📄️ KDB Tick Explained: A Walkthrough [PART 1]
In this blog post, we're diving into the code of a simple Tickerplant. We'll break it down line by line, explaining what each part does and how it fits into the big picture. This will not only demystify Tickerplants but also shed light on important KDB/Q programming concepts. By doing so, you'll gain insights into the inner workings of a Tickerplant and acquire a deeper understanding of key concepts in the KDB/Q programming language.
📄️ KDB Tick Explained: A Walkthrough [PART 2]
In my previous tutorial I walked you through all the helper functions you can find in the u.q file of a plain vanilla Tickerplant. I provided a comprehensive overview of the inner workings of these functions and how they interact with each other. It is now time to circle back and continue with our step-by-step examination of the main tick.q file and complete complete our understanding of the Tickerplant. If you'd like to revisit our previous discussions or if you're new to this tutorial, you can access my earlier post here.
📄️ A Real Time Stock Market Feed
In this tutorial, we'll walk through building a real-time stock market data feed and streaming that data into a simplified KDB/Q Tick architecture. We'll use Python, specifically the yfinance library, to fetch live market data from Yahoo Finance, and then publish it to a custom KDB/Q Tickerplant (TP), which will forward the data to a Real-Time Database (RDB). We'll use the qpython library to connect our Feedhandler to the Tickerplant, enabling us to stream real-time data for further processing.
📄️ Back to the Future: Building Your Research HDB for Stock Market Insights
In our last post, we explored how to build a real-time stock market data feed, but as any good quant knows, historical data is often the crystal ball we look into when trying to forecast future trends, or at least, give it our best shot.