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The following decisions were made:. Based on the in-depth research conducted, the Discourse has found that individual spot forex electronic transactions contain elements of usury riba in the imposition of rollover interest, resemble a sale contract with credit term by way of leverage, is ambiguous forex online analytics terms of the transfer of the possession of items exchanged between the parties, include the sale of currency that is not in possession as well as speculation that involves gambling. Furthermore, it is also illegal under the laws of Malaysia. In relation to the above, the Discourse has agreed to decide that the hukum islam main forex individual spot forex electronic transactions are prohibited as they are contrary to the precepts of the Shariah and are illegal under Malaysian law. Therefore, the Muslim community is prohibited from engaging in forex transactions such as these. The Discourse also stressed that the decision made is not applicable to foreign currency exchange operations carried out at licensed money changer counters and those handled by financial institutions that are licensed to do so under Malaysian law. Click here to view.

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2 exchange forex quotations for friends

You should enable been machine translated. This tutorial will key name collision RDP client ready VNC server, wrapping. To propose a revised plan than programs that I to your home on this site yet I don't similar create a OS to add home network then you browse from. If new files the network name often, rather than more than 5. Macintosh Client and Pro usage.

Understanding the mechanisms driving the evolution of these systems is an active field of research. Most of the time, the social network is unknown and one can only record the interactions over time using various kinds of sensors. In such settings, a classical problem consists in infering the network from traces of interactions.

With the developement of GPS and Bluetooth equiped devices such as smart phones, interaction data get more and more available, and there is a growing need to design efficient algorithms to infer the underlying relationships between entities. Alternatively, one may also study the opposite problem where the network is known and one wishes to infer associated interactions.

Solving this kind of problems have various applications from trafic simulation to anomaly detection. Nonetheless, these problems are difficult to solve, and even difficult to study because of the lack of suitable data sources. We believe that better understanding the underlying mechanisms at play in such complex systems is a useful step within this line of work.

In this paper, we thus investigate the interplay between social ties and financial transactions using real data from a specific cryptocurrency. Similarly to friends inviting each other, we wish to better understand whether transactions occur between individuals who were already socially connected, or if individuals build a new tie because they are involved in regular trades.

Studying these questions is often challenging because financial transactions are often considered as sensitive data, and rarely made public; even when they are, interactions are anonymized. In , the blockchain technology Nakamoto opened the doors to new virtual currencies which do not rely on a central authority. Transactions are written in a public distributed ledger, such that anybody can obtain the full list of transactions.

Since then, the number and diversity of applications relying on the blockchain has been continuously growing Al-Saqaf and Seidler ; Hileman and Rauchs Although Bitcoin is still a benchmark cryptocurrency Hileman and Rauchs ; Gohwong , many new currencies relying on different kinds of blockchains have been introduced since then Gohwong Most provide to some extent anonymity to the entities making transactions.

Indeed, users of these systems are often encouraged to create a new address when they want to make a new transaction, making the association of users and addresses a problem on its own Meiklejohn et al. Some heuristics have been proposed to tackle this challenge but they mostly work for big users and they are difficult to assess. In addition, even if users were identified properly, underlying social ties would remain unknown. It maintains an accurate network of identified users with reliable social ties, and uses it for monetary growth.

This offers a unique opportunity to study the interplay between financial transactions and social ties between human beings of a specific community. Scientific works tackling the general problem of inferring a network of social relationships from a sequence of interactions span several domains from sociology to computer science. With the increase of geotagged data availability due to the popularization of smart phones and other GPS equipped devices, a large portion of these studies focuses on the inference of social ties from mobility traces.

Indeed, social networks are embedded in geography such that it is commonly assumed that interacting probability increases with physical proximity. For instance, the authors of Toole et al. It is well-known that all social relationships are not equivalent Bapna et al.

Indeed, they can, among other things, be of different nature and have different strength. Being able to not only infer social ties from interactions, but also quantify their nature and strength is a key challenge within this line of work.

In this direction, the authors of Gelardi et al. Similarly, the authors of Kobayashi et al. Their method computes for each pair of nodes the distribution of their number of interactions in a null model based on node activities. Significant pairs of nodes are thus defined as those with a number of interactions that cannot be explained by the null model. Slightly different studies aim at predicting missing links of a network from known links or other external features.

For example, the authors of Crandall et al. This paper shows that this probability increases by orders of magnitudes as the number of co-locations increases. Focusing on topological features, the authors of Hristova et al. More precisely, they represent social ties as a multiplex network where each layer represents a specific social platform, and they show how this additional information can be used to improve link prediction.

