Guide indicators in cryptocurrency trading or the Truman effect in action. Weak correlations are in the arsenal of a trader.

How to use weak correlations in cryptocurrency trading? What is the Truman Effect? Guide indicators for cryptocurrencies – is it possible?


  1. Introduction
  2. Mining data on cryptocurrencies.
  3. Principles of searching for guides for cryptocurrencies.
  4. Guidelines for cryptocurrencies? … Are there any? …
  5. The Truman effect in action.
  6. Conclusion


On stock exchanges, traders often use the technique of trading using guide indicators. The principle is extremely simple. If the movement of the stock you are trading with a delay follows the movement of some index, this index is a predictor (oracle) for this stock.

Obviously, the success of this technique is based on the stability of the response movements of the instrument you are trading in response to the movements of the guide indicator. If repetition occurs only occasionally, you need to look for a “predictor” who will be more successful.

But … is it possible to use guide indicators in cryptocurrency trading? On stock exchanges, composite and derivative indices from stocks, shares of competing companies, etc. are often used as guide indicators.

Let’s first ask ourselves a question: what are expectations in the stock market? This is primarily faith. Often, stock prices are not clearly linked to the size and type of assets. As an example: to what extent do we believe that hydrocarbon technologies have outlived their usefulness? If the belief in wind turbines is inexhaustible, it is obvious that the shares of the companies concerned will skyrocket. There is no magic in this.

In the crypto world, faith takes on an even more weighty character. It is faith that is the basis of cryptocurrency rates. Have you seen faith in its concentrated form? Take a look at the charts of the crypto pairs. That’s her.

It is natural for a person to put everything “on the shelves”, to analyze everything around. This invisible roulette-scale: “better or worse” – it is with us everywhere. This is how cryptocurrency priorities are born. They are built from faith and a natural desire to order the world around us. One cryptocurrency is becoming “more important” in people’s minds than another.

But what if … one cryptocurrency pair helps predict the behavior of another? In other words, are there links: a led cryptocurrency pair and a leading cryptocurrency pair (guide indicator). By observing the behavior of the leading cryptocurrency pair, we will be able to predict the movement of the leading cryptocurrency pair. How many such bundles are there? This is what will be discussed.

Mining data on cryptocurrencies

By tradition, we consider the Binance crypto exchange as a source of data on cryptocurrency pairs. We will use Python 3.7.7 as processing tools. We use libraries such as:scipy,numpy,pandas,plotly. At the time of this writing, we have historical data on 1 160 cryptocurrency pairs. We will consider hourly data. Data mining is done using the Binance API. Sample size – 90 periods. We consider all cryptocurrency pairs available at the time of this writing

The subject of consideration will be the following indicator:

Growth_rate_Close = Close temp hour / Close last hour

Those. if, for example, this ratio is 1.015, the closing price has increased by 1.5%. If the value, for example, is 0.98, then the price has dropped by 2%. Thus, we analyze not the absolute values of the closing prices of cryptocurrency pairs, but their gains.

Principles of searching for guides for cryptocurrencies.

The technique of finding a guide is extremely simple. We make an assumption that some pair, for example, it will be pair_aavebusd_1h, is a good guide indicator for the pair_aavebtc_1h crypto pair.

We need to check the statement: if pair_aavebusd_1h grew an hour ago, now, at this hour, pair_aavebtc_1h should also grow. If such a connection exists and has a good level of significance, we can say that a guide indicator has been found for the pair_aavebtc_1h crypto pair – this is pair_aavebusd_1h. In other words, when analyzing the relationship, we take the data on the guide indicator from the previous period.

We are not talking about regression analysis here. We only need to determine the closeness and direction of the relationship. We are not talking about how much the leading cryptocurrency pair will grow, depending on the value of the guide indicator (the leading cryptocurrency pair). We are not trying to derive the functional dependence of such a relationship here. We only need to answer the question – will it grow after the guide and how stable such a connection is.

Looking ahead, we note that the existence of anti-guides is also possible – when there is a connection but … with a minus sign (ie, an increase in the leading cryptocurrency is accompanied by a decrease in the led cryptocurrency).

