> ## Documentation Index
> Fetch the complete documentation index at: https://docs.praison.ai/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.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
flowchart LR
    In[Stock Query] --> Agent[Finance Agent]
    Agent --> Out[Analysis Report]
    
    style In fill:#8B0000,color:#fff
    style Agent fill:#2E8B57,color:#fff
    style Out fill:#8B0000,color:#fff
```

Financial analysis agent with stock price, company info, and historical data tools.

***

## 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="your-key"
```

### Run — Python

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent
from praisonaiagents 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 praisonaiagents 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="your-key"
```

### Run — Python

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, Task, AgentTeam, Session
from praisonaiagents 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 praisonaiagents 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  |

## Next Steps

* [Data Analyst](/agents/data-analyst) for CSV/Excel analysis
* [Research Agent](/agents/research) for market research
* [Memory](/features/advanced-memory) for persistent context