A step-by-step walkthrough of how to build an AWS Lambda on top of a docker container image that is stored in the cloud — removing the previous 250 MB limit for code/dependencies.

Co-authored with Isaac Rhea and Cody Crofoot

On Dec 1st, the CEO of AWS, Andy Jassy, made an announcement at the annual re:Invent conference to support lambdas built on container images. This was one of the many announcements made at the conference, see the list here.

Photo by Hello I'm Nik 🎞 on Unsplash

This new feature makes it very convenient to build lambda-based applications using any and all existing container development workflows, as mentioned in the announcement. Another key benefit is the increase in the deployment package size of 250 MB for the code/dependencies and 50 MB for the function to 10 GB or 10000 MB. …


Learn step-by-step how to build a trading bot using python, Alpaca API, Google Cloud Platform, and email notifications.

The purpose of this article is to provide a step-by-step process of how to automate one's algorithmic trading strategies using Alpaca, Python, and Google Cloud. This example utilizes the strategy of pairs trading. Please reference the following GitHub Repo to access the Python script.

Step 1: Create accounts for Alpaca and Google Cloud Platform


How to leverage the Alpha Vantage API and Python to gather earnings calendar and previous earnings history for stocks.

Co-authored with Cody Crofoot and Isaac Rhea

Photo by Rohan on Unsplash

Introduction

Public companies are required to release quarterly and annual financial reports that anyone can access and analyze. Investopedia describes earnings as “perhaps the single most important and most closely studied number in a company’s financial statements.” The hype surrounding a company’s earnings announcement can result in extra volatility and big moves in its stock price. This could mean increased risk for your investments or potential opportunities to take advantage of. …


Understanding the accessible cloud technologies available in the market and when to use the different services for the variety of types of algorithmic trading

Co-authored with Isaac Rhea

Photo by Dallas Reedy on Unsplash

Introduction

The combination of computers and massive amounts of market data has made algorithmic trading the norm. Around 80% of trades in the US stock market are done by computers and algorithms. 80% of the stock market is now on autopilot (cnbc.com). Wall Street has been taking advantage of these tools for some time because they had the monetary resources to leverage technology that was relatively expensive and not available to the public.


An in-depth analysis of how to leverage SQL and Python while working with stock data.

Co-authored with Isaac Rhea

Photo by Jan Antonin Kolar on Unsplash

Introduction

When working with stock data it is crucial to find tools that allow for large amounts of data and simple ways to filter and derive meaning from it. In a recent article, we compared the speed of Pandas and JSON for working with data for a large number of stocks. As naïve beginners, we thought the best option was to create a giant CSV file with millions of rows holding the data for all of the stocks we were interested in…


Comparing the Performance of Pandas and JSON for Stock Market Data

Co-authored with Isaac Rhea

Photo by Veri Ivanova on Unsplash

Introduction

If you are interested in algorithmic trading you will need to start working with a lot of stock data. This will enable you to design and test strategies until you gain confidence to go live. Casting a wide net could mean that you take into consideration thousands of stocks. What is the best way to organize this data?

You could opt to pull in data from an API for each stock each time you are working on your analysis. However, it will probably be more efficient to store the data. You could store it as a…


Breaking down the steps for how to start a data science project and exploring the benefits of doing so

Co-authored with Isaac Rhea

Photo by Kvalifik on Unsplash

Introduction

The role of a data scientist has become one of the most desired careers. At the same time, there is actually a shortage of qualified individuals.


Learn how to solve data problems with AWS Sagemaker Notebook Examples and Sagemaker Studio JumpStart

Photo by Michael Dziedzic on Unsplash

Introduction

What if you could take a look at how others are breaking down data problems such as fraud detection or image classification using Machine Learning. Well, you can and AWS has made it incredibly easy. However, some of these examples are a bit hidden and for that reason, we are going to show you how to find these gems. We will also pull out a few particularly useful examples that you will want to add to your tool belt.

Steps to Complete Before we Begin the Hunt


Analyzing crypto data from the Coingecko API to build a crypto arbitrage scanner in python

Co-authored with Isaac Rhea

Photo by Alina Grubnyak on Unsplash

Introduction

Markets for currencies around the world trade 24 hours a day at volumes significantly higher than bond, stock, or futures markets. Participants in foreign exchange markets are hedging risk or speculating on future changes in currency values.

Another source of profits comes from taking advantage of short-term imbalances in currency valuations. Using lightning-fast algorithms, high-frequency traders identify arbitrage opportunities and quickly execute a series of exchanges that results in a small profit. See this article from the Corporate Finance Institute for a more in-depth explanation and examples.


A true story with some additional thoughts on the future of cryptocurrency.

Photo by Johann Siemens on Unsplash

My wife and I made our first purchase of Bitcoin on September 1st, 2020 when Bitcoin was at $12,066.22. We sold on May 12th, 2021 at $51,660.75 a few minutes after Elon Musk tweeted the following.

Elon Musk on Twitter: “Tesla & Bitcoin https://t.co/YSswJmVZhP" / Twitter

Ironically, we were planning on using the gains from the sale of our Bitcoin to go towards a large tree in our front yard. We were able to purchase and have the tree delivered all from our gains.

We started thinking about…

McKlayne Marshall

Learn | Build | Elevate

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