Bitcoin Reserve Risk Falls To 2015 Levels, What Happened To BTC’s Price That Year?

newsbtcPubblicato 2022-07-06Pubblicato ultima volta 2022-07-06

Introduzione

Bitcoin has returned to the $20,000 area after experiencing rejection. The cryptocurrency has been displaying some strength during today’s trading session despite a spike in the U.S. dollar which signals...

At the time of writing, BTC’s price trades at $20,300 with a 2.2% profit in the last 24 hours. Data from Material Indicators (MI) records an increase in buying pressure from Bitcoin whales with bid orders of over $1 million (brown in the chart below).

Large investors have been accumulating BTC over the past week as the cryptocurrency moved below its current levels. BTC Whales could have been preparing for bullish continuation. At the time of writing, every investor’s class except retail is jumping into BTC’s price action.

The cryptocurrency needs to break above $20,500 and continue above $22,000 to clear out any potential short-term downside risk.

Material Indicators records over $20 million in asks order for BTC’s price at around $20,500 until $22,000 alone. There is little resistance above those levels until $24,000 which stands as the next major area of resistance.

On the possibility that large investors were accumulating BTC expecting a larger move to the upside, MI wrote: "The FireCharts heat map and CVD both show that the purple class of whales which have historically had the most influence over BTC price have been trending up since the dump to $17.5k on May 18th. Too early to validate this as accumulation phase. Time will tell."

The key behind the current price action could be the U.S. dollar. The currency aggressively moved to the upside, to levels last seen in 2003, and could be about to retest previous lows.

As seen below, this could send the DXY Index to the 105 area or to its June range below 100, if these levels fail. Thus, providing some more room for BTC’s price to reclaim higher levels.

DXY Index (U.S. Dollar) sees some losses after a breakout on the daily chart. Source: Tradingview

Bitcoin Indicators Suggest Bullish Continuation

Quantum Economics analyst Jan Wüstenfeld indicated that BTC’s Reserve Risk dropped to 0.001, a metric used to measure long-term holders’ conviction. The last time Bitcoin saw a Reserve Risk this low was in 2015 before it began a persistent uptrend.

Currently, there are macroeconomics factors that could hurdle BTC’s price reclaim of previous highs. However, the current area could be a major accumulation zone for the coming months and a good place to apply a Dollar Cost Average (DCA) strategy. Wüstenfeld said: "Bitcoin reserve risk has fallen below the green box. The last and only other time this happened was in August 2015. One thing is sure, this cycle is genuinely different to other cycles due to the macro conditions, and we are partially seeing that as well in the indicators."

Letture associate

The Value Distribution of Stablecoins

**Summary: The Value Distribution of Stablecoins** The article argues that stablecoins are evolving from mere trading tools into broader channels for dollar access. It divides the stablecoin ecosystem into four layers to analyze how value is distributed: 1. **Issuance Layer:** Mints stablecoins, holds reserve assets, and captures the spread between reserve yield and user costs (e.g., Tether, Circle). This layer currently earns the largest profit margin. 2. **Infrastructure Layer:** Connects stablecoins to the traditional financial system, handling fiat on/off-ramps, banking integration, compliance (KYC/AML), and asset management (e.g., Bridge, BVNK). This is the "unglamorous" but critical work, building the essential bridges between crypto and real-world finance. 3. **Acquiring/Distribution Layer:** Integrates stablecoins into merchant systems, manages payment flows, and provides enterprise financial software (e.g., Stripe, Coinbase). They act as the access point for businesses. 4. **Application Layer:** The end-users and businesses that ultimately use stablecoins for payments, settlements, or as a store of value. They benefit from convenience but have little pricing power. The core thesis is that while the issuance layer currently dominates profits, the often-overlooked **infrastructure layer holds significant long-term potential**. The real challenge and barrier to mass adoption is not the on-chain transfer of stablecoins (which is simple), but the complex "last mile" integration into existing business workflows, banking systems, and regulatory frameworks across different countries. Companies in this layer are currently in a "land grab" phase, investing heavily to build networks, secure bank partnerships, and establish compliance pathways. While their position is currently pressured by the profitable issuers above and distribution platforms below, the article suggests that if stablecoins become a default financial rail for businesses, the infrastructure providers who have done the hard work of integration will ultimately gain strong pricing power and become entrenched, essential players.

