How to diversify a Long-term Crypto Portfolio

Last Friday it was my pleasure to give an Executive Talk at the Frankfurt School of Finance & Management. While the focus of my presentation was Digital Transformation, plenty of the questions raised by the audience were about the cryptocurrencies use case – and how to build a crypto portfolio. After I received some more emails from the participants asking for some guidance, I decided that is might be worth to publish this blog post.

The cryptocurrency market has returned over 1200% since the beginning of 2017. It is very hard to find this kind of return on investment (ROI) in the stock market or anywhere else. If you had made an investment of $500 in January, you would have made $6000 in less than a year! This guide is aiming to show you how to implement a cryptocurrency portfolio for a long-term investment.

In general, I recommend balancing your portfolio with up to five coins in the Top 10 market cap, making up 90-95% of your total cryptocurrency investment. And then add smaller coins (a.k.a. altcoins) – in projects you believe in – to fill up the remaining 5-10%. This mirrors the more technical analysis done by Timothy Chong on exploring a Markowtiz-style crypto optimization.

Bitcoin (50%)

Bitcoin (BTC) is the father of all blockchains, and is considered to be gold of cryptosphere. It is expected to grow step-by-step, without dramatic events. It is a safe long-term investment choice for many.

Price (as of time of writing): $16,708
Gain Over Past Year: 2,170%
Market Cap: $278 B (#1)
Circulating Supply: 16,734,237 BTC

Ethereum (15%)

Ethereum (ETH) is different to Bitcoin in that its sole purpose is not to be used as a medium of value exchange. Instead, Ethereum allows developers to build dApps using smart contracts. The tradeable currency of the Ethereum project is known as Ether.

Price (as of time of writing): $470
Gain Over Past Year: 5740%
Market Cap: $45 B (#2)
Circulating Supply: 96,272,074 ETH

Bitcoin Cash (10%)

Bitcoin Cash (BTC) is similar to Bitcoin in that it too is supposed to be a currency that is dedicated to serving as a medium for the purchase of various goods and services. The key difference between Bitcoin Cash and Bitcoin is that the former has an 8MB block size, whereas Bitcoin has a 1MB block size. A bigger block size allows Bitcoin Cash to process transactions faster than Bitcoin, and at a lower fee.

Price (as of time of writing): $1385
Gain Over Past 4 Month: 502%
Market Cap: $24 B (#3)
Circulating Supply: 16,849,738 BCH

Litecoin (10%)

Litecoin (LTC) is often marketed as being the silver to Bitcoin’s gold status. Being a hard fork of Bitcoin, Litecoin shares many similarities to the original coin. Litecoin can also be used as a value exchange coin. However, Litecoin’s block generation time of 2.5 minutes, compared to Bitcoin’s 10 minutes, and a different hashing algorithm (Scrypt), are features designed to produce a more innovative cryptocurrency.

Price (as of time of writing): $170
Gain Over Past Year: 4690%
Market Cap: $10 B (#5)
Circulating Supply: 54,255,483 LTC

Monero (10%)

Monero (XMR) is similar to Bitcoin in that it allows value exchange. However, Monero differs from Bitcoin in that it is focused on providing greater privacy to those that utilize their blockchain, using their stealth address mechanism. Anonymity is likely to become more and more important in a world where Bitcoin addresses can be traced. As more regulation starts entering the cryptocurrency space, an increasing number of individuals will gravitate towards privacy-focused coins such as Monero that can mask their transactions.

Price (as of time of writing): $264
Gain Over Past Year: 3370%
Market Cap: $4 B (#9)
Circulating Supply: 15,449,232 XMR

Electroneum (5%)

Electroneum (ETN) is a brand new cryptocurrency launching on September 14th – and is one of the projects were I see tremendous potential. Electroneum is developed to be used in the mobile gaming and online gambling markets, its goal is to be the most user-friendly cryptocurrency with wallet management and coin mining all possible on a mobile app.

