BTC whales and miners sold into institutional buyers in Q4 2020, on-chain data infers
Retail traders chased Bitcoin’s latest rally to new ATH while whales took profits
An original research report analyzing OKEx BTC/USDT trade data to assess market behavior and trends during Bitcoin's latest rally.
Written by OKEx Insights | Powered by Kaiko
A PDF of the following report has been included at the bottom of this page so that readers can view, download and share it at their convenience.
Despite all the setbacks, global shocks, disruptions and crises it brought, as it nears its end, 2020 is shaping up to be a great year for cryptocurrencies and Bitcoin, propelling the latter to its former highs — a prospect that appeared far too distant only months prior.
When Bitcoin rallies, the market tends to forget previous, long-drawn bearish stretches, and the sentiment shifts to manic euphoria as quickly as a thousand-dollar-candle appears on the BTC/USDT chart. And while it is exciting to see Bitcoin go up, analyzing its price action against market behavior can help paint a somewhat telling picture of how market participants drove or reacted to various price ranges, surges and pullbacks.
For this report, we've collaborated with blockchain data firm Kaiko once again and analyzed data from the BTC/USDT market on OKEx between August and November 2020. Our focus here will be on filtering and batching trading data based on both amounts and trade directions (whether they were sell or buy orders). By charting such trades against the price, we aim to assess how various market segments behaved as Bitcoin surged to its new all-time high by the end of November.
Before getting to the actual analysis, however, we'll introduce the methodology, and highlight the challenges faced in determining trade directions on any market. The discussion here requires an understanding of order books, makers and takers, and bids and asks — all concepts that are addressed in our in-depth guide to trading.
Since even a single market on a high-volume exchange like OKEx can execute hundreds of thousands of trades in a day, any digestible analysis needs purposeful data filtering in order to reveal insights otherwise buried. For this report, we collected daily trade data from the OKEx BTC/USDT market between Aug. 1 and Nov. 30, 2020.
Trade ranges and market personas
This data focused on the number of daily trades executed on the BTC/USDT market, their amounts, directions and the overall volume-weighted average BTC price for the day. However, since such a dataset includes millions of values, we further grouped these transactions by amount-based ranges. These ranges, apart from simplifying the visual representation of this data, also serve as descriptive categories of market participants.
All daily transactions on the OKEx BTC/USDT market were hence grouped into five ranges: transactions under 0.5 BTC, between 0.5 and 2 BTC, between 2 and 5 BTC, between 5 and 10 BTC and, finally, 10 BTC and above. While these ranges are largely arbitrary, they do generally line up with established market personas, such as retail traders, professional traders, large traders/whales and institutions.
Retail traders typically include speculators, casual day traders and small investors. They are often seen following market trends, as opposed to setting them. Professional traders, on the other hand, often trade for a living, and they use advanced trading techniques and tools, including technical analysis and algorithmic trading.
The distinction between large traders, whales and institutions is harder to make, as there are no strict thresholds for these traders. Typically, a whale is an entity holding enough coins to be able to move market valuations by selling a large number of them all at once. While a single whale may easily execute a few $100,000 trades a day, a large trader with only about $100,000 in trading capital can theoretically do the same. The same is true for institutions, which are known to conduct large buys and sells — but those trades could also be attributed to whales. Hence, while we discuss these market participants, our analysis refrains from specific attributions.
That being said, it is also important to acknowledge the gaps in this dataset and the relevant assumptions. Firstly, this data only spans one exchange and one market, and even though OKEx is one of the largest in the industry, it does not represent the entire space.
Secondly, while we can assume that all trades above 5 or 10 BTC are very likely to be either whales or institutions, we can't assume that whales and institutions trade only above these thresholds. In fact, it is in their interest to execute large trades in smaller batches so as to not affect market liquidity and, consequently, the market price.
Thirdly, this data does not account for over-the-counter services, which are often used for large transactions. These services place multiple, so-called child orders, often across days, to fulfill the parent (i.e., larger) orders, which makes it difficult to identify or attribute these trades accurately.
