As Bitcoin surged toward a new all-time high in late 2020, retail traders flooded exchanges to buy in—driven by excitement, momentum, and the fear of missing out. But behind the scenes, a different story was unfolding. While small investors chased price gains, large holders—often referred to as "whales"—quietly exited their positions, locking in profits at the top.
This article dives into blockchain trading data from August to November 2020 on the OKEx BTC/USDT market, powered by analysis from Kaiko. By segmenting transactions based on size and direction, we uncover how different market participants behaved during one of Bitcoin’s most pivotal bull runs. The findings reveal a classic market dynamic: retail buys high, whales sell high, and institutions accumulate during pullbacks.
Understanding Market Behavior Through Transaction Data
To decode who’s buying and who’s selling, we analyzed over 122 days of trading data across five transaction size tiers:
- Under 0.5 BTC – Retail investors
- 0.5–2 BTC – Professional traders
- 2–5 BTC – Large traders and early whales
- 5–10 BTC – Major whales and institutional players
- 10+ BTC – Institutional-scale entities and top-tier whales
Each tier reflects not just dollar value but also behavioral tendencies. Smaller trades tend to be reactive and emotional; larger ones often signal strategic positioning.
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How We Determine Buy vs. Sell Activity
In any exchange, every trade has a buyer and a seller. So how do we know who initiated the action?
The key lies in taker direction:
- If a trader uses a market buy order, they “take” liquidity from the order book—this is recorded as buying pressure.
- A market sell order removes sell-side liquidity, indicating selling pressure.
Using Kaiko’s taker_side_sell metric, we identified the net direction of daily trades across each size bracket. This allowed us to calculate net buying (positive) or net selling (negative) activity relative to Bitcoin’s volume-weighted average price (VWAP)—a more accurate reflection of true market sentiment than simple price charts.
Retail Traders: Buying High, Selling Low
Transactions under 0.5 BTC made up the largest volume of trades—representing everyday investors, day traders, and speculators.
Over the 122-day period:
- Net sellers on 65 days (53.28%)
- Net buyers on 57 days
At first glance, this suggests a relatively balanced stance. But when plotted against price movements, a clear pattern emerges.
Key Behavioral Insights:
- In August, as Bitcoin traded above $11,000, retail traders were predominantly selling—possibly anticipating a drop back to $10,000.
- Once prices dipped to that level in September, retail shifted to net buying.
- From October onward, they consistently bought higher—especially between $13,000 and $15,000.
- Near the peak ($19,000+), retail activity turned cautious but remained biased toward buying during rallies.
This behavior exemplifies trend-chasing: retail enters after momentum builds and exits during sharp dips.
“Retail investors often buy because prices are rising—not because fundamentals justify it.”
Their strategy worked until late November when Bitcoin spiked to nearly $20,000—then crashed around Thanksgiving. While others sold into strength, retail kept buying.
Professional Traders: Tactical Entry and Exit Points
The 0.5–2 BTC range represents skilled traders who use technical tools like Fibonacci retracements, moving averages, and algorithmic systems.
Key stats:
- Net sellers on 80 out of 122 days (65.57%)
- Only turned consistently bullish in October and November
Strategic Observations:
- Heavy selling occurred from August to September, as price fell from $11K to $10K.
- A major shift happened in mid-October, when Bitcoin broke above $11,500—a key resistance level.
- First significant buying spikes at $11,500 and $12,500.
- Temporary profit-taking at $13,500 and $15,300.
- Renewed accumulation began after $15,000 and continued toward the high.
Interestingly, many of these levels align with Fibonacci retracement zones from the 2017 peak to 2018 bottom:
- $12,000 ≈ 0.5 Fibonacci level
- $13,000 ≈ 0.618 level
- $16,000 ≈ 0.786 level
This suggests professional traders were using established technical models to time entries and exits—unlike retail, which reacted emotionally.
