Build your portfolio based on sector indexes of crypto market. Smart, Secure and Easy Pump & Dump Virtual Billion – Yaniv Hevron – Medium. Signal | Pump and Dump Signal Crypto Pump Signal - Crypto Investment Group One of the biggest cryptocurrency pump groups in the world. [blackcat] L1 Bitcoin Guppy Whale Pump Dump Oscillator this is just a study to investigate the pumps and dumps that have been happened in a crypto.
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This is described through indicator variables , of which the values may be directly indicative of an anomaly, and environment variables , whose variables are not directly indicative of an anomaly. The indicator variables are determined to be anomalous depending on the values of the environmental variables.
In the current context this means the goal is to locate the breakout indicators, with respect to the reinforcers Table 2. For the scope of this paper, we do not consider the reinforcer of whether a symbol pair was present on multiple exchanges, due to the amount of data available.
Thus, the goal is to locate corresponding price and volume spikes of coins with a low market cap that are trading for other cryptocurrencies. The anomaly detection technique utilised is a thresholding technique, inspired by previous research regarding denial of service attacks on a network Siris and Papagalou For a particular value, a simple moving average is computed by taking the average of previous values in a given time window, the length which is known as the lag factor.
In this way, one can compare a value to the trend over a time period, as opposed to a singular value, allowing for the detection of local anomalies in comparison to recent history. This type of thresholding algorithm, allows us to provide a functioning baseline which further research could then expand upon with more sophisticated algorithms.
Additionally, as more is learned about cryptocurrency pump-and-dump schemes, it is likely that more domain information e. If the high price at any given point is greater than the computed anomaly threshold for that point, then the point is determined to be anomalous. An instance x is a particular observation in the time series that is associated with the respective OHLCV values.
The goal is to detect local conditional point anomalies, that is the co-occurrence of both a price anomaly and a volume anomaly. There are perhaps other contextual indicators that could be investigated, though for the scope of this paper, only the two mentioned above will be looked at.
The market cap of a coin is defined as its price times the supply, and represents a way of judging the popularity, or size, of a coin. The top ten coins from the dataset and the percent of the total market cap they account for are shown in Table 4. This section investigates various values for the different parameters and shows how changing these affects the results found, with the goal of providing a suggestion for balanced parameters.
Hopefully, these parameters could then be taken to a real-time system, to be further monitored and tuned as time progresses. It is possible to formulate expectations based on the domain information presented in earlier sections. Additionally, since this paper only simulates real-time detection, it is possible to look forward in time, and see which of the alleged pumps were followed by a marked drop in price, which could be an indication of users dumping their coins, making it more likely that the preceding pump was the result of nefarious activity i.
While these may be interesting points to investigate, making the parameters stricter could help reduce false positives i. Ultimately the goal is to find a set of balanced parameters that filter the points detected down to a more reasonable number that can then be further assessed by humans. Figure 4 shows an example of an annotated candlestick chart using the initial parameters. We increased the estimation window to 24 h, so it required a more drastic change in comparison to the average.
This led to detecting alleged pump-and-dumps over 20 days, about 0. With the information gained from the previous two parameter sets, we attempted to find a balance between the two. This resulted in about 1. An illustration of how the percentage of symbols analysed relates to the percentage of pumps detected is shown in Fig.
Breaking down the pump-and-dumps on a symbol level allows for a look into which cryptocurrencies, are disproportionately often affected, and hence more vulnerable Table 6. This is consistent with the notion that specific coins may be targeted more often than others. Also interesting to note is that five of the top ten most pumped coins were pumped on the Bittrex exchange. Further research could perhaps investigate the properties of these coins, in an attempt to see if there are links between the most pumped coins.
The individual spikes have been muted in the figure, to highlight only the pump-and-dumps. The resulting graph depicts rather suspicious trading activity, with many periods of lower price and volume, followed by significant spikes in both. During the 9-day period shown eight pumps were detected. Regardless of whether it is directly the result of nefarious activity, it is still a pattern which raises question.
A core test of a pump-and-dump identification system is its real-world detectability. In Case 1 Fig. As a result of their coordinated efforts a large price and volume spike is visible, beginning exactly at the time at which the announcement took place. The chart depicts the results of a pump-and-dump promoted by the group Moonlight Signal , which was signalled to commence at 4 pm UTC on the 17th of August. Exchange: Binance. Once again, the warning signals of corresponding price and volume spikes are present, and the system correctly marks the strange activity at the announced starting time as fraudulent.
In this case we also observe the price and volume beginning to increase just prior to the announcement time, perhaps indicating insider trading by the group leaders. The chart depicts the results of a pump-and-dump promoted by the group Moonlight Signal , which was signalled to commence at 4 pm UTC on the 21st of August. The pump announcement in this case was given on the 4th of September , at p.
Once again, we observe corresponding price and volume spikes Fig. The reason for this is that the price continued to climb for a while after the pump, instead of immediately dumping. Thus, we can observe that sometimes the momentum caused by a pump group may actually persist for a period of time in this case about 24 h. The chart depicts the results of a pump-and-dump promoted by the group Moonlight Signal , which was signalled to commence at p.