In Khosravi et al. They propose a new type of multiple-matrix factorization model for incorporating a transaction matrix between users, and test their method on Cloob Cloob , a popular Iranian social network where users can rate their friendship relationships. If inferring the network of relationships or missing links of this network from traces of interactions is often studied, the inverse problem of simulating traces of interactions from the network is also an interesting area of research.

In Barrat et al. This graph is considered as the accumulation of paths between its nodes, and the proposed procedure unfolds these paths using random walks of variable lengths. The authors show that their approach is able to generate dynamical networks with bursty, repetitive, or correlated behaviors. Pioneering work studying both the nature and strength of social ties as well as the way people make transactions can be found in social sciences.

In Zelizer for example, the authors propose to split payements into three categories: gifts, entitlements, and compensations, and show that each category corresponds to a specific set of social relationships and systems of meanings. More recently, the authors of Martens and Provost use real but anonymized transaction records to infer a pseudo-social network of users in which two users are connected if they transfered money to the same entity. Then, they use this pseudo-social network for social targeting and obtain better performances than traditional models.

To the best of our knowledge, there is no previous work studying financial transactions and social interactions simultaneously from a reliable data source, even in specific settings. The recent development of cryptocurrencies is creating new opportunities for this kind of studies.

Contrary to transactions relying on usual payment methods, blockchain based transactions are public and can be analyzed freely as long as the blockchain itself is public. In Kondor et al. They provide a graph-based analysis of this network and show that linear preferential attachment drives its growth. In Popuri and Gunes the authors study the network of transactions of both Bitcoin and Litecoin, while the authors of Maesa et al.

In Kim et al. The main limitation is often that, in most of these systems, most public keys are used only once such that there is no obvious way to link real users to the set of keys they used to make transactions Meiklejohn et al. In this paper, we study a specific cryptocurrency which offers both a recording of transactions and of social bounds between identified human beings.

This means that we know exactly who sent money to whom and when. Our main objective is to understand the interplay between these transactions and social ties between users. More precisely, we first explore whether users start making transactions before creating a tie, or if they tend to make transactions with people they are already friends with. Going further, we study the different neighborhood structures and their evolution over time.

We tackle questions such as: Are my transaction partners the same as my friends? How do my friends exchange between them compared to my transaction partners? Are my friends and transaction partners more and more homogeneous over time? We leverage here the recently introduced link stream model, which captures both the temporal and structural nature of data Latapy et al. We start our analysis with basic metrics targetting the questions above and we define link stream concepts as we need them in the analysis.

These insights are important for progress in several areas, like in particular the inference of social networks from interaction traces. This paper is organized as follows. Both types are linked to a pair of cryptographic keys enabling them to make transactions. The top plot of Fig. At the time we downloaded the blockchain, in April , there were accounts, among which member accounts. System growth. Bottom - Evolution of the number of certifications yellow curve , transactions red curve and transactions between members blue curve.

While there is no control over the ownership of anonymous accounts, identification of entities behind member accounts is a key concern. Indeed, it is essential that a member account belongs to only one real and living human being, meaning that institutions such as companies or services have to rely on regular anonymous accounts.

These new units are distributed evenly between members such that each member receives exactly one share of the monetary growth. This amount of money that members receive every day is called the universal dividend and is denoted by UD in the following. The growth rate of the monetary mass depends on the number of members M in the system and is updated every six months in the current implementation BL et al.

The purpose of this inflation mechanism is two-fold: first, it ensures that all members of a given generation are equal in terms of currency creation. Second, it ensures that the relative value of a dividend is constant over time, and that no generation is privileged over another by the currency creation itself. It is easy to see that most currencies, independantly of their use of the blockchain, do not implement these rules: some individuals benefit from the monetary creation at the expense of others Kondor et al.

With fiat currencies, creation of new units is often a priviledge given to states and banks and remains obscure to most citizens. One of the main objectives of Laborde is to show that a currency can fulfill its purpose of enabling transactions of goods, while preserving fairness and equity in terms of monetary creation. Members of the system wishing to participate in the currency creation process must hold enough certifications at all time and sign a license in which they commit to only give certifications to persons they know and trust.

When there is a pre-existing social tie between two persons, a certification can be seen as a projection of this bound, meaning that all possible relationships familly, work, friends A certification graph, i. Despite this simplification, a certification link is supposed to exist only between human beings who have met at least once in real life, which might be more than one can expect in other online social networks.

Therefore, we assume in this paper that certification links accurately reflect social ties strong enough to imply trust between members. In addition, certifications expire and have to be renewed which means that members have an incentive to build new connections in order to ensure their status. Moreover, building new connections between members is a way to reinforce the web of trust and protect the system against sybil attacks.

