# Strategy Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Strategy", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

Google's Largest Acquisition Ever: What Makes Wiz Worth It?

Google has made its largest acquisition ever, acquiring cloud security company Wiz for $32 billion. This deal, which surpasses its 2012 purchase of Motorola Mobility, values Wiz at a price-to-sales ratio of over 45x—significantly higher than industry averages. Despite Wiz's rapid growth, with annual recurring revenue reaching ~$700 million in just a few years, the acquisition appears financially expensive at face value. Founded in 2020 by a team of Israeli cybersecurity veterans, Wiz became one of the fastest-growing software companies by focusing on agentless cloud security, enabling quick deployment and multi-cloud visibility. Its client base includes nearly half of the Fortune 100. For Google, the acquisition is strategic. It aims to integrate Wiz’s technology into Google Cloud to enhance its security offerings, attract enterprise clients, and compete more effectively against AWS and Azure. The deal, structured as all-cash, also prevents rivals from acquiring Wiz. However, challenges remain, such as integrating Wiz’s distinct corporate culture and maintaining its perceived neutrality as a now-Google-owned multi-cloud platform. Ultimately, Google is betting that the long-term strategic value outweighs the financial cost.

marsbit03/16 03:10

Google's Largest Acquisition Ever: What Makes Wiz Worth It?

marsbit03/16 03:10

The Bitcoin Game Plan That Shows How To Navigate BTC Below $100,000

A crypto analyst, ChainHub, has outlined his Bitcoin strategy for the coming months, suggesting that if BTC fails to break out soon, it may not be ready for a reversal. He identifies key demand zones at $53,000-$58,000 and $44,000-$46,000 as potential reversal points, with levels below $30,000 as next targets if these fail. While Bitcoin is still technically bullish, breaking higher without sufficient demand will be difficult. The analyst is dollar-cost averaging into short positions to hedge his spot holdings and plans to close all spot positions by early April. He expects sideways movement or slight gains before exiting spots and moving net short by late March. ChainHub anticipates a potential Bitcoin bottom around April 14-15, with the next bullish phase from late April to early May, targeting $93,000. He warns that altcoins have been unpredictable and may drop to zero before a meaningful rally, though the next cycle could be altcoin-driven.

bitcoinist03/14 01:02

The Bitcoin Game Plan That Shows How To Navigate BTC Below $100,000

bitcoinist03/14 01:02

Your Backtest Is Lying: Why You Must Use Point-in-Time Data

The article warns that backtests using revised historical data are misleading due to look-ahead bias. It illustrates this with a hypothetical trading strategy based on Bitcoin exchange outflows from Binance: entering the market when the 5-day moving average of BTC balance falls below the 14-day average, and exiting on the reverse. Initial backtest results using current data appeared reasonably successful, even comparable to buy-and-hold at times. However, the author argues this is deceptive because metrics like exchange balances are often revised retroactively as new data arrives, meaning a backtest uses information that wasn't available in real-time. Re-running the identical strategy using Point-in-Time (PiT) data—which is immutable and reflects only what was known when the data was first published—yielded significantly worse performance. The PiT-based strategy failed to capture key market upswings as effectively, resulting in meaningfully lower cumulative returns. The key takeaway is that only immutable, Point-in-Time data ensures a backtest accurately replays history without look-ahead bias, and that using revised data will produce lying backtests.

insights.glassnode03/13 14:12

Your Backtest Is Lying: Why You Must Use Point-in-Time Data

insights.glassnode03/13 14:12

Your Backtest Is Lying: Why You Must Use Point-in-Time Data

This article warns against a common pitfall in backtesting trading strategies: look-ahead bias caused by using revised historical data. It illustrates this with a hypothetical Bitcoin strategy based on exchange outflows from Binance. The strategy is built on the premise that sustained outflows (when the 5-day moving average of BTC balance falls below the 14-day average) are bullish, while inflows signal a sell-off. An initial backtest using standard, revised data shows the strategy performing comparably to a simple buy-and-hold approach. However, the author argues these results are misleading because the data has been updated with information that wasn't available in real-time. This data mutation creates an unfair advantage in the backtest. To demonstrate, the test is rerun using Point-in-Time (PiT) data—an immutable, append-only record that reflects only what was known on any given day. The results are significantly worse, as the PiT-based strategy misses key profitable moves. The key takeaway is that accurate backtesting requires immutable Point-in-Time data to avoid look-ahead bias and replay history honestly.

