Are you reading about the high profits others are making by trading cryptocurrencies? That may seem out of your reach if you don’t know how to read a chart or even place an order on an exchange. This is where Artificial Intelligence can help you become a crypto trader without any other prior knowledge or at least help you start playing in forecasting and get to know how the whole thing works.
- 1 Is crypto trading available for everyone?
- 2 Is technology making it easier for you?
- 3 Conclusion
Is crypto trading available for everyone?
The crypto market is highly volatile. Even experienced traders can face difficulties in this space, let alone a new trader. You need the passion and patience for unremitting market research, the ability to understand and act upon patterns, the speed to process them and the patience to wade through mountains of data each day. You need to be highly self-disciplined and focused, and you need to spend some time learning about trading stocks and foreign exchange markets to time your market moves correctly. You also need to achieve emotional control – a step that’s particularly hard for new traders.
The stress factor is high. According to digital currency investor Chad Willis, “Your eyes are constantly glued to the charts, using this, that, and the other method to pinpoint exact entry and exit points to maximize your gains. Blockfolio is refreshed at least 50 times per day on your cell phone, and you start to sweat if you can’t access prices for more than 15 minutes.”
Spending more than 40 hours a week, at least in the beginning, fretting and following the charts, actively engaging with different crypto communities and even losing money may damage your health, family life, and relationships. In fact, most new day traders lose over $21,000 in their first three months of trading, according to Robert Deel, author of The Strategic Electronic Day Trader.
Trading tools can really help to avoid, at least some of the the frustration and give you a lot of material to build your strategy on:
- Coinmarketcap is a platform where you can check how the market in behaving and learn in which exchanges coins are being traded. Right now it’s the most trusted index for historical snapshots of cryptocurrency market capitalizations rankings.
- Investing.com brings in one place information relating to the financial markets such as real-time quotes and streaming charts, up-to-date financial news, technical analysis, tools & calculators.
- Bitcoinwisdom can help to analyse Bitcoin and other major currencies with dynamic graphs that are highly interactive and fully customizable across major exchanges in different currency pairs that vary from fiat to pure crypto ones.
- Cindicator is a decentralized ecosystem that leverages AI and human intelligence for more efficient asset management in traditional financial and crypto markets. Here you can start being a forecaster, knowing more of how the markets work from day to day. It’s much easier to become a trader then.
How complex trading can get?
Markets are almost unpredictable. You never know when chaos may hit, which may be the best time to invest (if at all), which political, sociological, or economic events may cause you to win or lose, and so forth. This is where mathematical algorithms and statistics are largely helpful. They may not provide foolproof answers, but they certainly eliminate much of your risk.
[bctt tweet=”Mathematical algorithms and statistics may not provide foolproof answers, but they certainly eliminate much of your risk.”]
The math behind trading is daunting. You need, at the very least, some rudimentary knowledge of each of these technical market analyses: Phi and Fibonacci numbers, Ermanometry Research, and classical and advanced Elliott Wave Theory in order to estimate some sort of market prediction in regards to when it’s best to invest. You’ll also want to harness a fundamental analysis and a technical analysis, and have a deep understanding of both.
That’s when the innovative projects comes to market: “Our activity is the hybrid, or symbiotic intelligence,” Yury Lobyntsev, Chief Technology Officer and co-founder of Cindicator, said. “This system trains both people and machines. As a result, all participants become contributors rather than merely users, while the inner mental work becomes a capital which can be estimated.”
Most traders use fundamental analyses to evaluate the long-term decisions of a company, a stock, or the market as a whole. They’ll want to review all aspects of that market to assess its potential profit. Analysts and investors who use technical data, on the other hand, investigate the data on market activity such as trends, support and resistance levels, and trade volume in order to chart patterns in market movement.
While a fundamental analysis attempts to show intrinsic value in a market or stock, a technical analysis is meant to provide insight on the future activity of securities or the market as a whole.
Each and every day, you’ll find yourself analysing the following: long, short, cheap and expensive, fundamental, social, technical data, and everything volume driven. The monotony itself induces mistakes.
In short, it takes a machine to succeed.
How traditional trading works?