Determining trade direction on crypto exchanges
Even though we often come across expressions like "increased institutional buying" or "massive selling" in the course of general market discussions, in reality, all buying and selling has a counterparty. This means there cannot be "increased institutional buying" without there being sellers, nor can there be "massive selling" without there being buyers. To put it simply, on an exchange, any time you buy a coin or a token, someone else sells it, and vice versa.
In such a scenario, how can any trade be labeled as a buy or a sell? How can we determine if the market is biased toward buying or selling? This is where the concept of makers and takers comes into play.
Summarily put, makers add liquidity to any market by placing limit orders that sit on the order book, available to be filled by takers. Meanwhile, takers place market orders that, when filled, remove the limit orders (previously placed by the makers) from the order book, thereby reducing liquidity.
A common convention used in order to determine the direction of any trade is to consider it from the taker's perspective. If a taker places a market sell order, which is then filled by a maker's limit buy order, that trade is considered a sell transaction. Similarly, if a taker places a market buy order, which is then filled by an available limit sell order, the transaction is counted as a buy.
Our data partner for this report, Kaiko, has a detailed article on their work in terms of normalizing cryptocurrency trade data, and we use their taker_side_sell variable to determine the daily number of sell and buy transactions on the OKEx BTC/USDT market.
With our daily trading data grouped into amount-based ranges and further filtered with trade-direction data, we were able to move toward visualizing this data. In order to simplify this process, we decided to calculate daily percentages of buying and selling transactions, as well as to calculate their net differences.
The next step involved charting these values against Bitcoin's price to see how market behavior changed as the price of BTC appreciated. Since we're considering aggregate values, we chose volume-weighted average price, or VWAP — a way to measure the average price that an asset was trading at throughout the day — to accurately reflect the daily price change corresponding with the number of trades.
In some of the charts that follow, readers will be able to see Bitcoin's price for each day, coupled with the percentage of buy and sell transactions. A second set of charts will then show the net difference between buying and selling trades on a daily basis, and will more accurately represent the changing sentiment of market participants belonging to each of our grouped categories.
Additionally, to add another critical perspective, we will look at sets of charts that follow Bitcoin's price progression through this period, from around $10,000 to nearly $20,000, and note how the daily net buying (or selling) changes with price across these ranges, irrespective of chronological dates.
Finally, to conclude, we will highlight some of the key findings from these datasets by comparing them against each other in a table format — putting the insights extracted from our analysis into perspective.
Trades under 0.5 BTC — Retail traders
Trades under 0.5 BTC, as expected, represented the largest volume of daily transactions on the OKEx BTC/USDT market. These trades can be valued anywhere between $10 (0.001 BTC) and $5,000 (0.5 BTC) if BTC is priced at $10,000. We take $10,000/BTC as the reference price for this report because of its psychological significance as a key support level and because the period being analyzed in this report started with BTC trading around this level.
Looking at the entirety of this data, between Aug. 1 and Nov. 30, we have 122 days. When comparing the number of buying and selling transactions (as per the method explained above) for each day, we learn that traders in this range were net sellers for 65 days out of these 122, or 53.28% of the time. This means that on 65 days, selling trades were higher in number compared to buying trades, while buying trades were dominant on 57 days.
A daily chart (above) for the percentage of selling and buying trades against the volume-weighted average price shows that the buyers and sellers in this range (0.5 and below) were largely balanced, albeit a bit biased toward selling. We will now look at the net percentage difference against the price to identify more specific trends.
The chart above shows the net percentage difference between buying (positive values) or selling (negative values) on a daily basis, compared with the volume-weighted average price.
We can see here that the traders in this range were mostly selling when Bitcoin traded around $11,000 and above in August (shown by the dominance of values below 0.00%), possibly expecting a correction toward $10,000. This was seen in the shift toward net buying (values above 0.00%) starting from September, when Bitcoin dropped to $10,000 levels.
This buying took a backseat again as BTC traded above $11,000, all the way to $13,000, at which point it picked up again. However, buying interest largely peaked around $15,000, after which retail traders have seemingly been indecisive, mostly selling during the Thanksgiving crash (on and around Nov. 26) and cautiously buying on the bounceback.
