🕵🏻 Building an AI-Powered Stock Analyser using OpenAI Agents SDK and Dappier

You can also check this cookbook in colab here

Introduction

This notebook provides a step-by-step guide to building an AI-powered Stock Market Analyzer using OpenAI’s Agents SDK and Dappier. The analyzer generates real-time stock market insights based on user input, fetching financial news, market trends, breaking news alerts, and high-performing similar stocks dynamically. It then formulates a data-driven trading strategy, including entry/exit points, risk management, and investment recommendations.

By leveraging Dappier’s real-time search and AI-driven insights, this stock market agent helps users make informed and strategic investment decisions with up-to-date market intelligence. 🚀

Watch the Video Guide

If you prefer a visual walkthrough, check out the accompanying video guide below:

OpenAI Agents SDK

The OpenAI Agents SDK enables you to build agentic AI applications with a lightweight and production-ready API.

It consists of:

• Agents – LLMs equipped with instructions and tools
• Handoffs – Delegation of tasks to specialized agents
• Guardrails – Input validation for enhanced reliability

The SDK also provides built-in tracing for debugging and evaluation, making AI-powered workflows easy to build and scale.

Dappier

Dappier is a real-time AI data platform that connects LLMs and AI agents to rights-cleared data from trusted sources.

It specializes in web search, finance, news, and live events, ensuring AI applications can access up-to-date and verified information without hallucinations.

Install Dependencies

!pip install openai-agents dappier colorama nest-asyncio

Import Required Libraries

Python

import os
import getpass
import nest_asyncio
import asyncio
from agents import Agent, FunctionTool, Runner, function_tool
from colorama import Fore, Style
from dappier import Dappier
from openai.types.responses import ResponseTextDeltaEvent

Set Up API Keys Securely

To prevent exposing API keys in shared environments, use getpass to enter them securely.

Python

os.environ["DAPPIER_API_KEY"] = getpass.getpass("Enter your Dappier API Key: ")
os.environ["OPENAI_API_KEY"] = getpass.getpass("Enter your OpenAI API Key: ")

Initialize Dappier Client

Python

dappier_client = Dappier(api_key=os.environ["DAPPIER_API_KEY"])

Enable tracing for OpenAI Agents SDK

Python

from agents import set_tracing_export_api_key

set_tracing_export_api_key(os.environ["OPENAI_API_KEY"])

Define AI Functions for Real-Time Data Fetching

Fetching Real-Time Search Results

This function fetches real-time search results from Dappier based on the user’s query.

Python

@function_tool
def dappier_real_time_search(query: str, ai_model_id: str) -> str:
    """Fetches real-time search results from Dappier based on the user's query.

    Args:
        query (str): The user's query for real-time data.
        ai_model_id (str): The AI model ID to use for the query, dynamically set by the agent.
            Possible values:
            - "am_01j0rzq4tvfscrgzwac7jv1p4c" (General real-time web search, including news, weather, and events.)
            - "am_01j749h8pbf7ns8r1bq9s2evrh" (Stock market data with real-time financial news and stock prices.)

    Returns:
        str: A response message containing the real-time data results.
    """
    print(Fore.RED + f"CALLING TOOL - dappier_real_time_search: {ai_model_id}\n" + Style.RESET_ALL)
    print(Fore.GREEN + f"Query: {query}\n" + Style.RESET_ALL)
    response = dappier_client.search_real_time_data(query=query, ai_model_id=ai_model_id)
    return response.message if response else "No real-time data found."

Fetching AI-Powered Recommendations

This function fetches AI-powered content recommendations from Dappier based on the user’s query.

Python

@function_tool
def dappier_ai_recommendations(query: str, data_model_id: str) -> str:
    """Fetches AI-powered content recommendations from Dappier based on the user's query.

    Args:
        query (str): The user's query for recommendations.
        data_model_id (str): The Data Model ID to use for the query, dynamically set by the agent.
            Possible values:
            - "dm_01j0pb465keqmatq9k83dthx34" (Sports news)
            - "dm_01j0q82s4bfjmsqkhs3ywm3x6y" (Lifestyle news)
            - "dm_01j1sz8t3qe6v9g8ad102kvmqn" (Dog care advice from iHeartDogs)
            - "dm_01j1sza0h7ekhaecys2p3y0vmj" (Cat care advice from iHeartCats)
            - "dm_01j5xy9w5sf49bm6b1prm80m27" (Eco-friendly content from GreenMonster)
            - "dm_01jagy9nqaeer9hxx8z1sk1jx6" (General news from WISH-TV)
            - "dm_01jhtt138wf1b9j8jwswye99y5" (Local news from 9 and 10 News)

    Returns:
        str: A formatted response containing AI-powered recommendations.
    """
    print(Fore.RED + f"CALLING TOOL: dappier_ai_recommendations: {data_model_id}\n" + Style.RESET_ALL)
    print(Fore.GREEN + f"Query: {query}\n" + Style.RESET_ALL)
    response = dappier_client.get_ai_recommendations(query=query, data_model_id=data_model_id, similarity_top_k=5)
    results = response.response.results
    formatted_text = ""
    for result in results:
        formatted_text += (f"Title: {result.title}\n"
                           f"Author: {result.author}\n"
                           f"Published on: {result.pubdate}\n"
                           f"URL: {result.source_url}\n"
                           f"Image URL: {result.image_url}\n"
                           f"Summary: {result.summary}\n\n")
    return formatted_text or "No recommendations found."