insights.glassnode03/13 14:12

Your Backtest Is Lying: Why You Must Use Point-in-Time Data

insights.glassnode03/13 14:12

Oracle's 2026 Financial Report Analysis: Strategic Transformation of a Database Company in the AI Computing Era

Oracle's Q3 FY2026 earnings report reveals a strategic transformation from a traditional database software company to an AI infrastructure provider. Revenue reached $17.2 billion, up 22% YoY, with cloud services driving growth—OCI revenue surged 84% to $4.9 billion. The remaining performance obligation (RPO) grew 325% to $553 billion, indicating strong future contract revenue. Oracle is capitalizing on AI-driven demand for GPU computing, data centers, and high-performance networks, positioning OCI as a key platform for AI training and inference. This shift leverages its legacy database customer base while embracing cloud and AI convergence. However, the strategy requires massive investment, with planned capex of ~$50 billion in FY2026, funded by debt and equity offerings. While still a smaller player compared to AWS, Azure, and Google Cloud, Oracle aims to compete in the AI compute market by integrating data management, cloud infrastructure, and AI capabilities. The success of this bet depends on sustained AI demand, customer adoption, and ROI from investments. The report marks a pivotal moment in Oracle’s evolution toward becoming an AI infrastructure leader.

marsbit03/13 13:32

Oracle's 2026 Financial Report Analysis: Strategic Transformation of a Database Company in the AI Computing Era

marsbit03/13 13:32

From OpenSea to OpenRouter: The Business Strategy of a Serial Entrepreneur in 'Aggregation'

From OpenSea to OpenRouter: The "Aggregation" Playbook of a Serial Entrepreneur Alex Atallah, co-founder of OpenSea (the largest NFT marketplace, once valued at $13 billion), left at the NFT market's height before its collapse. He has now founded OpenRouter, an AI model aggregation platform that unifies API access to over 500 models (including Claude, GPT, and DeepSeek) and charges a 5% fee on transactions. OpenRouter simplifies developer access by providing a single interface, avoiding the complexity of dealing with multiple providers, separate registrations, and API inconsistencies. Its growth has been rapid: monthly transaction volume surged from $800,000 in October 2024 to $8 million by May 2025, with annual processed value exceeding $100 million. The company, recently valued at $500 million after a $40 million funding round led by a16z, benefits from the AI boom and tools like OpenClaw, which uses OpenRouter as a default provider. Atallah’s strategy remains consistent: he doesn’t bet on which specific asset or model will succeed but instead positions himself as the essential intermediary in fragmented market—aggregating supply and capturing value through transaction fees. His approach highlights the power of building platforms that become critical infrastructure, regardless of which trend emerges next.

比推03/13 11:34

From OpenSea to OpenRouter: The Business Strategy of a Serial Entrepreneur in 'Aggregation'

比推03/13 11:34

Buy BTC or MSTR? Analyzing the Capital Flywheel of MicroStrategy

MicroStrategy's mNAV (market cap to Bitcoin holdings ratio) has compressed to near parity, sparking debate about whether the premium will re-expand. The core argument centers on the company's ATM equity issuance strategy. Critics view it as shareholder dilution, while supporters see it as rational Bitcoin accumulation. However, both miss the deeper strategic shift: MicroStrategy is building a layered capital structure that operates differently across mNAV regimes. At ~1x mNAV (current phase), equity issuance is used to buy Bitcoin directly, justified by long-term undervaluation. In high mNAV regimes (3-4x+), equity becomes a tool to repay debt from preferred securities, not just acquire Bitcoin. The introduction of preferred stock attracts yield-seeking investors, creating a continuous funding source for Bitcoin purchases but also dividend obligations. The ATM acts as a proactive de-leveraging tool, building equity ahead of future payment needs. mNAV expansion may return not only from Bitcoin price appreciation but also from the market valuing MicroStrategy as a scalable Bitcoin capital markets platform. The company is evolving from a Bitcoin treasury into a financial engine with distinct investor segments: yield investors in preferred securities and growth investors in equity. This could form a self-reinforcing "capital flywheel": preferred demand funds Bitcoin buys, equity demand values platform growth, and Bitcoin appreciation strengthens the balance sheet. The discussion may shift from *if* mNAV premium returns to *how large* this financial platform can become.

marsbit03/13 11:08

Buy BTC or MSTR? Analyzing the Capital Flywheel of MicroStrategy

marsbit03/13 11:08

Why Is OpenAI Playing Catch-Up with Claude Code?