Analysts, also called chartists, are in high demand and needed by practically everyone – manufacturers, market research firms, management consultants, advertising agencies and even the government. They’re not always right, but skilled analysts can save investors a ton of money. Technical analyses may not yield 100% accurate predictions, but it helps to make the financial decisions of buying, holding, or selling stocks, and it helps to anticipate the future.
[bctt tweet=”Skilled analysts can save investors a ton of money.”]
Essentially, analysts believe that history repeats itself, that market psychology is predictable, and that traders often respond the same way when presented with similar stimuli. Once you know the trends you can profit from them. Traders also believe that all past, present, and even future information (like demand) is factored into existing asset prices. All you need to do is interpret the data correctly to make educated predictions about the market going forward.
According to the Bureau of Labor Statistics, the average pay for a market analyst in 2018 is $62,829 a year, with a high of $88k. Demand is projected to grow 23% from 2018 to 2026, much faster than the average for all other occupations.
Are the traditional trading tools good enough?
Big financial companies all over the world are investing in artificial intelligence machines. After all, AI has none of the problems humans have: it collects data effortlessly and fast, sifting through reams of information at any one point in time. It is never sad, worried, sick, or tired, and it produces accurate results, among several other benefits.
Kevin Maney said in a Newsweek article:
AI trading software sucks up enormous amounts of data to learn about the world and about stocks, bonds, commodities, and other financial instruments. They ingest books, tweets, news reports, financial data, earnings numbers, international monetary policy, even Saturday Night Live sketches—anything that might help the software understand global trends.
Results are faster, more specific, and far more precise than any of those conducted by even the top Wall Street Investors, according to a study done by Friedrich-Alexander University, where an international team of investors used AI algorithms to replicate parts of market data for real-time investment purposes. They didn’t just beat the market, they annihilated it. One model returned 73% annually from 1992 to 2015. That’s compared to a real market return of 9% annually. Gains were particularly rich during periods of unpredictability, when emotion dominates. When the tech bubble burst in 2000, one AI model reported a 545% gain. In 2008, the period of the global financial meltdown, that same AI model notched a remarkable 681% return.
Artificial intelligence short-circuits human bias, feeds you information, cuts down your time, and makes consistently correct predictions. But the last is not necessarily true. There are instances where AI, with all its advantages, can be disastrous. This is because AI simply feed you undifferentiated information. It gives you the logic and facts, but it can be impacted by unexpected and unpredictable parameters. They also can’t explain their decisions. It takes a human with experience, creativity, and intuition to sift through the data and decide which to accept, and which to reject. This is where the hybrid intelligence (HI) model comes in that combines the best of both fields.
Is technology making it easier for you?
Automating the process
People tend to get confused between artificial intelligence (AI) and the highly similar concept of machine learning (ML) – for good reason. The difference is fuzzy. The best way I like to think of it is that ML follows AI, or that AI is a subset of the other. To elaborate, AI refers to computers that are programmed with capacities of natural language processing, automation, image processing, and so forth. They are machines that replicate the processes of human behavior. Like Siri, they give you answers.
“We’re facing thousand of stocks to pick every day,” cofounder of Kavout, Alex Lu, told Techemergence magazine in 2017, “It’s a very daunting task. Today by using AI, we can actually do all the number crunching, look at all the news media, the social media, blogs, and also the real-time codes, we can basically scan thousands of stocks in real time and give you the best idea.”
That’s how AI helps you. Too busy or too tired to absorb the massive amount of information? Artificial machines like those used in Sentient Technologies, an AI company based in San Francisco, squeeze 1,800 days of trading into mere minutes.
You, on the other hand, still have to formulate conclusions, and that’s where machine learning (ML) comes in. It gathers all that information and kicks off predictions, advising you which markets you should enter and which to avoid. So ML is programmed by algorithms to predict the future. It is data-driven and data-oriented to recognize patterns, and is the kind of heuristic data used in search engine results. It asks: What is x if y, then scrounges all the conditions and draws the links in 1.5 fraction of the time it takes the human to do so. For instance, a user can say “Buy 3,000 units of Litecoin if the price of Bitcoin drops below $10,000 and the market cap reaches over $200 billion.” The algorithm then monitors the market and, once the conditions are met, executes the strategy accordingly.
[bctt tweet=”Machine learning makes it easier to find patterns hidden from human eyes.”]
Human Intelligence (HI) takes AI and ML to the next level by adding the human brain and wisdom of the crowd to the mix. Most of the projects use either AI, or ML, or crowd wisdom.
