Algorithmic trading, or algo-trading is the process of using algorithms for placing a stock trade based on a set of perceived market conditions. These algorithms are based on price, quantity or other mathematical model without the risk of human emotion influencing the buy or sell action. This workshop will walk your through some of the basic tools and concepts that algorithmic traders employ to build fully automated trading systems.
Monte Carlo Simulations involve repeated random sampling to model the probability of a complex problem that is difficult to predict using other methods due to the nature of the variables involved. We will use Monte Carlo Simulations to simulate and predict future stock movement by repeatedly sampling random stock values based on past results.
The goal of this workshop is not to become financial gurus. I doubt we'll be rich at the end, but hopefully we'll have learned different ways to build batch processing pipelines using AWS services and save up to 90% using EC2 Spot Fleets.
If you'd like to learn more: Basics of Algorithmic Trading: Concepts and Examples
- AWS account - if you don't have one, it's easy and free to create one
- AWS IAM account with elevated privileges allowing you to interact with CloudFormation, IAM, EC2, SQS, and AWS Batch
- A workstation or laptop with an ssh client installed, such as putty on Windows or terminal or iterm on Mac
- Familiarity with Python, Jupyter, AWS, and basic understanding of algorithmic stock trading - not required but a bonus
These labs are designed to be completed in sequence. If you are reading this at a live AWS event, the workshop attendants will give you a high level run down of the labs. Then it's up to you to follow the instructions below to complete the labs. Don't worry if you're embarking on this journey in the comfort of your office or home - presentation materials can be found in the git repo in the top-level presentation folder.
Lab 1: Setup the workshop environment on AWS
Lab 2: Explore the Algorithmic Trading Concepts with Jupyter
Lab 3: Deploy an Automated Trading Strategy
Lab 4: Leverage a Fully Managed Solution using AWS Batch
Throughout this README, we provide commands for you to run in the terminal. These commands will look like this:
$ ssh -i PRIVATE_KEY.PEM ec2-user@EC2_PUBLIC_DNS_NAME
The command starts after $. Words that are UPPER_ITALIC_BOLD indicate a value that is unique to your environment. For example, the PRIVATE_KEY.PEM refers to the private key of an SSH key pair that you've created, and the EC2_PUBLIC_DNS_NAME is a value that is specific to an EC2 instance launched in your account.
This section will appear again below as a reminder because you will be deploying infrastructure on AWS which will have an associated cost. Fortunately, this workshop should take no more than 2 hours to complete, so costs will be minimal. See the appendix for an estimate of what this workshop should cost to run. When you're done with the workshop, follow these steps to make sure everything is cleaned up.
- Delete any manually created resources throughout the labs.
- Delete any data files stored on S3.
- Delete the CloudFormation stack launched at the beginning of the workshop.
First, you'll need to select a region. For this lab, you will need to choose either the Oregon or Ohio Region.
SSH Key Pair Instructions (expand for details)
At the top right hand corner of the AWS Console, you'll see a Support dropdown. To the left of that is the region selection dropdown.
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Then you'll need to create an SSH key pair which will be used to login to the instances once provisioned. Go to the EC2 Dashboard and click on Key Pairs in the left menu under Network & Security. Click Create Key Pair, provide a name (can be anything, make it something memorable) when prompted, and click Create. Once created, the private key in the form of .pem file will be automatically downloaded.
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If you're using linux or mac, change the permissions of the .pem file to be less open.
$ chmod 400 PRIVATE_KEY.PEM
If you're on windows you'll need to convert the .pem file to .ppk to work with putty. Here is a link to instructions for the file conversion - Connecting to Your Linux Instance from Windows Using PuTTY
For your convenience, we provide a CloudFormation template to stand up the core infrastructure.
The template sets up a VPC, IAM roles, S3 bucket, and an EC2 Instance. The EC2 instance will run a Jupyter Notebook which we will leverage in Lab 2 and a small website that we will use in Lab 3. The idea is to provide a contained environment, so as not to interfere with any other provisioned resources in your account. In order to demonstrate cost optimization strategies, the EC2 Instance is an EC2 Spot Instance deployed by Spot Fleet. If you are new to CloudFormation, take the opportunity to review the template during stack creation.
Important: Prior to launching a stack, be aware that a few of the resources launched need to be manually deleted when the workshop is over. When finished working, please review the "Workshop Cleanup" section to learn what manual teardown is required by you.
