DevopsDaily

  Legal Disclaimer:  The Content is for informational purposes only. You should not construe such information or other material as legal, tax, investment, financial, or other advice. Nothing contained on our Site constitutes a solicitation, recommendation, endorsement, or offer by myself or any third-party service provider to buy or sell any securities or other financial instruments in this or in any other jurisdiction in which such solicitation or offer would be unlawful under the securities laws of such jurisdiction. So in the last blog, we talked about the data collected and how we stored it. Let's pick up where we left on and start building a model.  The neural network I built is a dense Neural Network, a model's architecture that takes information from the previous layers and then passes it to the next layer. This dense connectivity starts in the input layer and continues to the hidden and output layers. Connecting all the nodes to each other at each layer.  Let...

Legal Disclaimer:  The Content is for informational purposes only. You should not construe such information or other material as legal, tax, investment, financial, or other advice . Nothing contained on our Site constitutes a solicitation, recommendation, endorsement, or offer by myself or any third-party service provider to buy or sell any securities or other financial instruments in this or in any other jurisdiction in which such solicitation or offer would be unlawful under the securities laws of such jurisdiction. I have already talked about the two scripts, one that pulls the financial data from Yahoo Finance hourly, then looks for the TA signals in that data. The other script searches one thousand tweets daily, forty-two tweets every hour for Litecoin, and uses hugging face to analyze sentiment.  Let's talk about the data features we wanted to save and the tables we kept them in.  Transactions: We collect the following metrics open, high, low, close, adj close, volu...

 When I started this project, I had been looking at small Kubernetes clusters to make for my home lab. The issue was the size and power required to run them. In my bachelor days, I once owned a fully enclosed computer rack on wheels, which lived in the corner of my bachelor pad dining room. It was full of salvaged HP, Cisco, SGI, and Sun hardware from the dot.com crash. It's a true story; running it costs me about $300 extra monthly in power. When I got married, the spousal acceptance level of such a setup was a non-starter. Which made me rethink the whole X86 architecture or other rack-mounted hardware for a home lab. Linux has been running on ARM for a long time, and most significant projects have arm ports. This is fueled by the AWS Graviton platform and Apple's move to ARM-based M1 and M2 chips.   In 2019 Raspberry Pi 4B option came out with some decent specs: Cortex-A72 (ARM v8) 64-bit SoC @ 1.5GHz, 8GB RAM, 2 USB 3.0, and a fast Micro-SD card slot. Also, Google rele...

 I decided to rip off the bandaid and use my Python skills to do some Data Science and retire R, as it really wasn't serving me to learn neural networks. The first thing I had to do was to figure out all the Python libraries and this is what I settled on:  a client library for Twitter, a current stock data library, and a stock technical analysis library. After that, I found a great open API for NLP sentiment analysis. The next library I needed was data storage and manipulation. I used a db library to store the data and, finally, Numpy and Panda to manipulate the data while experimenting.  This is what I built to solve the problem.  I took around 1,000 tweets daily with the search term Litecoin and did sentiment analysis on them to find out of the 42 tweets per hour how many of those are positive, negative, or neutral, and stored that in a MySQL database table.  Next, I took the stock data from Yahoo Finance and ran it through technical analysis software, looking...

I had the privilege of working among some of the most intelligent people I have ever known for several years while I was head of IT and DevOps at Skytree (The machine learning company) back in 2013-2016, way before the AI LLM hype that has taken over the world.  I learned from and worked with giants in the industry. Some of them went on to build their own companies, and some are legends in the field. While there, I got a real-world education in ML and what it could do at scales that hadn't been tried before.   In my off hours, I took Coursera courses on ML and the R Programing languages classes. After about the second year, I knew enough about our product and ML in general that I often stood in the booth, gave presentations about our product to passers-by at conferences, and sat in as a sales engineer at client meetings.   Now the problem: While at Skytree, I talked to a colleague about ML and inference. With enough candle stick technical analysis stock dat...