Welcome to our first article on Binance. The focus of these article series will revolve around educational content on how to use various metrics, recapping the market, a big-picture crypto outlook, and much more.

In this article, we'll look at a big move in the market and use data to explain how liquidity cascades work. 

Volatility is back?

Bitcoin is well-known for its volatility. 20%+ moves in a day (or hours) are quite frequent for those of us who have been in crypto long enough. Since the start of the year, we are up over 40%!

But we'll save that for a future article. For this reading, we are going to go back all the way to December 2021, when we experienced one of these moves in the form of a market crash. 

Most large moves in bitcoin lead to periods of consolidation (“chop”). Price usually forms some sort of range (hence known as a "ranging market") and then continues to trade within this range. In this period of consolidation, retail traders are likely opening positions, hoping to catch the next wave up or the next wave down (“breakout”). Generally, the longer the range lasts, the more explosive the breakout, like a coiled spring. The underlying reason behind this is usually due to the fact that as time goes on, more and more participants start entering positions, hoping to finally catch this breakout. It is hard to sit on the sidelines, and the willpower to do so only diminishes as the range continues. As more and more positions enter, so do their stop losses, which brings us to our main topic: liquidity cascades. 

 We often see volatility coupled with liquidity cascades. A liquidity cascade is when price reaches a level that triggers liquidations or stop losses (executed via market orders), which then forces price further in the same direction, triggering even more liquidations and stop losses, which then move the price even further, and so forth.

This is likely what happened on Wednesday, January 5, 2022. In the image below, we can see that a range is forming. The two white circles show the lows in this range. There is a high chance that traders who entered into long positions placed their stop losses slightly below the lows. This area below is boxed in the red rectangle (45000–45400).

As the channel continues building, price again approaches the range lows and stops right above them (third white circle). Price then quickly rises, and any longs that enter here ("fomo longs") will most likely place their stops below the range lows. 

When stop losses are hit, market orders are used to execute. In this case, because we know where there may be a large number of stop losses (on long positions), triggering them would result in a rapid flood of market sell orders. Each market sell would eat up bids and lower the price, triggering even more stop losses and creating a liquidity cascade. 

“Data! Data! Data!” 📊📊📊

Let's use data to revisit some of our earlier assumptions.

 “Generally the longer the range lasts, the more explosive the breakout, like a coiled spring. The underlying reason behind this is usually because as time goes on, more participants start entering positions hoping to finally catch this breakout. It is hard to sit on the sidelines and the willpower to do so only diminishes as the range continues”   

How can we quantify this? Open Interest. 

In the image above, we see that Open Interest increases during this range across all the major exchanges (Binance, Bitfinex, Bybit, Bitmex and Deribit). 

💡Explanation: Open Interest is the total amount of open contracts (positions). If the open interest (OI) is 100 million, this means that there are $100 million longs and $100 million shorts in the market. Through open interest, we can actually measure whether or not more and more participants are entering or exiting. 

If we take those major exchanges and aggregate them into one indicator (the total of all OI), we can see that clearly more positions are entering the markets (see image below).

In addition to this data we can also look at Binance Global Accounts. We are fortunate that Binance shows the percentage of accounts that are net long across all accounts for that ticker.

For example, let’s say there are 1000 accounts on Binance (using 1000 for convenience). If 700 of them are in a net long position, then the long% is 70% since 70% of accounts are in a net long position. During this entire range, the Binance global accounts long% steadily increases (image below). If we couple this with the fact the OI is also increasing, it is probably be safe to say that retail (majority of accounts on Binance) were increasing their long exposure as the range continued, while the short side in Open Interest is likely represented by a few larger accounts.

While Binance provides us this data directly, Bitmex does not. For Bitmex, we approximate net longs and net shorts through our proprietary calculations. Throughout most of the range we see net longs (cumulative net longs) increasing, causing a large gap between net longs and net shorts (CLSD).

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Dissecting Orderflow 🕵️‍♂️

Let’s now dive into order flow. When we isolate CVD into large orders (orange line) and small orders (blue line), we see that small orders are the ones that are buying while larger orders are mostly selling. This goes hand in hand with Binance global accounts long%, indicating that it is likely retail entering long positions. 

Adding it all up… 🤔💭📊📝

So up to now we know that: 

Positions were increasing (OI increasing) – stop losses also increasing.

Retail were the one likely opening longs, indicating that long stop losses had been building and a potential target zone.

Small orders CVD (retail) is mostly buying, adding confluence to Binance data.

The range low is tapped multiple times (consolidation above support). Right below this area (red rectangle box) is likely where most of the long stops are.

Finally, when price does hit the stop-loss box (image above), price instantly teleports down. How do we know that large amounts of stops are triggered? Market Order Count.

We know that stops are executing via market orders. If a large amount of long stop losses are hit, the data will also show a large count of market sell orders and this is in fact what we see. There is a spike in the number of market sell orders and the difference between market buy orders (count) and market sell orders (count) is also very large (as sells greatly exceed buys).

Stay tuned for our next article, covering the most recent price spike, using a variety of unique data metrics.