In the chart below, we see the same daily net percentage difference against ascending VWAP values — simply, the lowest to highest VWAP values over our selected time period. This chart shows us how traders in this category reacted to price changes, regardless of chronology.
Once again, we can see that most of the buying was around $10,000 levels, while the majority of the selling was between $11,000 and $13,000, following which we see more buying until $15,000 levels. From there onward, retail traders appeared uncertain as to the market direction, but they followed the trend by selling on dips and buying recoveries.
Given its diversity, this range represents the largest chunk of market participants, including speculators, day traders and casual investors. Our data essentially shows that, in August, retail traders were not expecting the price to stay above $11,000 for long, and that they were seeking opportunities to buy below this level. After the September drop, however, they have been following the price surge and have been net buyers on most days — all the way to the new all-time high.
Trades between 0.5 and 2 BTC — Professional traders
Trades between 0.5 and 2 BTC represent the second-largest volume on a daily basis. These trades can be valued anywhere between $5,000 (0.5 BTC) and $20,000 (2 BTC) if BTC is priced at $10,000. For our purposes, we attribute this range to professional traders.
Looking at the 122 days between Aug. 1 and Nov. 30, traders in this range were net sellers for 80 days, or 65.57% of the time (compared to 53.28% for the retail range). Moreover, while retail traders were mostly net buying from September onward (i.e., the number of buying-dominated days in the month were higher), professional traders in this range only became net buyers in October and November.
The visual representation of daily sells and buys (in percentage terms) in this category against the VWAP shows a bias toward selling, as compared to the more balanced trend seen in the retail range.
The net percentage difference compared to the VWAP also highlights the predominant selling pressure throughout August and September, as the price dropped from $11,000 levels to $10,000 levels and subsequently recovered. The shift in sentiment only came in October, as the price broke through $11,500, after which professional traders have mostly been on the buying side, especially in November.
The first major buying peaks are seen on Oct. 18 and Oct. 21 (corresponding with $11,500 and $12,500) while similar selling action was seen on Nov. 1–2 and Nov. 10 (corresponding with $13,500 and $15,300 levels). The majority of the buying in this range started after the $15,000 price level, however, and continued, for the most part, until the all-time high — with the exception of the Thanksgiving crash.
Charted against ascending VWAP values, the net percentage difference shows mostly selling activity until about $12,000, buying interest around $13,000 levels, subsequent selling until $15,500 and then a major shift toward buying post-$16,000.
This range (0.5 to 2 BTC) represents the beginning of relatively larger trades and is likely to include professional traders who use technical analysis and charting techniques alongside algorithmic trading. This could explain the pattern here, somewhat, since a Fibonacci retracement, drawn between the 2017 high and the 2018 low, shows $12,000, $13,000 and $16,000 as price levels corresponding with the 0.5, 0.618 and 0.786 Fibonacci levels, as shown below.
Trades between 2 and 5 BTC — Large traders and whales
Transactions in this range are valued anywhere between $20,000 (2 BTC) and $50,000 (5 BTC) if BTC is priced at $10,000. While these figures are not indicative of whales, per se — as traders not holding millions of dollars worth of BTC can also make these trades — they do mark the threshold from which we can start considering large traders and whales as participants.
Interestingly, in this dataset, of the 122 days between Aug. 1 and Nov. 30, traders were net sellers on 86 days, or 73.50% of the time. Moreover, unlike professional traders — who became net buyers in October and November — the traders in this range were net sellers throughout.
Our percentage values chart above shows selling trades in this range overtaking buying trades for the majority of the period, with some exceptions. Notably, a more balanced approach can be seen during the time in which Bitcoin was trading under $11,500.
The net percentage difference chart above shows buying interest around $11,500 levels (with the highest peak in mid-October recorded at this level), followed by $15,000. Notably, after $15,000, the selling pressure only intensified in this range, with most selling trades recorded from Nov. 21 to Nov. 30, often as the price rose.
The net percentage against the ascending VWAP chart further confirms our observations, with notable selling post-$15,000 and especially after $18,000, all the way up to the all-time high.
