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I recently wrote an article on the subject of support and resistance SAR that was published and have received an unusually high number of inquiries on the subject. First of all, let me say unequivocally that the mother lode of trading proficiency lies in identifying SAR and understanding how to trade these lines. In my mind, there is nothing more to trading than understanding the structure and context in which the e-mini contract is trading; most important in understanding the structure of the market is to identify where real support and real resistance can be found. Support and Resistance SAR is generally considered one of the cornerstones of technical analysis. In my personal trading, I assign SAR levels the highest level of scrutiny because most of the really good trades I have executed originate by judging the direction of the market price at SAR levels. Of course, this all sounds great but determining these significant price levels and determining their importance, relative to other SAR lines, is truly the meat and potatoes of learning to trade e-minis.



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Tether is a stablecoin, namely a cryptocurrency associated with an underlying security. Tether provides one of the most relevant ways to buy bitcoins and has been the centre of many controversies.

In fact, it has been hypothesized that new tethers are issued without the underlying reserves, and that new massive Tether emissions are the basis of strong speculative movements on the Bitcoin, with consequent bubble effects.

In the course of this article, we conduct a Social Network Analysis focused on the Tether transaction graph to identify the main actors that play a leading role on the network and characterize the transaction flow between them.

From our analysis, we conclude that 1 the Tether transaction network does not enjoy the Smallworld property, with the robustness and reliability it carries with it; 2 cryptopcurrency exchanges are the nodes with the greatest centrality; 3 even Assortativity is not found, as the subjects who move Tether on a large scale do not give continuity to their presence and operations, therefore do not get a chance to consolidate stable links between them; and 4 among the exchanges, Bitfinex, which has co-ownership and co-administration relationships with the Tether issuer, can be mostly associated with the Rich-gets-Richer property.

Stablecoins are cryptocurrencies whose values are tied to external assets, such as the US dollar or gold, in order to maintain a stable price. There are also algorithmic stablecoins that, to keep volatility under control, use an algorithm, mostly working under the principle of issuing more coins when the price increases, and buying them from the market when the price falls.

Stablecoins should thus in theory combine the best of two possible worlds, namely the disintermediation in the management of payments that characterizes cryptocurrencies such as Bitcoin and Ether and the stability of fiat currencies, such as the Dollar, the Euro and the Yen.

If these promises are kept, stablecoins could become more common for buying and selling goods and services than other cryptocurrencies, which are highly volatile due to the strong speculative activities they are subject to and consequently are perceived to be unreliable for this purpose.

And they could in turn become viable alternatives to those fiat currencies that are unstable from being linked to national economies affected by instability, hyperinflationary trends, financial defaults and other problems in the same category.

A digital stablecoin and a real-world asset are thus tied together, with the money in the reserve acting as a collateral for the stablecoin. A user is in this way allowed to redeem one unit of a stablecoin for one unit of the asset supporting it. Based on such general principles, several stablecoin projects were started.

Internet giant Facebook launched the Libra project, later renamed Diem 1 , with great fanfare in with the idea of creating a stablecoin for universal use in e-commerce; however, the project had to face heavy objections raised by regulators and supervisors, both in Europe and in the United States, and to date is substantially stalled. More successful, if less ambitious, were projects like USDC 2 , Binance USD 3 and Pax 4 , launched, respectively, by leading exchanges coinbase and Binance, and trust company Paxos, all backed by the US Dollar; they focus on building a safe bridge between traditional banks and cryptocurrencies, whose buyers and sellers can go through the intermediate steps provided by such stablecoins, that are accepted by the banks by virtue of being both dollar-backed and regulated.

Terra 5 is another promising initiative aimed at creating algorithmic stablecoins. While deserving full credit for pioneering the practice of stablecoins, Tether is, however, far from having an irreproachable reputation of proven remedy against speculative or even illicit practices in the cryptocurrency market. On the contrary, it is suspected to be a deflagrator of speculative trends where large masses of tethers are in turn used to buy large masses of bitcoins so as to artificially push up the price of the latter and consequently generate bubbles that affect the whole world of cryptocurrencies, as the value of altcoins generally depends on that of Bitcoin.

As a matter of fact, the issuing company of Tether has never given official evidence of holding enough reserves to cover the quantity of tethers issued 7. Furthermore, there are co-ownership and co-administration relationships between such issuing company and Bitfinex 8 , one of the largest cryptocurrency exchanges. These suspicions, coupled with the lack of transparency in the management of the currency and in the relationships between the various companies mentioned above, have led to a series of legal actions against diverse physical and legal persons involved in Tether trafficking 9.

