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1.3 Social Investment Network The trustless nature that blockchain and cryptocurrencies provide is incredibly powerful, but we believe a bridge of social trust is still needed for us to achieve adoption and access at scale, and invest in the future together. The Ethos platform will also be an inherently social platform – where users can share their expertise and be rewarded for helping community members understand the intricacies of the cryptocurrency space and overall investment process. Ethos Ratings invite users to quickly and easily share their opinions on different assets, helping paint a picture of community sentiment towards the unique aspects and overall impressions of a coin as a potential investment. Ethos Reviews allow users to share more in-depth thinking, analysis and experiences about diverse assets, and answer questions from other users, with quality information and answers being recognized by other community members and rewarded with tokens and reputation points.


Cindicator works by using a large dataset that is transferred to a mathematical block consisting of a machine learning model ensemble (cleaning, clustering methods, linear regressions, Bayesian models, xgboost on decision trees, genetic algorithms, and neural networks). Machine learning models dynamically calculate various weightings for each forecaster, identify stable systematics in their errors and calculate corrections for the errors, eliminate noise, and generate nal predictions and trading signals. At the core of our Hybrid Intelligence system is the synergy of the collective intelligence of a large group of dissimilar decentralised analysts combined with articial intelligence (machine learning, and a self-learning model based on a variety of dynamic feedbacks).

The articial intelligence system is only the rst stage which generates a large amount of 'raw' data. Next, Cindicator's 'black box' is used, with the following core elements: (1) the system and methods dening the condence weight (with constant adjustments after each question and trade) for each user, which takes into account: - the personal track record of each member's accuracy, divided into clusters (signal types, instrument types, links between answers, etc.); - dynamic feedback following each trade with regard to the value (prot or loss) of each user's forecast; - the predictive model, which (in a very short time) is capable of dening superforecasters in the group. (2) trading strategies and models to seek the best possible way of using the enriched data to create trading robots: - testing of various trading strategies and hypotheses; - constant backtests and forward tests to adapt the models to the constantly changing market environment.

55% - budget for continuation of scientic work, infrastructure development, creation of new products, development of a Hybrid Intelligence platform. The budget will be allocated between these areas as set out below: - development: data science, machine learning, AI modules, mobile applications, web versions, products, API, web-hosting, server capacity; - trading: trading services and terminals, development of trading algorithms and infrastructure; - operational costs: salaries, oce rent, other operational costs.

20%  Hybrid Intelligence portfolio for technology validation, the accumulation of valid trading data and formation of a dynamic motivational ETH/BTC pool for forecasters. The trading cases of this portfolio will also serve to make up a history of transactions, which will contribute to growing interest and demand for Cindicator products in the professional market of investors and traders. 10%  marketing: promotion of the collective intelligence platform in order to achieve signicant growth in forecaster numbers. 5%  legal support, improvement of company's legal structure, protection of investors' rights. 5%  monthly forecaster compensation fund. 5%  acquisitions and future partnerships for the synergetic development of the Hybrid Intelligence ecosystem.

5.6 Technologies (libraries, algorithms) - Languages: Python, Scala, R; - Libraries: NumPy, SciPy, Pandas, scikit-learn, matplotlib, seaborn, keras, Theano, xgboost; - Algorithms: regressions, clusterisations, ARIMA, boosting, decision trees, random forest, deep learning; - Infrastructure: Django/Flask/Tornado, Postgres, MongoDB, Re-dis, MS Azure, Hadoop, Spark.

In the future, as our technology develops and amount of data increases, we plan to: - Implement neural networks and deep learning; - Implement a trading robot based on reinforcement learning, which will independently analyse the market and learn from its own mistakes; - Develop modern mathematical models to build predictive models for the market; - Collaborate and cooperate with data scientists from leading universities (Stanford, Berkeley, Princeton, SPSU) and companies (Google Research, IBM) in nance, data science, and ML/DL; - Create a platform for managing trading robots; - Develop the market2vec algorithm (a vector representation of nancial assets' data). We believe that the merging of such areas as control dynamics, game theory and technical analysis, machine learning, and behavioural analysis is a very promising eld.

