AI Disrupts Finance! DeepSeek + LangGraph Creates Revolutionary Stock Analysis Assistant (Source Code Included)

- **Trend Analysis**: Stock price breaks above the 200-day moving average, indicating a strengthening mid-term trend  - **Potential Risks**: Debt ratio exceeds the industry average by 30%  - **Operation Suggestion**: If it pulls back to the 50-day moving average, consider building positions in batches 

Through the DeepSeek inference engine, we have comprehensively considered technical trends, market positioning, as well as potential risks and opportunities to provide comprehensive analysis and decision support.

4. Streamlit Visualization Interface

Input the stock code with one click (for example, AAPL for Apple), and the analysis results will be displayed in real-time:

1. Avoid Over-Reliance:
   
   The AI model may not capture sudden market events or policy changes, so investors should combine their own experience and judgment, avoiding complete reliance on the suggestions provided by AI.
2. Data Quality First:
   
   The accuracy of AI analysis heavily relies on the quality of input data. Therefore, it is essential to use authoritative and timely data sources to avoid analysis results skewed by data delays or errors.
3. Risk Control is Key:
   
   Even if AI suggests buying a certain stock, investors still need to set stop-loss and take-profit points to control potential risks and protect investment returns.

Before starting to write code, you first need to install the necessary Python libraries:

!pip install openai langchain yfinance pandas numpy streamlit langgraph langchain-community -i

OPENAI_API_KEY = "Replace with your API key"  # Replace with your API keyOPENAI_API_BASE = "https://api.deepseek.com/v1"  # DeepSeek API address
# Initialize DeepSeek model
def get_llm():    return ChatOpenAI(        api_key=OPENAI_API_KEY,        model_name='deepseek-reasoner',  # Use DeepSeek inference model        openai_api_base=OPENAI_API_BASE  # Specify API address    )

def compute_technical_indicators(stock_data: pd.DataFrame) -> Dict[str, str]:    if stock_data.empty:        return {"Error": "No price data available"}
    # Calculate 50-day and 200-day moving averages    stock_data['MA_50'] = stock_data['Close'].rolling(window=50).mean()    stock_data['MA_200'] = stock_data['Close'].rolling(window=200).mean()
    # Calculate volatility (30-day standard deviation)    stock_data['Volatility'] = stock_data['Daily Return'].rolling(window=30).std()
    # Calculate RSI    delta = stock_data['Close'].diff()    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()    rs = gain / loss    stock_data['RSI'] = 100 - (100 / (1 + rs))
    return {        "50-day MA": f"${stock_data['MA_50'].iloc[-1]:.2f}",        "200-day MA": f"${stock_data['MA_200'].iloc[-1]:.2f}",        "Volatility": f"{stock_data['Volatility'].iloc[-1]:.4f}",        "RSI": f"{stock_data['RSI'].iloc[-1]:.2f}"    }

def draw_conclusion(state: AgentState) -> AgentState:    facts = "\n".join(state["facts"])    prompt = PromptTemplate(template="""Based on the following financial data:     {facts}     Please provide a structured analysis of the potential trends of the stock:     - **Trend Analysis**     - **Market Position**     - **Potential Risks**     - **Operation Suggestions**     """, input_variables=["facts"])
    chain = prompt | llm    try:        response = chain.invoke({"facts": facts})        state["conclusion"] = response.content    except Exception as e:        state["conclusion"] = f"Error in conclusion generation: {str(e)}"
    return state

Users only need to input the stock code (for example, “TSLA” for Tesla), and the system will automatically analyze and display the latest stock market data and deep reasoning conclusions. Here is a result of a practical operation:

In this article, you learned how to utilize DeepSeek’s powerful reasoning capabilities and LangGraph’s defined analysis process capabilities to build an intelligent stock analysis tool. By performing practical operations to obtain stock data, calculating technical indicators, and generating structured analysis reports.Get the complete code now, and start your intelligent investment journey!

• AI Empowering Investment Decisions: By combining DeepSeek’s reasoning capabilities with LangGraph’s process definition capabilities, an intelligent stock analysis assistant has been created, enhancing the efficiency and accuracy of investment decisions.

• Advantages of DeepSeek and LangGraph: DeepSeek is an open-source AI model adept at handling complex reasoning and computational tasks, particularly suitable for financial quantitative analysis and data modeling. LangGraph, based on the LangChain framework, provides workflow management capabilities with flowchart structures, making it convenient to break down complex stock market analysis tasks into multiple subtasks.

• Core Construction of the Intelligent Analysis Assistant: This assistant includes four core parts: data scraping and cleaning, technical indicator calculation, DeepSeek inference engine, and Streamlit visualization interface. Through these modules, users can obtain historical stock prices, calculate key technical indicators, and generate structured analysis reports.

• Usage Notes: When using the AI assistant, avoid over-reliance, ensure data quality, and set risk control measures such as stop-loss and take-profit points to protect investment returns.

• Future Upgrade Directions: Future upgrades may include multi-factor model integration, real-time news interpretation, and personalized strategy adaptation into the analysis system, further enhancing prediction accuracy and comprehensiveness.

WeChat ID | Lao Yu Fishing

www.LaoYuLaoyu.com