Create AI Agent

This AI agent will determine whether to fetch real-time search results or AI recommendations based on the user’s query.

Python

agent = Agent(
    name="Dappier Assistant",
    instructions="""
    You analyze the user's query and determine whether to use real-time search or AI recommendations.
    If the query involves stocks, finance, or current events, use `dappier_real_time_search`.
    If the query is about recommendations (e.g., news, lifestyle, sports, pets), use `dappier_ai_recommendations`.
    You MUST provide `ai_model_id` or `data_model_id` as necessary.
    Format responses in structured Markdown.
    """,
    tools=[dappier_real_time_search, dappier_ai_recommendations],
)

Generate Task Prompt

A function to dynamically generate a task prompt based on the user’s given stock symbol.

Python

def generate_stock_agent_prompt(stock_name: str) -> str:
    return f"""Generate a detailed stock market analysis and trading strategy for {stock_name}, using real-time financial data. Follow these steps:

### **1. Fetch Financial News:**
Use **Dappier's real-time search** to retrieve the latest financial news related to {stock_name}. Identify key insights such as earnings reports, company announcements, regulatory changes, and macroeconomic factors affecting the stock.

### **2. Fetch Market Trends:**
Analyze **real-time market trends** for {stock_name} using Dappier's data. Extract key performance indicators such as:
   - Recent price movements (intraday, weekly, monthly)
   - Trading volume fluctuations
   - Support and resistance levels
   - Institutional investor activity

### **3. Fetch Breaking News & AI Recommendations:**
Use **Dappier AI Recommendations API** to detect any **breaking news** or high-impact reports that could influence stock price movement, such as:
   - Mergers & acquisitions
   - SEC filings and insider trading reports
   - Macroeconomic events affecting the industry

### **4. Find Similar Stocks with Strong Performance:**
Identify **similar stocks** that have historically performed well under similar conditions. Compare:
   - Price correlation with {stock_name}
   - Industry trends and sector-wide movements
   - Alternative investment opportunities based on risk-reward assessment

### **5. Generate a Trading Strategy:**
Based on all the gathered insights, generate a detailed **trading strategy** that includes:
   - **Entry Points:** Optimal buy price based on historical data & support levels
   - **Exit Points:** Ideal selling price based on resistance levels & profit margins
   - **Risk Management:** Suggested stop-loss levels & risk-reward ratio
   - **Investment Timeframe:** Short-term (day/swing trading) vs. long-term (investment)
   - **News-Driven Adjustments:** How to react to sudden news or volatility

### **6. Output:**
Present the findings in a structured format:
   - **Latest financial news for {stock_name}**
   - **Market trends & technical indicators**
   - **Breaking news alerts from Dappier AI**
   - **Comparison with similar high-performing stocks**
   - **A complete trading strategy tailored to {stock_name}**

Ensure that the recommendations are **data-driven**, clear, and actionable for the user.
    """

Get User Input and Run AI Agent

This function collects user input dynamically, generates the task prompt, and executes the AI agent asynchronously.

Python

async def main():
    """Asks user for stock details dynamically and executes AI agent"""

    # Ask for stock details dynamically
    stock_name = input("Enter the stock that you are intersted to invest in: ")

    # Generate task prompt
    task_prompt = generate_stock_agent_prompt(stock_name)

    print(Fore.BLUE + f"\nExecuting AI Agent with prompt:\n{task_prompt}" + Style.RESET_ALL)

    # Execute AI agent with streaming results
    result = Runner.run_streamed(agent, task_prompt)

    print("\n\n=== Streaming Start ===\n\n")

    async for event in result.stream_events():
        if event.type == "raw_response_event" and isinstance(event.data, ResponseTextDeltaEvent):
            print(event.data.delta, end="", flush=True)

    print("\n\n=== Streaming Complete ===")

nest_asyncio.apply()
asyncio.run(main())

Enter the stock that you are intersted to invest in: Apple

Executing AI Agent with prompt:
Generate a detailed stock market analysis and trading strategy for Apple, using real-time financial data. Follow these steps:

### **1. Fetch Financial News:**
Use **Dappier's real-time search** to retrieve the latest financial news related to Apple. Identify key insights such as earnings reports, company announcements, regulatory changes, and macroeconomic factors affecting the stock.

