As the cryptocurrency world takes off and news spreads about traders profiting from their investments, those who are less familiar with technology and finance are struggling to take advantage of this young market. Different cryptocurrencies operate within different platforms and in different economies, requiring their users to be knowledgeable about more than the basic functions of exchanges or coin orders.
Like many other industries, developers are working with artificial intelligence to bridge the gap between emerging markets and those who lack experience. This technology aims to support new traders with guidance in how to predict currency fluctuations and understanding how blockchain markets operate.
Cindicator is a new technology that intends for crypto traders to make more successful and informed decisions by providing a huge database of analyzed trends and market patterns, as well as daily input from professional financial forecasters. At the heart of this platform is AI that generates predictions and probabilities about particular economic events and overall schemas, with continual machine learning that develops and alters theses algorithms.
Trading 101: The Complexities of Cryptocurrency
Recent studies indicate that approximately 91% of active traders fail to make a profit, due to insufficient knowledge and experience in the realm of trading and forecasting. An extremely erratic market can prevent even the most experienced traders from knowing where their investments will end up, despite extensive market research and financial proficiency.
Becoming an expert trader often requires dedication, patience, and a passion for technology. Beyond navigating the plethora of daily data, which must be analyzed for any patterns to be recognized, traders tend to make the best decisions when also well versed in foreign markets and basic trading stocks.
Additionally, stress is constantly high. Basic investments come with inherent risk but the crypto world demands constant attention as it functions in a decentralized space, where banks and financial institutions cannot manipulate it.
The responsibility is therefore on the individual trader, who must study charts, monitor prices, and adapt entry margins as the currency fluctuates. The demand that early-stage trading places upon an individual can damage their personal lives by taking away free time. Robert Deel, a leading expert in day trading, claims that beginner traders typically lose about $21,000 in the first three months of activity.
Similar to regular investing and trading practices, those in the crypto market utilize algorithms, statistics, and mathematical principles to guide their decision-making. Unpredictable markets are affected by various sociological and political events that are sometimes difficult to decipher, hindering individuals from understanding when the best time to invest may be.
Basic market analyses in both the crypto and traditional financial models are based upon mathematical tools such as Ermanometry research, Fibonacci and Phi numbers, and Elliott Wave Theory. These conventional axioms are essential to elementary market predictions for both fundamental and technical analyses.
Existing models require an educated and experienced analyst or chartist. For any organization or agent existing within the economic matrix – including consultants, advertisers, manufacturers, private investors, and the government – an expert in financial patterns in essential to save money and garner profits.
Even though analysts cannot guarantee accurate decision-making and predictions, their expertise is much further advanced than the common investor, which usually saves a client some money. These experts operate on the assumption that market behavior is predictable, settling into historical patterns that can be evaluated.
Traders, who typically make similar decisions when faced with analogous circumstances, uphold these patterns. Understanding the trends allows analysts to make informed decisions and factor their predictions into present prices. In this sense, analysts are experts in collecting and analyzing relevant market data but this skillset often takes years to develop.
Those who utilize technical data to inform their decisions research factors like resistance, support, trade volume, and overarching trends to bring out patterns, with the intention to more accurately understand where the market or specific stocks are heading. Fundamental analyses, by contrast, take into consideration all market features in an attempt to guide long-term decisions for a snapshot of profit potentiality. This is generally understood as capturing the intrinsic value of a stock.
The high demand of such experts has allowed these market analysts to earn an average of $62,829 per year, according to the Bureau of Labor Statistics. Boasting a growth rate greater than most other occupations, the industry is expected to expand 23% over the next 8 years.
Regardless of which method an analyst follows, they are constantly juggling multitudinous volumes of information, increasing their vulnerability to error. Efforts to reduce incidences of mistake are swiftly moving towards the incorporation of computers and AI.
Current Crypto Trading Tools and the Future of AI
Multiple platforms exist that attempt to provide potential traders with as much information as possible to make the best investment decisions, while implementing the aforementioned mathematical principles of economic forecasting. Bitcoinwisdom monitors and interprets the activity of major cryptocurrencies before communicating that data through interactive graphs.
Similarly, Coinmarketcap explains which coins are being exchanged in which sites, as well as a general overview of crypto economic behavior. It appears to be recognized as the leading source of historical rankings within the crypto market. Investing.com is also highly utilized for its centralizing of financial information such as real-time quotes, calculators, and streaming charts.
Cindicator is the newest addition to this toolbox, combining human and artificial intelligence for a comprehensive understanding of cryptocurrency trends and efficient management of investments and assets. This platform is designed to evolve with the market so that traders can become successful traders.
AI is an appealing route for investors for its ability to collect thousands of types of data each second and to simultaneously extrapolate patterns and future behaviors. It can operate in multiple realms at once and does not react to the same emotional stimuli that can distract humans.