The Cindicator team has created a seamless hybrid system that allows artificial intelligence, machine learning and its huge pool of collective human intelligence to work together – maximizing the strengths of humans, AI, and ML. And we decided to dig in to understand how it works.
Cindicator, founded in December 2015 by serial technical entrepreneurs Mike Brusov, Artem Baranov and Yuri Lobyntsev, is a decentralized system that integrates machine learning and artificial intelligence with the collective intelligence of financial analysts, data-scientists, traders, and investors. Its purpose is to answer questions regarding the price of company shares or other financial tools in the near future. To date, the startup attracts a total of 73,420 analysts and 11,900 token holders from different backgrounds, all over the world. Considering that the company is is just over two years old, that’s a huge volume of interest for a toddler!
In a February 2017 pilot project using the Moscow Stock Exchange, Cindicator was able to drive an estimated 47% yield per annum for experimental investment portfolios compared to top hedge funds.
Data science and machine learning (ML) can be used to accurately forecast the actual behaviour of financial instruments based on data from the market and from forecasters’ predictions. To achieve this goal, two major approaches are used in Cindicator: superforecasting and the “wisdom of the crowd.”
Collecting the required data:
- The company studies its forecasters, identifying behavioural patterns and common factors.
- Clusters forecasters into various factors that includes whether or not forecasters analyse the market, follow the trend, use technical or fundamental analyses, and so forth.
- Explores behavioural patterns: How often forecasters make mistakes, in which situations they err, and how forecasters react to a dramatic change in the market and to different economic events.
- Conducts experiments with groups and clusters.
- Conducts experiments with predictive models, and uses them to build the boosting algorithm.
- Conducts a time series analysis of the market and the predictions of forecasters.
- Validates machine learning models and optimises their parameters.
The main source of random error stems from forecaster input errors (where the user indicated the wrong ticker symbol or specified an incorrect number order). These errors adversely affect the work of models and displace its metrics.
For data cleanup, which is the process of detecting and correcting faulty data, there can be used the following methods: IQR, Grubbs Test, and GESD.
Each forecaster and investment instrument has a distinctive behavioural pattern. Algorithms consider these patterns and apply either different weights or different models according to the situation. The company also developed a reinforcement learning (RL) model based on behaviorist psychology to program the computer to independently adjust for different situations.
All of its models can be divided into two classes:
- Superforecasting models (in which models are built on various forecaster clusters and cluster ensembles).
- The wisdom of the crowd model (in which models are built on the predictions of all forecasters).
The company develops mathematical models for descriptions and predictions based on the concept of phase transition and game theory. Phase transitions measure the external conditions of the market to know when to risk investing in a certain stock, while game theory – widely used in economics – makes logical decisions in areas, or situations, of conflict.
On top of that, fractal geometry can be used to forecast critical points, such as points where the market experiences increased tension.
To assess the accuracy and quality of the models, the company can perform tests based on historical data to see whether or not to proceed (this is also called “back-testing”), and it uses both standard and advanced statistical instruments (e.g., RMSE, ROC, MAE, Pearson’s correlation coefficient) for each trading strategy.
Different ML/DL approaches can be included:
- Bayesian statistics that applies probabilities to statistical data. In reference to market trading, it can tell us the answer to, say, the likelihood that a certain investment will work out better than another.
- Bayesian Belief Networks, or Bayesian networks, that refers to multiple events or to random processes that depend upon each other. In investing, Bayesian networks could be used, for instance, to predict short or long-term success, given a variety of market conditions, time frames, and trends.
- Hidden Markov Model (HMM), where the system being modeled is assumed to have unobserved (i.e. hidden) states. A person, being human, only sees surface aspects and tends to make his or her decisions accordingly. The Markov Model includes invisible factors, saving you money.
- Various regression models to understand which of the independent variables are related to the dependent variable, and to explore these relationships. Linear regression helps us identify price trends without the bias of the human mind, so we can see if there is any sort of connection between two sorts of variables, e.g., price and time, and if so, in which direction.
AI predictions can be improved by merging its AI results with the professional wisdom of a diverse range of forecasters from different countries with different professional backgrounds, all with unique personal experiences.
The system consists of four types of indicators: Stocks, futures, financial economic reports, and some political news which can directly or indirectly impact the financial markets and share prices.