### **2. Fetch Market Trends:**
Analyze **real-time market trends** for Apple using Dappier's data. Extract key performance indicators such as:
    - Recent price movements (intraday, weekly, monthly)
    - Trading volume fluctuations
    - Support and resistance levels
    - Institutional investor activity

### **3. Fetch Breaking News & AI Recommendations:**
Use **Dappier AI Recommendations API** to detect any **breaking news** or high-impact reports that could influence stock price movement, such as:
    - Mergers & acquisitions
    - SEC filings and insider trading reports
    - Macroeconomic events affecting the industry

### **4. Find Similar Stocks with Strong Performance:**
Identify **similar stocks** that have historically performed well under similar conditions. Compare:
    - Price correlation with Apple
    - Industry trends and sector-wide movements
    - Alternative investment opportunities based on risk-reward assessment

### **5. Generate a Trading Strategy:**
Based on all the gathered insights, generate a detailed **trading strategy** that includes:
    - **Entry Points:** Optimal buy price based on historical data & support levels
    - **Exit Points:** Ideal selling price based on resistance levels & profit margins
    - **Risk Management:** Suggested stop-loss levels & risk-reward ratio
    - **Investment Timeframe:** Short-term (day/swing trading) vs. long-term (investment)
    - **News-Driven Adjustments:** How to react to sudden news or volatility

### **6. Output:**
Present the findings in a structured format:
    - **Latest financial news for Apple**
    - **Market trends & technical indicators**
    - **Breaking news alerts from Dappier AI**
    - **Comparison with similar high-performing stocks**
    - **A complete trading strategy tailored to Apple**

Ensure that the recommendations are **data-driven**, clear, and actionable for the user.


=== Streaming Start ===


CALLING TOOL - dappier_real_time_search: am_01j749h8pbf7ns8r1bq9s2evrh

Query: Apple Inc. latest financial news

CALLING TOOL - dappier_real_time_search: am_01j749h8pbf7ns8r1bq9s2evrh

Query: Apple Inc. market trends

CALLING TOOL: dappier_ai_recommendations: dm_01j0pb465keqmatq9k83dthx34

Query: Apple Inc. breaking news and high-impact reports

CALLING TOOL - dappier_real_time_search: am_01j749h8pbf7ns8r1bq9s2evrh

Query: stocks similar to Apple with strong performance

### **Stock Market Analysis and Trading Strategy for Apple Inc. (AAPL)**

---

#### **1. Latest Financial News:**

1. **Why the Market Dipped But Apple (AAPL) Gained Today**
    - Apple (AAPL) closed at $173, showing resilience despite broader market dips.
    - [Read more](https://www.zacks.com/stock/news/2170442/why-the-market-dipped-but-apple-aapl-gained-today)

2. **Apple Faces Ominous Setup Heading into Earnings**
    - Analysts warn of potential headwinds ahead of Apple's earnings report.
    - [Read more](https://www.marketwatch.com/story/apple-faces-ominous-setup-heading-into-earnings-analyst-warns-5cef6ff1)

These articles provide insight into how Apple's stability is perceived amid potential market challenges.

---

#### **2. Market Trends & Technical Indicators:**

- **Recent Price Movements:** Apple has seen volatility, with contrasting opinions on product performance.
- **Trading Volume Fluctuations:** Increased interest despite uncertainties in new product launches.
- **Support and Resistance Levels:** Analysts highlight strong support at lower levels in preparation for earnings reports.
- **Institutional Investor Activity:** Significant institutional investments remain, reflecting confidence.

Articles emphasize both product-related concerns and long-term growth potential.

---

#### **3. Breaking News Alerts from Dappier AI:**

Currently, no specific breaking news reports directly affecting Apple's stock have been detected. General sector movements and broader economic factors may play a role without significant direct high-impact announcements.

---

#### **4. Similar Stocks with Strong Performance:**

Stocks typically compared with Apple include high-performing tech giants like Microsoft (MSFT) and Google (GOOGL). Analysis of current trends shows these companies have faced similar macroeconomic pressures, yet maintain robust institutional support.

---

#### **5. Trading Strategy:**

- **Entry Points:** Consider buying around identified support levels to capitalize on potential upticks post-earnings.
- **Exit Points:** Target resistance levels for a potential profit margin, assessing ongoing product performance and market responses.
- **Risk Management:** Utilize stop-loss orders synchronized with support levels, maintaining a favorable risk-reward ratio.
- **Investment Timeframe:** Align with a mix of short-term strategies around earnings and long-term holding for sustained growth.
- **News-Driven Adjustments:** Stay vigilant of any regulatory changes or quarterly reports. Adjust positioning promptly in response to new developments.

---

This strategy is tailored to current market conditions and centered around potential earnings impacts and broader sector performance. Adjustments may be needed based on further information and real-time data changes.

=== Streaming Complete ===

Conclusion

This notebook provides a structured guide to building an AI-powered Stock Market Analyzer using OpenAI Agents SDK and Dappier.

It covers:

• Real-time data retrieval for financial news, market trends, and breaking news alerts
• AI-powered recommendations to identify high-performing similar stocks
• An agent-driven workflow to generate actionable trading strategies

This AI analyzer can be extended further by integrating real-time portfolio tracking, options trading insights, and automated risk assessment tools for a more comprehensive investment assistant. 🚀