Price (as of time of writing): $0.12
Gain Over Past 1 Month: 30%
Market Cap: $257 M (#44)
Circulating Supply: n/a (launched recently)

Other altcoins have high potential as well. WTC, VTC, and VEN are also good worth to be considered. What is your cryptocurrency investment strategy? I’m happy to discuss more ideas in the comments!

This blog post is an opinion and is for information purposes only.  It is not intended to be investment advice.

[Update 12 Dec 2017] Frequently asked questions after blog post release:

Q: Isn’t it too late to start buying Bitcoins now?
A: No. I’m very confident that Bitcoin will hit the 100,000 EUR/BTC mark by the end of 2018.

Q: Where can I buy bitcoins, ether and other coins?
A: Coinbase is a US-based cryptocurrency exchange where you can buy and sell Bitcoin, Ethereum, and Litecoin. bitcoin.de is the first Germany-based exchange where you can buy and sell Bitcoin, Ethereum, and Bitcoin Cash.

Q: How can I store my cryptocurrencies in a secure way?
A: Either you can generate and print a paper wallet, or you can get a hardware wallet, such as the Ledger Nano S.

5 Takeaways from Tableau’s Hybrid Transactional/Analytical Processing

What makes Hyper so fast?
The Future of Enterprise Analytics: Hyper can handle both OLTP and OLAP simultaneously. In the future it will address NoSQL and graph workloads.

1. What is Hyper’s key benefit?

Hyper is a Hybrid transactional/analytical processing (HTAP) database system and replaces Tableau Data Extracts (TDE). The change will be mostly transparent for end users, other than everything being faster. Hyper significantly improves extract refresh times, query times and overall performance.

2. What is Hybrid transactional/analytical processing?

As defined by Gartner:

Hybrid transaction/analytical processing (HTAP) is an emerging application architecture that “breaks the wall” between transaction processing and analytics. It enables more informed and “in business real time” decision making.

The two areas of online transaction processing (OLTP) and online analytical processing (OLAP) present different challenges for database architectures. Currently, customers with high rates of mission-critical transactions have split their data into two separate systems, one database for OLTP and one so-called data warehouse for OLAP. While allowing for decent transaction rates, this separation has many disadvantages including data freshness issues due to the delay caused by only periodically initiating the Extract Transform Load (ETL) data staging and excessive resource consumption due to maintaining two separate information systems.

3. Does Hyper satisfy the ACID properties?

Hyper, initially developed at the Technical University of Munich and acquired by Tableau in 2016, can handle both OLTP and OLAP simultaneously. Hyper possesses the rare quality of being able to handle data updates and insertions at the same time as queries by using hardware-assisted replication mechanisms to maintain consistent snapshots of the transactional data. Hyper is an in-memory database that guarantees the ACID properties (Atomicity, Consistency, Isolation, Durability) of OLTP transactions and executes OLAP query sessions (multiple queries) on the same, arbitrarily current and consistent snapshot.

4. What makes Hyper so fast?

The utilization of the processor-inherent support for virtual memory management (address translation, caching, copy on update) yields both at the same time: unprecedentedly high transaction rates as high as 100,000 per second and very fast OLAP query response times on a single system executing both workloads in parallel. This would support real-time streaming of data in future releases of Tableau. These performance increases come from the nature of the Hyper data structures, but also from smart use of contemporary hardware technology, and particularly nvRam memory. Additional cores provide a linear increment in performance.

5. What does this mean for Tableau?

With Hyper now powering the Tableau platform, your organization will see faster extract creation and better query performance for large data sets. Since Hyper is designed to handle exceptionally large data sets, you can choose to extract your data based on what you need, not data volume limitations. Hyper improves performance for common computationally-intensive queries, like count distinct, calculated fields, and text field manipulations. This performance boost will improve your entire Enterprise Analytics workflow.

Join our “The Future of Enterprise Analytics” events and get a sneak peak at upcoming features and the Tableau Roadmap: 14th of November in Düsseldorf and 6th of December in Frankfurt.