Additionally, its computational skills are beyond what any human can achieve, allowing for accurate results. A study conducted through Friedrich-Alexander University showed that AI algorithms provided more specific and precise results more quickly than the highest Wall Street investors and advisors. Through different years and various financial conditions, AI was able to report gains of up to almost 700% with a particular strength during the most volatile periods.
For this reason, it seems that AI can navigate the market better than humans when emotional factors such as stress begin to affect operation. In this way, it functions more efficiently while circumventing some human shortcomings.
It is essential to note, however, that AI is not invincible. Humans can explain their decisions and react to data, rather than accepting and utilizing it all. The human traits of intuition, creativity, and various experiences mold important decision-making processes and can often contribute to a better outcome. To take advantage of both, the hybrid intelligence model (HI) inhibits a sole reliance on either.
How Technology Helps
Obviously, these various technological tools and platforms are developed to facilitate human activity and work towards a particular goal, specifically within the economic realm. Introducing AI is the next step in automating the process to enhance efficiency and to maximize profits.
AI itself works by imbuing computers with human skills such as image and natural language processing to replicate human action. As such, these machines automate the basic computational faculties of humans so that a far greater amount of data can be processed in a given moment.
Economic markets can be difficult to comprehend at their most fundamental level of operation. The newly emerging crypto market, however, is expanding at such a rapid rate that it is almost impossible to stay fully up-to-date with it. Each day, thousands of assets are exchanged and tangibly alter the market in some way.
New AI technology can collect the data of what is going on within each trade as well as that derived from news and social media sources, all in real time. Sentient Technologies, located in San Francisco, reports utilizing AI machines that compute 1,800 human days of trading in just a few minutes. Beyond making crypto trading more convenient, AI is driving the economy further by exponentially increasing the number of trades completed per day.
As these machines continue to interact with such markets, the process known as machine learning (ML) becomes relevant. Just as humans test and reject certain hypotheses, ML allows AI to derive conclusions from its experiences within the economy. Demonstrating its ability to infer, AI predicts which markets will be stable and where value will flow.
In other words, ML is the result of AI algorithms that have collected, analyzed, and tested data inferences for a set of accepted patterns. Once its suggestions are proffered, traders can input their own knowledge and set specific constraints on their asset management strategies and then wait for AI to perform the action. This HI model is the foundation for Cindicator, which is aimed at extracting the greatest benefit from both AI and human experience within the crypto realm.
Cindicator: Decentralized HI
Derived from the concept of crowd indicating, Cindicator is founded in the concept of discovering greater truths from a group of collaborative minds, rather than from a single source. Launched in 2015 by well-known entrepreneurs Yiru Lobyntsev, Mike Brusov, and Artem Baranovand, this platform relies on HI, a combination of AI with ML and the collaborative efforts of human activity. Professionals across the industry contribute to its operation, including investors, traders, and analysts.
Already having grown quite rapidly for its recent launch, Cindicator is supported by almost 12,000 token holders and hosts the work of nearly 75,000 analysts around the world. Its main function is to function as the main financial tool for investment portfolios by completing tasks such as managing company share prices. Bolstered by a strong track record, this platform was able to prompt a 50% yield over top hedge funds in the Moscow Stock Exchange.
As previously discussed, HI is an appealing technology for its ability to combine the strengths of both artificial and human intelligence, while also avoiding the pitfalls of each. Like ML, human input from experienced financial professionals with a variety of international economic backgrounds improves the predictions generated by AI.
These analysts have an active role within Cindicator by contributing questions that direct and shape where AI focuses its analysis. Four key indicators are mainly relied upon, including financial economic reports, stocks, predictions, and political news media. Media is the least ‘relevant’ indicator in the sense that it may only indirectly sway overall prices and markets but this is done with the intention of increasing awareness of outlying variables.
The AI platform features a bot that generates more questions to help keep the information provided to users relevant and operate across multiple timelines and specific coin economies. These questions are fielded by professional forecasters, who verify the probabilities generated by AI, and feed the answers back into the system to facilitate greater ML.
Once the information is collected, the program can return to the data and test their predictions. It then gives each forecaster a weight based on its ability to correct their errors, eliminate confounding data, recognize systemic errors, and creating final prediction probabilities.
By combining market information and the input of professional forecasters, ML accounts for both sets of data and continually alters its approach to analysis. Again, HI is driven by the concept of discovering greater wisdom from a group of sources.
AI actually studies the forecasters to learn about their biases, behavioral patterns, use of fundamental or technical analysis, and how accurately their predictions match the current data. It tracks how they respond to extreme fluctuations, how often they make mistakes, and if there are similarities in the circumstances in which they are wrong.
Concurrently, it continually adapts its algorithm and tests alternative predictive models and optimizes its own learning parameters. In this way, both artificial and human intelligence improve based on the feedback from the other.
Crunching the Numbers
Forecasters are dependent upon computers for basic mathematical processing, which is subject to human miscalculation from input errors. Such random and unnoticed errors are often debilitating in accurate predictions and can make otherwise perfectly operational models appear dysfunctional.
