Building an AI Stock Forecasting API

🧠 Build an AI Stock Forecasting API with LangGraph, OpenAI, and FastAPI
Want to predict stock prices using modern AI tools? In this guide, we’ll build a production-grade API using LangGraph, OpenAI, and FastAPI — powered by AI agents that:

✅ Analyze financial news
✅ Analyze stock charts & trends
✅ Predict price forecasts with confidence levels & sentiment
✅ Suggest short- and long-term selling targets
✅ Return elegant HTML for direct frontend display

🧱 Project Architecture
Layer Tech Stack
🧠 AI Reasoning LangGraph + OpenAI GPT-4
📉 Stock Data Yahoo Finance via yfinance
📰 News Custom scraper or NewsAPI
🔁 Backend FastAPI
⚡ Caching Redis / FAISS
🧩 Agents Modular, parallel AI agents

🧠 AI Agent Design
We created three modular AI agents:

NewsAgent: Fetches and summarizes the latest news for a stock.

TrendAgent: Analyzes historical prices and technical patterns.

PredictionAgent: Uses output from News & Trend agents + stock history to predict:

Stock prices for 7d, 15d, 1mo, and 3mo

Short- and long-term sell targets

📈 Bullish, 📉 Bearish, 🤝 Neutral sentiment

🔍 Confidence level: High / Medium / Low

🧭 Process Flow Diagram

Here's how the LangGraph flow looks:

Start([🔄 Start API Call]) --> NewsAgent[🗞️ News Agent]
Start --> TrendAgent[📊 Trend Agent]
NewsAgent --> Combine[🔗 Merge Outputs]
TrendAgent --> Combine
Combine --> PredictionAgent[🔮 Prediction Agent]
PredictionAgent --> Return([📤 Return HTML Result])

NewsAgent and TrendAgent run in parallel

PredictionAgent uses their output to forecast intelligently

🔮 Prediction Agent Sample Logic

def predict_future(stock, news, trend):
history_summary = get_history_summary(stock)

prompt = (
    f"Stock: {stock}\n\n"
    f"News: {news['summary']}\n"
    f"Trend: {trend['analysis']}\n"
    f"History: {history_summary}\n\n"
    f"Predict future stock price ranges:\n"
    f"  - 7d, 15d, 1mo, 3mo\n"
    f"Suggest:\n"
    f"  - Short-term and long-term sell targets\n"
    f"Provide sentiment (Bullish/Neutral/Bearish) and confidence level\n\n"
    f"Output as styled HTML with headers and bullet points."
)

response = client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": prompt}],
    max_tokens=800
)
return {"html_prediction": response.choices[0].message.content}

🧪 Example HTML Output

📈 15-Day Price Forecast

• 7 Days: ₹3,420 – ₹3,480
• 15 Days: ₹3,500 – ₹3,570

🎯 Sell Targets

• Short-term: ₹3,550
• Long-term: ₹3,700+

📊 Sentiment

🚀 Bullish – Positive momentum and rising volumes.

🔍 Confidence

High – Aligned news and technical signals, with historical support.

⚡ Frontend Integration
Return the prediction as HTMLResponse from your FastAPI backend:

from fastapi.responses import HTMLResponse
@app.post("/predict", response_class=HTMLResponse)
async def predict_stock(...):
result = await flow.invoke(input)
return result["html_prediction"]

Inject in React or Vue:

💾 Caching for Performance
Use Redis or FAISS to cache:

Past news summaries

Processed trend data

Full prediction HTML for frequently requested stocks

import redis

redis_client = redis.Redis(host="localhost", port=6379)
redis_client.set(stock_symbol, prediction_html, ex=3600)
🚀 Deployment Tips
Use uvicorn or gunicorn to serve FastAPI

Host on Render, Fly.io, or your own VPS

Secure .env for API keys

Use openai>=1.0.0 (new SDK format)

🎯 Final Thoughts
With LangGraph + FastAPI + OpenAI, you've created a fully modular, explainable stock forecasting API that:

Thinks like an AI co-analyst 🧠

Responds in seconds 🔁

Presents beautiful HTML results 🌐

Gives confidence-driven decisions for buyers & investors

For collab DM me on LinkedIn

Let me know your thoughts or suggestions on this idea in the comments below! 👇