DataTrading
is an innovative project in the world of trading and consulting, which provides
a set of analytical and forecasting tools for trading in stock and crypto
exchange markets and is completely based on artificial intelligence. Big
DataTrading Limited was registered as a legal entity on January 23, 2017 with a
representative office in Hong Kong. With the support of a professional team and
investors, DataTrading already has a developed MVP that shows results, which
exceed many trading strategies in terms of profitability.
There are
many analytical and trading strategy services in the modern world. The
fundamental difference between DataTrading and all other companies is that we
use machine learning and neural networks to solve the tasks. 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.
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.
REVIEW OF
USED TECHNOLOGIES
1. Technical Indicators
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.
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.
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.
DataTrading system will use all types of learning for different tasks, in this
section we will stop on the description of the supervised learning, as the most
obvious and understandable.
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.
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.).
5.
Ensemble of Neural Network
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.
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. DataTrading system will be built taking
into account the fundamental analysis carried out by the methods of machine
learning.
7.
News analysis
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.
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. 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.
9.
Self-learning algorithms
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.
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
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 betting 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.
- Trading advisor
The trading advisor is one of the key services
of DataTrading system. The task of the service is to help traders efficiently
trade on exchanges with any financial instruments. The trading adviser monitors
the status of the selected instruments in real time and gives trading signals
for buying or selling.
- Scoring of ICO/IPO
Scoring is the classification of the
researched series of objects into different groups according to implicit
factors. 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.
- Open constructor of
machine learning models
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.
- Quality control of
machine learning
It is expected that
DataTrading system will be interesting not only for traders who want to get a
reliable forecast of the dynamics of markets and investment tools, but also for
developers in the field of machine learning who will train algorithms and
thereby earn money by offering trained models to other participants of the
system. In order to maintain the high level of quality of all forecasting
tools, the module of quality control of machine learning will be included in
the DataTrading system.
- A marketplace of
trained machine learning models
It use in
market screeners, trading advisers, scoring, forecasting, etc. This model will
be further used in screeners, trade advisors or for scoring ICO / IPO. If
desired, these models can be published in the marketplace of machine learning
models and sold to other participants of the system.
- External modules
(integration with broker platforms)
Our team will begin
integrating the platform into the most popular brokerage platforms immediately
after the release of the first version of DataTrading. If integrated
successfully, the trading advisors of DataTrading will be used for the
placement of orders in these systems based on the received trading signals.
- Blockchain infrastructure
for transparency.
DataTrading system will use
the blockchain to provide:
· transparency of agreements between all users of the platform;
· quality control of artificial intelligence;
· control of intellectual property (without disclosing technological
features of the implementation).
AUTHOR
ETH (ERC20) Address 0x2f024C06ac5F3bD661864c39c13e838925Cd193f
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