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Click on one of these CloudFormation templates that matches the region you created your keypair in to launch your stack:
Region Launch Template N. Virginia (us-east-1) 
Ohio (us-east-2) Oregon (us-west-2) Dublin (eu-west-1) Tokyo (ap-northeast-1) Seoul (ap-northeast-2) Sydney (ap-southeast-2) -
The template will automatically bring you to the CloudFormation Dashboard and start the stack creation process in the specified region. Click Next on the page it brings you to. Do not change anything on the first screen.
First Screen
Parameters Screen
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Select a password to use for the Jupyter Notebook. You will use this password in Lab 2.
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The default port for the Jupyter Notebook is 8888. Some corporate firewalls and VPNs will block this port. You can change the JupyterPort to 443 to get around this.
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Select your ssh key.
Important: On the parameter selection page of launching your CloudFormation stack, make sure to choose the key pair that you created in step 1. If you don't see a key pair to select, check your region and try again.
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After you've selected your ssh key pair, click Next.
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On the Options page, accept all defaults - you don't need to make any changes. Click Next.
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On the Review page, under Capabilities check the box next to "I acknowledge that AWS CloudFormation might create IAM resources." and click Create. Your CloudFormation stack is now being created.
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Periodically check on the stack creation process in the CloudFormation Dashboard. Your stack should show status CREATE_COMPLETE in roughly 10-15 minutes. In the Outputs tab, take note of the Jupyter and Web Server values; you will need these in the next two labs.
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Under the CloudFormation Outputs, click on the URLs for Jupyter and Web Server. Each should load a web page confirming that the environment has been deployed correctly. We have created a self-signed certificate for the Jupyter Notebook. You will see messages about an unsafe connection. It is safe to ignore these warnings and continue. The steps will differ depending on your browser.
Certificate Warning
Jupyter
Web
If there was an error during the stack creation process, CloudFormation will rollback and terminate. You can investigate and troubleshoot by looking in the Events tab. Any errors encountered during stack creation will appear in the event log.
You've completed Lab 1, Congrats!
The Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations and narrative text.
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Log into the Jupyter Notebook using the Jupyter URL output from the CloudFormation Template using the password you configured when building the stack.
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Click on the notebook named monte-carlo-workshop.ipynb and it should open in a new tab.
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Follow the instructions in the Notebook to complete Lab 2. If you're new to Jupyter, you press shift-enter to run code and/or proceed to the next section. When you're done with the Notebook, return here and we'll take the concepts we learned in this lab and build our own automated pipeline.
You've completed Lab 2, Congrats!
Now that we understand the basics of our trading strategy, lets get our hands dirty building out the batch processing pipeline.
We will start by creating a managed message queue to store the batch job parameters.
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Go to the SQS Console, if you haven't used the service in this region, click Get Started Now. Otherwise, click Create New Queue.
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Name your queue "workshop". Select Standard Queue. Click Quick-Create Queue.
NOTE: Queue Name is Case Sensitive
For regions that don't yet support FIFO queues, the console may look different than shown. Just name the queue and accept the defaults.
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Save the queue ARN and URL for later use.
Our EC2 instances run with an Instance Profile that contains an IAM role giving the instance permissions to interact with other AWS services. We need to edit the associated policy with permissions to access the SQS service.
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Go to the EC2 Console.
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Under Instances, select the instance named montecarlo-workshop.
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Scroll down and select the IAM Role.
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You should see two attached policies. One will be an inline policy named after the workshop. Click the arrow beside the policy and click Edit policy.
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Click on Add additional permisions. Click on Choose a service and select or type SQS.
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Click on Select actions. Under Manual actions, check the box beside All SQS actions (sqs:*).
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You will see a warning that you must choose a queue resource type. Click anywhere on the orange warning line. Under Resources, click on Add ARN.
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In the pop-up window, paste the ARN that you saved previously. Click Add.
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Click on Review Policy and then click Save changes.
The CloudFormation template deployed a web server that will serve as the user interface. We need to configure it with our SQS queue
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Launch Web Site using the URL from the CloudFormation output.
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Click Configuration
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Configure the SQS URL, S3 Bucket Name, and AWS Region using the output values from the CloudFormation stack.
