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🚀 Docker Setup for Trading Agents

This guide provides instructions for running the Trading Agents application within a secure and reproducible Docker environment. Using Docker simplifies setup, manages dependencies, and ensures a consistent experience across different machines.

The recommended method is using docker-compose, which handles the entire stack, including the Ollama server and model downloads.

Prerequisites

Before you begin, ensure you have the following installed:

Docker
Docker Compose (usually included with Docker Desktop)

⚡ Quickstart

For those familiar with Docker, here are the essential steps:

# 1. Clone the repository
git clone https://github.com/AppliedAIMuse/TradingAgents.git
cd TradingAgents

# 2. Create the environment file
cp .env.example .env

# 3. Edit .env and set your API Keys or pick local LLM settings to run locally

# 4. Build the app
docker-compose build

# 5. Run the command-line app
docker-compose run -it app

Step-by-Step Instructions

Step 1: Clone the Repository

git clone https://github.com/TauricResearch/TradingAgents.git
cd TradingAgents

Step 2: Configure Your Environment (`.env` file)

The application is configured using an environment file. Create your own .env file by copying the provided template.

cp .env.example .env

Next, open the .env file and customize the settings. The most important variables are LLM_PROVIDER and OPENAI_API_KEY.

To use the local Ollama server:
```
LLM_PROVIDER="ollama"
```
To use external provider like OpenAI:
```
LLM_PROVIDER="openai"
OPENAI_API_KEY="your-api-key-here"
```
Note: If you use an external provider, the Ollama service will not start, saving system resources.

Step 3: Run with `docker-compose` (Recommended)

This is the simplest way to run the entire application.

Build and Start the Containers

The following command will build the Docker image, download the required LLM models (if using Ollama), and start the application.

# Use --build the first time or when you change dependencies
docker-compose build

# On subsequent runs, you can run directly
docker-compose run -it app

The first time you run this, it may take several minutes to download the base image and the LLM models. Subsequent builds will be much faster thanks to Docker's caching.

Running on GPU machines

For running on GPU machines, uncomment the deploy gpu resource section in docker-compose.yml and run the commands.

docker-compose run -it app

View Logs

To view the application logs in real-time, you can run:

docker-compose logs -f

Stop the Containers

To stop and remove the containers, press Ctrl + C in the terminal where docker-compose run is running, or run the following command from another terminal:

docker-compose down

Step 4: Verify the Ollama Setup (Optional)

If you are using LLM_PROVIDER="ollama", you can verify that the Ollama server is running correctly and has the necessary models.

Run the verification script inside the running container:

docker-compose exec app python test_ollama_connection.py

Step 5: Run Ollama server commands (Optional)

If you are using LLM_PROVIDER="ollama", you can verify run any of the Ollama server commands like list of all the models using:

docker-compose exec app ollama list

✅ Expected Output:

Testing Ollama connection:
  Backend URL: http://localhost:11434/v1
  Model: qwen3:0.6b
  Embedding Model: nomic-embed-text
✅ Ollama API is responding
✅ Model 'qwen3:0.6b' is available
✅ OpenAI-compatible API is working
   Response: ...

Alternative Method: Using `docker` Only

If you prefer not to use docker-compose, you can build and run the container manually.

1. Build the Docker Image:

docker build -t trading-agents .

2. Test local ollama setup (Optional): Make sure you have a .env file configured as described in Step 2. If you are using LLM_PROVIDER="ollama", you can verify that the Ollama server is running correctly and has the necessary models.

docker run -it --env-file .env trading-agents python test_ollama_connection.py

for picking environment settings from .env file. You can pass values directly using:

docker run -it \
  -e LLM_PROVIDER="ollama" \
  -e LLM_BACKEND_URL="http://localhost:11434/v1" \
  -e LLM_DEEP_THINK_MODEL="qwen3:0.6b" \
  -e LLM_EMBEDDING_MODEL="nomic-embed-text"\ 
  trading-agents \
  python test_ollama_connection.py

To prevent re-downloading of Ollama models, mount folder from your host and run as

docker run -it \
  -e LLM_PROVIDER="ollama" \
  -e LLM_BACKEND_URL="http://localhost:11434/v1" \
  -e LLM_DEEP_THINK_MODEL="qwen3:0.6b" \
  -e LLM_EMBEDDING_MODEL="nomic-embed-text"\ 
  -v ./ollama_cache:/app/.ollama \
  trading-agents \
  python test_ollama_connection.py

3. Run the Docker Container: Make sure you have a .env file configured as described in Step 2.

docker run --rm -it \
  --env-file .env \
  -p 11434:11434 \
  -v ./data:/app/data \
  --name trading-agents \
  trading-agents

4. Run on GPU machine: For running on GPU machine, pass --gpus=all flag to the docker run command:

docker run --rm -it \
  --gpus=all \
  --env-file .env \
  -p 11434:11434 \
  -v ./data:/app/data \
  --name trading-agents \
  trading-agents

Configuration Details

Live Reloading

The app directory is mounted as a volume into the container. This means any changes you make to the source code on your local machine will be reflected instantly in the running container without needing to rebuild the image.

Persistent Data

The following volumes are used to persist data between container runs:

./data: Stores any data generated by or used by the application.
.ollama: Caches the Ollama models, so they don't need to be re-downloaded every time you restart the container.

GPU troubleshooting

If you find model is running very slow on GPU machine, make sur you the latest GPU drivers installed and GPU is working fine with docker. Eg you can check for Nvidia GPUs by running:

docker run --rm -it --gpus=all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark

or

nvidia-smi

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