How can you use analytical skills to analyze research data in real-time?

One of the things I've found helpful was establishing acceptable market definitions and developing a suitable framework to drive the right conversation. Very often, a near perfect analysis falls prey to opaque requirements, conflicting agendas, or an unseemly vision. Setting the table, so to speak, generally creates buy-in, boosts credibility, and strengthens the bridge to building understanding in pursuit of research that's actionable.

To analyze research data in real-time, define research objectives, select appropriate analytical tools, establish data sources, implement data preprocessing, apply descriptive statistics, visualize data, perform statistical analysis, monitor key metrics, use time series analysis, conduct predictive modeling, perform text and sentiment analysis, integrate feedback loops, automate routine analyses, collaborate with stakeholders, document and communicate insights, and ensure data security and compliance. This approach allows for timely decision-making, adaptation to changing conditions, and the extraction of valuable insights from dynamic datasets.

First of all, I need to correctly formulate the new task: What data do I use? How do I measure this data? Is this data enough? How reliable is this data?4 What do I need to find or what dependency in the data do I want to establish? The most important and difficult part of the research is the choice of a mathematical model for further research. I need to test the model using historical data. This is an important step to determine the relevance of the model. If the model is not completely relevant, I try to understand the reason.

The purpose of my latest research: is there a stable trend for volatile instruments in a volatile financial market? This is a purely mathematical problem that still does not have a clear answer. My choice was the ideas of the great mathematician B. Mandelbrot. He suggested that financial reality is essentially a multifractal with variable dimensions. My assumption coincided 100% with B. Mandelbrot, but I did not understand why his model was not successful, i.e. often not relevant? At some point, I began to understand the reason: his model contained redundant parameters, which complicated the analysis. That is, by simplifying the model, it became relevant!

Of course, this type of research requires many supporting tools to obtain intermediate data. This could be program code, graphical tools, etc.

Data analysis led me to a discovery: financial reality is essentially a modified Fibonacci sequence! That is, previous data determines future market values! This result has been tested on dozens of financial instruments. Thanks to this discovery, it was possible to identify the fractal regularity of financial reality. The next stage in the research was: creating a data library for various assets.

In this project, I was able to carry out precise mathematical research (without simplifications of financial reality) and obtain analytical formulas that answer the question: what is a trend line for a volatile market? Essentially, these formulas establish the rule for the development of the internal dynamics of the financial market!