This pattern indicates how large traders, and possibly whales, bought at low levels, around $11,000, and decided to take profits on the way up, especially near the all-time high, where they seem to have sold when retail and professional traders were buying.
Trades between 5 and 10 BTC — Large traders and whales, continued
With trades in this range going from $50,000 (5 BTC) to $100,000 (10 BTC) if Bitcoin is priced at $10,000, this segment overlaps with the previous one (2–5 BTC), as they are both likely to include large traders and whales.
Their similarity is also reflected in the net selling days of this range between Aug. 1 and Nov. 30 standing at 85, or 72.65% (compared to 86, or 73.50%, of the previous range). Similarly, like the previous range, traders here also remained net sellers from August to November.
The daily percentages of buys and sells in this category are also very similar to the previous category, mostly leaning toward net selling, with some respite between September and mid-October. However, one interesting observation here is a net buying peak on Nov. 24, which corresponds with the day's $1,000+ candle.
The net percentage chart above further highlights this trend near the tail end, showing buying activity in this category on Nov. 24 and Nov. 26–29 (with the exception of Nov. 25 and Nov. 30). Looking at the net percentage difference chart for the previous range (2–5 BTC), we don't see similar buying activity on these days.
Notably, this buying activity not only corresponds with the green candle on Nov. 24 but also the bottom found around $16,000 on Nov. 27 and the subsequent price reversal, as shown below.
Before we analyze this trend any further, it is prudent to discuss our last category, which groups trades of 10 BTC and above. For now, the last chart for the current range is the net percentage compared to the ascending VWAP.
This chart highlights the selling bias throughout this range, with three notable exceptions: when traders in this range bought Bitcoin priced at $11,500, $15,500 and $18,500. We will, however, revisit the trend mentioned earlier, where buying activity in this range increased near all-time highs.
Trades of 10 BTC and above — Institutions
Our last category comprises trades worth 10 BTC and above, which start from $100,000 when Bitcoin trades at $10,000. Given the sheer size of these trades, they can be attributed to whales and institutions, albeit with the reservations highlighted in the introduction.
This dataset is also the smallest of all, volume-wise, given the low number of actual daily trades of 10 BTC or more. That being said, a quick look at the net selling days of this range shows a more balanced trend, with only 64 of the 122 days, or 54.70%, dominated by selling trades.
Moreover, unlike all previous categories, no extreme selling pressure can be seen for any month in this group. In fact, unlike the last two categories, this one had a net buying trend in October.
The daily percentages reflected in the chart above show a balanced approach, with extremes on both sides. However, these values are also a result of the unavoidably limited sample size of this range.
The net percentage difference chart above shows a clearer trend, with the majority of the buying taking place between mid-September and the end of October as Bitcoin traded between $10,000 and $11,500.
While there was some buying interest around the $15,500 mark, it was followed up by selling pressure. However, once again, like the category before this one, we see net positive buying on Nov. 24, Nov. 26 and Nov. 29.
Daily net % difference of sells and buys of 10 BTC and above against ascending VWAP. Source: Kaiko and OKEx
Finally, the ascending VWAP against net percentage difference chart just confirms how traders in this range favored buying at lower prices (i.e., under $12,000) and, subsequently, selling during the price rise — with some exceptions, though, especially near the tail end of the rally.
Putting things into perspective
While the extensive discussion above does highlight interesting insights, we will now summarize some of the key findings below for accessibility and a well-rounded perspective.
The table below highlights each month's dominating trend, denoted by the plus (buying) or minus (selling) symbol next to the percentage value. The last column has the same figure for the entire 122 day period between Aug. 1 and Nov. 30.
|Trading range||August||September||October||November||Total for the period|
|0 and 0.5 BTC||-80.65%||+56.67%||+58.06%||+53.33%||-53.28%|
|0.5 and 2 BTC||-90.32%||-93.33%||+58.06%||+63.33%||-65.57%|
|2 and 5 BTC||-93.55%||-76.67%||-54.84%||-56.67%||-73.50%|
|5 and 10 BTC||-90.32%||-73.33%||-58.06%||-56.67%||-72.65%|
|10 BTC or more||-67.74%||50.00%||+58.06%||-56.67%||-54.70%|
The table above shows heavy net selling throughout August across all trading ranges. This can be explained by the fact that August marked the first time Bitcoin broke into the $11,000 price range after almost 12 months — and traders were eager to take profits.