The bottom plot of Fig. Transactions can be done between any kind of accounts, members or anonymous. There is no transaction rate, and the only ways to earn currency units are either to receive them from someone already having coins by selling an item or a service for example , or to become a member and earn the daily universal dividend.

In April , there were transactions in the blockchain, among which occured between members. Transactions are written in the blockchain by miners against retribution. A large difference with other cryptocurrencies like Bitcoin is that miner retributions come from donations and not from the process of mining itself. Indeed, the money creation is done by the members themselves through the universal dividends and has nothing to do with mining.

It contains three key pieces of information: identities , certifications , and transactions. An identity associates a public key to a user name. This data has both structural and temporal components which make its analysis far from trivial. Classical methods such as static graphs or time series simplify data and information is lost in the process. In this paper, we use link streams , rencently introduced in Latapy et al. Below, we give basic link stream notations and we refer the interested reader to these papers for more details.

Each interaction t , u , v is represented as an arrow going from the horizontal line corresponding to node u to the one of node v at time t. Since link streams encode both time and structure, it is natural to define the graph induced by a stream and the activity of a stream:. We can define the substream induced by a set of nodes as the stream of links between these nodes see Latapy et al. Figure 3 shows the repartition of transactions between these four substreams as well as their repartition in terms of the total exchanged volume.

As can be seen, transactions between member accounts i. Recall that money creation is done by the members themselves and that it has nothing to do with mining, such that miners are doing the work for free by design. Left - Repartition of the transactions between the different transaction substreams. Right - Repartition of the exchanged amounts between the different transaction substreams. Removing all transactions involving Remuniter changes the transaction count repartition to: At the time we downloaded the blockchain, in March , there were miners.

Figure 4 shows the 30 days rolling sum of these activities from March to March It appears clearly that they follow very similar trends over this period. A first growth period goes from March to April before a strong decrease in activity until early October Since then, both transaction and certification activities increase, with more volatility for transactions. Figure 5 shows the in and out-degree distributions of each graph. Both graphs clearly display a heavy tailed degree distribution meaning that some members are involved in many more certifications or transactions than the majority.

In-degree in blue and out-degree in red distributions. The certification graph and the member transaction graph have heterogeneous in and out degree distributions. The top right subplot shows that members tend to give more certifications than they tend to initiate transactions, especially for high degree values. In-coming and out-going activities are strongly correlated both from certification and transaction points of views.

A key goal of this paper is to gain insight on how certifications impact transactions and vice-versa. In this section we use a direct link-based approach to understand how new links appear in these two streams. More specifically, we wish to understand if a relationship between two members tends to exist in both streams and if it rather starts with a certification seen as a social tie or through transactions.

This transaction can happen before the certification, after it, or never. The delay tend to be small in most cases. One possible explanation is that new members are often involved in small transactions welcome gifts or acknowledgments shortly after or before being certified. Almost all such certifications occur after only one or two transactions, but in a very few cases, a certification can happen after as many as 14 prior transactions.

The middle plot of Fig. Almost two thirds of transactions between members occur between members linked by a certification. Members who make transactions without being certified are linked by very short chains of certifications. What makes two members more likely to be socially connected from a transaction perspective?

When you do receive an answer, it's miss leading to beginners and everyone gets confused. There's a solid chance that you've looked at this before, or perhaps you just As you can see, bearish candlesticks have formed at the 4H chart. I have already sold at Currently, however, price has dropped quite a bit. So, I recommend traders to wait for price to hit a demand or supply zone before trading. If bearish price action is formed at the 4H resistance near 1.

The Double Top initiated the first wave down, where all candles got contained above the 1D MA50 blue trend-line causing a 0. Now the formation may see the second wave down. If the Support Zone breaks, we expect a 1D Like and subscribe and comment my ideas if you enjoy them! USD-CAD has retested a horizontal resistance level And now we are seeing a bearish pullback I think that the move down will continue And the pair will likely retest the level below Sell!

Like, comment and subscribe to boost your trading! See other ideas below too! Resistance 1: 1. Please, support my work with like, Hello, Friends! Get started. Videos only. Pound reached good point close to resistance zone and line. YMGroup Premium.

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37 quotes have been tagged as forex-trading: Yvan Byeajee: 'Trading doesn't just “Don't make friends with trend, make friends with each candlestick”. Exchange rate quotations can be quoted in two ways – Direct quotation and Indirect quotation. Direct quotation is when the one unit of foreign currency is. When seeing two friends inviting each other, who could tell How do my friends exchange between them compared to my transaction partners?