So, we have at our disposal data on more than 1160 cryptocurrency pairs. It is necessary to calculate how each cryptocurrency pair can predict the rest of more than 1159. Python to the rescue!

Earlier in the article “How and how to analyze the connections of cryptocurrency pairs?” it was noted that the Spearman (not Pearson) correlation coefficient can be used to analyze the relationships between cryptocurrency pairs, due to the fact that the movements of cryptocurrency pairs do not follow a normal distribution. He will be taken by us as a basis.

Guidelines for cryptocurrencies? … Are there any?…

There are a lot of pairwise combinations from more than 1160 crypto-pairs. Finding the exact amount is, in principle, possible using the following formula.

It is referred to as the number of combinations from n to k (k in this case is equal to two). This is a truly gigantic amount given the amount of data available. But … we don’t need all the combinations. Of interest are only those relationships of pairs that are of practical interest. In this regard, we will select only those links that have a correlation coefficient value higher than 0.4. As a result, we have a sample of the strongest relationships between crypto pairs. It has the following characteristics:

Count        300

mean        0.097337

std        0.419350

min        -0.528515

25%        -0.410778

50%        0.406569

75%        0.426944

max        0.532138

The number of potential guide indicators is 300 positions (you can find out about the composition of guide indicators at the current time at In principle, for trading on a crypto exchange, it is not so little. But … the maximum value of Spearman’s coefficient is only 0.532138. It would seem … what is the point in guides who are most suitable for what – to predict the movement by only about 50% … We might as well flip a coin! By the way – the lowest feedback has a value of -0.528515. You can observe the following histogram for the available sample:

Due to the fact that we cut off the middle part of the sample, left and right “pillars” arose. So… is there any reason to be sad?….

Let’s be patient and consider several correlation fields. An example would be the following pairs: pair_adabnb_1h and pair_adatusd_1h; pair_adabusd_1h and pair_dotbkrw_1h; pair_algotusd_1h and pair_avabtc_1h; pair_audbusd_1h and pair_hbarbusd_1h.

In this correlation field, the pair_adabnb_1h pair acts as a guide (the increases in its closing prices are located on the horizontal axis), and the pair_adatusd_1h pair is the led one. The cloud of values is elliptical. The color of the dots is the “novelty” of the data.

Do not forget that we are dealing with time series. The earliest data are blue; as they approach the latest, they turn yellow. All four plots show that the colors are quite well mixed (do not form color clusters). This indicates the stability of the form of bonds within the correlation field. The last two charts show a negative relationship between cryptocurrency pairs. In principle, this could be the end of our research, but … there is one most curious moment. Let’s digress from formulas and graphs and recall one wonderful film.

The Truman effect in action

This is the film “The Truman Show” directed by Peter Weir. The motion picture was released in 1998. Jim Carrey brilliantly played the main role and was awarded the Golden Globe. You can read the full description of the film script on Wiki. We will only recall some of the plot details. They will be very useful to us in order to understand: how guide indicators can work in cryptocurrency trading.

So, the main character of the film (Truman) lives in an illusory world. This world was created in order to observe Truman himself, who, without knowing it, is the main character of the reality show. His life from birth under the supervision of hidden video cameras. The city in which he lives is actually a shooting range. Gradually, Truman begins to understand that what is happening around is a fake; he manages to escape into the real world, he becomes free.

If you look at the film from the point of view of numerical analysis, you can come to the idea: the illusory world that was around Truman and would remain real for him if he did not leave the city (Sihaven). How can you get out of the city? It is necessary to stand in its center and … move in one direction. Those. with an increase in this value – the distance of movement in one direction – Truman’s habitual worldview can change completely. The old system of world perception is destroyed, a new one is created. This is a very, very important point.

The very existence of the old worldview is a set of factors with certain characteristics. Changing one of the characteristics «breaks the dome of the system», it is no longer there.