The object of the investigation is therefore the Tether transaction graph. The basic hypothesis we want to test is the following: if Tether is indeed used essentially for speculative purposes, the subjects involved, even if not necessarily conniving in market manipulations, would be nodes where the Tether is in turn exchanged, directly or indirectly, with other currencies; in other words, they would be exchanges or entities related or cooperating with exchanges.

We should also have structural indications that such a network reflects the bubbles consequence of the speculative waves associated with this currency. On the contrary, we would expect such a network to be as evanescent, labile and unreliable just as bubbles are. But at the same time a network of this type should have easily repeatable patterns, so that it can recreate itself and reappear over time, as the periodicity of bubbles, especially financial ones, teaches us.

This appears all the more plausible and applicable in the case of Tether, if it were confirmed that the force behind its various speculative waves is always suspect number 1, namely Bitfinex.

The remainder of this article is structured as follows: Section 2 makes a concise review of works that are related to the topics explored in this article, while Section 3 similarly recalls the relevant SNA concepts as well as illustrates the design principles followed to carry out the study, Section 4 describes its results, and Section 5 describes limitations and future work.

Section 6 closes the article by summarizing the main conclusions thus reached. There are various methodologies to analyze the connectedness characteristics of financial markets, including cryptocurrencies, drawing both from techniques originating from econometry and quantitative finance, and from the adaptation of SNA approaches. On the SNA side, Kondor et al. We therefore refer to the discussion of the results reported therein in the following sections of this article. VAR models are very powerful tools to describe economic and financial historical series, which can also be used for prediction, and as such have been applied to issues of stablecoin design, as illustrated below.

The work of Pagnottoni and Dimpfl aims to identify which are the most crucial platforms for Bitcoin in terms of price discovery, thus drawing up, in a period between January and March , a list of cryptocurrency exchanges, headed by OKcoin and ranked by impact on the price formation process. In turn, Giudici and Pagnottoni examines the relationships of Bitcoin exchanges during the price increase and subsequent decline, performing a connectedness study to assess the effects of spillover and lead-lag in the relationships between exchanges that treat the same asset and consequently identifying Bitfinex and Gemini as leaders among exchanges in terms of return spillover transmission.

Another example is the work of Giudici and Pagnottoni , where the return connectedness across the major Bitcoin exchanges is evaluated, as the price can be different depending on the trading venues. As a result, Bitfinex and coinbase are identified as leading exchanges in the price formation process with a significant return spillover to other exchanges. VAR and VEC models can explain effectively the price formation process and quantify the impact the exchanges can have on market dynamics.

On the other hand, SNA techniques can give a more general prospective about the transaction flow between the nodes of the network supporting a cryptocurrency, including the exchanges, and account for centrality roles in market dynamics. Regarding stablecoins and the Tether in particular, for an overview of existing options for stablecoin design see Mita et al. Giudici et al. These analyses corroborate the hypothesis that the use of Tether is essentially speculative, and possibly tainted by dodgy issuance practices, in that the effective availability of collateral reserves to guarantee the tethers placed on the market is under strong doubt, while instead the dollars would enter Bitfinex in reverse direction, i.

Judging by the reaction to the updated paper, sophisticated and experienced traders in the ecosystem appear to fully understand this concept. To reduce the spike in the bitcoin price in to such simplistic terms is facile. It is also an insult to the millions of people in our community that believe in the sound principles governing the digital currency economy.

As a matter of fact, the results of our analysis are fully compatible with the conclusions reached in the Griffin and Shams study. On the other hand, this does not exclude that the activities around the Tether, including the bubble effects, be characterized by a systemic complexity greater than the involvement of a single actor, and our analysis can also be extended to this expanded scenario.

As the title of our article states, beyond the specific aspects related to the Tether, ours wants to be a contribution to the study of financial bubble phenomena, for which cryptocurrencies have represented a particularly fertile ground, perhaps as an inevitable downside of the great entrepreneurial and intellectual vivacity that continues to characterize the community that has grown up around them.

From this point of view, we are part of a growing line of contributions to financial forensics in the cryptocurrency sector, which also includes the identification of short-term bubbles deriving from Punp-and-Dump schemes, addressed by Li et al. Social Network Analysis SNA is a methodology based on the assumption that complex structures can be modeled as networks, by exploiting the mathematics of graph theory and viewing the relationships between the entities involved as social interactions.

