Topic: [ANN][DTT]🔺ICO DataTrading - trade forecasting by artificial intelligence 🔺📈 - page 8. (Read 6021 times)

Novermber 2017: token pre-sale.
December 2017: token sale.
Q1 2018: expansion of the team of developers, acceleration of the development process. Development of a Stock screener and crypto assets on the basis of neural networks.
Q2 2018: creation of a trading advisor, forecasting of stock quotations/ crypto assets, development of analytical tools for fundamental and news analysis of the stock and crypto market on the module of artificial intelligence.
Q3 2018: development of an open constructor of trading strategies with the ability to connect and train neural networks available to users of the system / community (with the possibility of further sale of trained models to other users for tokens).
Q4 2018: implementation of self-learning neural networks (in the future will be available to the community for making their own forecasts). Scoring ICO / IPOs by artificial intelligence. Start of active marketing promotion of the system.
Q1 2019: DataTrading 1.0 release: developed analytics for stock and cryptocurrency markets based on machine learning and artificial intelligence. Integration of DataTrading 1.0 with the key brokerage platforms. Beginning of implementation of DataTrading 1.0 in the Neural networks ensemble system, preparation of infrastructure for working with Level2 data (work with data in the context of orders).
Q2 2019: active involvement of experts and professional market participants, marketing, further promotion of the system.
Q3 2019: DataTrading 2.0 release: analytics based on a neural network ensemble. Getting started with Level2 data.
Q1 2020: DataTrading 3.0 release: analytics on neural networks based on Level2 data.




Seems like a great plan!

I am so proud of you

2012: getting Started with Artificial Intelligence and Neural Networks.
2014: the beginning of work with the technology of blockchain and cryptocurrencies.
Q1 2015: development of Neural Networks for the analysis of the stock market and the market of CO2 quotas.
Q1 2016: implementation of the first version of the DataTrading system in the form of a plug-in for Bloomberg Terminal.
Q2 2016: the beginning of funds management based on the forecasts of the DataTrading system in the New York Stock Exchange.
Q3 2016: development of the concept of a trade advisor, a generator of trading strategies, stocks and cryptocurrencies screener on neural networks with self-training.
Q1 2017: registered legal entity Big Data Trading Limited, successful use of the DataTrading system to manage funds on the New York Stock Exchange, start of managing funds in the cryptocurrency markets. Q2 2017: expansion of the team and preparations for the token sale.






What did you do with AI in 2012?


What was the project?Can I read about it?

Payment for personal or collective orders
It is planned to implement an order placement unit on the basis of the DataTrading marketplace, which can be used in different ways. For example, a trader or a group of traders can place an application for the development of an exclusive forecasting model or screener for a particular market. Its infrastructure will ensure the preservation of the rights of both customers and performers: if the trained model meets the conditions of the order, the developer will have a guarantee of receiving a reward. In addition, this module can be used to organize challenges, competitions, etc. In such a scenario, any competition is an order and the developer whose model will show the best performance on the quality control module will be considered the winner of the competition and will receive a reward. For more details on using the machine learning marketplace, see  section 3.6 .


Need a webinar on all of that I guess...

Purchasing of models and strategies developed by other system participants in the marketplace
Each user has the opportunity to purchase alternative models developed by other system participants in addition to using standard models of forecasts, scoring, and scanning available in various versions of the DataTrading subscription. Depending on the terms of use, set by the developer, the models may have:
● limitation on the maximum number of users (in order to ensure the uniqueness of the result of the forecast and, as a consequence, to limit the circle of traders who can benefit from the use of this model, thus increasing the interest of traders in this model);
● limited or unlimited time of use (the trader can either purchase the eternal right to use a model or for a certain period of time, for example, for a month with the possibility to extend the right to use in the next period)
 


Purchasing

Subscription to use the services of the system
Each user should pay for subscription in order to gain access to the system’s services, screeners of financial instruments, trading strategies, ICO / IPO scoring modules, performing operations in the marketplace, etc. There will be different options for subscription, so a trader can choose the package of services that he needs.
Payment for the use of machine learning constructor
If a developer or trader wants to use the DataTrading system to develop his own trading advisor, screener, scoring or forecasting model, he can purchase a subscription to access the machine learning constructor. In addition to the subscription, the user will be asked to pay for the use of computing power of the system (calculated as the number of hours spent on the calculation multiplied by the cost of one hour of calculation). The resulting training models can be used either for the user’s own trading or for sale in the marketplace. For more information on using the machine learning constructor, see  Section 3.4. “Open Constructor of Machine Learning Models” .

DataTrading token (DTT) will be used to ensure the efficient operation of the economic model of the DataTrading system. The initial distribution of DTT will start during the token sale (for more details see Section 6 “Token sale” ).
DTT will serve as an internal system currency and will be used to pay for services of the system, or to reward developers. At the moment, there are several scenarios of using DTT:
● subscription payment for using the services of the system;
● subscription payment for access to the machine learning constructor, payment of the use of
computing power to create and train own models;
● purchasing models and strategies developed by other participants of the system in the
marketplace;
● payment of personal or collective orders.



So DTT is not a security?