Let’s take the following example. The standard temperature in the office while the author is writing these lines is +20 degrees Celsius. What happens if you lower it to -60? Most likely for the author it will be fatal. Any system is a set of factors describing it with ranges of values ​​admissible for it (the system). The formation of forecasts within the system (with standard values ​​of factors for it) is a very unreliable thing from the point of view of the effectiveness of such forecasts. And vice versa. The behavior of the system becomes more and more predictable as soon as one of the factors forming it reaches the extreme (minimum or maximum) values. What does this have to do with guide indicators?

Look carefully at the position of the points when the guide indicator (Growth_rate_Close from pair_adabnb_1h) is higher than 1.005. The lion’s share of the points are above the horizontal line corresponding to the value 1. Here the guide indicator is practically not mistaken in its predictions.

In other words, if the leading cryptocurrency pair (guide indicator) exceeds the value of 1.005, in most cases we will have an increase in the dependent cryptocurrency pair. Let’s call this the Truman effect. Areas in which the data have a corresponding feature are Truman zones.

For extremely positive Growth_rate_Close values from pair_adabnb_1h, in most cases, positive Growth_rate_Close values from pair_adatusd_1h will be observed. This is when the guide indicator will work pretty well.

If the value of Growth_rate_Close from pair_adabnb_1h is less than 0.995, then a «mirror» situation arises: the guide indicator foreshadows a fall in price and its predictions are also successful. The number of points that lie above the horizontal line corresponding to the value 1 is minimal. We also note the second Truman zone on the chart:

Notice how the guide indicator behaves within the range 0.995-1.005. How effective is it? He doesn’t work here. From the word at all. This is nothing more than a prototype of the «Sihaven City»:

You can look at the correlation fields for the other three links. The situation is very similar. The only difference is in the boundaries of the guide indicators for the Truman zones.

For pair_adabnb_1h and pair_adatusd_1h – more than 1.005 and less than 0.995

For pair_adabusd_1h and pair_dotbkrw_1h – more than 1.005 and less than 0.995

For pair_algotusd_1h and pair_avabtc_1h – more than 1.01 and less than 0.99

For pair_audbusd_1h and pair_hbarbusd_1h – more than 1.002 and less than 0.998


In the process of searching for guide indicators, the interconnection of cryptocurrency pairs is important. In each case, you need to find two such cryptocurrency pairs, where one is the leading, the second is the follower. By the way, relationship and correlation are not the same thing. Correlation speaks, first of all, of a linear relationship. Why are we using correlation? The point is this. Identifying guide indicators and assessing their quality are computationally intensive. We have a huge number of combinations of crypto pairs that need to be analyzed. Correlation, even if it is weak, is an effective way to catch the partial interconnectedness of cryptocurrency pairs. Why consider weak correlation values? Suppose, on the whole, there is no obvious linear connection over the entire correlation field. But … it is quite possible that you can find something interesting in some parts of the correlation field! In the Truman zones, the guide indicator works quite well. Determination of such areas is not particularly difficult – it is enough to compare the growth rates of the leading and trailing crypto pairs on the chart.

So, the use of guide indicators is possible in the cryptocurrency market. Some cryptocurrency pairs can act as guide indicators for other cryptocurrency pairs. It should be understood that the market changes almost every week. New interesting relationships may emerge; new pairs appear.

But … why one cryptocurrency can follow another, what are the fundamental reasons? … In our opinion, the leading cryptocurrency becomes when belief in it becomes more significant in relation to the leading cryptocurrency. But … how long is it? How constant is people’s faith? In the same character?

We think the answer is obvious. Watch the market. Use monitoring data. Belief in symbols (in the broadest sense of the term) exists and is inert. Cryptocurrency trading is based on the constancy and inertia of belief in symbols. Using the Truman zones, you can understand when the system of interconnection of two crypto pairs becomes practically predictable. Obviously, Truman zones with pronounced borders will not be present for all cryptocurrency pairs. Data can be cluttered with outliers. Analyze, explore the graphs of the correlation fields. Perhaps it is you, dear reader, who will be the first to detect Truman zones on a certain schedule.

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