Expert guides, insights and articles updated for 2026
Published 3 hours ago
Checking your portfolio and seeing -30% in a day feels like chaos. But the reputation-making drawdowns—the 70%+ wipeouts people warn about—usually aren’t random. They tend to follow a repeatable script.
This piece is a post‑mortem of 2018, March 2020, and 2022 to answer one question: what tends to trigger 70%+ crypto drawdowns? Then we’ll compare those setups to today’s market structure—ETFs, stablecoins, on-chain visibility, and deeper derivatives—to see what risks are genuinely lower, and which ones are still sitting there waiting for the wrong week.
One expectation up front: this is not a price call. It’s a framework for reading risk so headlines don’t do all your thinking for you.
Big drawdowns usually require a stack of fragilities. The recurring trio is:
A key term you’ll see throughout:
A few structural features make crypto more crash-prone:
We’re not trying to predict the next crash. We’re identifying the mechanics that turn “a bad week” into a generational drawdown.
2018 is the classic example of what happens when a boom is built on thin liquidity + relentless new supply + retail momentum.
The 2017 cycle featured:
One practical detail mattered: many projects that raised funds needed to pay expenses in fiat, which often meant selling crypto over time—extra supply pressure as the market weakened.
When prices started falling, the unwind fed on itself:
A lot of 2018’s pain wasn’t just “people sold.” It was where they sold:
The vehicles change, but the warning signs rhyme:
March 2020 is the reminder that crypto doesn’t need a crypto-native scandal to crash. Sometimes it just gets hit by the financial weather.
The early COVID panic created a global dash for cash—a scramble for USD liquidity. In those regimes:
The speed came from leverage + market mechanics:
Definitions, in plain English:
In fast markets, the “plumbing” matters:
(Some traders reported platform instability during that period across the industry. The useful takeaway isn’t blaming a single venue—it’s that operational stress rises when volatility spikes.)
What stopped March 2020 (broadly) wasn’t a crypto-native rescue. It was macro:
Signals to remember from March 2020:
2022 wasn’t “just a bear market.” It was a credit event.
The 2020–2021 boom normalized “earn” products and high yields. Under the hood, much of that yield depended on:
The core issue: balance sheets were often opaque, so when stress hit, nobody knew who was solvent.
Early 2022 saw a major crypto-native failure involving a prominent stablecoin/protocol design. The sequencing matters for historians, but the mechanism is what risk readers should remember:
As the year progressed, multiple centralized entities across lending, funds, and exchanges failed or entered distress (including widely known names like 3AC, Celsius, Voyager, and FTX).
Contagion followed a familiar path:
2022 had plenty of risk-off moments, but the dominant emotion was different:
That distinction matters because solvency fear causes runs—and runs create forced sellers even when fundamentals haven’t changed.
What stopped it (broadly):
Signals to remember from 2022:
Different headlines. Same underlying recipe.
Leverage isn’t only “I borrowed on an exchange.” It can hide in:
The tell isn’t the product name. It’s the fragility: can positions be forcibly closed into a falling market?
In calm conditions, liquidity looks abundant. In panics:
That’s why drawdowns overshoot: the market isn’t calmly “deciding” fair value—it’s searching for the level where forced selling finally meets real buyers.
Systemic events often involve an entity that is:
When that breaks, it’s not just reputational damage. It becomes mechanical selling pressure.
When the narrative shifts from:
…behavior changes. People withdraw first and ask questions later. That’s when a drawdown becomes nonlinear.
| Period | Primary trigger | Dominant leverage | Liquidity condition | Dominant fear | How it stabilized (broadly) |
|---|---|---|---|---|---|
| 2018 | Post-bubble unwind after ICO boom | Margin/speculative positioning + supply overhang | Thin, fragmented (esp. alts) | “The boom was a mirage” | Sellers exhausted + slow confidence rebuild |
| March 2020 | Global dash-for-cash | Derivatives + cross-asset deleveraging | Liquidity evaporated fast | “Need USD now” | Macro liquidity response + risk appetite return |
| 2022 | Counterparty/credit failures | Rehypothecation + credit chains | Liquidity + trust crisis | “Who’s solvent?” | Bankruptcies/closures + forced deleveraging |
The biggest mistake is assuming “different” automatically means “safer.” Usually it means different trade-offs.
Spot Bitcoin ETFs (where available) change access:
But there’s a catch:
Stablecoins are now the main settlement layer for crypto trading:
But they also introduce systemic concentration risk:
On-chain analytics gives you visibility you don’t get in TradFi:
But it doesn’t solve everything:
Derivatives are deeper than in earlier cycles:
But the same machinery can accelerate cascades:
More derivatives can mean more shock absorbers—or more explosives—depending on positioning.