Entities actors in networks correspond to nodes, while their interactions are represented as connecting edges. The specificity of SNA is indeed its focus on interaction, with the possibility of identifying properties that can be traced back to different types of interaction, and actors of a totally different nature that nevertheless can fit within the same type of interaction. For example, an energy distribution network and the set of people who gravitate around a certain work context e.

These properties, that are computed from structural and quantitative input data, provide formally rigorous yet empirically grounded bases for qualitative assessments of aspects such as the robustness and efficiency of the investigated networks which can be harnessed for the purposes of diagnostics, auditing and decision-making.

In the context of cryptocurrencies such as the Tether, the nodes are money holders and the edges that connect them are the transactions through which money change hands from one actor to another. A first broad differentiation of networks is annotative, in that it stems from annotating the connections between nodes as either directed or undirected. In directed networks each edge involves a sender node at one end and a receiver node at the other, while in undirected networks the interactions between connected entities can be in both directions.

Moreover, in the case of weighted networks, the strength of the interactions is recorded by assigning a weight to the edges. That said, the properties of a network are based on some relevant parameters and how they relate to each other, namely:.

The term degree refers to the number of connections of a node. There are many measures of centrality, for example Betweenness Centrality hinges on the geometric characteristics of the network. The assumption is that important vertices are bridges over which information flows, so if information spreads through shortest paths, the important nodes must be on many shortest paths.

To carry out the SNA, we used Gephi 10 , an open source software specifically dedicated to the analysis and visualization of social networks, in combination with R and the igraph 11 library, so as to operate on the Tether transaction data obtained through data mining.

Moreover, we computed the value of the network statistics using the available Gephi plugins, such as graph average degree. The following sections describe the details of the data mining process and the SNA performed.

In our analysis, we considered only the tokens traded on the OmniLayer. A dataset of Tether transactions represents the context of our study. It includes the data provided by Erskine , obtained from Blockspur 14 , until about February Also, we mined transaction data from OmniExplorer.

Using a script from Erskine , the data is summarized for all single transactions by aggregating all transactions by sending and referral address, summing the total amount of Tether exchanged.

In the end, we obtained a dataset of aggregated Tether transactions starting from October to February 15, i. In Figure 1 , there are the ten most common transaction types extracted from our dataset. The transaction type specifies in which way the tokens are sent. For example, the Simple Send is a basic type of transaction where a sending address gives a certain amount of coins to a reference address. Transactions of this type can not be used to send Bitcoins.

Also, Create Property - Fixed is used to create a new Smart Property where a fixed number of tokens is set. Moreover, there are transaction types related to the Distributed Exchange i. The description of all the available transaction types is provided by the OmniLayer specification For our analysis, we considered only Simple Send transactions. Figure 2 shows the transaction amount over time, starting from to To provide visual feedback of our dataset, in Figure 3 we show a plot of a network graph built using the top transactions until March , aggregated by sender and receiver address.

Moreover, using the transaction data, we built different versions of the transaction graph, i. The ten most common transaction types from our dataset. Values on the y axis are reported on a logarithmic scale. Network graph from the top transactions until March Colors are related to modularity class , and node sizes describe the nodes in-degree left and out-degree right.

In most cases, to compute and extract the relevant properties of the Tether transaction network, we resorted to Gephi. Gephi provides a wide range of algorithms for the extraction of network characteristics that can be used for both visualization and analysis and classification purposes. We have therefore extracted both the topological characteristics and the properties that can be associated with the nodes of the network, such as centrality and authority.

The basic interaction with the functionalities offered by Gephi takes place via GUI, which severely limits the possibility of a massively concurrent execution of analyzes and computations. Reason why we first performed a preliminary analysis using the GUI, then we used the Gephi Toolkit, a headless version of Gephi, through which we automated and performed all the analyzes on all the network snapshots as resulting from the data preparation process.

In our analysis, the following features provided by the Gephi Toolkit were applied:. For each node, the values of in-degree, out-degree and overall degree are computed.

Besides, through the average weighted degree Gephi accounts for the link weights if the network is a weighted network. Using the node degree it is possible to evaluate the influence of a node in its neighborhood. The network diameter is the shortest path between the two most distant nodes of the network.

The betweenness centrality measures how many shortest paths go through a node, thus identifying the nodes that act as bridges between other nodes. The closeness centrality measures how easily reachable is a node. The eccentricity is the distance from a given node to the farthest one in the network.



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