Both analysts and predictive tools have behavioral biases that affect their ability to accurately understand patterns in the data. ML in the HI model allows for the algorithm to grant different forecasters individual weights, dependent upon their accuracy in various situations.
Cindicator is also unique in that it has introduced a method for Reinforcement Learning in the program, which allows the machine to continuously adapt to different circumstances. In this way, its functionality takes two directions in its classification: the wisdom of the crowd, which combines the input of multiple origins, and superforecasting, which builds upon numerous predictive actions.
The concepts of both game theory and phase transition, fundamental to basic economic theory, are infused into the model by accounting for logical decisions amongst conflict and how external factors contribute to the risk of investment, respectively.
In addition to ML, the program can look back to historical trends to test its models using advanced statistics and determine how to proceed. Statistical theory also verifies specific probabilities with tried and tested calculations. For example, the Hidden Markov Model, which operates on the presupposition that there are invisible but relevant factors in any given set of conditions, points to natural human tendencies, such as inherent biases.
Similarly, the type of mathematics performed by Bayesian scholars has advanced basic probability forecasting, such as that required in economic trading, and recognizes that entire networks of events are causally dependent. This allows statisticians to consider multiple factors at once, resulting in more accurate predictions.
Lastly, regression models grant mathematicians the ability to view and analyze numerous variables and determine which of those aspects are most closely related. Economically, this might be useful for understanding something like whether prices are more closely linked to other currencies or political events.
Like many other emerging technologies that involve cryptocurrency, Cindicator uses a token system based in the blockchain. Providing a public ledger, the blockchain insures security in that any individual can monitor the daily transactions. Tokens are increasingly popular because they incentivize users and can be used to gain further access to the platform’s services; moreover, they democratize the process by giving each user a stake in the company.
There are two kinds of tokens within Cindicator: their CDN utility and infrastructure coins. This platform works by giving members access to some of the most accurate financial predictions available. With a direct role in the strategic development of cryptocurrencies, all participants benefit and receive guidance from an HI framework.
Beyond the average user, forecasters are rewarded with CDN tokens for accurate predictions, their contribution to ML, and frequent activity. In this effort to incentivize their continual participation, they are publicly reviewed based on their performance and can profit from the overall, monthly trade gain of Cindicator users. Again, their ratings can change depending upon their precision and activity, thereby affecting their monthly winnings.
There does not appear to be a lot of information currently available about the infrastructure tokens, but they do seem to give users privileges beyond what the utility tokens offer. It can be inferred that infrastructure tokens should be associated with a greater number of services regarding investment decisions, trading tools, and economic strategies.
Outside of the platform’s boundaries, the company seeks to make connections with various private investors, financial institutions, and hedge funds, offering them access to their exchange sites for a percentage of the income earned through them.
Those interested in Cindicator are most likely drawn to its ability to radically change one’s success in the crypto realm. Infrastructure tokens appear to be the key to getting the most out of the strategies and tips provided by both professional analysts and AI through the HI system.
Practically, this platform was developed to generate continually adapting trading tools, strategies, and indices for an improved and comprehensive grasp of how to navigate smart investment trading. With this aim, the user is confronted with the network itself, which consists of expert forecasters from diverse financial and personal backgrounds who actively predict economic events – in both the crypto and traditional markets – and push the AI technology to advance its knowledge. They are rewarded for the usefulness of their expertise and encouraged to continue participating.
Also key to the structural framework of Cindicator are its bots. The first is the most obvious in that it is responsible for the HI model that continuously works upon the predictive analytics in both crypto and traditional financial economics. It collects and organizes data, extrapolates patterns, recognizes market indicators, and deduces conclusions from ML.
The second bot works on a broader scale, monitoring exchange prices and market activity across various currencies to identify real-time asset changes and symptoms of market volatility for the best chance at comprehending the market and strategizing investment decisions.
The strength of Cindicator appears to be its blending of both human and artificial intelligence, reflecting an inherent acceptance of the fallibility of both. Humans often err because of emotional stressors, incomplete knowledge, or computational mistakes but their ability to react to sudden changes or make decisions with years of experience are unmatched by technology. Computers instantly sift through data that might otherwise take a human weeks to complete and can execute actions with apparently perfect logic, though they are also liable to failure from time to time.
By combining these strengths in the HI model, Cindicator aims to bring together the most advanced technology for financial forecasting. Both the computers and top analysts learn from each other and move toward increasingly precise accuracy.
For these reasons, it seems that those interested in learning more about crypto trading might want to take a look at Cindicator for better informed and more focused strategies. Because the crypto industry is still so new and this type of technology has not been utilized before, it seems difficult to say where the future of Cindicator will go.
Its founders intend for it to become more accurate as it experiences more of the market, so current users might have a completely different experience than those who join after it has been around for longer. That being said, it appears that relatively beginner traders can use its services and benefit from its extensive data collection and analysis.