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Click Submit and then click Home to return to the home page.
- Enter you simulation details. You can select whatever values you'd like, but too large of an iteration count, may take a long time to complete. We recommend the following configuration.
- Stock Symbol (e.g. AMZN)
- Short Window = 40 days
- Long Window = 100 days
- Trading Days = 1260 (5 years)
- Iterations = 2000
- Preview Only = unchecked - You can use this to see the json message placed on the queue.
- Go to the SQS Console and select your queue.
- Under Queue Actions, select View/Delete Messages.
- Click on Start Polling for Messages
- You should see the message that was created by the web client. Explore the message attributes to see what we will be passing to the worker script
- Now that we have messages on the queue, lets spin up some workers on EC2 spot instances.
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From the EC2 Console, select Spot Requests and click Request Spot Instances.
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Select Request and Maintain to create a fleet of Spot Instances.
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For Total target Capacity, type 2
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Leave the Amazon Linux AMI as the Default.
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Each EC2 Instance type and family has it's own independent Spot Market price. Under Instance type(s), click Select and pick a few instance types (e.g. c3.xlarge, c3.2xlarge, and c4.xlarge) to diversify our fleet. Click Select again to return to the previous screen.
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For Network, pick the VPC we created for the Spot Monte Carlo Workshop.
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Under Availability Zone, check the box next to the first two AZs. The Network Subnet should auto-populate. If the subnet dropdown box says "No subnets in this zone, uncheck and select another AZ
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For Key pair name, choose the SSH Key Pair that you specified in the CloudFormation template.
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Under Security groups and IAM instance profile, select the name with the prefix spot-montecarlo workshop.
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We will use User Data to bootstrap our work nodes. Copy and paste the spotlabworker.sh code from the repo We recommend using grabbing the latest code from the repo, but you can review the script below.
#!/bin/bash
# Install Dependencies
yum -y install git python-numpy python-matplotlib python-scipy python-pip
pip install --upgrade pandas-datareader fix_yahoo_finance scipy boto3 awscli
#Populate Variables
echo 'Populating Variables'
REGION=`curl http://169.254.169.254/latest/dynamic/instance-identity/document|grep region|awk -F\" '{print $4}'`
mkdir /home/ec2-user/spotlabworker
chown ec2-user:ec2-user /home/ec2-user/spotlabworker
cd /home/ec2-user/spotlabworker
WEBURL=$(aws cloudformation --region $REGION describe-stacks --query 'Stacks[0].Outputs[?OutputKey==`WebInterface`].OutputValue' --output text)
echo 'Region is '$REGION
echo 'URL is '$WEBURL
echo "Downloading worker code"
wget $WEBURL/static/queue_processor.py
wget $WEBURL/static/worker.py
echo 'Starting the worker processor'
python /home/ec2-user/spotlabworker/queue_processor.py --region $REGION> stdout.txt 2>&1-
Under Instance tags, click on Add new tag. Enter Name for Key. Enter WorkerNode for Value.
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We will accept the rest of the defaults, but take a moment at look at the options that you can configure for your Spot Fleet
- Health Checks
- Interruption behavior
- Load Balancer Configuration
- EBS Optimized
- Maximum Price
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Click Launch.
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Wait until the request is fulfilled, capacity shows the specified number of Spot instances, and the status is Active.
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Once the workers come up, they should start processing the SQS messages automatically. Feel free to create some more jobs from the webpage.
In the previous step, we specified two Spot instances, but what if we need to process more than two jobs at once? In this optional section we will configure auto-scaling so that new spot instances are created as more jobs get added to the queue.
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Go to the CloudWatch console, and click on Alarms.
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Click on Create Alarm. Select SQS Metrics.
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Scroll down and select ApproximateNumberOfMessagesVisible. Click Next
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We will create a threshold for scaling up. Name the alarm, set the threshold for >= 2 messages for 2 consecutive periods. Delete the default notification actions and hit Create.
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Repeat these steps for the scale down policy. Name the alarm appropriately. Set the threshold for <= 1 message for 3 consecutive periods.
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Return to Spot Requests in the EC2 Console.
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Select your fleet and go to the Auto Scaling tab at the bottom pane.
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Click Configure. On the next screen, click on Scale Spot Fleet using step or simple scaling policies
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Under the ScaleUp and ScaleDown policies, configure the appropriate alarms under Policy trigger.