While our system correctly marked the corresponding price and volume spikes at the specified time, it failed to identify them as being the result of a pump-and-dump. In Case 4 Fig. Similarly, to Case 3, our system again fails to mark the anomalous spikes as a pump-and-dump, for the same reason of the price not dipping quickly enough afterwards.
In order to correctly identify these cases in which the price maintains momentum for some time after the announcement, a potential improvement could be made to the algorithm whereby decreasing volume is also taken into consideration. This paper attempted to introduce to the crime science community the problem of cryptocurrency pump-and-dump schemes. With cryptocurrencies becoming increasingly popular, they are also becoming a more likely target for criminal activity.
Cryptocurrency pump-and-dump schemes are orchestrated attempts to inflate the price of a cryptocurrency artificially. We identified breakout indicators and reinforcers as criteria for locating a pump-and-dump and investigated the data using an anomaly detection approach.
We were also able to show that using a limited set of parameters it is possible to detect pumping activity in the data as well as subsequent dumping activity. Moreover, we monitored two pump-and-dump groups in order to obtain several cases of real life pump-and-dump schemes which we then applied our detection algorithm to, in order to demonstrate its performance in real scenarios.
Besides locating potential pump-and-dumps, we found evidence of clustering in the data. Translated to the environmental criminology literature, this pattern resembles repeat victimisation Farrell and Pease ; Kleemans ; Weisel ; Farrell The clustering can be exploited for preventative purposes since efforts can be concentrated towards the clusters, finding out what makes them attractive targets, and implementing strategies to help mitigate potentially nefarious activity.
Consider an exchange which requires additional verification for users trading certain symbol pairs which are determined to be vulnerable. Such an intervention would increase the effort required to trade and hence to pump the vulnerable coin. When considering how to increase the risk, an example could be a system in which the automated detection of anomalous trading activity is used in cooperation with humans. A major challenge for pump-and-dump prevention might lie in coordinating the efforts between private bodies such as cryptocurrency exchanges and government bodies.
While governments are catching up on the problem and have allocated more resources to the mitigation of pump-and-dump schemes, exchanges might have little incentive to cooperate because they benefit from trading activity on their platforms. Finally, a move towards more government regulation—in our data less regulated exchanges were targeted disproportionately more frequently—might undermine the very concept of cryptocurrency trading as a decentralised exchange without government interference.
In the current investigation, we resorted to publicly available data and provided a framework for the future analysis of cryptocurrency pump-and-dumps. However, several limitations merit attention. First, the accuracy of flagging an alleged pump-and-dump is dependent upon the parameters chosen and cannot be ascertained absent a ground truth of confirmed pump-and-dumps. Our analysis should be treated as a first attempt to place the topic in the academic literature.
Second, the dataset only covers 20 days of data with hourly granularity. While this was sufficient for the scope of this paper, future research would want to attempt to collect more substantial quantities of data and at a smaller granularity e. Third, as with any flagging system, there is a decision to be made how many false positives are acceptable i. Arguably, an exchange would want to avoid announcing a coin of being used for fraudulent activity if this were not the case.
This compromise is particularly complex in real-time settings so an interesting alternative avenue for future research might be to move towards the identification of early warning signals that can highlight suspicious trading at a point in time where the costs of false positives are relatively low e. In order to minimise the likelihood of Type I errors i. Thus, a cost for both Type I and Type II errors needs to be determined, and a balance struck between the two. Thus, a desirable area for future research would be to create of a database of confirmed pumps.
While labour intensive to do in a fully manual way, the creation of such a database could likely be achieved through a smart combination of automated and manual tasks e. Such a database could be used as a means of testing the accuracy of a detection algorithm, as well as allowing for the use of supervised machine learning methods.
Two lines of research seem particularly interesting for an extension of cryptocurrency pump-and-dump identification. First, identifying vulnerable coins and understanding the characteristics of those coins that are repeatedly targeted in more detail would allow for efficient resource allocation of detection systems e. Second, moving away from exchange trading data, the modus operandi of pump-and-dumps could be examined in more detail. A particularly promising path for future studies could be the linguistic analysis of the coordination of pump-and-dumps in online chat groups, on the one hand; and the means by which misinformation about specific coins is spread on, for example, social media, on the other hand.
This paper has attempted to provide a first look into research for cryptocurrency pump-and-dump schemes. Ultimately, it is the hope that the information presented in this paper will serve useful as a basis for further research into the detection of these fraudulent schemes. Bartels, K. Click here to buy the next Microsoft: the penny stock rules, online microcap fraud, and the unwary investor.
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Now, it seems that the Squid hype is no more. Developers that were behind the crypto project have reportedly left after the price crashed to nearly zero. The dramatic SQUID price fluctuation occurred over just a matter of days and the price drop happened in just 5 minutes. SQUID is not the only token to have scammed investors with a pump and dump.
In the past, numerous tokens have taken the market by storm with false hype before suddenly falling due to a rug pull. The four pro gamers, along with a number of influencers, promoted the token to their large following. Another coin that left investors empty-handed was SafeTrade. While SQUID is certainly not the first pump and dump scam to hit the market, it could well be the biggest.
Unfortunately, pump and dump schemes are not illegal for cryptocurrencies. This means that scams occur regularly in the market. The easiest way to spot a pump and dump scheme is to look for unknown coins that suddenly surge in value. Another pump and dump clue are paid ads.
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