In the rapidly evolving field of AI coding assistants, OpenAI, which once led the generative AI wave with ChatGPT, has found itself in the unexpected position of playing catch-up against Anthropic’s Claude Code. Through interviews with OpenAI executives, engineers, and developers, the article reveals that OpenAI’s early lead in AI programming—via its Codex project—was deprioritized as the company shifted resources toward ChatGPT and multimodal models. This strategic shift allowed Anthropic, founded by former OpenAI members, to focus intensely on coding capabilities, leading to the successful launch of Claude Code. OpenAI later reorganized internal teams and accelerated development of its AI programming products, such as the reasoning-based model o1 and later o3. Despite these efforts, Claude Code gained significant traction, especially after integration with tools like Cursor, which OpenAI attempted to acquire unsuccessfully. A proposed acquisition of Windsurf also failed due to tensions with Microsoft, OpenAI’s major partner. By late 2025 and early 2026, OpenAI’s Codex began narrowing the gap, with user growth rising to about 40% of Claude Code’s usage. The competition reflects broader industry trends where AI agents are increasingly automating cognitive work, raising questions about the future of software development and white-collar jobs. Despite progress, concerns around safety and societal impact remain as AI coding tools become more powerful and pervasive.

marsbit03/13 07:39

Why Is OpenAI Playing Catch-Up with Claude Code?

marsbit03/13 07:39

a16z: To Crypto Founders, Enterprises Don't Buy the Best Technology

a16z addresses crypto founders building for enterprises, arguing that companies don't buy the "best" technology—they choose the least disruptive upgrade path. Enterprise adoption is often hindered not by a lack of vision or education, but by product misalignment with organizational realities. Decision-makers prioritize minimizing downside risk over maximizing gains, as failures carry severe personal and institutional consequences. The real "buyer" is often a coalition of stakeholders (legal, compliance, risk, finance) with veto power, not just technical teams. Third-party consultants and system integrators act as gatekeepers, legitimizing new tech through familiar frameworks. Founders must tailor pitches to specific enterprise constraints, avoid "rip-and-replace" narratives, and design products that integrate into existing workflows. Successful adoption requires professionalism, predictability, and incremental integration rather than ideological purity. Examples like Uniswap’s collaboration with BlackRock and LayerZero’s Zero blockchain illustrate strategies that minimize risk while extending existing systems. The key is to become the "hedge" option in enterprise experiments, focusing on secure, auditable, and governable solutions.

marsbit03/13 00:37

a16z: To Crypto Founders, Enterprises Don't Buy the Best Technology

marsbit03/13 00:37

Strategy’s STRC Funds 7,000 Bitcoin Purchase as Experts Warn High-Yield Risks

Strategy has purchased 7,000 Bitcoin this week using funds raised through its high-yield preferred stock, STRC, which offers investors an 11.5% yield with monthly payouts. The company has bought over 11,000 BTC in the past two weeks and accumulated roughly 34,000 BTC since STRC’s launch. STRC is a perpetual preferred stock designed to trade near its $100 face value, though it has occasionally dipped below that level, prompting Strategy to raise dividends to attract buyers. Experts, including Two Prime CEO Alexander Blume, caution that high yields like STRC’s come with elevated risks, especially given Bitcoin’s volatility. Despite the risks, strong investor demand continues to fund Strategy’s rapid Bitcoin acquisitions.

TheNewsCrypto03/12 14:24

Strategy’s STRC Funds 7,000 Bitcoin Purchase as Experts Warn High-Yield Risks

TheNewsCrypto03/12 14:24

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