Topic: [ANN][DTT]🔺ICO DataTrading - trade forecasting by artificial intelligence 🔺📈 - page 10. (Read 6021 times)

2.3. Artificial neural networks
Artificial neural networks are one of the methods of machine learning and serve to solve many tasks, such as image recognition problems, discriminant analysis, approximation, clustering methods, decision making, forecasting, etc. Artificial neural networks are built on the principle of the organization and functioning
of biological neural networks (networks of nerve cells of a living organism). Neural networks can find and identify relationships between input parameters (even if these relationships are not known in advance) and make very accurate forecasts based on the found patterns.

Then the method (or algorithm) of machine learning is chosen, data processing and model configuration are carried out, and the learning begins. If during the testing of the learning outcomes it was revealed that the relationship between the factors was not found, or it was very weak, then a new stage of training is conducted, for which a larger sample, another set of data (features) or different model settings is used. This process continues until the system finds the parameters and configuration of the model that reveal the relationship between the characteristics (characteristics of the car) and the results of observations (the price of the car). After successful training and testing the model is used for forecasting, i.e., predicting the cost of a new car depending on its characteristics. In our example, it looks like this: a set of features (for example, Mazda, crossover, gasoline engine, 3 liters, manual transmission, 205 hp, 11 l / 100 km) is used in the model and the model makes a prediction about the price. It is important to understand that a well-trained model will make an accurate forecast about the price of such a car even if such configuration was not in the training sample. This is because the model does not fit the results to the learning data (finds the closest configuration), but determines the relationship between the factors and how much each factor (body type, car brand, liter, etc.) affects the desired parameter (car price).

Here is a schematic example. Let us suppose, the task is to predict the price of a new car, depending on its parameters. To solve this problem, a training sample using the methods of machine learning is used, which consists of a number of observations (the more observations, the greater the accuracy of training). Each observation is the same and consists of a number of parameters (signs): the car’s brand, the type of body, the type of engine, the capacity, the type of gearbox, the amount of horsepower, fuel consumption and so on. In the training sample, for each set of characteristics, the price of the car is known, for example:
● observation 1: Ford, sedan, gasoline engine, engine capacity 1.5 liters, manual transmission, 105 hp, fuel consumption 8 liters per 100 kilometers, price — 15,000 $;
● observation 2: Ford, hatchback, gasoline engine, engine capacity 1.5 liters, manual transmission, 115 hp, fuel consumption 9.5 liters per 100 kilometers, price — 21,000$;
● observation 3: Toyota, sedan, diesel engine, 1.8 liter engine, automatic transmission, 120 hp, fuel consumption 8.9 liters per 100 km, price — 19,000$;
● ...

The simplified general task for supervised machine learning is as follows. There are many situations (experiments, observations) and the values of certain features that somehow influence the results of the experiment. The task is to identify the relationship between the set of signs and the results of observations (experiments)1. The process of identifying and establishing this dependence is called the learning process. The data used for training, the values of attributes and the results of observations for which are known, called training samples. If during the learning on the training sample an explicit relationship between the signs and the results of the observations was determined, it is considered that the aim of the training was achieved and the developed model is used to work with data where the results of the experiments are unknown.

2.2. Machine learning
Machine learning is a big subsection of the science of artificial intelligence, which involves the use of various data analysis algorithms, during which the system learns and independently finds interrelations between input parameters, and can make conclusions, decisions or predictions in the context of the tasks. Unlike the traditional approach in programming, in which the task is solved by creating certain set of rules and commands, the machines are trained on a large number of input data and this gives them the opportunity to learn how to perform the task.

Thus, the use of technical indicators for the analysis of investment tools in DataTrading system will be a part of the first stage of data processing. Further, the results of application of technical indicators will be used in the machine learning module, where along with other feature detection they will serve as input layer for the process of Artificial intelligence learning.
We want to emphasize that while using data from  technical indicators as one of many input layers for machine learning, the system can give much more accurate and  relevant  forecasts than standard trading strategies that would use these same indicators as the sole basis for trading signals.

