Pump and dump watchlist _ CryptoMarkets money laundering fees

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Lot of newbies fall for pump and dumps and lose money. This is not only due to their lack of understanding of the crypto scene but as well as a lack of relevant resources to help them identify pump and dumps to avoid them and/or take advantage of them.Money laundering fees to help prevent that we are building a pump and dump watchlist to highlight potential pump and dumps.

For a start we are using trading volume surge as a key indicator of possible pump and dumps. The logic is that a sudden trading volume surge can only be justified either by a very big news or a pump-and-dump. This list can serve as a trigger point, and you can easily jump on twitter and verify if there were any news justifying the exponential trading volume.

Here is how we are currently identifying pnds: we identify the cryptos with average daily volume between $100k and $2M, and 24h volume at least 5x above their average daily volume.

You can see this list in our beta tool here https://beta.Digitalassetdb.Com. We have a section on the page called exponential volume surge.

Please have a look and share your feedbacks in the comments.

What do you think of this approach?Money laundering fees

Would you add other selection criteria to make this list more accurate?

What value do you see in maintaining a pump and dump watchlist since the crypto space is extremely volatile anyways?

Edit 1: we now show all assets with an average daily volume above $100K and 24h volume at least 5x above their average daily volume.

Edit 2: reports are now generated every 5 minutes.

Edit 3: added exponential surge by price

Edit 4: we have made lot of new update over the past few days. We posted about it in the part 2 of this post here let us know what you think.

Edit 5: exponential spikes are now based on weekly average price and volume instead of monthly previously.

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The logic is that a sudden trading volume surge can only be justified either by a very big news or a pump-and-dump.

This is a gross oversimplification. When you’re talking about low volume cryptos, a single whale who takes a legitimate interest in a coin can cause this type of surge, which would be a good buy signal rather than a sell/avoid signal if you happen to know that’s the case.Money laundering fees similarly, if a large group of individuals, who aren’t a pump and dump group, take a legitimate interest in a coin, they may move on it in unison. There’s also virality / tipping points to consider, wherein a coin hits a critical mass either in social media promotion, or small, incremental pieces of news, that add up to an acute surge in interest. In other cases, the NEWS doesn’t really break out in a public way; where either insiders are moving early on it, or simply people who are in the slack or telegram channels. These are just a few examples of behaviour that can lead to a surge in volume/price that don’t indicate nefarious intent or are a result of BIG NEWS.

The TL;DR is the same as in every other thread like this, where someone is peddling a simplfication to help noobs: gross oversimplifications are dangerous, DYOR.

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Sorry, I was on mobile before. Prove action should be price action.

Anyway, what I would do is grab historical data for an exchange or 2, where you know whether or not something was a pump or not, and then run the algorithm against that to see if it identifies it correctly (it would check the data as if it were real time, but of course, much faster since it doesn’t have to actually wait).

If you aren’t able to access that data, you would have to run it against live data and wait for the actual results, which would take a lot more time, and would require a lot more effort. Hopefully you can get some historical data that has enough detail for you to follow up.

When people create stock market algorithms for trading, they test effectiveness against historical data to guess as to whether or not it will be successful in the future. What you’re doing is a bit more subjective (whether or not something is a pump might be a little up for debate), but if you follow the same steps you could improve your algorithm.Money laundering fees

I unfortunately don’t have any examples since most of my college coursework is on bullshit that has no practical use, and I’m currently trying to support a family while getting a degree…So I don’t have much time for leisure coding. I bet you could find similar stuff for wall street online in some other subs (pretty sure there’s an /r/algotrading that might be neat to look at).

Hope that helps!

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