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Click Save
- Check your S3 Bucket. In a few minutes, you should see results start appearing the bucket.
- If you monitor the SQS queue for messages you should see them being picked up by the worker nodes.
In the next lab, we will use AWS Batch to create a managed batch process pipeline. We will reuse our existing queue, so let's terminate our EC2 Spot worker fleet.
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From the EC2 Console, select Spot Requests and click Request Spot Instances.
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Check the box beside the Spot fleet request containing your worker nodes. The correct request will have a capacity of 2 and the shortest time since it was created.
IMPORTANT: Take care not to cancel the Spot fleet request responsible for our workstation node (Jupyter/WebClient). It will have a capacity of 1 and the instance type will be m4.large.
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Under Actions, select Cancel Spot request.
You've completed Lab 3, Congrats!
- Each job is handled fully by one worker. Maybe you could look at adding more parallelism to task scheduler.
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Go to the AWS Batch Console. The following instructions use the first-run wizard. If the wizard does not show, replace the path at the end of the URL with /wizard. (e.g. https://ap-southeast-2.console.aws.amazon.com/batch/home?region=ap-southeast-2#/wizard)
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Select/Enter the following values
- How would you like to run your job ? : No job submission and hit Next
- Compute environment name : montecarlo-batch-worker
- Service role and EC2 instance role : Leave it defaulted to "Create a new Role"
- Provisioning Model : Spot
- Maximum bid price : 100
- Spot fleet role : Select the role containing your workshop name
- Allowed instance types : optimal
- Minimum vCPUs : 0
- Desired vCPUs : 0
- Maximum vCPUs : 20
- VPC Id : VPC as created earlier
- Subnets : Any two subnets in the VPC
- Security groups : Security Group as created earlier
- Job queue name : montecarlo-batch-worker
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Click Create . It will take less than one minute for the setup to complete. Once complete, click on View Dashboard
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Go to Job Definition , hit Create and enter the following details
- Job definition name : montecarlo-queue-processor
- Job role : Select the one that appears in drop down, as created during setup
- Container image : ruecarlo/montecarlo-workshop-worker:latest
We have created a docker container image containing the required libraries and the Worker code that we used in the previous lab. This container image is stored on Dockerhub. This is the image that we are pulling for our batch job.
- Environment variables (Key) : REGION
- Environment variables (Value) : Name the region you are using, example us-east-1
- Leave everything as default and click Create job Definition
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Now we are ready to submit a job (with the definition created above) and run it against the compute environment created above. Go to Jobs , select Submit job and enter the following details
- Job name : montecarlo-batch-first-run
- Job definition : Select the one created above
- Job queue : Select the one created above
- Leave everything as default and click Submit Job
This will create the EC2 Instances using Spot price as bid during creating the compute environment. This process may take 2-3 minutes. When you refresh the screen, you will see the staus of the job getting transitioned from submitted > pending > runnable > starting > running.
- Once the job reaches Running state, check your S3 Bucket. In a few minutes you should see results start appearing the bucket.
- If you monitor the SQS queue for messages you should see them being picked up by the worker container.
- Use AWS QuickSight to build visualizations, perform ad-hoc analysis, and quickly get business insights from your data. You will need to create a json manifest file with your Amazon S3 data location. Use the following template as a starting point:
{
"fileLocations": [
{
"URIPrefixes": [
"s3://YOUR_S3_BUCKET_NAME/"
]
}
],
"globalUploadSettings": {
"format": "CSV"
}
}
Hopefully you've enjoyed the workshop and learned a few new things. Now follow these steps to make sure everything is cleaned up.
- In the EC2 Console > Spot Requests, click Cancel Spot request under Actions. Make sure Terminate instances is checked.
- In the SQS Console, delete the queue that you created earlier. This is located under Queue Actions.
- In the S3 Console, locate the resultsBucket that was created for your workshop. Click on the bucket and select Empty bucket. You will need to copy and paste the bucket name in to confirm the action.
- Under AWS Batch, click on your running job and click Terminate job. Under Job definitions, click on your job definition and select deregister. Go to Job queues, then disable, and delete the configured job queue.
- In the CloudFormation template, select the workshop stack and select Actions and then Delete stack.
The estimated cost for running this 2.5 hour workshop will be less than $5.