.1. Technical Indicators
Technical indicators are traditional mathematical tools for assessing and forecasting trends in the behavior of the price of financial instruments, based on the values of statistical indicators of trading (price, time of transactions, trading volume, etc.). Nowadays there are hundreds of technical indicators (apart from the variations of the most famous ones).
Almost every trader is familiar with technical indicators, technical analysis and algorithmic trading are based on them. There has been a long debate on how effective technical indicators are and whether they can be used for decision-making. Usually experienced traders rarely make decisions based on one indicator only. In the majority of cases, each trader chooses several indicators and makes decisions to expand or reduce the position after carefully analyzing them and taking into account his own experience, knowledge of the market and intuition.
Technical indicators serve as the basis for most automated trading strategies in trading systems; trading signals on the opening or closing of trading positions are generated based on their combination. DataTrading system uses some technical indicators in its algorithms in order to aggregate incoming data and conduct primary analysis and selection, but it is not making trading decisions based only on technical indicators.

1.2. Solution
Our team has been working on DataTrading project since 2015. DataTrading aims to make the use of artificial intelligence affordable and convenient for traders so they can trade on the stock exchanges without the need to study of the mathematical foundations of this technology. We want to offer traders
a ready-made toolkit that will help them trade on different stock exchanges and to receive the income, which is higher than the market level. We expect that even a novice trader will be able to get a good profit and increase his professionalism with DataTrading trading advisor. In addition, anyone who is interested in these technologies will be able to develop their own model of artificial intelligence on the DataTrading platform even without special education and use it for their own trade or for sale to other users of the system.

The fact that there is a wide range of fields where artificial intelligence is applied raises the issue of the possibility of using this technology for the analysis of exchange markets and the formation of trade strategies on its basis. The success of using AI for trading on stock exchanges is confirmed by various researches  [7] . A number of large hedge funds are actively using different instruments of artificial intelligence to make investment decisions. Profitability of investments of these funds in most cases exceeds the profitability of those investments that were made with using traditional analytical tools and technical indicators  [8]   [9]   [10]   [11] .
Thus, it can be argued that artificial intelligence is very effective in forecasting exchange markets and can bring good profit. Nevertheless, most traders today do not have the opportunity to use this technology for their own trading. The problem is that in order to use AI effectively there is a need to study a large volume of mathematical apparatus and  "spend a lot of time"  in mastering the methodology of developing of AI. It is also necessary to find or purchase, select and correctly process a large amount of primary, inordinately diverse data from different sources, so that the results of analysis and trading strategies of AI are as accurate as possible. All these factors significantly complicate the access of ordinary traders for the use of artificial intelligence when trading on exchanges.

The possibility to make money by forecasting the price movement of financial instruments has always attracted a large number of participants to the securities market. Many people earned good money by trading on stock exchanges, but many of them also went bankrupt. For centuries, mankind has been developing a mathematical model for forecasting these markets and with varying degrees of success different mathematical tools are used to make investment decisions nowadays.
In the middle of the twentieth century new technologies for the analysis and processing of information began to be actively developed, which were called an artificial intelligence (AI). Nowadays the potential of artificial intelligence seems to be comparable with the capabilities of the human brain, and in many cases exceeds it [ 1 ] [ 2 ] [ 3 ]. It is possible to provide automatic control of transport, to recognize visual and sound images, to identify individuals, to play intellectual games, to model engineering products, to create works of art, etc. with the help of AI. In addition, artificial intelligence copes well with the task of finding the implicit relationships between a huge number of factors and their influence on the object of study. For example,
AI can diagnose patients on the basis of medical card data and predict the health of patients in the
future [ 4 ] [ 5 ] [ 6 ].

The fundamental difference between DataTrading and all other companies is that we use machine learning and neural networks to solve the tasks. These revolutionary instruments will also be available to traders and the community so that they can develop their own models for forecasting markets.
Trained models will be able to make a profit for each client of Data Trading: they can be used for trading as well as for selling to other market participants. The machine learning constructor will be easy to develop, so that every client and even those without specialist education can use it.
DataTrading service develops its own constructor of trading strategies and will also implement a full analytical tool for stock and cryptocurrency markets on neural networks, namely:
● Screener of shares / crypto assets;
● Trade advisor;
● Scoring of ICOs / IPOs;
● Constructor of trading strategies with the ability to connect and train neural networks
available to the community; implementation of self-learning neural networks.

1.) Irreversible: After confirmation, a transaction can‘t be reversed. By nobody. And nobody means nobody. Not you, not your bank, not the president of the United States, not Satoshi, not your miner. Nobody. If you send money, you send it. Period. No one can help you, if you sent your funds to a scammer or if a hacker stole them from your computer. There is no safety net.

