The Reality of Bitcoin's Election Correlation | The Giver
Summary
The Giver argues Bitcoin’s election correlation may be backwards: the rally reflects liquid election-risk hedging, not proof that a Trump win mechanically sends BTC to $100K. Event-driven funds can express a scalable Trump proxy through IBIT, CME futures and spot BTC more easily than through Polymarket or unfamiliar energy names. Because that money is “not sticky” and has not recycled into ETH or SOL, he expects much of it to unwind regardless of who wins.
He estimates the election capital’s cost basis around $61K–$63.5K, making $70K a natural profit-taking level after a roughly 10% three-week return. CME open interest added about $3.5 billion from October 10 to October 16, while the hosts cited ETF inflows of $250 million, $550 million, $400 million and $450 million across October 11–16. His trade preserves the asymmetric upside while expressing skepticism elsewhere: “I am long Bitcoin and short everything.”
Four buyer classes explain why a BTC rally need not become a broad crypto cycle. Speculators typically generate the deepest troughs and highest peaks; The Giver assumes ETF and Saylor-style passive buyers are price-agnostic and have healthy time horizons; carry funds might buy IBIT while shorting CME futures; and event-driven buyers arrive specifically to monetize election moves. That fourth group is moving price now, in The Giver’s view, but “that capital will unravel.”
Jonah’s strongest pushback is that Trump could give Bitcoin the same reflexive option value Elon Musk once gave DOGE. After Musk’s first mention, buyers held DOGE because he “might mention it in the future,” sustaining months of outperformance; a Trump victory could similarly keep speculation alive through inauguration. If BTC remains above $70K and beyond its all-time high for a month, headlines could finally draw in the large pool of investors who currently own no crypto.
The Giver accepts that right tail but rejects the assumption that 2021-style liquidity automatically returns with Trump. Crypto’s prior $2.7 trillion peak came with stimulus, 0% rates, leverage and 90% LTV rehypothecation, allowing $100,000 to become two or three times as much effective buying power. Today’s market has seen limited fresh capital since January–March, so he thinks a durable expansion needs stabilized financial conditions and equities, not merely an election result.
His trading framework begins with consensus positioning and asks whether existing flows can support it without new money. Only then does he consult volume and open interest; the more everyone treats FOMC, CPI or jobs data as decisive, the less weight he gives it and the more willing he is to flatten or fade the event. Jonah’s memorable framing is not whether conditions are euphoric, but whether they will get “more euphoric or less euphoric.”
The memecoin discussion supports The Giver’s thesis that small memecoins can produce unusually uncorrelated returns during periods of macro uncertainty. His regression of SOL against BTC gave an R-squared of roughly 0.6 since 2021; he separately described SOL’s BTC correlation as around 0.7 in 2023 and 0.8 year-to-date as SOL grew, while POPCAT had the lowest statistical covariance. The Giver said GOAT could potentially reach $2 billion but was very unlikely to matter in a year; Jonah expects a BODEN-like “wild arc” followed by collapse.
Deep dive
1. A zero-profit cycle made survival more important than being right
The Giver entered crypto in mid-2021 with little market experience, DCAing during the summer drawdown. DOT bought near $10 was around $60 when he next checked, turning an accidental entry into an obsession with understanding DeFi, treasury-backed tokens and why prices moved.
His first analytical post attracted roughly 1,000 likes and 2,000 followers overnight. Using a pseudonym—and sometimes remaining anonymous or unbranded outside Twitter—became a deliberate test: he wanted his work evaluated “on the merit of my thinking,” without professional identity making readers too eager to agree.
The brutal education came while shorting LUNA from roughly $100. He repeatedly took profits and reloaded at $70, $40, $30 and $20, briefly making about $250,000 in hours; after continuing despite another trader’s warning that the short was becoming crowded, the reversals erased those gains and roughly half his crypto net worth.