Data Science Toolbox: How to use Julia with Tableau

R allows Tableau to execute Julia code on the fly
R allows Tableau to execute Julia code on the fly

Michael, a data scientist, who is working for a German railway and logistics company, recently told me during an FATUG Meetup that he loves Tableau’s R and Pyhton integration. As he continued, he raised the raised the question for using functions they have written in Julia. Julia, a high-level dynamic programming language for high-performance numerical analysis, is an integral part of newly developed data strategy in the Michael’s organization.

Tableau, however, does not come with native support for Julia. I didn’t want to keep Michael’s team down and was looking for an alternative way to integrate Julia with Tableau.

This solution is working flawless in a production environment since several months. In this tutorial I’m going to walk you through the installation and connecting Tableau with R and Julia. I will also give you an example of calling a Julia statement from Tableau to calculate the sphere volume.

1. Install Julia and add PATH variable

You can download Julia from julialang.org. Add Julia’s installation path to the PATH environment variable.

2. Install R, XRJulia and RServe

You can download base R from r-project.org. Next, invoke R from the terminal to install the XRJulia and the RServe packages:

> install.packages("XRJulia")
> install.packages("Rserve")

XRJulia provides an interface from R to Julia. RServe is a TCP/IP server which allows Tableau to use facilities of R.

3. Load libraries and start RServe

After packages are successfully installed, we load them and run RServe:

> library(XRJulia)
> library(Rserve)
> Rserve()

Make sure to repeat this step everytime you restart your R session.

4. Connecting Tableau to RServe

Now let’s open the Help menu in Tableau Desktop and choose Settings and Performance >Manage External Service connection to open the External Service Connection dialog box:

TC17 External Service Connection

Enter a server name using a domain or an IP address and specify a port. Port 6311 is the default port used by Rserve. Take a look on my R tutorial to learn more about Tableau’s R integration.

5. Adding Julia code to a Calculated Field

You can invoke Calculated Field functions called SCRIPT_STR, SCRIPT_REAL, SCRIPT_BOOL, and SCRIPT_INT to embed your Julia code in Tableau, such as this simple snippet that calculates sphere volume:

6. Use Calculated Field in Tableau

You can now use your Julia calculation as an alternate Calculated Field in your Tableau worksheet:

Using Julia within calculations in Tableau (click to enlarge)
Using Julia calculations within Tableau (click to enlarge)

Feel free to download the Tableau Packaged Workbook (twbx) here.

Further reading: Mastering Julia

Social Media and the Customer-centric Data Strategy #data17 #resources

Social media marketing mix
Do you analyze your social media marketing mix? | Photo Credit: via Richard Goodwin

With over 3 billion active social media users, establishing an active presence on social media networks is becoming increasingly essential in getting your business front of your ideal audience. These days, more and more consumers are looking to engage, connect and communicate with their favorite brands on social media.

Adding social media to your customer-centric data strategy will help boost brand awareness, increase followership, drive traffic to your website and generate leads for your sales funnel. In 2017, no organization should be without a plan that actively places their brand on social media, and analyzes their social media data.

Once you’ve started diving into social media analytics, how do you bring it to the next level? This session covers a customer-centric data strategy for scaling a social media data program.

Here are the links (i.e. additional resources) featured during the session to help you formulate your social media data program in order to build a stronger presence and retrieve powerful insights:

The Data Opportunity

TC17 Social Media Slides: The Data Opportunity

Focus on relevant metrics for your strategy

TC17 Social Media Slides: Sentiment Analysis

How to get Social Media in Tableau?

TC17 Social Media Slides: 3rd Party Platform Talkwalker

Tips to Level Up

TC17 Social Media Slides: Unshorten URLs in Tableau with R

Tutorials and Slide Set

The slides and tutorials presented at Tableau Conference on Tour in Berlin are also available on SlideShare, and on YouTube in English and German.

English Tutorials

German Tutorials

Slide Set

Data Strategy: Erstickt Innovation zwischen Berichtswesen und Data Discovery?

