Unveiling the Methodology Behind Top Researchers' Crypto Macro Analysis
TechFlow Selected TechFlow Selected
Unveiling the Methodology Behind Top Researchers' Crypto Macro Analysis
The market rally will be a slow process, and the use of derivatives and leverage is unlikely to drive the market in the short term.
Navigating Web3 tides with focused insightsTopic: Unveiling the Macroeconomic Analysis Methodology of Top Crypto Researchers
Guests:
Zheng @ZnQ_626
-
Founder, LUCIDA
-
Champion, Bgain Digital Asset Trading League Season 1 (Hybrid Strategy Group), 2019;
-
Runner-up in April, Champion in May, and Season Third Place, TokenInsight Global Asset Quantitative Competition (Composite Strategy Group), 2020;
-
Season Third Place, TokenInsight x KuCoin Global Asset Quantitative Competition (Composite Strategy Group), 2021;
Vivienna @VV_watch
-
Research Partner, BuilderRocket Accelerator
-
Entered the industry in 2017
-
Former investment researcher at Foxconn's blockchain investment fund
-
Former DeFi researcher at Huobi
-
Passionate about macro research
HighFreedom @highfree2028
-
Entered the industry in 2016
-
Background in computer science & finance
-
Currently a securities firm researcher
-
Specializes in cycle timing based on USD liquidity and macro analysis
Albert @assassinaden
-
Quantitative private equity fund manager
-
Former quant researcher in foreign exchange markets, focusing on statistical arbitrage and relative value strategies
-
Expertise in non-delta strategies and macro research
-
Emphasizes resilience across market cycles and ability to navigate bull and bear markets
Unpacking the Macro Analysis Framework
Zheng@LUCIDA:
As the crypto industry evolves, its correlation with the broader macroeconomy continues to strengthen, making macro analysis an essential component of market understanding. Today, we'll dive into this topic. Let's start with the first question: could each of you share your framework and methodology for analyzing macroeconomic conditions, along with the underlying logic?
HighFreedom:
Macro analysis for crypto consists of two parts: off-chain (non-crypto-native) macro, and on-chain macro (crypto-native, with Bitcoin chain data as the core).
For off-chain macro, my analytical framework resembles an inverted triangle with three layers.
The first layer includes various data points such as employment, GDP, inflation, and PCE.
The second layer involves summarizing these data. Although the raw data may appear scattered, they can ultimately be categorized into two types: indicators of economic strength and measures of inflation levels. This is because the Federal Reserve (responsible for monetary policy like interest rate changes and balance sheet adjustments) and the Treasury Department (responsible for fiscal policy like government spending) aim to maximize employment while maintaining price stability—essentially ensuring strong economic performance without runaway inflation.
The third layer focuses on the composition and future expectations of U.S. dollar liquidity. Key components include bank reserves, the Fed’s balance sheet, Treasury account balances, and overnight reverse repo (RRP) levels. Monitoring changes in these factors—alongside the Treasury’s quarterly refunding announcements—helps assess current and future liquidity conditions.
Moreover, employment and inflation are interrelated. For example, when deciding whether to cut rates or halt quantitative tightening, the Fed weighs both metrics. Therefore, a holistic analysis is crucial for accurately forecasting shifts in dollar liquidity. By combining quarterly refunding updates with key economic indicators, we gain deeper insight into the actions of the Fed and Treasury, enabling better predictions of future liquidity trends.
These constitute off-chain factors—elements external to crypto, such as macroeconomic indicators, policy shifts, and investor sentiment. Though not native to the crypto space, their impact is significant as they shape the broader market environment and investor expectations.
On-chain macro refers to internal crypto market dynamics, primarily derived from Bitcoin’s on-chain data. This includes metrics such as long-term vs. short-term holder supply, profitability levels, and other behavioral signals. Analyzing this data allows us to understand investor behavior and market momentum within the crypto ecosystem.
Each approach offers distinct insights: off-chain analysis provides the big-picture context, while on-chain analysis reveals granular market rhythms. Combining both perspectives creates a comprehensive macro framework essential for effective crypto market analysis.
