What’s next for Bitcoin?

We have seen plenty of drama lately in the Bitcoin arena. Technological questions aside, it is time to re-evaluate the involvement in a market with looming roller coaster dynamics.

Originally, our motivation to favor cryptocurrency over other asset classes as a subject to automated trading, resulted from these four considerations:

  • Bitcoin exchanges like Bitstamp or Gemini are easily accessible from a software developers point of view. They have modern, well maintained and well documented APIs, and you don’t need to work for a financial services institution to receive access.
  • The trading hours are 24×7, so the trading bot does not run idle 2/3 of the time, which seems like a waste of resources.
  • Cryptocurrencies are cool. I don’t blame you if you beg to differ, with Bitcoin going more and more mainstream nowadays. But back in 2014 there was no dispute about it being the coolest thing since the UNIVAC 9000 series.
  • Little regulation. Don’t get me wrong: Regulation is a very very very good thing. After the 2010 flash crash, regulators in all major markets started to look very closely on automated trading, and put sensible restrictions in place. Since then we have seen a few more flash crashes, but non of them was nearly as severe as the 2010 incident. And this was certainly not due to a more responsible behavior of the market participants. So regulation is a good thing. But that said, if automated trading is what you want to do, it complicates your life. In German law, Bitcoin is neither a currency nor a security, so it is mostly unregulated, which made our project a little easier.

The last point was originally an advantage, but it seems to turn into a hassle now, because as it stands today, the market seems to go crazy. This is a problem, because it becomes inherently unpredictable.


It might not be obvious, but with a deep neural network the predictive performance, at the end of the day, still depends on the ability to find statistical relationships, however hidden and convoluted they might be. In a market with constantly changing influencing factors, these interrelations are hard to find, even under normal conditions. Add craziness as another complexity layer and your neural network’s only output will be white noise. At least with mine, this is the case.

So what can we do?

I hear many people talking about tulips lately. They refer, of cause, to the Tulip mania in the early 17th century. They point out parallels of today’s Bitcoin exchange rate to the historic tulip prices, to point out that Bitcoin is a case of an irrationally inflated bubble that is doomed to burst.

Indeed there seems to be a good portion of irrationality. In the past, whenever we saw a hike in the Bitcoin price, it came with an obvious explanation: The disappropriation of Russian bank customers in Cyprus; the Indian demonetarization policy; gambling in China. The last event in this series was the cancelled hard fork in November. Although the problem addressed by the proposed fork has not been solved yet by any means, since then the price has more then tripled. I don’t believe, that many of the buyers have a firm grasp of blockchain hard forks. It just does not justify the latest price hike.

Use this finding as input for a little ducktesting,  and you will likely come to the conclusion that we are, in fact, dealing with a bad case of a speculative bubble.

So is this the time to abandon Bitcoin and blockchain technology and move on the the next cool thing? Or walk back to something more conservative?

Let’s come back to the tulips: What happened after the bubble has burst? Take a walk through almost any neighborhood in almost any western community, and you see, that, while the tulip bubble is gone, the tulips are still there.  They can be found in most private and public gardens. They cover a significant share of the land surface of the Netherlands and represent a small but notable share of the Dutch economy.

To me, this looks like a blueprint for the further way of Bitcoin. The current craze has the beautiful side effect, that for the first time people with no immediate need and no interest in the technology, create Bitcoin wallets and acquire cryptocurrency. No matter if the price stabilizes at the current level or it crashes and then stabilizes at a much lower level: The wallets will still be there and people will still own Bitcoin and know a lot more about it then a few months ago.

No matter how this ends: When it’s over, Bitcoin will likely be ubiquitous in more and more areas, like tulips are today. We might finally enter a phase where Bitcoin will be used the intended way: as a currency.

As a conclusion: This is not the time to leave the field of Bitcoin. If anything it is a good time to enter the area of cryptocurrencies and blockchain technology, because no matter if the current market is a bubble or just very healthy growth: It will contribute to a much broader use of the technology in the next few years.