Our models use dierent ML/DL approaches, such as: - A Bayesian approach; - Bayesian Belief Networks; - HMM; - Using various models as separate predictors which serve to build up the boosting; - Building various regression models; - Using various algorithms of clustering for segmentation and aggregation of forecasters. We compose clusters of superforecasters and ensembles of clusters, on which we run various algorithms; - Using historical data on investment instruments in addition to user signals. Models based on time series analysis are also used

Our models are optimised and back test-assisted due to the pipelines involved. Dierent models demonstrate their own specic behaviors for dierent investment instruments. Each model has its own settings (the length of the sliding window, the form of the function for calculating weights or penalties, the depth of the decision tree, and others). Tuning of parameters is done for each model with regards to each nancial asset. Each model is constantly learning on the basis of new data. To assess the accuracy and quality of our models, we perform back-testing and use both standard scores (RMSE, ROC, MAE, Pearson's correlation coecient) and their intrinsic evaluation functions for each trading strategy.

5.2 Data science and machine learning (ML) ML is employed by Cindicator to accurately forecast the actual behaviour of nancial instruments based on data from the market and forecasters' predictions. To achieve this goal, two major approaches are used: superforecasting and the wisdom of the crowd. We undertake this work in several ways: 1. We study our forecasters, identifying behavioural patterns and common factors. - We cluster forecasters: into bears or bulls, those who narrow or expand price levels, analyse the market or not, follow the trend or not, use technical or fundamental analysis etc; - We explore behavioural patterns: how often forecasters make mistakes, in which situations they are mistaken, and how forecasters react to a dramatic change in the market and dierent economic events. 2. We conduct experiments with groups and clusters. 3. We conduct experiments with predictive models and use them to build the boosting algorithm. 4. We conduct time series analysis of the market and the predictions of forecasters. 5. We validate machine learning models and optimise their parameters

Business logic module: - Backend system with basic business logic that works with events; - Administrative system; - Viewing data and indicators; - Mobile applications (iOS + Android); - Web application (under development). Prediction module: - Data acquisition; - Filtration and cleaning of acquired data; - Feature extraction; - Forming of hypotheses and mathematical models; - Validation and optimisation of parameters for predictive models; - Synthesis of accurate predictions. Trading module: - Data acquisition from the predictive module; - Integration with exchanges, acquisition and processing of resulting data; - Back tests and forward tests for parameters of trading strategies; - Implementation of trading strategies through trading robots.

Active trading of traditional nancial assets: stocks, futures, and foreign exchange markets on the basis of Cindicator technologies, as well as data and signals retrieved from the consensus of Hybrid Intelligence. This part of the portfolio is used to demonstrate the capabilities of Hybrid Intelligence in traditional markets. It can also be treated as protective in relation to the entire cryptoportfolio. In the case of a strong fall in the cryptomarket, a portion of the funds may be transferred to cryptoassets with the purpose of earning a prot upon the rehabilitation of that market. USD will be used as a benchmark to estimate the results. Active management of the third part of the portfolio will begin within a few months after the end of the Token Sale. To do this, we will need to complete the preparation of the entire trading infrastructure, such as accounts and legal structure (it is necessary to establish a separate legal entity for the fund and to obtain the necessary licenses). This portfolio will be managed by our team of traders and trading robots, who will use the data, signals, and analytics obtained through the Hybrid Intelligence technology. We will apply various strategies in the nancial markets (both crypto and traditional) within dierent time horizons, from short-term trades to long-term investments. The choice of strategy and assets invested will draw on a positive evaluation from Hybrid Intelligence, as well as successful testing in the form of back and forward tests.


Additional Information

ICO Start Date:Apr 4th, 2018  ICO End Date:May 8th, 2018Total Cap:360,000,000Hard Cap:30,000 ETHSoft Cap:6,000 ETHICO Price :$0.13 | 0.00016667 ETHBonus:20%Country:Belize



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