Bitcoin whale watch

This approach is referred to as a "sale wall. Cryptocurrencies run on a decentralized blockchain technology , which allows users to stay anonymous. This makes it impossible to link specific accounts to specific individuals or entities. Whales and market cap Cryptocurrencies with a smaller market capitalization or market cap are more vulnerable to whales.

Market cap is a statistic used to assess a cryptocurrency's proportionate value on the crypto market. You can simply find out a crypto's market cap by multiplying the current market price of a single share with the circulating supply. It's a widespread misperception that a cryptocurrency's price directly affects market value estimation.

However, even a slight change in value can significantly impact its market cap. Instead, liquidity and volume, two separate albeit linked concepts , determine how much cash is required to produce such a price hike. While liquidity is essentially the extent to which a crypto user can buy or sell cryptocurrency without significantly influencing the price, volume refers to the amount of cryptocurrency exchanged within a specific time frame.

Since there are multiple transactions in the trade volume and potentially high quantities of orders spanning various price ranges, whales won't easily influence a liquid market with high volume. As a result, it becomes less unpredictable, making it increasingly challenging for a whale to dramatically impact the price without a significant amount of crypto. In comparison, a cryptocurrency with a smaller market cap and less trade volume can easily be manipulated for smaller sums of crypto without substantially influencing that particular crypto's price and market cap.

Identifying a crypto whale enables average users to watch their movement on the market while trying to predict the whale's next action plan. This allows the user to make money while avoiding potential losses. Crypto whales have influenced some of the largest cryptocurrencies in the world, including BTC. As a result, smaller investors need to keep tabs on the biggest crypto users and stay informed of any significant changes to their crypto wallets to adjust their investment strategy accordingly.

Additionally, there are specialized cryptocurrency websites that provide tracking and "watching" services for crypto whales with various metrics to help smaller investors. Additionally, they allow users to vote for specific cryptocurrencies they find most appealing to gauge and offer insightful data on the most popular coins and tokens.

Well-known whales Crypto whales tend to be high net worth individuals who often make headlines due to their positions and status in their respective fields. Crypto whales can also be large organizations that hold large amounts of crypto. Here are some well-known crypto whales: Brian Armstrong: Brian Armstrong is the CEO of Coinbase, one of the biggest cryptocurrency exchanges in the world.

Coinbase processes crypto transactions worth billions of dollars every day. It has since gone on to include various subsidiaries, including crypto debit and credit cards, Bitcoin mining , and venture capital funds. Winklevoss twins: Cameron and Tyler Winklevoss gained notoriety by claiming fellow Harvard student Mark Zuckerberg had stolen their idea of their university's social media platform. They now hold more than 70, bitcoins, along with other holdings. Presently, GDAX allows users to see the buy and sell limit order volume via the "depth chart" shown below.

The problem with this is that the depth chart does not tell us where the volume behind buy and sell walls is coming from. It could be from a few whales seeking to manipulate the market, or it could be from a large number of individuals who have placed orders around a modal point.

In technical terms, there is no way to see how much of present resistance or support levels are due to individuals vs. This difference is quite important, as it impacts how quickly walls can be pulled, etc. This simple app allows you to access just that information, by focusing on individual limit orders that constitute large walls, and particularly emphasizing the largest orders, enabling the user to spot whales that may be manipulating the present price via various methods.

How to spot a whale: This is perhaps the most important question when it comes to the purpose of this app. I think a real world analogy helps to illustrate this point: Imagine that you are looking to understand if there is any whale activity at your local coffee shop I know this is silly but I promise it is a good example; stay with me here. You could sit near the ordering area and write down each individual order.

If someone comes along and orders lattes, that would get your attention--you have spotted a whale! What if, however, someone was ordering 5 lattes, and then getting in the back of the line again, and ordering another 5 lattes, repeating this until they had purchased a total of lattes? Both of these individuals had a whale-like impact on market movement, but if all you care about is the size of each individual order, you will miss out on the sneaky whales that may be splitting their orders across a ladder of price-points.

We have to make some assumptions. To continue the coffee shop metaphor, we aren't able to actually look at the people in line for coffee; instead, we have a blindfold on and are jotting down what we hear the barista say. We hear "1 order for 5 lattes" once and write it down, but if we hear "1 order for 5 lattes" different times in close succession, then we can make the safe assumption that we are dealing with a whale, as this is analogous to seeing orders for 10 ETH stacked in a ladder of price-points in close succession on GDAX.

As can be seen from the UI screenshot below, for each currency pairing the user can easily examine the most obvious whales hiding amongst the walls. This methodology makes our analysis robust both in times of price stability--when there is both resistance and support similar in magnitude--as well as during times of large price fluctuation--when there may be very little support or very little resistance.

Thanks to the creative coloring algorithm behind the visualization, the brightest colors are those most likely to be whales. The colors become progressively darker as the number of distinct orders at a price-point increases, allowing for easy visual identification of whales in the market. In addition to the main views which provide at-a-glance information about the largest orders, users can freeze the live refresh within the app in order to zoom in on particular sections of the order book, or to take better advantage of the tooltip capabilities of the Plotly visualization.

Given that we now track both single price-point and ladder price-point whales, there are often times when bubbles overlap the linebar charts. When this happens, simply freeze the visualization and zoom in on a particular area to separate the two in a more detailed view.

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