Topic: [ANN][DTT]🔺ICO DataTrading - trade forecasting by artificial intelligence 🔺📈 - page 9. (Read 6021 times)

This Constructor will be effectively used by both professionals in artificial intelligence, as well as ordinary traders or anyone who is interested in the topic of machine learning. Thus, we want to create a community of professionals and enthusiasts around DataTrading platform who will share their experience as well as develop and promote the ideas of blockchain and machine learning.
In most cases you do not need to input raw data into the system for the implementation of training, all market data for machine learning is constantly available and will be updated in real time (the data preparation will be handled by system administrators) in DataTrading platform. The user only has to choose those values   that are necessary for training a particular algorithm. However, if the user wants to use some specific or unique data, he will have the opportunity to upload and connect them to the model.
The resulting models can be used both for the developer’s own trading or for its sale to other platform participants. The system will implement automatic quality control, the user will be confident in the properties, accuracy and capabilities of the model, which he will purchase from a third-party developer.

3.4. Open constructor of machine learning models
About the constructor
Machine learning is a great technology; its application for the exchange analysis shows the results far exceeding traditional tools. Unfortunately, at the moment, access to these technologies is limited to a certain number of scientists, data-scientists and developers. The reason is in the lack of good tools for developing and teaching prediction models, rather than in the complexity of the mathematical apparatus that underlies the machine learning.
One of the tasks of DataTrading system is to overcome the existing barrier and to make it possible to use the available mathematical model without its in-depth study. To implement this goal, we are developing a machine learning constructor.
The Constructor of DataTrading Machine Learning is one of the components of the system, which is an interface that allows any member of the system to design a machine learning model, select and process the necessary data, train the model and perform the test of the results. The system will be designed in such a way that no special knowledge will be required to complete all these operations, only the understanding of the general principles of machine learning.

It is planned that all data on IPO and ICO projects will be constantly kept in the system and updated. If the user is subscribed to this service, the scoring data of all the future IPOs and ICO projects will be available to him. If the project is not in the system (for example, the project is only in the process of preparing an IPO/ICO), the user can enter all necessary data about the project and get the results of the analysis. In addition, in order to prevent the adjustment of the project parameters for obtaining good scoring results, different defense mechanisms will be provided (for example, deliberately slowing down the receipt of the analysis results, for the impossibility of enumeration of the parameter).

3.3. Scoring of IPO/ICO
Scoring is the classification of the researched series of objects into different groups according to implicit factors. For example, scoring is widely used in the banking sector to identify creditworthy clients, as well as investment-attractive projects or companies. Multiple studies  [14]   [15]   [16]   [17]   [18] , as well as our own experience gained in the DataScoring project, shows that the use of neural networks for scoring in comparison with linear algorithms gives a significant increase in accuracy.
At the moment, the cryptocurrency world is oversaturated with ICO-projects, many of which are scams. We are confident that it is possible to use machine learning for scoring ICO projects in order to identify potentially successful or failed projects even before the sale of tokens. At the moment, a sufficient amount of data on projects with different histories has already been accumulated. It is possible to train neural networks and make forecasts for new projects on their basis. Also, machine learning can be used not only to classify projects, but also to forecast the behavior of the price of the token after entering the exchange.
What is more, artificial intelligence can be successfully used for scoring IPO projects: an even larger amount of historical data will provide more accurate scoring results and a forecast of the dynamics of stock prices on stock exchanges.

Just like in case of the stock screener, the trade advisor works with the results of machine learning algorithms. When developing trading signals, not only the trading data for the selected stock, commodity or crypto currency is taken into account, but the state and dynamics of the entire market. When setting up or using trading advisors, you do not need to know or assign links between different market indicators or to know patterns of market movement — the system will find and identify them. The trading signals of the adviser will be based on the identified trends.
Each trading signal (buying or selling.) will be accompanied by a probabilistic evaluation of the success of trading action and its profitability. In addition, individual parameters (warning thresholds) can be set for each instrument, such as acceptable profitability, riskiness, and so on. After the integration of DataTrading system into the trading platforms, the trading advisor can be used for automated trading (for those instruments that will be available in these sites).

DataTrading screeners are based on machine learning and artificial intelligence. When forecasting and selectingfinancial instrument, not only the dynamics of the price of an individual instrument is taken into account, but also the movement of the whole market, the industry, the fundamental analysis, the order book and its change, news analysis, etc. Technical indicators will also be used during the training, but only for the primary aggregation of information and will be one of many parameters of the input data layer.
3.2. Trading Advisor

3.1. Screener of stocks / crypto assets
More than 45,000 of companies’ shares are being traded on the stock exchanges of the world; the market of derivatives is even greater  [12] . The number of crypto instruments is more than 1 thousand  [13] . How to choose those financial instruments (stocks, crypto-currencies, derivatives, etc.), which at the moment give the best result, from such a variety of options? Screeners will solve the abovementioned task.
The screener is an tool for selecting financial  instrument  (usually, shares of companies) with the help of the specified filters. Indicators of financial performance, any indexes or technical indicators can serve as filters in the modern screeners.
The main task of DataTrading screener is to find and show financial instrument that will bring maximum profitability in the short, medium or long term. The screener will recommend a trading strategy (play on raising or lowering), the expected profitability in the chosen time interval, the riskiness of investments and the likelihood of implementing the proposed strategy. Thus, choosing an financial instrument will be extremely simple; this screener will be successfully used by both experienced traders and beginners.

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.

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.

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.

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.

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.

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.

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.

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

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” ).

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.)

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.

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.

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)