Build Your Own Trading Agent Locally Using Ollama and Google Gemma3 – Part 1

Run Advanced LLM Models like GEMMA On Your Desktop PC Without the Cloud — A Practical Guide for Traders & Investors

Modern trading is no longer just about price charts and indicators. AI-driven agents are becoming a game-changer in how traders analyze markets, test ideas, and even automate decision-making. While tools like ChatGPT are amazing, they often come with recurring costs, data privacy concerns, and reliance on internet access. That’s where Ollama comes in — an open-source platform that lets you run Large Language Models (LLMs) like Google’s Gemma3 on your own PC.

Pair Ollama with Gemma3, Google’s powerful multimodal AI model family, and you’re ready to build your own private AI trading assistant — completely offline.

What is Ollama?

Ollama is a local model runner that lets you:

• Run popular open-source LLMs like LLaMa, Gemma, Mistral
• Interact with models via command line, web interface, or Python
• Avoid cloud costs and keep all your prompts and data private
• Build intelligent agents that live entirely on your system

Think of it as your personal ChatGPT engine — hosted and owned by you.

Why Google’s Gemma3 Models?

Gemma3 is Google’s latest open model family based on Gemini technology. It’s designed for high performance with lower hardware requirements.

Key reasons why traders should explore Gemma3:

• ✅ 128K Token Context Window: This is the big one. Feed the model large documents like quarterly reports, 10-Ks, or entire datasets in one shot. No need to chunk your inputs.
• ✅ Available Sizes (1B to 27B): You can choose smaller models (like 4B or 12B) that run smoothly even on consumer-grade hardware.
• ✅ Multimodal: The models can understand text and images. Want it to interpret a chart screenshot or annotated trade journal image? It can.
• ✅ Multilingual: Support for 140+ languages — useful for global macro analysis or following international news.
• ✅ Strong Reasoning & Code Abilities: With excellent scores on logic, reasoning, and math benchmarks, Gemma3 can assist in Python scripting, backtesting logic, or understanding financial concepts.
• ✅ Runs Locally: These models are designed to run efficiently even on laptops with decent GPUs. No GPU? Still works with CPU (though slower).

Minimum System Requirements

Here’s what you need to run Ollama and Gemma3 models smoothly:

• OS: Windows 10/11 64-bit
• RAM: At least 8 GB (16+ GB recommended for 12B model)
• Disk Space:
  • Gemma3 4B: ~3.3 GB
  • Gemma3 12B: ~8 GB
• GPU (Optional):
  • NVIDIA GPU with 6 GB VRAM or more (for faster responses)
  • CPU-only mode works, but will be slower
• Python: 3.8 or later (for Python integration)

Step-by-Step Guide: Install Ollama + Gemma3 (No Docker Required)

1. Download and Install Ollama for Windows

• Visit https://ollama.com/download
• Download the Windows installer
• Run the .exe and complete installation
• Ollama will install and launch a local background service

By default, it runs on http://localhost:11434

2. Install Google Gemma3 Models

Open Command Prompt and run:

ollama pull gemma3:4b   # ~3.3 GB
ollama pull gemma3:12b  # ~8 GB

To run a model and test:

ollama run gemma3:4b

Then type your question or prompt and get a reply.

3. Set Up Python + Ollama SDK

# Create a virtual environment
python -m venv ollama_env

# Activate it (on CMD or PowerShell)
ollama_env\Scripts\activate

# Install the Python library
pip install ollama

🧪 Sample Python Code (Streaming Response)

from ollama import chat

stream = chat(
    model='gemma3:4b',
    messages=[{'role': 'user', 'content': 'Explain the Difference between Volume and Liquidity'}],
    stream=True,
)

for chunk in stream:
    print(chunk['message']['content'], end='', flush=True)

This streams the response in real-time, which is faster and more responsive than waiting for the full output.

4. Install Open WebUI (Optional Web Chat)

pip install open-webui
open-webui serve

Visit: http://localhost:8080 and start chatting with your local AI.

Building a Trading Agent

Once you have Ollama and Gemma3 running:

• You can connect via Python and send prompts programmatically
• Read news headlines, feed into model, get summaries
• Analyze earnings report PDFs, structured trade logs, or financial tables
• Use it as your local strategy coder

Coming in Part 2: We’ll show how to build AI trading agents that can analyze data, suggest strategies, and run autonomously.

Ready to Build Your First Local Trading Agent?

With Ollama and Gemma3, the age of private, on-device AI for finance is here. No more monthly API bills, no privacy trade-offs.

Set it up in minutes and unlock a new era of AI-assisted investing — right from your desktop.

Stay tuned for Part 2: Fine-tuning Gemma3 and Building Trading Agents for Financial Workflows.