Albert:
I’d like to add that beyond Fed and Treasury policies, we should also consider micro-level economic factors like bank deposits. For instance, during high-interest-rate environments, retail investors might prefer parking money in savings accounts. However, this can affect overall market liquidity. Historically, after banking crises in the 80s–90s, deposit growth slowed significantly. Similarly, post-SVB collapse in 2023, we observed declining bank deposits alongside recovering equities and other asset classes. Additionally, international flows—such as Japanese yen carry trades involving U.S. assets—can inject supplementary liquidity into global markets.
In high-rate cycles, individuals tend to favor interest-bearing deposits. But when banks face solvency risks, capital often migrates toward equities, short-term Treasuries (~5% yield today), ETFs, derivatives, or stocks seeking higher returns.
When confidence in banks wanes—seen in the 90s and again in 2019—investors reallocate funds to relatively safer alternatives. Money market fund inflows have surged since 2023, reaching 20-year highs, reflecting heightened demand for liquidity and safety.
Ideally, we'd incorporate all potential influences—including overseas liquidity—but practical constraints limit full integration. While Japanese and European carry trades do matter, their effects are hard to quantify. Thus, we typically prioritize U.S.-centric analysis, treating offshore developments as secondary—even though offshore dollar markets cannot be ignored entirely.
Vivienna
On Twitter, I published an article exploring how U.S. liquidity impacts crypto prices. My focus was domestic factors, excluding offshore dollar markets due to data limitations. When analyzing Bitcoin prices, I categorize drivers into three groups:
First, observable indices: federal funds rate, Treasury yields, DXY, gold prices. These form the basis of market expectations but don’t linearly correlate with risk assets. For example, rate hikes usually tighten liquidity and hurt risk assets, while cuts have the opposite effect. However, transmission mechanisms—affected by economic, financial, and sentiment cycles—delay immediate impacts.
Second, liquidity indicators: Fed balance sheet size, RRP usage, Treasury General Account (TGA) balance. These directly influence dollar liquidity and thus affect growth-risk assets like Bitcoin. Fed QE, RRP drawdowns, or TGA depletion boost liquidity and benefit risk assets.
Third, sentiment indicators: dot plots, Fed speeches, labor reports, CPI prints. These sway short-term expectations and trading cycles. Traders should focus on expectation shifts—not just headline numbers.
How Macro Factors Influence the Crypto Market
Zheng@LUCIDA:
So how do your macro frameworks translate into actual crypto market applications? In other words, how do these models guide your trading and help you generate profits?
HighFreedom:
I see profit generation through three lenses:
First, earning directional returns: identifying major market trends and holding spot positions patiently without frequent trading.
Second, capturing volatility returns: using quant strategies to exploit price swings regardless of trend direction.
Third, harvesting liquidity premiums: lending capital during bull markets to traders in need, earning high interest.
I believe macro factors influence crypto mainly through two channels: liquidity and penetration rate. Liquidity determines the total available capital, while penetration reflects the proportion allocated to crypto like Bitcoin.
Operationally, I go all-in on spot during bull runs—especially on blue-chip cryptos like BTC—as per the first method above.
I occasionally use part of my portfolio for coin-denominated longs but avoid frequent multi-directional trading during bull phases. The key is identifying market tops and bottoms by synthesizing diverse signals: miner costs, market热度, borrowing rates, funding fees, etc.
In H2 2021, BTC peaked after Nasdaq and dollar liquidity peaks—a pattern suggesting that when liquidity peaks, risk assets should prepare to exit. Hence, I closely monitor liquidity metrics to gauge proximity to market extremes.
I advocate information orthogonality—gathering inputs from multiple independent sources—to build robust market views. This reduces operational errors. I also adapt risk management dynamically to protect capital amid volatility.
Vivienna:
I recommended Victor Sperandeo’s book *The Principles of Professional Speculation* on Twitter. He outlines two core principles: (1) market movements reflect fundamental economic forces shaped by political systems and policies; (2) participants’ psychology dictates price movement patterns and timing.
Macro analysis must address both. First, grasp fundamentals: economic indicators, production-consumption cycles, saving-investment behaviors, tech innovation trajectories. Second, anticipate shifts in investor psychology—which offers stronger trading signals. Critics often dismiss macro analysis because practitioners overemphasize data while neglecting evolving expectations. Successful trading requires interpreting not only present realities but also changing narratives and market博弈.
