How to make profits in cryptocurrency trading with machine learning
If you think we have grown neurons in a laboratory, it is not far from the reality. Just kidding 😉(but take a look at “What is an Artificial Neuron?” to know the truth). As lucky as you are, let’s discover an amazing new world.

We assume that anyone reading this article doesn’t know a lot about machine learning and cryptocurrency. But don’t worry, everybody can understand. All concepts are simplified and you can find pictures and gifs to help you to figure out what we are talking about.

Let’s introduce what cryptocurrency is, and if you’re still with us at that time, we’ll introduce machine learning.

“A cryptocurrency (or crypto currency) is digital asset designed to work as a medium of exchange that uses strong cryptography to secure financial transactions, control the creation of additional units, and verify the transfer of assets. […]Cryptocurrencies use decentralized control as opposed to centralized electronic money and central banking systems.” , Wikipedia Cryptocurrency

“Machine learning is the idea that there are generic algorithms that can tell you something interesting about a set of data without you having to write any custom code specific to the problem. Instead of writing code, you feed data to the generic algorithm and it builds its own logic based on the data.”

Wikipedia Machine learning

We will probably use the term “Neural network” to speak about machine learning, you only need to understand that a “neural network” is a kind of a machine learning technique and “Long Short Time Memory(LSTM)” is a type of neural network (cf “LSTM” ).

LSTM

LSTM for “Long Short Time Memory” is a model design that processes past data to give a prediction. It’s a type of neural network.

Let’s illustrate this with an example.

If I tell you “give me the next number after 6”, what can you say ? Naturally you will give “7” because it is the next one, but why not “8” if the series is “N+2” ?

Ok, now I tell you “give the next number after 3, 6,… “ and now you can answer with “9” or “12”, because the link between 3 and 6 can be “N+3” or “N*2”. Then I tell you “give me the next number after 0,3,6” and you see that the answer is “N +3”.

Here, you implemented, in your brain, a simple series check. You used the past values to predict the next one. However, you need enough values in the example range to be able to predict the right result, or at least the most probable one.

Your brain is able to make that for a large variety of “data”. For example, when you look at a ball thrown in the air, you can predict where it will fall, because your brain analyses the position at different steps, and can predict the next steps until the final position.

With LSTM, it is a bit more complicated but the basics are the same:

we send a “chronological series” to let the neural network find the next probable value. A huge dataset, build on a long period, is not needed. You need enough data, to split the dataset in small chronological chunks, for the neural network to be able to understand and predict the next value.

Different trading algorithms types:

There are 2 types of trading algorithms characterized by different speeds, turnover rates, and order-to-trade ratios that leverages different frequency financial data and electronic trading tools. While there is no single definition of High-frequency trading and Low-frequency trading, among their keys attributes are very short-term(HFT) investment horizons or longer(LFT).

High-frequency trading(HFT): It takes between 300 to 400 milliseconds for the human eye to blink. During this time a common “HFT” has passed around 400 orders, why is it so fast ? because it only communicates with one and only one server and this is the point, it is not possible to do “HFT” algorithm on cryptocurrency because the technology is decentralized. This kind of trading needs big infrastructures (super calculators, extremely fast connection, not everybody can have it).

Low-frequency trading (LFT): Considering this high hardware requirement for HFT, we rather focused on low-frequency trading. Furthermore it has already happened HFT program disturbed the market (example link).

We chose the cryptocurrency market because data is easy to get (API, Kaggle datasets, etc…) and it is a decentralized asset.

What about Data ?

We used a software to get the data from “Bittrex” — an exchange platform of cryptocurrency — then save it to a database. (Crawler Bittrex)

Our sample of data once collected has this format:
['Quentin Le Baron']
['Technology', 'Machine Learning', 'Cryptocurrency', 'Bitcoin', 'Tech']