> ## Documentation Index
> Fetch the complete documentation index at: https://praison.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Finance Agent

> Learn how to create AI agents for financial analysis and investment recommendations.

Get real-time stock prices, company fundamentals, and historical trends with a single Agent — no financial-data API glue code required.

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonai_tools import get_stock_price, get_stock_info, get_historical_data

agent = Agent(
    name="FinanceAnalyst",
    instructions="Analyze stocks and return an investor-friendly summary.",
    tools=[get_stock_price, get_stock_info, get_historical_data],
)

agent.start("Compare AAPL and MSFT year-to-date.")
```

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    subgraph "Finance Agent"
        User[📋 Stock Query] --> Agent[🤖 Agent]
        Agent --> Tools[🧠 Finance Tools]
        Tools --> Report[✅ Analysis Report]
    end

    classDef input fill:#6366F1,stroke:#7C90A0,color:#fff
    classDef process fill:#189AB4,stroke:#7C90A0,color:#fff
    classDef output fill:#10B981,stroke:#7C90A0,color:#fff

    class User,Agent input
    class Tools process
    class Report output
```

## Quick Start

<Steps>
  <Step title="Simple Usage">
    Give an Agent the finance tools and ask a question.

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    from praisonaiagents import Agent
    from praisonai_tools import get_stock_price, get_stock_info, get_historical_data

    agent = Agent(
        name="FinanceAnalyst",
        instructions="You are a financial analyst. Analyze stocks and provide insights.",
        tools=[get_stock_price, get_stock_info, get_historical_data],
    )

    agent.start("Analyze Apple (AAPL) - current price and 6-month trend")
    ```
  </Step>

  <Step title="With Configuration">
    Add memory to track a portfolio across sessions.

    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    from praisonaiagents import Agent
    from praisonai_tools import get_stock_price, get_stock_info, get_historical_data

    agent = Agent(
        name="FinanceAnalyst",
        instructions="Track a portfolio and compare positions over time.",
        tools=[get_stock_price, get_stock_info, get_historical_data],
        memory=True,
    )

    agent.start("Compare AAPL and GOOGL, then remember my preference for tech stocks.")
    ```
  </Step>
</Steps>

***

## How It Works

A user asks a financial question, the Agent calls the finance tools to pull live data, then returns an investor-friendly summary.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
    participant User
    participant Agent
    participant Tools as Finance Tools

    User->>Agent: "Compare AAPL and MSFT year-to-date"
    Agent->>Tools: get_stock_price / get_historical_data
    Tools-->>Agent: Prices, fundamentals, history
    Agent-->>User: Investor-friendly comparison
```

***

## Configuration Options

The finance tools take no configuration — pass them to any `Agent`. For the underlying tool signatures see the tools reference.

<CardGroup cols={2}>
  <Card title="yfinance Tools" icon="wrench" href="/tools/yfinance_tools">
    Stock price, company info, and historical data tool signatures.
  </Card>

  <Card title="Agent Reference" icon="user" href="/features/agents">
    Full Agent parameters and options.
  </Card>
</CardGroup>

***

## Simple

**Agents: 1** — Single agent with finance tools for comprehensive stock analysis.

### Workflow

1. Receive stock query
2. Fetch real-time price data
3. Retrieve company information
4. Analyze historical trends

### Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents praisonai yfinance
export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"
```

### Run — Python

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonai_tools import get_stock_price, get_stock_info, get_historical_data

agent = Agent(
    name="FinanceAnalyst",
    instructions="You are a financial analyst. Analyze stocks and provide insights.",
    tools=[get_stock_price, get_stock_info, get_historical_data]
)

result = agent.start("Analyze Apple (AAPL) stock - current price and 6-month trend")
print(result)
```

### Run — CLI

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "Analyze Tesla stock performance" --tools yfinance
```

### Run — agents.yaml

```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
framework: praisonai
topic: Stock Analysis
roles:
  finance_analyst:
    role: Financial Analyst
    goal: Analyze stocks and provide investment insights
    backstory: You are an expert financial analyst
    tools:
      - get_stock_price
      - get_stock_info
      - get_historical_data
    tasks:
      analyze_stock:
        description: Analyze Apple (AAPL) stock - current price and 6-month trend
        expected_output: A comprehensive stock analysis
```

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai agents.yaml
```

### Serve API

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonai_tools import get_stock_price, get_stock_info, get_historical_data

agent = Agent(
    name="FinanceAnalyst",
    instructions="You are a financial analyst.",
    tools=[get_stock_price, get_stock_info, get_historical_data]
)

agent.launch(port=8080)
```