Illiquid NFTs then deteriorated, insufficient liquidity compromised his remaining positions, and the principal left on FTX disappeared. Despite being directionally right at several points, he finished the entire cycle with no profit: “markets can remain irrational longer than you can remain solvent.”
2. Valuation describes worth; flows explain public-market prices
The Giver’s traditional-finance experience taught him to distinguish what an asset should be worth from what people are willing to pay, or what they paid previously and use as a precedent. In public markets, he says, the difficult additional task is understanding capital concentration and the economic forces shaping that gap.
His Bitcoin process starts with an ordinary supply-and-demand curve, then a hypothesis about where consensus has accumulated. The key question is whether enough volume and capital exist to support that consensus “without new money coming in”; if support looks insufficient, he checks volume and open interest.
He weights macro inversely to how intensely everyone else watches it. When FOMC, CPI or jobs data become universally decisive, he is likelier to flatten or take a contrarian view; crypto’s advantage is rapid feedback, because a directional thesis usually declares itself within one or two weeks, or “at most a month.”
3. Above $60K, ETF scale turned Bitcoin into the cleanest Trump trade
The hosts saw a changed market after BTC reclaimed $60K: real ETF demand, equities at all-time highs and investors moving down the risk curve. The cited October ETF flows—$250 million, $550 million, $400 million and $450 million—were described as “mega numbers,” reinforcing Bitcoin’s role as an accessible institutional Trump trade.
Polymarket might absorb $50,000 or $500,000, whereas IBIT can accommodate $50 million or $100 million. GEO Group provided another observed Trump proxy because its detention-center funding is election-sensitive, but the hosts argued no other trade combined Bitcoin’s liquidity, accessibility and upside.
The Giver began the month short, flipped long around Golden Week to scalp an easing-driven risk move, then flattened and ran his lightest book in three or four months. When BTC dipped from $60K to $59K, he added small ETH and SOL shorts before the market accelerated through $61K–$65K.
4. The election hedge may be causing Trump odds, not following them
The familiar framework—Trump wins and BTC reaches $100K, Kamala Harris wins and it falls to $40K—struck The Giver as “somewhat lazy analysis.” Avi agreed with only half of that framing, explicitly rejecting $40K as a necessary Harris outcome.
His “chicken or the egg” alternative is that Bitcoin itself has become a liquid hedge for election risk. A manager exposed to Harris-beneficiary industries can buy BTC as a Trump proxy without researching coal or energy companies, confronting mandate restrictions or accepting an illiquid prediction-market position.
The light-bulb moment came as BTC rallied while ETH, SOL and other assets eroded against it. Trump Media had separately moved from about $10 to $30 in two weeks without new guidance, reinforcing his conclusion that BTC was being used as an election hedge rather than bought because crypto-native fundamentals had changed.
He places this mercenary capital’s cost basis around $61K–$63.5K. At $70K it owns a roughly 10% gain on a three-week trade, attractive enough in traditional finance to justify closing rather than rolling the dice: “that money’s coming out.”
5. Four buyer classes explain Bitcoin’s dominance over alts
The first cohort is the traditional speculator, historically crypto’s dominant participant and perhaps more than 75% of the previous cycle’s base. These traders create the asset class’s very high peaks and deep troughs through leverage, reflexivity and changing appetite.
Passive buyers are newer: ETF allocators and, to some extent, Saylor. The Giver assumes they are price-agnostic, diversified and conditioned toward long horizons; their repeated support in the $50Ks and $60Ks after liquidation events suggests a materially stickier bid.
Carry buyers such as Millennium are price-insensitive but rate-sensitive. They might own IBIT and short CME futures—the “basis trade in real life”—capturing basis without making a lasting directional statement, so their gross exposure should not be confused with fresh speculative demand.
Event-driven buyers are the marginal force now, in his model—the same temperament that traded a rapidly closing Grayscale discount or rumors of a Trump strategic reserve. Their money remains inside IBIT, CME or BTC and “doesn’t recycle elsewhere,” explaining why Bitcoin can rise as ETH and SOL ratios weaken.