Post-2022, many participants adapted:
That likely reduces certain “single-giant-blowup” scenarios. But risk can migrate to:
Relative to the height of the yield bubble:
March 2020 can happen again in spirit:
Stablecoin confidence events remain a tail risk because stablecoins sit in the middle of market plumbing.
If you only watch exchange metrics, you can miss the real buildup:
The theme: leverage doesn’t die—it relocates.
When someone says “crypto could drop 70% again,” the first question should be: which crypto?
A 70% drawdown in an alt can be a straightforward liquidity event. In BTC, it usually takes broader systemic stress.
This isn’t about calling tops. It’s about recognizing when the recipe is forming: leverage + vanishing liquidity + counterparty fear.
Look for clusters, not single signals:
If multiple are flashing, assume the market is more fragile than it looks.
Practical signals you can observe:
Ask boring questions when the market is euphoric—because you won’t have time later:
Practical rules that reduce regret:
Historically, the biggest drawdowns rarely come from a single headline. They typically require a stack of fragilities: (1) leverage that can be forcibly unwound (liquidations, margin calls), (2) liquidity that disappears when everyone sells at once, and/or (3) counterparty risk where a trusted lender/exchange/fund can’t meet obligations or becomes a forced seller.
2018 followed the ICO boom, when capital flowed into many thinly traded tokens and supply expanded quickly. As demand faded and prices fell, forced selling and reflexive deleveraging amplified declines—especially where liquidity was shallow and buyers stepped away.
March 2020 was mainly a global “dash for cash” liquidity shock. Crypto sold off alongside equities as investors raised dollars, cut risk, and met margin calls. Inside crypto, derivatives liquidations plus thin order books turned a fast drop into a cascade, even though the trigger wasn’t crypto-native.
2022 was dominated by counterparty and solvency fears after major failures across the crypto credit stack. When trust breaks, withdrawals accelerate, collateral haircuts rise, and even “good” collateral (like BTC/ETH) gets sold to meet redemptions—creating contagion that’s different from a normal risk-off move. (The exact sequencing varies; the key is the trust-to-forced-selling loop.)
It’s when too many people have borrowed exposure (via margin or derivatives), and a price drop forces positions to close automatically. Those forced sales push price lower, which triggers more forced sales—often called a liquidation cascade.
Common warning lights include persistently one-sided perpetual futures funding, rising open interest relative to spot volume/market depth, frequent liquidation spikes, and an unusually large futures basis (often tied to crowded carry trades). No single metric predicts a crash, but clusters matter.
An asset can be tradable (it has a price) but not liquid (you can’t sell size without moving price a lot). In panics, order book depth vanishes, spreads widen, and markets gap—so even modest selling can cause outsized moves.
Both effects can be true. ETFs can broaden access and bring a buyer base that may be more “sticky” than pure momentum traders. But they can also make BTC more flow-driven and more linked to traditional risk appetite—so large creation/redemption flows could amplify moves during sharp risk-off periods.
Stablecoins improve settlement and 24/7 liquidity—often stabilizing day-to-day trading. But they add concentration and confidence risk: if an issuer faces reserve, banking, liquidity, or regulatory stress, redemptions or depegs can become a market-wide shock.
It helps, but it’s not a full solution. On-chain data can reveal some reserves and flows, yet it usually can’t show off-chain liabilities, hidden leverage, or related-party obligations. Proof-of-reserves can improve confidence, but “assets ≠ liabilities” remains the core limitation.
Leverage can migrate into offshore venues, OTC credit, structured yield products, cross-margin setups, and options strategies that are effectively short volatility. When conditions flip, these can still unwind quickly.
BTC and ETH generally have deeper liquidity and broader holder bases than most altcoins, so their crash dynamics differ. Altcoins can experience air pockets more easily due to thinner books and higher concentration. The same shock can produce very different drawdowns across assets.
If you remember one thing from 2018, March 2020, and 2022, make it this:
70%+ drawdowns usually happen when leverage meets disappearing liquidity, and then trust breaks (or the other way around). The headline changes. The mechanism repeats.
The useful stance isn’t permanent bearishness. It’s conditional awareness: when leverage is crowded, liquidity is thin, and counterparties look shaky, you don’t need a crystal ball—you need a plan.
So the next time “crypto wipeout” trends, try this instead of doomscrolling:
Watch conditions, not price targets. That’s how you replace fear with context—and context with better decisions.
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