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The Value Distribution of Stablecoins

The Value Distribution of Stablecoins The article argues that stablecoins are evolving from a mere trading tool into a broad "dollar channel." It analyzes the industry's value chain through four layers: 1. **Issuance Layer (e.g., Tether, Circle):** The top layer that mints stablecoins, holds reserve assets, and captures the thickest interest rate spread. 2. **Infrastructure Layer (e.g., Bridge, BVNK):** Connects stablecoins to the traditional financial system, handling critical but complex "dirty work" like fiat on/off-ramps, banking integration, compliance (KYC/AML), and cross-border settlement. 3. **Acquiring/Distribution Layer (e.g., Stripe, Coinbase):** Embeds stablecoins into merchant systems, manages payment flows, and integrates with enterprise software. 4. **Application Layer:** End-users and businesses that ultimately use stablecoins for payments, settlement, or storing value. The author posits that while the issuance layer currently captures the most profit, the most overlooked and potentially critical layer is infrastructure. The core challenge for stablecoin adoption isn't the on-chain transfer (which is simple), but bridging the gap between blockchain and the real-world financial system. This involves solving practical problems for businesses: fiat conversion, reconciliation, tax handling, and user onboarding. Infrastructure companies are currently in a difficult "land-grab" phase—building networks, securing banking relationships, and achieving compliance country-by-country. They face pressure from both the profitable issuance layer above and distribution platforms below. However, the author suggests this layer is building a crucial moat. Once stablecoins become a default business rail, the infrastructure players who have done the hard work of integration may gain significant, durable value and pricing power.

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Why Is Nvidia Borrowing $20 Billion When It's Not Short of Cash?

Nvidia's recent announcement to issue at least $20 billion in senior notes, despite holding a strong cash position with over $48.6 billion in free cash flow last quarter, is not a sign of financial need. Instead, it represents a strategic move to leverage its high credit rating (recently upgraded to AA by S&P) to secure low-cost, long-term debt. This capital will support long-term AI infrastructure investments, data centers, R&D, supply chain prepayments, and strategic investments, while allowing the company to continue aggressive shareholder returns through stock buybacks and dividends. The decision reflects a mature capital management strategy: using debt to finance long-term growth assets is more efficient and less dilutive to shareholders than equity financing. It signals that Nvidia, like other tech giants (Alphabet, Meta, Amazon), is entering a new phase of heavy AI capital expenditure, shifting from a pure growth story to a story about capital allocation, credit strength, and long-term ecosystem positioning. The key question for investors is whether Nvidia can maintain its high cash flow generation and ensure that returns from these AI investments justify the cost of capital over the long term. The bond issuance amplifies its expansion capabilities but also ties its valuation more closely to the broader AI investment cycle's sustainability and profitability.

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Why Is Nvidia Borrowing $20 Billion When It's Not Short of Cash?

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Liberland Fires Tech Secretary After Alleged Blockchain And Website Takeover Attempt

Liberland's congress has voted to remove Technology Secretary Dorian Stern Vukotić, following a resolution accusing him of attempting to hijack the Liberland.org domain, removing administrative multisig protections, blocking the president from voting, and launching unauthorized tokens. The incident serves as a case study in blockchain governance, highlighting risks that go beyond smart contracts to include control over domains, admin keys, and off-chain authority. For the crypto industry, it underscores the need to scrutinize decentralization claims against operational realities, where a small group can still compromise governance. The story reflects a broader market shift where infrastructure security and governance are becoming as critical as price action. Editorial framing should treat the development as a verified information signal about governance risks, avoiding overstatement of Liberland's legal status and leaving room for follow-up evidence.

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Liberland Fires Tech Secretary After Alleged Blockchain And Website Takeover Attempt

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How to Do Research Well: Deliberately Practice the Real Skills That Matter

No one truly teaches you how to do research. You're often given a desk, a pre-selected problem, and vague instructions to "create something new." Consequently, many people reverse-engineer the job based on visible outputs—papers, posts, announcements—learning only how to *appear* like a researcher rather than how to *become* one. True research capability is built from stacking small, trainable skills, nearly all of which can be developed through deliberate practice. **Pick Your Own Problem:** Most researchers absorb problems from advisors or trends, lacking the underlying reasoning. Choosing a problem you genuinely care about, as John Schulman advises, leads to original work. Develop "taste" like a muscle: predict experiment outcomes, guess paper results from methods, and track which findings remain important over time. **Upgrade Your Inputs:** Relying on shared reading lists (arXiv hot lists, filtered group chats) leads to unoriginal conclusions. Undervalued old literature often holds crucial insights (e.g., MoE, LSTM, backpropagation). Richard Sutton's "The Bitter Lesson" or Claude Shannon's 1952 talk on creative thinking are more predictive than lengthy modern surveys. Breadth matters as much as depth: draw from neuroscience, mechanism design, hardware knowledge, and honest statistics. Read papers directly, especially appendices and limitations sections. **Write Everything Down:** As Paul Graham noted, writing exposes flaws in seemingly mature ideas. Writing is the cheapest defense against self-deception. Following Feynman's principle, Darwin programmatically wrote down facts contradicting his theory to combat memory bias. Maintain a detailed log of hypotheses, setups, predictions, results, and updated understandings. Reviewing past logs fosters essential humility.

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How to Do Research Well: Deliberately Practice the Real Skills That Matter

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