🧠 Google ADK Stock Market Research Agent: Real-Time Investment Reports with Dappier

pip install google-adk dappier

from dappier import Dappier

client = Dappier()

def real_time_web_search(query: str) -> str:
    """
    Perform a real-time web search. Use this for general queries that do not include a specific stock ticker.
    """
    try:
        return client.search_real_time_data_string(
            query=query,
            ai_model_id="am_01j06ytn18ejftedz6dyhz2b15"
        )
    except Exception as e:
        return f"Error: {str(e)}"

def stock_market_data_search(query: str) -> str:
    """
    Fetch real-time stock market data. Use this tool only when querying with a stock ticker symbol.
    """
    try:
        return client.search_real_time_data_string(
            query=query,
            ai_model_id="am_01j749h8pbf7ns8r1bq9s2evrh"
        )
    except Exception as e:
        return f"Error: {str(e)}"

from google.adk.agents import LlmAgent
from datetime import datetime
from .tools import real_time_web_search

GEMINI_MODEL = "gemini-2.0-flash"
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

# --- Initial Agent to Resolve Ticker ---
ticker_resolution_agent = LlmAgent(
    name="TickerResolutionAgent",
    model=GEMINI_MODEL,
    instruction=f"""
As of {timestamp}, take a user-provided company name and use real-time web search to resolve its stock ticker symbol.
Return only the stock ticker symbol in uppercase.
""",
    description="Resolves stock ticker symbol from company name.",
    tools=[real_time_web_search],
    output_key="ticker_symbol"
)

from google.adk.agents import LlmAgent
from datetime import datetime
from .tools import real_time_web_search

GEMINI_MODEL = "gemini-2.0-flash"
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")

# --- Initial Agent to Resolve Ticker ---
ticker_resolution_agent = LlmAgent(
    name="TickerResolutionAgent",
    model=GEMINI_MODEL,
    instruction=f"""
As of {timestamp}, take a user-provided company name and use real-time web search to resolve its stock ticker symbol.
Return only the stock ticker symbol in uppercase.
""",
    description="Resolves stock ticker symbol from company name.",
    tools=[real_time_web_search],
    output_key="ticker_symbol"
)

financials_agent = LlmAgent(
    name="FinancialsPerformanceAgent",
    model=GEMINI_MODEL,
    instruction=f"""
As of "{timestamp}", use real-time web search with the timestamp to extract financial performance data for {{ticker_symbol}},
including Revenue (TTM), Net Income (TTM), YoY revenue growth, gross margin, and recent quarterly trends.
Include any earnings trends or management commentary.
""",
    description="Extracts financial metrics using real-time web search.",
    tools=[real_time_web_search],
    output_key="financials_summary"
)

benchmarking_agent = LlmAgent(
    name="CompetitiveBenchmarkingAgent",
    model=GEMINI_MODEL,
    instruction=f"""
As of {timestamp}, identify 3-5 peer companies in the same sector as {{ticker_symbol}} using real-time web search.
Extract and compare metrics: P/E ratio, revenue, stock price, market cap.
Highlight where {{ticker_symbol}} outperforms or underperforms.
""",
    description="Performs competitive benchmarking using real-time web search.",
    tools=[real_time_web_search],
    output_key="competitive_benchmarking"
)

news_agent = LlmAgent(
    name="StockNewsAndSentimentAgent",
    model=GEMINI_MODEL,
    instruction=f"""
As of {timestamp}, fetch real-time financial news
for "{{ticker_symbol}}". Categorize by: Earnings, Analyst Ratings, Market Moves, Partnerships, Legal/Regulatory.
""",
    description="Fetches categorized stock news using stock_market_data_search.",
    tools=[stock_market_data_search],
    output_key="categorized_news"
)

parallel_web_research_agent = ParallelAgent(
    name="WebResearchAgentGroup",
    sub_agents=[company_overview_agent, financials_agent, benchmarking_agent],
    description="Gathers structured company data in parallel."
)

parallel_stock_insights_agent = ParallelAgent(
    name="StockInsightsAgentGroup",
    sub_agents=[stock_snapshot_agent, news_agent],
    description="Gathers real-time stock insights in parallel."
)

report_generator_agent = LlmAgent(
    name="InvestmentReportGenerator",
    model=GEMINI_MODEL,
    instruction=f"""
As of {timestamp}, compile a comprehensive investment report for **{{stock_ticker}}**, formatted as markdown. Synthesize the outputs of all tasks: company overview, financial performance, competitive benchmarking, real-time stock snapshot, and categorized financial news.

The report must include the following structured sections:

1. **Quick Summary**  
2. **Company Profile Table**  
3. **Financial Performance Table**  
4. **Competitive Benchmarking Table**  
5. **Real-Time Stock Snapshot**  
6. **Categorized Financial News**  
7. **Insight Section**  

All the related information is below:

{{company_overview}}

{{financials_summary}}

{{competitive_benchmarking}}

{{stock_snapshot}}

{{categorized_news}}

Output only the final markdown report.
""",
    description="Generates a full investment report from aggregated data.",
    output_key="final_markdown_report"
)

sequential_pipeline_agent = SequentialAgent(
    name="StockMarketResearchPipeline",
    sub_agents=[
        ticker_resolution_agent,
        parallel_web_research_agent,
        parallel_stock_insights_agent,
        report_generator_agent
    ],
    description="Orchestrates multi-agent flow: resolves ticker, gathers data, and generates report."
)

# Entry point
root_agent = sequential_pipeline_agent