[ANN][DTT]🔺ICO DataTrading - trade forecasting by artificial intelligence 🔺📈

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.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.

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.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.

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.

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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.

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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.

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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$;
● ...

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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).

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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.

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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.

The mathematical model for artificial neural networks was proposed in the 50-60s of the twentieth century, but for a long time it did not find its practical application due to the fact that even the most basic neural networks required very powerful computer calculations and were for a long time unfeasible or unreasonably expensive for application. In the second half of the first decade of the 21st century, rapid technological progress made parallel computing of neural networks on graphic cards possible and effective and a new era of practical application and development of machine learning began.
Due to its ability to identify non-linear mathematical patterns of time series and quickly adapt to changes in market trends, neural networks are one of the most effective and accurate tools for predicting the behavior of markets in general and their specific components in particular. Traditional technical indicators usually take into account only historical data on the volume and price level of orders of one investment instrument in their forecasts, while a neural network can take into account the movement of prices throughout the market as a whole, by industry and by specific companies in particular. In addition, the neural network can take into account the financial and operational performance of companies to build the forecast, as well as information from news channels, which is almost impossible to implement in the technical analysis. On the basis of the revealed interrelations, the trained model can make extremely accurate forecasts for the price of the company’s stock, goods or crypto currency (depending on the market in question)

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2.4. Data Mining and Deep Learning
Data mining is a set of methods designed to search hidden and nontrivial knowledge in a large amount of data that was previously unknown and which can be used in subsequent analysis or decision making. The purpose of data mining is the extraction of information from a set of data and their transformation into understandable structures for further use (through various interpretations, visualizations, etc.).
Deep training is a set of machine learning methods for solving complex problems of modeling of high-level abstractions with a large amount of input data. An example of such problems can be recognition of images, “understanding” of computer algorithms for texts, finding relationships and regularities in a vast amount of disparate information, etc. Among other things, deep learning methods are also used to solve the data mining tasks.

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2.4. Data Mining and Deep Learning
Data mining is a set of methods designed to search hidden and nontrivial knowledge in a large amount of data that was previously unknown and which can be used in subsequent analysis or decision making. The purpose of data mining is the extraction of information from a set of data and their transformation into understandable structures for further use (through various interpretations, visualizations, etc.).
Deep training is a set of machine learning methods for solving complex problems of modeling of high-level abstractions with a large amount of input data. An example of such problems can be recognition of images, “understanding” of computer algorithms for texts, finding relationships and regularities in a vast amount of disparate information, etc. Among other things, deep learning methods are also used to solve the data mining tasks.

Data Mining and deep learning methods will be an essential part of the DataTrading system. The main algorithms that will be used include convolutional neural networks, recurrent neural networks, networks with long short-term memory (LSTM networks). It is also planned to visualize the found dependencies of the results of the mining date (including the results of the fundamental analysis).
2.5. Ensemble of Neural Networks
Ensemble of Neural Networks is a set of neural network models that collectively decide on the formulated problem.
A simplified model of this architecture looks as follows. There is a certain number of neural network models in the system that are differently trained (possibly on different incoming data) and give different forecasts for the same parameter (for example, the company’s stock price). The final decision is made by a separate neural network that takes into account the accuracy of prediction of a model in the past and corrects its influence on the forecasted parameter as a whole, thus combining the forecasts into one and making it more accurate.

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An example. Let us suppose that there are three neural networks that predict the price of the Ethereum, with such differences2
● neural network 1: the input receives data on the history of the change in the price of the etherium as well as data on the movement of prices of 10 other cryptocurrencies
● neural network 2: as an input receives data on the price of the Ethereum and the volumes of transactions, general indexes of the crypto-currency market, data on the volumes of orders placed for each price cluster
● neural network 3: uses the same data as the neural network 2, but has different settings (another number of neurons, the number of hidden layers, the learning rate, etc.)

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Obviously, all three neural networks will give a different forecast for the price of Ethereum. When using ensemble of neural networks in DataTrading system, the final decision will be made by a separate neural network that takes into account the accuracy of the forecasts of each network in the past and corrects the overall forecast of all networks.
The work on the development of the application of Ensemble of Neural Networks in DataTrading system is planned immediately after the release of the first version of DataTrading 1.0. We expect that in 6 months after the release of the first version of the system the ensemble of neural networks will be available for the users of the platform (see  Section 5 “Road map” ).