6. Trump could still give Bitcoin a DOGE-like speculative tail
Jonah’s pushback separated the buyer’s instrument from the buyer’s reason. If buyers expect Bitcoin to flourish under a friendlier Trump regime, they need not close immediately after the result; they may hold for a perceived friendly regulatory environment and follow-on reflexive inflows.
The memorable analogy was DOGE after Elon Musk’s first mention. Buyers accumulated it solely because he might mention it again, and it outperformed for months; Trump could create the same “ever-present” right-tail event around Bitcoin through election night and perhaps inauguration.
Sustained price matters more than briefly printing a record. Jonah argued that BTC has repeatedly retreated immediately after making all-time highs; holding above $70K and beyond the high for more than a month could generate headlines that bring currently flat, non-crypto investors into the market.
The Giver called this reasonable, which is why his expression was “long Bitcoin and short everything” rather than outright short BTC. But he contrasted today with 2021’s $2.7 trillion market, stimulus and 0% rates, when 90% LTV rehypothecation could turn $100,000 into two or three times that amount of effective buying.
7. The sharpest disagreement is whether profit-takers or outsiders dominate
Jonah challenged the empirical core directly: had The Giver actually met traditional-finance managers hedging election-sensitive equities with Bitcoin? His honest answer was mixed—people were acutely aware of election beneficiaries, but he had not asked whether they were implementing the exact hedge professionally or personally.
The Giver instead pointed to Trump Media and what he thought—possibly incorrectly—was about a 20% difference between the two proxies. Jonah accepted that a fund would bank a quick 10% gain at scale, but argued those exits would be “spit in the ocean” beside a Trump-triggered tsunami of new buyers seeking perceived safety and regulatory friendliness.
The Giver’s rebuttal was about sequencing: new adoption takes time, while a sharp election-night rally can be sold immediately. CME open interest had increased about $3.5 billion from October 10 to October 16; falling CME OI would be the clearest warning that the event position was unwinding.
The Giver also resisted a blanket alt short, arguing that SOL and DeFi could receive a Trump bid. His June–August regression found only a weak linear relationship between Trump odds and BTC; if second-order beneficiaries are not already responding, they are either underpriced longs or evidence of absent crypto-native capital. He chose the latter.
8. Memecoins monetize uncertainty before size restores correlation
Jonah introduced GOAT as the convergence of AI and meme speculation: Marc Andreessen gave a robot $50,000, a coin emerged, and it appreciated roughly 100-fold in five days. The Giver thought it could potentially reach $2 billion quickly but was very unlikely to matter in a year because an AI coin lacks the mass appeal of “a cute dog in a hat.”
Jonah compared GOAT with BODEN: an “oh my God” ascent toward an absurd valuation, followed by collapse because neither possesses lasting cultural power. The Giver still considered memecoins permanent gambling instruments and expected periodic rips whenever BTC remained above $60K, noting that funds had begun allocating to them.
The Giver’s portfolio-level claim is that young memecoins can deliver uncorrelated crypto returns. His SOL regression against BTC produced an R-squared of roughly 0.6 since 2021; he separately described SOL’s BTC correlation as 0.7 in 2023 and 0.8 year-to-date as SOL grew. Among MOG, POPCAT, WIF and PEPE, slower-growing POPCAT showed the lowest statistical covariance.
His small historical sample linked memecoin highs to uncertainty: WIF ran from $0 to $2 billion or $2.5 billion from December to March amid skepticism about whether the BTC ETF would keep capital abundant, and performed well during Bitcoin’s $49K–$73K run; PEPE moved from $3 billion to $6 billion during April–May geopolitical stress; MOG and MEW doubled around ETH ETF uncertainty; and POPCAT cleared $1 billion during September’s rate-cut debate. He said the current election uncertainty is a critical period for identifying a replicable memecoin process, while explicitly calling the sample limited.