Idea: Adopting Serverless for Trading Operations

I’m not very into day-trading, but I see the potential in the market from time to time, so I came up with this idea to create an automated trading system using AWS services exclusively.

The system will use Lambda, Timestream, EventBridge, S3, SQS and SageMaker to create a serverless architecture for monitoring and trading on the stock market, using the Twelvedata and Coinbase APIs for pulling in market data and executing trades, respectively.

To start, I will use EventBridge as an alternative to cron-jobs to add symbols to an SQS queue for ingestion. This ties in with the use of serverless architecture. For FOREX, the schedule will run every hour, and for Crypto and stock market, it will run every 15 minutes. This is a good balance as I’m not a professional trader and don’t need to use too many API calls.

I will have five Lambda functions:

1. The first Lambda function will listen to the SQS queue and query Twelvedata for the mentioned symbols. It will then insert the data directly into Timestream.
2. The second Lambda function will be triggered by an alert from Timestream when new data is available. For safety (and to start with), I have configured this alert to trigger hourly. The function will throw the data at the SageMaker model. If the model predicts a positive yield, the Lambda function will pass the symbol to the third lambda function via another SQS Queue.
3. The third Lambda function will execute a transaction on Coinbase.
4. The forth Lambda function will monitor Twelvedata and Coinbase for hot & trending symbols and add them to the monitoring queue, triggered by another EventBridge Schedule.
5. The fifth Lambda function will create a *.csv dataset from the data within Timescale.

I will use Secrets Manager to securely store the API keys for Twelvedata and Coinbase.

I’m not an AI expert and don’t know much about the specifics of training a model, so I’ll be using the SageMaker canvas feature to train the model. The canvas feature is the easiest way into training AI Models that doesn’t require making a Python script.

Finally, at the end of each day, I’ll extract a dataset from the Timestream database into a *.csv and store it in S3, then pass this file onto SageMaker for training. I’ll use one last EventBridge schedule to trigger this workflow.

Hopefully by following this approach, I’ll have a fully functioning market monitoring and trading system.