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Large Traders & Whales: Selling Into Strength
Now we enter the domain of serious capital: 2–5 BTC and 5–10 BTC trades.
These are not casual investors. They’re capable of moving markets with single orders.
2–5 BTC Range:
- Net sellers on 86 days (73.5%)
- No sustained buying phase
- Minor accumulation around $11,500 and $15,000
- Aggressive selling intensified after $18,000, peaking near the all-time high
5–10 BTC Range:
- Net sellers on 85 days (72.65%)
- Similar pattern: early accumulation below $12K, then gradual distribution
- Notable exception: strong buying on November 24, coinciding with a +$1,000 candle
- Additional buying seen from Nov 26–29, right after the Thanksgiving crash
This tells a powerful story: large players had been offloading for weeks—but stepped in to buy when panic hit.
Institutional-Scale Players: Patience and Precision
The final tier—10+ BTC transactions—likely includes hedge funds, family offices, and ultra-high-net-worth whales.
Despite smaller trade volume, their behavior was distinct:
- Net sellers on only 64 days (54.7%)
- Showed clear accumulation from mid-September to late October, when BTC traded between $10,000 and $11,500
- Limited participation above $15,500
- Also bought during the Nov 24–29 dip
Unlike mid-tier whales who dumped aggressively at the top, institutions displayed a more balanced approach—accumulating low and selectively adding during volatility.
They didn’t chase price. They waited.
Monthly Breakdown: Who Was In Control?
| Period | Retail | Pros | Mid Whales | Big Whales | Institutions |
|---|---|---|---|---|---|
| Aug 2020 | Sell | Sell | Sell | Sell | Neutral |
| Sep 2020 | Buy | Sell | Sell | Sell | Neutral |
| Oct 2020 | Buy | Buy | Sell (less) | Sell (less) | Buy |
| Nov 2020 | Buy | Buy | Sell | Sell | Mixed |
Key takeaway: As retail and pros piled in during October and November, larger players began exiting—especially those in the 2–5 BTC range.
By the final week of November:
- Bitcoin hit ~$19,850
- Retail remained net buyers
- All other groups were net sellers
- Highest profit-taking? The 2–5 BTC bracket
Yet during the Thanksgiving crash—from $19K to $16K—everyone panicked… except whales and institutions. They bought what others were dumping.
Frequently Asked Questions (FAQ)
Q: What defines a "whale" in crypto markets?
A: There's no strict definition, but traders moving 2+ BTC regularly are often considered whales. Their trades can influence short-term price action due to size.
Q: Why do retail investors tend to lose money in bull markets?
A: Because they buy based on emotion and momentum—not strategy. They enter late in rallies and exit during corrections, effectively buying high and selling low.
Q: How can I tell if whales are buying or selling?
A: Track large transaction volumes on-chain or via exchange-level taker data. Sudden spikes in >5 BTC sell orders near resistance levels may signal distribution.
Q: Did institutions really buy during the Thanksgiving crash?
A: Yes. Data shows net buying from both 5–10 BTC and 10+ BTC groups during Nov 24–29, even as price dropped sharply—a sign of strategic accumulation.
Q: Is it too late to invest if I missed the rally?
A: Not necessarily. Bitcoin has historically seen multiple cycles of growth. Strategic entry during consolidation phases often yields better long-term returns than chasing peaks.
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Final Thoughts: The Cycle Continues
The narrative is familiar:
- Whales buy low, often when news is negative
- Retail discovers the trend late, fueled by media hype
- Price surges, triggering FOMO
- Smart money exits, while emotional traders take over
- A correction follows—and the cycle resets
In late 2020, this script played out perfectly. Whales accumulated below $12,000 and sold near $20,000. Institutions added during dips. Retail chased gains—and got caught at the top.
For today’s investors, the lesson is clear: follow the data, not the noise.
Whether you're an individual trader or building long-term wealth, understanding who is trading—and when—can make all the difference between profit and regret.
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