Deploying Zipline in a Cloud Environment for Scalable Backtesting

Backtesting is a critical component of developing any algorithmic trading strategy. It allows traders to see how their strategies would have performed in the past, providing insights and confidence before going live with actual trading. Zipline is a popular open-source algorithmic trading simulator that traders and quants use for backtesting their strategies. However, running Zipline on local machines can be restrictive due to computation limits. Deploying Zipline in a cloud environment offers scalable, flexible options to overcome these limitations.

Setting Up Your Cloud Environment

To deploy Zipline in a cloud environment, the first step is setting up a server. Several cloud providers such as AWS, Google Cloud, and Azure offer virtual machine services that are suitable for this purpose.

Creating a Virtual Machine on AWS

Step 1: Log in to your AWS Management Console and navigate to Amazon EC2.

Step 2: Click on the "Launch Instance" button, and choose an Amazon Machine Image (AMI). For a basic setup, the Amazon Linux 2 AMI is a suitable choice.

Step 3: Select an instance type. For heavier computation, choose a type that offers greater CPU and memory capacity.

Step 4: Configure the instance's firewall to allow SSH connections and any other ports your application may need.

Installing Zipline

Once your virtual machine is running, connect to it via SSH. The next step involves installing Zipline.

ssh -i your-key.pem ec2-user@your-instance-address

Ensure you have Python installed, as Zipline is a Python-based library. Consider using a virtual environment to manage dependencies smoothly:

sudo yum install python3
python3 -m venv zipline-env
source zipline-env/bin/activate

Now, we're ready to install Zipline using pip:

pip install zipline-reloaded

Setting Up Data Feeds

With Zipline installed, the next critical component is setting up your data feeds. Zipline supports data from various sources, but setting up your own data retrieval system in the cloud allows for scalability and flexibility.

from zipline.data.bundles import register
from zipline.data.bundles.quandl import quandl_bundle

register("quandl", quandl_bundle)

You can automate data fetching using scheduled scripts. For continuous integration, use crontab on Linux instances to update your data regularly.

crontab -e
0 2 * * * zipline ingest -b quantopian-quandl

Running Backtests

Now, Zipline is set up and ready for running backtests. Prepare your algorithm script and deploy it into the server. Use the command below to execute your backtest:

zipline run -f algo.py --start 2020-1-1 --end 2021-1-1 -o backtest_result.pickle

Scaling the System

To scale your backtesting environment, consider automating deployment scripts and utilizing services like AWS Lambda or Google Cloud Functions for on-demand processing at scale. For distributed workloads, containers can also be used.

# Example Dockerfile for containerizing Zipline
FROM python:3.8-slim-buster
WORKDIR /app
COPY . .
RUN pip install zipline-reloaded
CMD ["zipline", "run", "-f", "algo.py"]

Deploy these containers using Kubernetes to manage clusters effortlessly.

Conclusion

Deploying Zipline in a cloud environment paves the way for scalable, high-performance backtesting of trading strategies. By following these steps, traders can leverage cloud capabilities to conduct thorough testing and ensure their strategies are well-vetted before real-world application. With the right setup, the cloud doesn’t just offer scalability—it offers endless possibilities for innovation in algorithmic trading.