Syeda Samina Hussain

Posted on Feb 14

A Smart Financial Assistant with Google Vertex AI & Gemini API

#gemini #vertexsprintai #google #googlecloud

Introduction

With the rise of AI in finance, people are looking for intelligent systems that can analyze stock trends, provide investment insights, and assist in decision-making. In this blog, I’ll walk you through my Smart Financial Assistant, a project built using Google Vertex AI and Gemini AI.

This assistant can:

Fetch real-time stock data** from Alpha Vantage
Provide AI-generated investment insights** using Gemini
Explain financial concepts** for beginners

Let’s dive into the implementation, code, and real-world outputs!

Prerequisites

Before you start building the Smart Financial Assistant, ensure you meet the following requirements:

✅ Google Cloud Platform (GCP) Account – You need a GCP account to access Vertex AI and deploy AI models. If you don’t have one, you can sign up here.

✅ Enable Vertex AI – Go to the Google Cloud Console and enable the Vertex AI API for your project.

✅ Basic Understanding of Vertex AI – You should have some knowledge of Google Vertex AI, including:

How to initialize Vertex AI
How to deploy AI models
Basic concepts of function calling with AI

✅ Google Cloud SDK Installed – Install the Google Cloud CLI for authentication and managing your project. Download it here.

✅ Python Installed – Ensure you have Python 3.x installed on your system to run the AI scripts.

1. Project Overview

The Smart Financial Assistant is designed to:

✔️ Use Google Vertex AI for AI-powered interactions

✔️ Integrate Gemini function calling for answering financial queries

✔️ Provide basic insights on stock trends, market updates, and investments

✔️ Lay the groundwork for future enhancements, including real-time financial analysis

2. Tech Stack Used

Google Vertex AI: Manages AI models & cloud deployment
Gemini AI: Processes financial-related queries
Python: For coding and implementing the AI model
Google Cloud SDK: For authentication and API access

3. Implementation Steps

Step 1: Setup Google Vertex AI

Ensure you have Google Cloud SDK installed and Vertex AI enabled in your Google Cloud project.

# Authenticate Google Cloud (Run in your terminal)
gcloud auth application-default login --no-launch-browser
gcloud config set project your-project-id

Step 2: Initialize Vertex AI in Python

from google.auth import default
from vertexai import init
from vertexai.generative_models import GenerativeModel, Content

# ✅ Replace with your actual Google Cloud project ID
PROJECT_ID = "your-project-id"
LOCATION = "us-central1"

# ✅ Initialize Vertex AI
init(
    project=PROJECT_ID,
    location=LOCATION,
    credentials=None  # Replace with actual credentials
)

Step 3: Integrate Gemini for AI Function Calling

# ✅ Initialize Gemini Model
model = GenerativeModel("gemini-2.0-flash-001")

# ✅ Sample User Query (Financial Question)
user_query = "What are the latest stock market trends?"

# ✅ AI Processing
response = model.generate_content([Content(role="user", parts=[user_query])])

# ✅ Display AI Response
print("Financial Assistant Response:", response)

4. Smart Financial Assistant: AI + Stock Market Data

Now, let’s integrate real-time stock data into our assistant!

Code Implementation

import requests
from vertexai.generative_models import GenerativeModel, Content, Part

# ✅ Alpha Vantage API Key (Replace with actual key)
API_KEY = "your_api_key_here"

# ✅ Function to Fetch Stock Data
def get_stock_price(symbol):
    """Fetches real-time stock data for the given symbol from Alpha Vantage."""
    url = f"https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=5min&apikey={API_KEY}"
    response = requests.get(url)
    data = response.json()
    time_series = data.get("Time Series (5min)", {})
    if not time_series:
        return f"❌ No data found for {symbol}. Please check the stock symbol."
    latest_timestamp = max(time_series.keys())
    stock_data = time_series[latest_timestamp]
    return (
        f"📈 Stock Data for {symbol}:\n"
        f"- Open: {stock_data['1. open']}\n"
        f"- High: {stock_data['2. high']}\n"
        f"- Low: {stock_data['3. low']}\n"
        f"- Close: {stock_data['4. close']}\n"
        f"- Volume: {stock_data['5. volume']}\n"
    )

Example Output

📈 Stock Data for IBM:
- Open: $198.40
- High: $200.00
- Low: $197.50
- Close: $198.90
- Volume: 3,200,000

5. Check Out the Code on GitHub!

💻 GitHub Repository: Click Here

#AI #Finance #VertexAISprint #GoogleCloud #FinTech #MachineLearning #StockMarket

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