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2.6. Fundamental analysis
Fundamental analysis is the estimation of the company’s internal value, stock, currency, derivative or product based on an analysis of the main influencing external and internal factors.
Different methods are used to estimate intrinsic value of various types of financial instruments. For example, the main indicators of financial and production activity of a company and indices of its business activity can be analyzed to find the value of a company and its shares. The main macroeconomic factors such as nominal and real interest rate, economic growth rates, GDP, trade balance, inflation, etc. are evaluated for the analysis of exchange rates. Adoption and real use of the technology by business and the ordinary people, legal regulation at national levels, the emergence and development of competing projects play an important role for the evaluation of crypto-currencies. To assess the value of the product on commodity exchanges, the main factors affecting the value of the commodity are estimated, such as the volumes of production (for raw materials markets), the weather (for agricultural goods), the dynamics of the cost of competing and competing goods, the change in the cost of the resources necessary for the extraction or production of this commodity goods, the state of technological progress in the industry, etc.
Nowadays there is no univocal methodology how to conduct a fundamental analysis

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Nowadays there is no univocal methodology how to conduct a fundamental analysis — each analyst based on his experience takes into account certain factors, conducting a fundamental analysis of an financial instrument. Although a certain mathematical model can be used in the process of fundamental analysis, the analyst’s subjective influence on the results of the analysis is very high: he chooses the factors, determines the influence of each indicator on the final results, outlines the formulas and coefficients used. And although in some cases it is possible to partially algorithmize certain evaluation processes and aggregate them, it can be argued that the fundamental analysis was not amenable to automation.
Nevertheless, the use of various methods of machine learning can partially or completely replace the role of the analyst in the fundamental analysis. In addition, it is likely that the fundamental analysis carried out by artificial intelligence can produce more accurate results and forecasts than the traditional one, since machine algorithms can better locate and determine hidden regularities between factors.

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2.7. News analysis
Any trader knows that the behavior of the price of financial instruments is influenced, among other things, by the news flow, directly or indirectly related to this instrument. Positive news about the company’s activities (for example, the introduction of new technologies or the acquisition of competitors, or promising trends in the industry) leads to an increase in the share price of this company, while negative news reduces the cost of shares.
With the development of machine learning technologies and the development of methods of deep learning (using semantic analysis, convolutional neural networks, recurrent neural networks, networks with long short-term memory, etc.), it became possible to analyze arbitrary texts by computer algorithms and transfer the obtained analysis results to forecasting modules as input layers. In the DataTrading platform, specially trained neural networks will be used to continuously monitor the entire news flow and to identify information signals that can affect the price of stocks, crypto-currencies and other financial instruments and, based on these signals, the strategies of the trade advisors will be immediately adjusted.
Example. Suppose that a trader uses the DataTrading system to monitor the commodity market of wheat.

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Example. Suppose that a trader uses the DataTrading system to monitor the commodity market of wheat. Most likely, the model used for the forecast at the training stage will reveal the relationship between the price of wheat and the price of fuel materials. If during the analysis of the news flow the system finds news that will lead to an increase in the price of fuel (for example, the decision of the OPEC countries to reduce oil production), it will connect this input signal with an increase in the price of wheat in the near future and advise the client: to buy wheat at the actual price (because of the probable increase in the price and the opportunity to play on the growth of the market), or to stay in position before the price increases to a certain level. Of course, this is a greatly simplified and idealized example. In fact, the factors affecting the price of goods, shares or crypto currency are much larger, in addition, the relationship between the two factors may not always be permanent, so all forecasts are made with an indication of the probability of implementing predicted events.

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2.8. Order book
Order book — all orders for the purchase and sale of an investment instrument or commodity at a certain point in time and their dynamic change on a particular exchange. Information includes the price and volume of orders. Depending on the exchange, orders with the same price level can be combined into one order (without the possibility of knowing the number of participants behind this application), the others do not.
You can evaluate the supply and demand for an financial instrument on the market at a given moment in time after analyzing the information from the order book. There is a number of algorithms and indicators that use an Order book to develop a trading strategy.

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DataTrading system will comprehensively use information from the Order book during the process of machine learning: neural networks and algorithms will find the relationship between the state of the Order book and the dynamics of price changes over the entire period of quotations and form a trading strategy on the basis of the identified relationship and the current state of the bids. It should be noted that trading strategies will be based not only on the analysis of the Order book, the results of training will also be influenced by many other factors, such as market analysis in general, price movements of related investment tools, news analysis, etc.

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2.9. Self-learning algorithms
Machine learning methods such as “supervised learning” are usually used to solve the problems of detecting trends and dependencies. In such methods,  features  are indicated, the system’s response to these features are known for each observation and the system should establish the relationship between the features and the results of observations. The disadvantage of this approach is the complexity of the initial configuration of the system: it is required to go through a lot of parameters and conduct a large number of learning experiments in order to choose the optimal configuration of the model.
Self-learning algorithms solve the above-mentioned problem: such algorithms can independently sort out the settings of their system and the types of data on which training is conducted, in order to identify the optimal parameters and fix them. If in case of ordinary systems it requires constant observation and participation of the experimenter during training, the role of the human being in self-learning systems is minimized, the system is very autonomous.

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3. OVERVIEW OF DATATRADING SYSTEM
DataTrading is a cloud with a set of open and customizable analytical tools for trading, provided on a subscription or purchase basis, consisting of the following modules:
- screener of financial instruments;
- trading advisor;
- scoring of ICO/IPO;
- open constructor of machine learning models;
- quality control of machine learning;
- a marketplace of trained machine learning models for use in market screeners, trading advisers, scoring, forecasting, etc.;
- external modules (integration with broker platforms);
- blockchain infrastructure for transparency.