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Compare AAPL and GOOGL stocks"}'
```

***

## Advanced Workflow (All Features)

**Agents: 1** — Single agent with memory, persistence, structured output, and session resumability.

### Workflow

1. Initialize session for portfolio tracking
2. Configure SQLite persistence for analysis history
3. Execute multi-tool analysis with structured output
4. Store results in memory for trend comparison
5. Resume session for ongoing portfolio monitoring

### Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents praisonai yfinance pydantic
export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"
```

### Run — Python

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, Task, AgentTeam, Session
from praisonai_tools import get_stock_price, get_stock_info, get_historical_data
from pydantic import BaseModel

# Structured output schema
class StockAnalysis(BaseModel):
    symbol: str
    current_price: float
    recommendation: str
    key_metrics: list[str]
    risk_factors: list[str]

# Create session for portfolio tracking
session = Session(session_id="portfolio-001", user_id="user-1")

# Agent with memory and tools
agent = Agent(
    name="FinanceAnalyst",
    instructions="Analyze stocks and return structured investment reports.",
    tools=[get_stock_price, get_stock_info, get_historical_data],
    memory=True
)

# Task with structured output
task = Task(
    description="Analyze Apple (AAPL) stock with buy/sell recommendation",
    expected_output="Structured stock analysis",
    agent=agent,
    output_pydantic=StockAnalysis
)

# Run with SQLite persistence
agents = AgentTeam(
    agents=[agent],
    tasks=[task],
    memory=True
)

result = agents.start()
print(result)

# Resume later for portfolio review
session2 = Session(session_id="portfolio-001", user_id="user-1")
history = session2.search_memory("AAPL")
```

### Run — CLI

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "Analyze AAPL stock" --tools yfinance --memory --verbose
```

### Run — agents.yaml

```yaml theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
framework: praisonai
topic: Stock Analysis
memory: true
memory_config:
  provider: sqlite
  db_path: finance.db
roles:
  finance_analyst:
    role: Financial Analyst
    goal: Analyze stocks with structured output
    backstory: You are an expert financial analyst
    tools:
      - get_stock_price
      - get_stock_info
      - get_historical_data
    memory: true
    tasks:
      analyze_stock:
        description: Analyze Apple (AAPL) stock with buy/sell recommendation
        expected_output: Structured stock analysis
        output_json:
          symbol: string
          current_price: number
          recommendation: string
          key_metrics: array
          risk_factors: array
```

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai agents.yaml --verbose
```

### Serve API

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonai_tools import get_stock_price, get_stock_info, get_historical_data

agent = Agent(
    name="FinanceAnalyst",
    instructions="Analyze stocks and return structured reports.",
    tools=[get_stock_price, get_stock_info, get_historical_data],
    memory=True
)

agent.launch(port=8080)
```

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
curl -X POST http://localhost:8080/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Analyze TSLA", "session_id": "portfolio-001"}'
```

***

## Monitor / Verify

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai "test finance" --tools yfinance --verbose
```

## Cleanup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
rm -f finance.db
```

## Features Demonstrated

| Feature           | Implementation                  |
| ----------------- | ------------------------------- |
| Workflow          | Multi-tool stock analysis       |
| DB Persistence    | SQLite via `memory_config`      |
| Observability     | `--verbose` flag                |
| Tools             | yfinance (price, info, history) |
| Resumability      | `Session` with `session_id`     |
| Structured Output | Pydantic `StockAnalysis` model  |

## Best Practices

<AccordionGroup>
  <Accordion title="Attach only the tools you need">
    The finance tools call live market APIs, so each extra tool adds latency and cost. Pass `get_stock_price` alone for quote lookups, and add `get_historical_data` only when the question needs trends.
  </Accordion>

  <Accordion title="Enable memory for portfolio tracking">
    Set `memory=True` when the user asks follow-up questions about the same tickers. The agent then compares new quotes against prior turns instead of starting from scratch.
  </Accordion>

  <Accordion title="Ground recommendations in retrieved data">
    Instruct the agent to cite the figures it fetched. This keeps summaries verifiable and prevents the model from inventing prices when a tool call fails.
  </Accordion>

  <Accordion title="Reach for a specialised agent when you need more">
    For document-heavy market research, pair this Agent with the Research Agent; for CSV/Excel portfolios, hand off to the Data Analyst Agent.
  </Accordion>
</AccordionGroup>

## Related

<CardGroup cols={2}>
  <Card title="Data Analyst" icon="chart-line" href="/docs/agents/data-analyst">
    Analyze CSV/Excel portfolios and generate insights.
  </Card>

  <Card title="Research Agent" icon="magnifying-glass-chart" href="/docs/agents/research">
    Conduct market research across the web.
  </Card>
</CardGroup>