(Legacy) Free Crypto Solana Cloud Trading on DYDX V3 AWS

Learn how to obtain api key and secrets from the d33 exchange by inspecting page, going to storage, and expanding traded stock exchange to view credentials, then keep them secure.

Learn to structure Python training code for AWS Lambda by defining a lambda handler, placing imports at the top, ensuring indentation, and keeping the main code inside the handler.

Secure the trading code by storing dydx api key, secret, and passphrase in AWS environment variables and handling ethereum address and api key via strings.

Set up an alchemy http provider for dydx v3, create an account with sms verification, obtain app id and api key, and integrate the https key for ethereum mainnet code.

Explain python time handling in an aws schedule: daily 12 a.m. utc trigger, one-minute delay, and a loop that sleeps until the target start time before running the main code.

Explain how to fetch position and price data from the exchange, compute open size from long and short positions, and retrieve the recent close price daily with 50 rows.

Explain a rate-of-change model for Solana trading on dydx, using a threshold and n to decide hold or sell, and to determine order size and leverage.

This lecture outlines a market order status function that checks order success, retries within a timer, and returns true or false based on the outcome.

Identify the best price from the Solana order book to execute timely buys or sells on dydx, leveraging best bid and ask prices for repeatable, optimized trades.

Explore the core dydx order function, covering order IDs, retry limits of ten, safety timers, and validations via market order status and price from the order book.

Explore order conditions in the dydx sol price factor code: sell all serena when no position is recommended; sell excessive serena when target_sol_USD is negative; buy serena to meet target.

Set up aws accounts and install docker to automate python-based crypto trading on solana using dydx v3 aws.

Create and configure a private repository in AWS elastic container registry by searching for repositories, selecting private repositories, naming the repo, and saving the URI for later steps.

Prepare Windows workflow to upload Python scripts to AWS using a Dockerfile and requirements.txt, configure Ethereum address, API key, and HTTP provider, then start cloud trading.

Extract the Amazon web services account id, region, and repository name from the repo uri to prepare for uploading images.

Log in to the repository via the command prompt after starting Docker and AWS, replacing variables with your region and account ID to authenticate successfully.

Navigate to the project folder using cd, then paste the path to replace the old path with the new one containing docker file, requirements.txt, and python file.

Learn to build and tag an image, configure the AWS account ID and region, and push the image to the AWS repository for cloud deployment.

Push your package to the cloud and create a new Lambda function using a container image. Set the image name, browse the repo, upload, select, and finalize the function creation.

Configure a function with a five-minute timeout, set an EventBridge daily trigger at 12 a.m., and securely provide three environment variables from AWS secrets for scheduled execution.

Learn to check your lambda function history by viewing CloudWatch logs, inspecting Python code execution records, and using these debugging insights to fix issues.

Redeploy a Lambda image by deploying a new image and selecting the repo to update the function. Check the uploaded time for the image, test the function, and monitor code.