Abbildung 4: Interaktives Dashboard zur Darstellung von variablen Abhängigkeiten in Tableau
Abbildung 4: Interaktives Dashboard zur Darstellung von variablen Abhängigkeiten mit TensorFlow in Tableau

Der erste Schritt auf dem Weg zu besserer Entscheidungsfindung im Unternehmen, ist zu verstehen, wie gute (oder schlechte) Entscheidungen zustande kamen. Genau wie manche Unternehmen formale Prozesse für Aktivitäten haben, wie z. B. What-if-Analysen, prädiktive Wartung und Bestimmung von Abhängigkeiten in Korrelationen (siehe Abbildung 4), so müssen sie formale Prüfprozesse für Entscheidungen im gesamten Unternehmen einführen. Dies soll jedoch keinesfalls dazu dienen, die an schlechten Entscheidungen Beteiligten zu bestrafen, sondern den Entscheidungsfindungsprozess und -stil des Unternehmens im Allgemeinen verbessern.

Die Rolle der IT nähert sich hierbei wieder ihren Wurzeln an und statt eine Berichtefabrik für den Rest des Unternehmens zu unterhalten, wird die IT wieder zum Dienstleister und Partner, der die Infrastruktur für eine Data Discovery bereitstellt. IT-Mitarbeiter werden entlastet und erhalten den Freiraum, ihre professionelle Energie und Kreativität in den Dienst der Innovation zu stellen, und die Mitarbeiter in den Abteilungen sehen ihre Datenfragen nicht am Flaschenhals Berichtswesen verhungern. Nur so lassen sich die Investitionen in Business Intelligence und Analytics optimal in den Dienst der strategischen Ziele des Unternehmens stellen.

Abbildung 5: Anforderungen, Fähigkeiten und Ziele einer Datenstrategie (TC17-Präsentation)
Abbildung 5: Anforderungen, Fähigkeiten und Ziele einer Datenstrategie (TC17-Präsentation)

Moderne Unternehmen sehen sich vielen analytischen Anforderungen (siehe Abbildung 5) gegenüber, und diese Anforderungen werden unweigerlich schneller wachsen, als Unternehmen sie bedienen können. Es ist daher unerlässlich, Analytics als lebenswichtigen Teil der eigenen Datenstrategie zu verstehen und entsprechend zu planen.

Dabei ist ein umfassender Betrachtungswinkel sinnvoll, denn die wachsende Nachfrage nach Analysen und Erkenntnissen wird mehr und mehr von den kundenbezogenen Abteilungen wie Marketing oder Support ausgehen. Dementsprechend wird auch das Budget für Analytics verstärkt aus diesen Abteilungen kommen, statt aus einem zentralisierten IT- oder BI-Budget. Dort, wo viele Kundendaten vorhanden sind, wird der CMO bald mehr für Analytics ausgeben als der CIO. Und dort, wo Mitarbeiter über gut integrierte, intuitive Werkzeuge für komplexe Analysen verfügen, können gute Instinkte und datenbasierte Entscheidungen Hand in Hand für den Erfolg sorgen.

Dieser Beitrag ist der fünfte Teil der Datenstrategie-Serie:

Teil 1: Die Notwendigkeit einer modernen Datenstrategie im Zuge der digitalen Transformation
Teil 2: Steigern smarte Erkenntnisse den Business Impact?
Teil 3: 10 BI & Analytics Trends, die in keiner Datenstrategie fehlen dürfen
Teil 4: Wie unterstützen Analysen Ihre Entscheidungsfindung?
Teil 5: Erstickt Innovation zwischen Berichtswesen und Data Discovery?

TC17 Data Strategy Title Slide Möchten Sie mehr zu den neuesten Trends im Bereich Datenstrategie erfahren? Dann freue ich mich, wenn Sie an meinem Vortrag “Building an Enterprise Big Data & Advanced Analytics Strategy” auf unserer Tableau Conference TC17 (9.-12. Okt., Las Vegas) teilnehmen.