Creating an AI-Powered Stock Market Insight Chatbot

5 min readJul 24, 2024

Building an AI chatbot using Llama, Flask, and basic HTML & JavaScript to guide newcomers through stock market complexity — Overcoming the Stock Market Learning Curve

Simplifying Stock Market Navigation

When I first dipped my toes into the world of investing, I quickly realized how overwhelming it could be. The stock market, with its intricate terminology, constantly shifting conditions, and myriad of investment options, felt like a maze with no clear way out. For anyone new to investing, this complexity can be even more daunting.

That’s why I decided to create a chatbot designed specifically to assist newcomers like I once was. My goal is to offer a friendly and accessible resource that makes navigating the stock market a little less intimidating.

This chatbot is here to be your guide and support system. Whether you’re unsure about which investments suit your goals, trying to understand your risk tolerance, or just looking for clarity on market conditions and financial jargon, it’s got you covered. By harnessing advanced AI technology, the chatbot provides personalized advice and explanations, helping you make informed decisions and build confidence in your investment journey.

If you’re interested on how this chatbot was developed and want to dive into LLM technology yourself, I’ve put together a quick procedure outlining the steps I took. It’s a great starting point if you’re interested in creating your own chatbot or exploring the possibilities of AI in finance.

1. Setting Up Your Environment

First things first: let’s get our tools ready. You’ll need Python and pip installed on your computer. If you haven’t already installed Flask, transformers, and torch, don’t worry—it’s a breeze. Just open your terminal and run:

pip install flask transformers torch

This will set up everything you need for our backend. If you’re using a Llama model from HuggingFace or another source, make sure you follow their setup instructions.

2. Choosing the Right Model

The model name you use will depend on where you’re getting the Llama model from and the exact version of the model you want to use. Llama models are typically hosted on platforms like Hugging Face. For instance, if you’re using Hugging Face, you might use a model like "huggingface/llama-7b", "huggingface/llama-13b", or another variant.

3. Fine-Tuning the Model

To ensure that the Llama model can provide stock market insights, you’ll need to fine-tune it on relevant financial data. Here’s a step-by-step guide:

a. Collect Financial Data

Gather Data: Collect data relevant to the stock market, including financial news articles, stock analysis reports, historical market data, and company earnings reports.
Preprocess Data: Clean and preprocess this data to make it suitable for training. This might involve tokenization, text normalization, and structuring the data into a format that the model can learn from.

b. Fine-Tune the Model

Prepare Training Scripts: Use a script to fine-tune the model. Here’s an example using Hugging Face’s transformers library:

from transformers import LlamaTokenizer, LlamaForCausalLM, Trainer, TrainingArguments
from datasets import load_dataset

model_name = "huggingface/llama-7b"  # Replace with your chosen Llama model name
tokenizer = LlamaTokenizer.from_pretrained(model_name)
model = LlamaForCausalLM.from_pretrained(model_name)

# Load and preprocess your financial dataset
dataset = load_dataset('path/to/your/financial_dataset')  # Replace with your dataset path
tokenized_dataset = dataset.map(lambda x: tokenizer(x['text'], truncation=True, padding='max_length'), batched=True)

training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    per_device_train_batch_size=4,
    per_device_eval_batch_size=4,
    num_train_epochs=3,
    weight_decay=0.01,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset['train'],
    eval_dataset=tokenized_dataset['validation'],
)

trainer.train()

Save and Deploy: After training, save your model and deploy it in your chatbot application.

4. Backend Development with Flask

Building the Flask Backend

Let’s dive into the backend of our chatbot. Create a file named app.py. This will be the brain of your chatbot, handling user queries and generating responses. Here’s a simple script to get you started:

from flask import Flask, request, jsonify
from transformers import LlamaTokenizer, LlamaForCausalLM
import torch

app = Flask(__name__)

# Load your fine-tuned Llama model and tokenizer
model_name = "path/to/your/fine-tuned-model"  # Replace with the path to your fine-tuned model
tokenizer = LlamaTokenizer.from_pretrained(model_name)
model = LlamaForCausalLM.from_pretrained(model_name)

def predict():
    data = request.json
    user_query = data.get('query', '')
    if not user_query:
        return jsonify({"error": "No query provided"}), 400
    # Encode and generate response from Llama
    inputs = tokenizer.encode(user_query, return_tensors='pt')
    with torch.no_grad():
        outputs = model.generate(inputs, max_length=150, num_return_sequences=1)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return jsonify({"response": response})
if __name__ == '__main__':
    app.run(debug=True)

This script sets up a simple Flask server that listens for queries, processes them with the Llama model, and returns the responses. Once you have this file ready, you can start your server by running:

python app.py

You’ll see your Flask server running at http://127.0.0.1:5000. Exciting stuff!

5. Frontend Development

Creating a Simple Interface

Next, we’ll build the user interface where people can interact with your chatbot. Create a file named index.html. This will be the webpage that your users see and interact with. Here’s a basic example:

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Stock Market Chatbot</title>
    <script>
        async function getResponse() {
            const query = document.getElementById('query').value;
            const response = await fetch('http://127.0.0.1:5000/predict', {
                method: 'POST',
                headers: {
                    'Content-Type': 'application/json'
                },
                body: JSON.stringify({ query })
            });
            const data = await response.json();
            document.getElementById('response').innerText = data.response;
        }
    </script>
</head>
<body>
    <h1>Stock Market Chatbot</h1>
    <input type="text" id="query" placeholder="Ask about stock predictions">
    <button onclick="getResponse()">Submit</button>
    <p id="response"></p>
</body>
</html>

In this HTML file, we create a simple interface with an input field for user queries and a button to submit them. When the button is clicked, the JavaScript function getResponse() sends the query to your Flask backend and displays the response on the page.

6. Testing Your Chatbot

Running the Show

Now comes the fun part — testing! Open index.html in your web browser. You should see a field where you can type your query. Go ahead and try asking something like:

User Input:

What is the stock market prediction for Tesla?

The Flask backend will process your query using the Llama model and send back a response. Here’s what you might see:

Backend Response:

{
  "response": "As of the latest analysis, Tesla's stock market prediction shows an optimistic trend due to recent innovations in their electric vehicle technology and expansion into new markets. However, market volatility and competition could impact future performance."
}

And on your web page, you’ll see:

As of the latest analysis, Tesla's stock market prediction shows an optimistic trend due to recent innovations in their electric vehicle technology and expansion into new markets. However, market volatility and competition could impact future performance.

7. Deployment and Scaling

Taking It Live

Once you’re happy with your chatbot, it’s time to share it with the world. You can deploy your Flask application to a cloud platform like Google Cloud Platform (GCP). Services such as Google App Engine or Google Kubernetes Engine (GKE) are great choices for scaling and managing your application.

Containerization

Consider using Docker to containerize your application. This makes deployment easier and more consistent across different environments.

That’s it! You’ve successfully built a fully functional AI-powered stock market chatbot using Llama and Flask. From setting up your development environment to deploying your chatbot, you’ve covered it all. I hope this guide helps you get a clear idea of how to leverage AI to create practical and engaging solutions. Happy coding.

If you’re interested, you can read more about it on my GitHub or feel free to contact me via email or instagram (@natgluons). Have a good day!