September was mostly marked by Bitcoin's drop to $10,000 again and the subsequent consolidation between $10,000 and $11,000. While we see retail buying in this month, all other ranges were again net sellers — except for traders in the 10 BTC or above range, who were neutral.
October marked another rally that took Bitcoin from around $10,500 to $13,500. While retail traders expectedly joined this run, it also swayed professional traders. Even though large traders and whales remained net sellers, their selling in October was not as extreme as the months prior. Notably, traders in the 10 BTC or above range also became net buyers in October.
Finally, November saw both retail and professional traders continue to buy into the rally, while large traders, whales and possibly institutions took profits. This behavior can be explained by Bitcoin's first unsuccessful attempt to test its 2017 all-time high on Nov. 25, which resulted in a steep drop and the crash around Thanksgiving. The psychological resistance here, as well as the need for risk management, may have prompted whales and institutions to unwind their positions at these levels while retail and professional traders continued to remain hopeful.
To put this into perspective, we will consider the last week of November and whether traders in our ranges were net buying or selling on those days.
|Trading range||Nov. 24||Nov. 25||Nov. 26||Nov. 27||Nov. 28||Nov. 29||Nov. 30|
|0 and 0.5 BTC||Selling||Buying||Selling||Buying||Buying||Buying||Buying|
|0.5 and 2 BTC||Buying||Buying||Selling||Buying||Buying||Buying||Selling|
|2 and 5 BTC||Selling||Selling||Selling||Selling||Buying||Selling||Selling|
|5 and 10 BTC||Buying||Selling||Buying||Buying||Buying||Buying||Selling|
|10 BTC or more||Buying||Selling||Buying||Selling||Neutral||Buying||Selling|
This table shows how everyone except the majority of retail traders took profits as BTC reached its new all-time high on Nov. 30. However, the highest relative profit-taking activity was seen in the range of 2 to 5 BTC trades. As discussed earlier, traders in this range mostly bought Bitcoin under $11,500 and were taking profits as BTC appreciated all the way up to the all-time high. They seem to have only executed more buying trades on Nov. 28, when the recovery after the Thanksgiving crash started.
Similarly, all of the trader personas except whales and institutional traders were panic-selling during the Thanksgiving crash on Nov. 26, when the price dropped from $19,000 to $16,000 levels. This shows how large whales and institutions actively bought BTC relatively "cheap" as it was being sold by everyone else while the price collapsed.
It would appear that retail traders have been chasing Bitcoin's price rally — as in buying as the price goes up — from $11,000 levels to the recent all-time high on OKEx of around $19,850. While professional traders also joined the rally later, large traders (and possibly whales) seem to have taken profits as institutions (and possibly whales) remained somewhat balanced, despite some bias toward selling.
A quick analysis of the last week of November is also telling, in that it shows that whales and institutions bought the Thanksgiving price drop, which was followed by a price recovery.
Overall, it appears that large traders, whales and institutions that accumulated Bitcoin around $10,000 levels decided to take profits during this rally, while retail traders mostly kept adding to their positions during the price surge. The result of this could be that retail traders will be trapped in the short- to mid-term — with BTC currently trading below $19,000 — but given how overall market sentiment remains bullish, their losses may be short-lived.
With all the hype and mania surrounding a Bitcoin bull run, the data discussed in this report serves as a reminder that large traders, whales and institutions are in the business of buying low and selling high. It is not in their interest to continue buying coins at new highs and making them even more expensive. Ultimately, as the data shows, they seek to drive the market, shake out retail traders in panic and capitalize on opportunities to buy relatively cheap coins. For retail traders, and everyone else in between, the choice seems to be between two options: swimming with the tide or against it.
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