In this blog, I share some images from an application called Storm. I wrote the program many years ago. Storm has the ability to generate 3-dimensional plumes from a stream of data. It also has an unusual feature that allows the user to trade based on the kinetics - effectively eliminating the need to know about pricing. At this time, I would like to draw a clear distinction between *trading* and *investing*. I should also point out that I used Storm for recreational purposes so I could - in a literal sense - "play" the market usually during interesting times in history such as WWII, Vietnam, and the Tech Bubble. When I was developing the program, it occurred to me that probably some gifted individuals - maybe even children - could trade by kinetics much better than me. I also used Storm to examine precipitation and hurricane data, electrocardiogram signals, earthquake data, and other lengthy streams of data that conventional software seemed poorly designed to present graphically. However, the main purpose of this blog is to share details about the plume structure, which might assist in the visualization of dynamic data more generally.

I present three types of plumes below: Paranoid, Reluctant, and Reactionary. Each plume is presented in three different formats: surface image (left); depth image (middle); and transitional (right). In a transitional image, the surface data is algorithmically squeezed into fixed boundaries. I generally used the transitional image for simulated trading purposes. My basic rationale behind these plumes for trading purposes is as follows: *repetitive change indicates trend*. However, incidents of repetitiveness tend to be distributed in an evasive manner. Repetitiveness should also not be confused with repetition particularly as it relates to stocks. The reductive nature of the index - the pricing - tells us almost nothing about the underlying investment or the activities of stakeholders: this makes it unclear whether at any given time pricing represents fair or flawed valuation. I found the usefulness of kinetic representation in how it allows for sense of tempo and rhythm.

In order to generate these plumes, it is necessary to load the data into stacks: (1) can hold the price for today; (2) the price for yesterday; (3) the price from 2 days ago; and so on. Therefore, (1)-(3) represents the price for today less the price from 2 days ago. Sometimes rather than hold the actual price, I find it helpful to load the moving average - averaging from today into the past - maybe over 10 days. So these are *differential plumes.*

Paranoid Plume

A Paranoid differential plume is jumpy. A person could logically expect it to be jumpy because the plume structure is based on the following differential pattern: (1)-(2) (1)-(3) (1)-(4) (1)-(5) (1)-(6) (1)-(7) and so forth from left to right across the surface - the differential forming the depth of the plume. I found it difficult to fit a Paranoid into fixed boundaries. It routinely hits a boundary and keeps banging against it. The surface appears muddy or blurry. I have tried playing the transitional image, but it is fairly fast-paced and stressful.

Reluctant Plume

In a Reluctant differential plume, it is assumed that the underlying numbers have certain convective qualities - that pricing flows gradually. In order to capture this phenomenon, the following differential pattern is used: (1)-(2) (2)-(3) (3)-(4) (4)-(5) (5)-(6) (6)-(7) and so force across the surface. The surface appears icy. The kinetics will tend to be contained in a tight centre - pulling to the boundaries only under exceptional circumstances. The transitional image is easier for people like me to play - that is to say, mature people that to enjoy the scenery. However, a lot of potential gains can be lost by the time a boundary is reached. (The push is at the Paranoid.)

Reactionary Plume

The surface appears bumpy on the plume. It is a cross between a Paranoid and a Reluctant differential plume. Here is the pattern - adjusted slightly to clarify the sequencing: (1)-(2) (2)-(4) (3)-(6) (4)-(8) (5)-(10) (6)-(12) (7)-(14) (8)-(16) (9)-(18). There is an assumption that both convection and abruptness are relevant. I can usually play the transitional image of this plume comfortably.

Transitional Profile

In order to fit the differential plume into fixed boundaries, I exploited the cyclical patterns on the surface images - indicating movement from one extreme to another - e.g. from all turquoise to all gold. I eliminated the amplitudes and kept the polarities such that all turquoise *on the surface differential* created one boundary (-1 to the left) and all gold the other boundary (+1 to the right). (The colour scheme for the transitional is different.) Due to limited computer processing capabilities at the time, I could not flip between different types of plumes from this aerial perspective. I usually played the head or face of the transitional image as shown below. I often combined all three of transitional wave patterns. The face is the front of the transitional image - the left being the *lightest* part of the plume and the right being its *heaviest* - the top conceptually representing *overvaluation* and bottom *undervaluation*. So the objective of the game is to catch the wave at the bottom and sell at the top. In Paranoid play, the wave might reach the bottom, bounce around, and then dive. So I quickly discovered that tempo was an important issue - and no particular stock necessarily gave a good sense of it.

Through this approach I found it possible to do simulated trading without knowing the price. Moreover, the visualization provided a great deal more information than the price on its own. I also found the games fairly entertaining. I wondered if it were possible to maybe to test for different aspects of cognitive problems using Storm. I imagined some brilliant young person perhaps with a musical or dance background being able to anticipate hurricanes and earthquakes.

© 2019 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

**Technical**

- Free Books and Resources for DSC Members
- Learn Machine Learning Coding Basics in a weekend
- New Machine Learning Cheat Sheet | Old one
- Advanced Machine Learning with Basic Excel
- 12 Algorithms Every Data Scientist Should Know
- Hitchhiker's Guide to Data Science, Machine Learning, R, Python
- Visualizations: Comparing Tableau, SPSS, R, Excel, Matlab, JS, Pyth...
- How to Automatically Determine the Number of Clusters in your Data
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- Fast Combinatorial Feature Selection with New Definition of Predict...
- 10 types of regressions. Which one to use?
- 40 Techniques Used by Data Scientists
- 15 Deep Learning Tutorials
- R: a survival guide to data science with R

**Non Technical**

- Advanced Analytic Platforms - Incumbents Fall - Challengers Rise
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- How to Become a Data Scientist - On your own
- 16 analytic disciplines compared to data science
- Six categories of Data Scientists
- 21 data science systems used by Amazon to operate its business
- 24 Uses of Statistical Modeling
- 33 unusual problems that can be solved with data science
- 22 Differences Between Junior and Senior Data Scientists
- Why You Should be a Data Science Generalist - and How to Become One
- Becoming a Billionaire Data Scientist vs Struggling to Get a $100k Job
- Why do people with no experience want to become data scientists?

**Articles from top bloggers**

- Kirk Borne | Stephanie Glen | Vincent Granville
- Ajit Jaokar | Ronald van Loon | Bernard Marr
- Steve Miller | Bill Schmarzo | Bill Vorhies

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives**: 2008-2014 | 2015-2016 | 2017-2019 | Book 1 | Book 2 | More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central