Build an AI-Powered Stock Analyzer Using Streamlit, Olostep, and OpenAI

Build an AI-Powered Stock Analyzer Using Streamlit, Olostep, and OpenAI.

Ever wanted to analyze stocks using AI without manually going through dozens of websites?

In this tutorial, you’ll build a Streamlit app that:

Uses Olostep's scraping API to fetch stock data from MarketWatch
Sends the content to OpenAI GPT-4 to rate stocks based on their performance and description
Visualizes the investment score with interactive charts

🛠️ Requirements

Make sure to install the following dependencies:

pip install streamlit openai requests python-dotenv matplotlib beautifulsoup4

Create a .env file and add:

OPENAI_API_KEY=your_openai_key
OLOSTEP_API_KEY=your_olostep_key

🔧 Step 1: Scrape Stock Pages with Olostep

Use Olostep’s scrape endpoint to fetch the latest data for stock tickers from MarketWatch.

def scrape_stock_page(ticker):
    headers = {
        "Authorization": f"Bearer {os.getenv('OLOSTEP_API_KEY')}",
        "Content-Type": "application/json"
    }
    payload = {
        "formats": ["markdown"],
        "country": "US",
        "url_to_scrape": f"https://www.marketwatch.com/investing/stock/{ticker}"
    }
    res = requests.post("https://api.olostep.com/v1/scrapes", headers=headers, json=payload)
    return res.json().get("result", {}).get("markdown_content", "")

🧠 Step 2: Analyze Stock Content with OpenAI GPT-4

We ask GPT-4 to evaluate and assign a score from 0 to 100 for each stock.

def analyze_stocks_with_gpt(tickers, markdowns):
    prompt = "You are a stock analyst. Based on the following markdowns from MarketWatch, rate each stock on a scale of 0–100 as an investment opportunity. Return only JSON format like:

"
    prompt += '''{
  "scores": [
    {"stock_name": "AAPL", "score": 92},
    {"stock_name": "MSFT", "score": 88}
  ]
}'''


    for ticker, md in zip(tickers, markdowns):
        prompt += f"Ticker: {ticker}
Content:
{md[:3000]}

"

    response = openai_client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
    )

    return json.loads(response.choices[0].message.content.strip())

🎨 Step 3: Visualize Results in Streamlit

def plot_scores(scores):
    stock_names = [item['stock_name'] for item in scores]
    values = [item['score'] for item in scores]

    fig, ax = plt.subplots(figsize=(10, 4))
    ax.bar(stock_names, values, color="green")
    ax.set_ylabel("Investment Score")
    ax.set_title("AI-Scored Stocks by OpenAI GPT-4")
    st.pyplot(fig)

🚀 Step 4: Complete Streamlit App

st.set_page_config(page_title="AI Stock Analyzer", page_icon="📈")
st.title("📈 AI Stock Analyzer with Olostep + OpenAI")

tickers_input = st.text_input("Enter comma-separated stock tickers (e.g., AAPL,MSFT,TSLA)")

if st.button("Analyze"):
    tickers = [t.strip().lower() for t in tickers_input.split(",") if t.strip()]
    markdowns = [scrape_stock_page(ticker) for ticker in tickers]

    if not any(markdowns):
        st.error("No data found.")
    else:
        st.info("Analyzing stocks with GPT...")
        scores = analyze_stocks_with_gpt(tickers, markdowns)
        st.success("Analysis complete!")
        plot_scores(scores['scores'])

✅ Example Output

Stock	Score
AAPL	91
TSLA	84
MSFT	89

💼 Use Cases

Retail investors screening multiple stocks
Analysts comparing high-volume tickers
Developers building AI-based investing tools
Newsletter writers looking to automate stock ideas

🔮 What's Next

Export scores to a Google Sheet or PDF
Use historical stock data and financial ratios
Add GPT explanation for each score
Deploy on Streamlit Cloud

💡 Final Thoughts

This app combines real-time scraping, AI rating, and visual insights into one seamless experience. It’s ideal for anyone looking to use AI to make smarter investment decisions at scale.

Happy investing! 📈