Deep Reinforcement Learning for Bitcoin trading

It’s been more than a year, since the last entry regarding automated Bitcoin trading has been published here. The series was supposed to cover a project, in which we have used deep learning to predict Bitcoin exchange rates for fun and profit.

We have developed the system in 2014 and operated it all through the year 2015. It has performed very well during the first 3 quarters of 2015, … and terribly during the last quarter. At the end of the year we have stopped it. Despite serious losses during the last three months, it can still be considered a solid overall success.

I have never finished the series, but recently we have deployed a new version, which includes some major changes, that hopefully will turn out to be improvements:

  • We use Reinforcement Learning, following DeepMind’s basic recipe (Deep Q-learning with Experience Replay) from the iconic Atari article in Nature magazine. This eliminates the separation of prediction and trading as distinct processes. The inference component directly creates a buy/sell decision instead of just a prediction. Furthermore the new approach eliminates the separation of training and production (after an initial training phase). The neural network is trained continuously on the trading machine. No more downtime is needed for re-training once a week, and no separate compute hardware is lying idle with nothing to do for the other six days of the week.
  • We use Deeplearning4J (DL4J) instead of Matlab code for the training of the neural network. DL4J is a Java framework for defining, training and executing machine learning models. It integrates nicely with the trading code, which is written in Java.

This will change the course of this blog. Instead of finishing the report on what we have done in 2014, I am now planning to write about the new system. It turns out, that most of the code we have looked at so far, is also in the new system, so we can just continue where we left off a year ago.

Predicting Bitcoin Prices

In this initial blog series, I am going to report on an automated bitcoin trading system, that I have build in 2014 and sucessfully operated during 2015.

The decision making component in this trading system incorporates machine learning methods: mainly a neural network and – in a data preparation step – principal component analysis (PCA).

The code was written in Java and Matlab. It is not always pretty, so please when reading through it, keep in mind, that this has started as a hobby project.

Some of the code I can not publish, which I will explain when I come to it. But I will point out how to fill in the gaps.

I would like to encourage people to rebuild the system, use it to try out their own ideas and share them with the rest of us. Also I want to point out, that while bitcoin trading is a good point to start, it is certainly not the only area, where these methods are applicable.

Why is bitcoin a good point to start? Because of an excellent technological infrastructure and immediate financial rewards, to name a few reasons. Also Bitcoin is cool, which for me has some value on it’s own.

In the 12 months of operation, the system initiated roughly 11000 transactions on Bitstamp, a Bitcoin exchange which among other things allows to trade Bitcoin against fiat currency (USD). The system yielded a gross revenue a little above 26%. After transaction fees, a pre-tax return around 20% remained. The result after taxes is a wholy different story, which we will talk about in a later post.

Now, a buy and hold strategy during this year would have given me the same revenue during this time interval, even with less transaction fees. But I could not have known that in the beginning of the year.

The approach of the trading system is obviously completely different. It tries to predict small movements in the near future (a few minutes) based on observed market activities, news, economic data and a few other factors. In essence, it exploits the prices’ volatility. The beauty of this is, that it works almost as well, when the overall direction is southwards.

During the first months of the year, while doing it’s first clumpsy, inexerienced trading steps, the system has recorded the input data and added it to an increasingly larger body of training data. The neural network has been trained and retrained several times, each time with more input data. The results turned out increasingly better. From January to April the trading yielded net negative results while the overall market went sidewards. After that the results where positive, even during a severe market decline in November. The last training took place in May. Due to memory constraints (and because the training time has passed 24 hours), training with more data would have made a different approach necessary. Since the results were already satisfactory, I have decided to stick to what I have. So that is, where we are now: Having quite some room for improvement.

In the next few posts, I will very briefly lay out the theoretical foundation to the project, before we take a closer look into the code.