Prerequisites

  • Python 3.10 or higher
  • PraisonAI Agents package installed
  • yfinance package installed
  • Basic understanding of financial data

​
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 yfinance
2

Import Components

Import the necessary components:

from praisonaiagents import Agent, Task, PraisonAIAgents
from praisonaiagents.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],
    self_reflect=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 = PraisonAIAgents(
    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"

​
Examples

​
Basic Financial Data Agent

from praisonaiagents import Agent, Task, PraisonAIAgents
from praisonaiagents.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],
    self_reflect=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 = PraisonAIAgents(
    agents=[finance_agent],
    tasks=[finance_task],
    process="sequential"
)
agents.start()

​
Advanced Market Analysis with Multiple Agents

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

​
Best Practices

​
Common Patterns

​
Market Analysis Pipeline

# 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 = PraisonAIAgents(
    agents=[collector, analyst],
    tasks=[collect_task, analyze_task]
)

Prerequisites

  • Python 3.10 or higher
  • PraisonAI Agents package installed
  • yfinance package installed
  • Basic understanding of financial data

​
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 yfinance
2

Import Components

Import the necessary components:

from praisonaiagents import Agent, Task, PraisonAIAgents
from praisonaiagents.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],
    self_reflect=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 = PraisonAIAgents(
    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"

​
Examples

​
Basic Financial Data Agent

from praisonaiagents import Agent, Task, PraisonAIAgents
from praisonaiagents.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],
    self_reflect=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 = PraisonAIAgents(
    agents=[finance_agent],
    tasks=[finance_task],
    process="sequential"
)
agents.start()

​
Advanced Market Analysis with Multiple Agents

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

​
Best Practices

​
Common Patterns

​
Market Analysis Pipeline

# 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 = PraisonAIAgents(
    agents=[collector, analyst],
    tasks=[collect_task, analyze_task]
)