2.) Pseudonymous: Neither transactions nor accounts are connected to real-world identities. You receive Bitcoins on so-called addresses, which are randomly seeming chains of around 30 characters. While it is usually possible to analyze the transaction flow, it is not necessarily possible to connect the real world identity of users with those addresses.

3.) Fast and global: Transaction are propagated nearly instantly in the network and are confirmed in a couple of minutes. Since they happen in a global network of computers they are completely indifferent of your physical location. It doesn‘t matter if I send Bitcoin to my neighbour or to someone on the other side of the world.

4.) Secure: Cryptocurrency funds are locked in a public key cryptography system. Only the owner of the private key can send cryptocurrency. Strong cryptography and the magic of big numbers makes it impossible to break this scheme. A Bitcoin address is more secure than Fort Knox.

5.) Permissionless: You don‘t have to ask anybody to use cryptocurrency. It‘s just a software that everybody can download for free. After you installed it, you can receive and send Bitcoins or other cryptocurrencies. No one can prevent you. There is no gatekeeper.

 

Bitcoins can only be created if miners solve a cryptographic puzzle. Since the difficulty of this puzzle increases the amount of computer power the whole miner’s invest, there is only a specific amount of cryptocurrency token that can be created in a given amount of time. This is part of the consensus no peer in the network can break.

 

Revolutionary properties
If you really think about it, Bitcoin, as a decentralized network of peers which keep a consensus about accounts and balances, is more a currency than the numbers you see in your bank account. What are these numbers more than entries in a database – a database which can be changed by people you don‘t see and by rules you don‘t know?

What are miners doing?
 

Principally everybody can be a miner. Since a decentralized network has no authority to delegate this task, a cryptocurrency needs some kind of mechanism to prevent one ruling party from abusing it. Imagine someone creates thousands of peers and spreads forged transactions. The system would break immediately.

So, Satoshi set the rule that the miners need to invest some work of their computers to qualify for this task. In fact, they have to find a hash – a product of a cryptographic function – that connects the new block with its predecessor. This is called the Proof-of-Work. In Bitcoin, it is based on the SHA 256 Hash algorithm.

What are cryptocurrencies really?
If you take away all the noise around cryptocurrencies and reduce it to a simple definition, you find it to be just limited entries in a database no one can change without fulfilling specific conditions. This may seem ordinary, but, believe it or not: this is exactly how you can define a currency.

Take the money on your bank account: What is it more than entries in a database that can only be changed under specific conditions? You can even take physical coins and notes: What are they else than limited entries in a public physical database that can only be changed if you match the condition than you physically own the coins and notes? Money is all about a verified entry in some kind of database of accounts, balances, and transactions.

How miners create coins and confirm transactions

Let‘s have a look at the mechanism ruling the databases of cryptocurrencies. A cryptocurrency like Bitcoin consists of a network of peers. Every peer has a record of the complete history of all transactions and thus of the balance of every account.

After seeing all the centralized attempts fail, Satoshi tried to build a digital cash system without a central entity. Like a Peer-to-Peer network for file sharing.

This decision became the birth of cryptocurrency. They are the missing piece Satoshi found to realize digital cash. The reason why is a bit technical and complex, but if you get it, you‘ll know more about cryptocurrencies than most people do. So, let‘s try to make it as easy as possible:

To realize digital cash you need a payment network with accounts, balances, and transaction. That‘s easy to understand. One major problem every payment network has to solve is to prevent the so-called double spending: to prevent that one entity spends the same amount twice. Usually, this is done by a central server who keeps record about the balances.

In a decentralized network, you don‘t have this server. So you need every single entity of the network to do this job. Every peer in the network needs to have a list with all transactions to check if future transactions are valid or an attempt to double spend.

Few people know, but cryptocurrencies emerged as a side product of another invention. Satoshi Nakamoto, the unknown inventor of Bitcoin, the first and still most important cryptocurrency, never intended to invent a currency.

In his announcement of Bitcoin in late 2008, Satoshi said he developed “A Peer-to-Peer Electronic Cash System.“ 

Do you people agree?

It is a currency associated with the internet that uses cryptography, the process of converting legible information into an almost uncrackable code, to track purchases and transfers.

Cryptography was born out of the need for secure communication in the Second World War. It has evolved in the digital era with elements of mathematical theory and computer science to become a way to secure communications, information and money online.