Two financial analysts generate around 10 to 15 yes/no questions every day. In addition, the service adds automatically generated questions by bot (five to seven questions per day), resulting in a total of 15 to 22 questions a day. Questions are about the price levels of different financial assets, macroeconomic indices, and events that significantly influence the market.
- Create a forecast of the minimum and maximum price levels of Bitcoin for the upcoming seven days.
- Will the Tesla stock price surge to $345 during market hours on Friday?
- Will the US unemployment rate be greater than or equal to 4.5%, according to the June 2nd report?
- Will Bancor collect more than $100M during the first week of its ICO?
- What is the probability of Trump’s impeachment in the next three months?
The audience of forecasters (including the superforecasters that are 2% of the pool of specialists) chooses a probability for each event. The answers are collated and fed through the artificial intelligence system which synthesises accurate forecasts using machine learning algorithms.
The machine learning models calculate various weights for each forecaster. These include:
- Identifying stable systematics in their errors
- Calculating corrections for the errors
- Eliminate irrelevant aspects that would corrupt the data
- Generating final predictions and trading signals
Cindicator gives you strategies, indices, sentiments, trading bots and tools, and SaaS products. It consists of the following:
- The Cindicator Platform, where a diverse team of financial analysts (also called forecasters) answer financial questions and predict event outcomes. The questions are divided into several sections and cover both crypto and traditional financial markets. The best forecasters receive
- Cindicator Bot 1.0, a system that works with predictive analytics, data, and market indicators powered by hybrid intelligence to support traditional and crypto financial market analysis. The bot provides trading indicators for crypto and fiat assets, data from thousands of analysts, and results of dozens of ML models.
- The Cryptometer Bot 2.0 that measures prices across multiple exchanges to anticipate and detect early signs of cryptocurrency market volatility and provides you with real-time price movements on your selected crypto assets.
Cindicator functions through Cindicator utility tokens (CND) that democratizes access to highly accurate predictive analytics and creates of a system of governance where all participants take part in and benefit from the strategic decision-making process. By buying tokens, CND token holders get exclusive access to part of the hybrid intelligence infrastructure that varies according to whether they buy their basic utility tokens (CND) or their infrastructure tokens, with the latter providing more privileges.
Cindicator incentivizes its collective human participants, called forecasters, for adding their expertise to its AI/ ML resources with its CND. These utility tokens reward the forecasters for their current predictions, and aims to incentivise their active participation for the future.
The company also rewards these forecasters through its robot trader that makes deals on the exchange, with the subsequent distribution of part of the trade gain among users. Forecasters are publicly rated and, at the end of each month, rewarded a part of the company’s trade gain in proportion to the quality of their forecasts. The more accurate their predictions – the higher the monthly reward they receive. Erroneous forecasts, and low activity downgrade the rankings.
“In the last two months,” project co-founder and CTO Yury Lobyntsev told Hightech in July, 2017, “we paid a total of $5000 to 30 best forecasters. The maximum forecaster payment was $120.”
Finally, the company provides hedge funds, banks, and private investors with access to its API trade signals in exchange for a part of the income received by them.
Studies show that only 9% of active traders make a profit, meaning an eye-boggling 91% fail. It’s extraordinarily difficult to amass the amount of information needed to succeed, which is why hedge funds, brokers, and other financial firms turn to artificial intelligence and machine learning. But even then there are errors since computers fail, too.
What Cindicator does is merge computer intelligence with human intelligence so that you get the most accurate predictions. In short, Cindicator looks at all the fundamentals and technical specs for you, managing all the number crunching, flipping through the news media, social media, blogs, and all the real-time codes. It even scans thousands of stocks in real time. Its computers do part of the labor, and its team of human experts do the rest.
Want to know where the word “Cindicator” came from? Crowd indicator. This refers to the famous “wisdom of the crowd” concept, which says that a group of people is more likely to provide the right answer than a single person. That’s Cindicator’s forte.
[bctt tweet=”A group of people is more likely to provide the right answer than a single person.”]
Using Cindicator won’t make you win automatically, it won’t make you a professional trader over night but your decisions will be much more deliberate and well-considered. It’s a small wonder then that Inc. Russia listed Cindicator as one of Russia’s 25 most promising technology companies.