Ensuring transparency of mutual settlements between all users of the platform
As in traditional cryptocurrencies, all mutual settlements between users of the system will be recorded in the blockchain and anyone will be able to conduct their audit. Also, the blockchain will provide pseudonymity of the participants. It means that the whole history of operations of any address will be available in the registry of the blockchain due to openness and transparency, but the users will not know which participant is hidden behind which address.
Providing quality control of artificial intelligence and preservation of intellectual property
Each trained model of artificial intelligence will undergo automatic quality control. All key information on this model will be recorded in the blockchain without a possibility to be changed or tampered. In the blockchain will be recorded:
● results of back-testing and quality control;
● the author of the model;
● version of the model;
● date of creation;
● description;
● hash parameters and settings of machine learning algorithms;
● other data.




What do they mean by other data?


What other data?

Quite controversial




Having considered all the advantages and disadvantages of implementing the blockchain systems, we came to the conclusion that the most relevant decision would be to create a private blockchain of the Ethereum network and make its public monitoring service (such as etherscan.io) available. The hash of all the last “unsaved” blocks from private blockchain will be recorded in the public blockchain Ethereum in order to ensure reliability and avoid the situation of “double waste”. In this way, we can ensure the unchangeability of the entire history of the DataTrading blockchain, while making all transaction costs inside the blockchain zero for the system participants.

I don't really understand it


Blockchain is a technology of distributed ledger, usually used for the decentralization of information and management systems. The technology of blockchain in its various implementations underlies all cryptocurrencies.
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).


Explain more on it pleaseeee

Also,

What is more, traders will be able to prepare collective or individual applications for the development of machine learning models for exclusive use, and the system will guarantee the interests of all parties (a user who make a purchase as a result will receive a model of appropriate quality, and the developer will receive the appropriate payment). In addition to development orders, any other applications may also be submitted to the marketplace, for example, open challenges with or without a prize fund, applications for consulting services and so on.

Traders are sometimes interested in using unique models that are not available to other participants of the system, so reducing the number of customers who can use this strategy simultaneously increases the competitiveness of this model in the marketplace. Also, the developer can create exclusive right to use the model (only one trader can use this model). Since the system will keep a constant record of quality control and settings of all models, developers will not have the opportunity to cheat and sell the same model or its easy modification.

3.6. Marketplace of machine learning models
Each user of DataTrading system has the opportunity to develop his/ her model of machine learning. 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.
The developer sets the conditions for using each model, for example:
● cost of use;
● time of use at the specified price (one month, year, all the time);
● the maximum number of traders who can purchase this model (from one (exclusive use) to an
unlimited number).

Based on the results of automatic testing multifactorial rating will be formed for each model. The rating will include an assessment of accuracy, profitability, riskiness, and other parameters. Thus, the rating will give a possibility to evaluate adequacy of the model and make a decision about the expediency of buying or renting. In addition, after the publication of the model in the marketplace, regardless of its use, its forecasts and strategies will be constantly monitored and checked with real data, thus the rating of the model will be constantly updated depending on its efficiency. There are scenarios which claim that some models will be removed from the marketplace after a certain time (for example, if new trends appeared on the markets and they were not present during the training of an old model).

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. The marketplace will be able to get only those models that give an acceptable level of errors and which show a high accuracy of forecasts on historical data. All data of testing and quality control will be entered in the blockchain and will be available for monitoring and auditing. In addition, the models will be checked for similarity to other existing developments, to ensure the preservation of intellectual property developers.

what is going on to BTC right now?

Do you like me shot articles?

3.5. 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.






 

Suppose that the cost of a monthly subscription to the machine learning Constructor costs 100 DTT, and the cost of training per hour is 10 DTT. The user has 500 DTT and he is ready to spend all of them on developing his own model of forecasting a certain market. After using 100 DTT for subscription to access the constructor, the user has 400 DTT left. Suppose that the user spent 3 experiments for 12 hours of training each, using 360 DTT (3 x 12 x 10). Having on the balance of 40 DTT, the user starts another experiment (with an estimated calculation time of about 10-12 hours). After 4 hours of calculation, the user’s balance is reset to zero (40 — (4 x 10)) and the calculations for the fourth experiment terminate prematurely. Let us suppose that despite the lack of results for the fourth experiment, the user will find the results for any of the three experiments acceptable. If the model obtained as a result of the experiment can pass the tests of internal quality control, then the user has the opportunity to expose this model for sale, setting any price. If in our case, the user sets a price of 100 DTT for buying a model he has trained, then 6 customers are enough to make the user’s expenses pay off and bring profit to him and to users of his model.

Here is an example of one of the scenarios for using the system. In order to start developing models, the user needs to purchase a subscription to gain access to the Constructor and should have a non-zero DTT balance. The system will set the price of computing (training) per hour (in DTT). After setting all the necessary parameters and starting calculations after each hour of training, the system will take a certain amount of DTT from the user’s balance.

Cost of development
The machine learning process consumes a lot of computing power, the complexity of the calculations is directly proportional to the volume of incoming data and the configuration of the model (for example, for neural networks this is the number of neurons, the number of hidden layers, the activation function, etc.). In order to make the computing capacities of the system fair and economically justified, we will use the DataTrading Token (DTT) token as a calculated internal system unit.