market data for analysis.",
    agent=data_agent,
    name="data_collection"
)

analysis_task = Task(
    description="Analyze collected market data for insights.",
    agent=analysis_agent,
    name="trend_analysis"
)

# Run agents
agents = AgentTeam(
    agents=[data_agent, analysis_agent],
    tasks=[data_task, analysis_task],
    process="sequential"
)
agents.start()

Best Practices

Agent Configuration

Configure agents with clear finance focus:

from praisonai_tools import get_stock_price, get_stock_info, get_historical_data

Agent(
    name="FinanceAnalyst",
    role="Market Analysis Specialist",
    goal="Analyze financial data accurately and efficiently",
    tools=[get_stock_price, get_stock_info, get_historical_data]
)

Task Definition

Define specific analysis objectives:

Task(
    description="Analyze market trends and generate insights",
    expected_output="Detailed market analysis"
)

Common Patterns

Market Analysis Pipeline

from praisonaiagents import Agent, Task, AgentTeam
from praisonai_tools import get_stock_price, get_stock_info, get_historical_data

# Data agent
collector = Agent(
    name="Collector",
    role="Data Collector",
    tools=[get_stock_price, get_historical_data]
)

# Analysis agent
analyst = Agent(
    name="Analyst",
    role="Market Analyst",
    tools=[get_stock_info]
)

# Define tasks
collect_task = Task(
    description="Collect market data",
    agent=collector
)

analyze_task = Task(
    description="Analyze market trends",
    agent=analyst
)

# Run workflow
agents = AgentTeam(
    agents=[collector, analyst],
    tasks=[collect_task, analyze_task]
)

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