As Soros noted, economic history is built on falsehoods, not truths. Profits come from identifying mispriced trends, riding them, and exiting before the illusion collapses. This aligns with the earlier point: to spot distortions, one must first understand what’s correct. For example, if governments apply contractionary policies during recessions, or try to stimulate via rate cuts without understanding transmission lags, you won’t foresee consequences—or know when to exit ahead of consensus.
Albert:
Let me break down how macro frameworks influence crypto markets and our trading strategies:
Since 2020, we’ve discussed a persistent theory—the liquidity chain. Based on risk profiles, assets like commodities, FX, equities follow a hierarchical order. At the top is cash—the foundation of all assets—with near-zero risk (except inflation). If even cash becomes risky, it implies systemic reset.
Next comes bonds, especially sovereign debt—low-risk fixed income. Then corporate bonds and equities offering higher returns. Commodities rank lower due to greater volatility. At the bottom sits crypto—highest risk and volatility.
This explains HighFreedom’s observation about BTC peaking after Nasdaq and liquidity peaks.
When liquidity expands, it flows from FX → bonds → equities → commodities → finally crypto. The unwinding process reverses this sequence. Understanding this flow is critical.
We use this to time trades. Watching for early signs of tightening/expansion allows us to anticipate reactions. We track interbank rates and bond futures—they react first to policy shifts. Then we analyze options markets, where implied volatility reveals forward-looking sentiment.
Our strategy hinges on these macro views. During hiking cycles, when sentiment turns bearish, we buy cheap puts when volatility is low. We adjust option portfolios based on shifting expectations to profit from volatility mean reversion.
We rely on volatility convergence, particularly near-term vol. Long-dated volatility may stay elevated longer. So we’re buyers in the far end, using calendar spreads to capture value differences between tenors. Our portfolio earns from relative pricing inefficiencies across maturities.
Bitcoin’s Position Among Asset Classes
Zheng@LUCIDA:
The next question is lighter—and we’ve already touched on it indirectly: where does Bitcoin stand among traditional assets? I recall in early 2019, amid geopolitical tensions, gold surged and so did Bitcoin. Many then viewed BTC as a safe haven.
However, following the 2020–2021 bull run and 2022’s downturn, the consensus shifted: Bitcoin is now seen as riskier than conventional risk assets. Do you agree with this classification, or do you have alternative descriptions?
HighFreedom:
That characterization seems accurate. Medium-term, Bitcoin is undoubtedly a higher-risk asset. But long-term, I’m confident it can evolve into a safe haven. Right now, we’re in transition. What defines a safe-haven asset? Let’s discuss possible prerequisites:
First, large market cap: sufficient scale to absorb institutional inflows without distortion.
Second, reduced volatility: though historically volatile, BTC’s swings have narrowed—sometimes dipping below gold’s.
Third, rational, stable participants: as adoption shifts from retail/crypto natives to traditional institutions, market behavior stabilizes.
Once met, Bitcoin could mature into a true safe haven—large, liquid, low-volatility—like gold. Even major shocks would cause only minor ripples.
Vivienna:
"Digital gold" is a widely accepted analogy. Bitcoin shares gold’s scarcity (fixed supply) and functions as store of value and medium of exchange.
But gold pricing is complex. During wars or crises, its safe-haven appeal shines. If base liquidity remains ample but fear spikes, BTC may track gold higher—driven by sentiment alone.
Yet if base liquidity is tight—e.g., during recession fears—even intense fear fails to lift volumes. Here, structural liquidity sets the floor, and BTC behaves more like a risk asset.
Thus, BTC-gold correlation depends on prevailing liquidity and perception of BTC’s nature. Most of the time, BTC correlates strongly with U.S. equities. It leads on the downside during contraction/deleveraging and accelerates faster on recovery/leveraging.
Global asset managers typically cap gold exposure at 5%, given unpredictable drivers. While gold has industrial uses, it’s largely driven by speculation and emotion—lacking solid fundamentals. That makes it hard to justify allocations to LPs based solely on recession forecasts—too subjective.
Bitcoin faces similar challenges convincing traditional allocators. Like gold, its inflation hedge property manifests over long horizons. With rising mining difficulty, BTC’s stature may grow further.
If more traditional asset managers enter, BTC allocations could converge toward gold-like levels.
Albert:
From a macro perspective, both gold and Bitcoin possess dual identities—they can act as either risk or safe-haven assets. This seems contradictory but follows logical underpinnings.
During crises, both serve as capital sanctuaries. Amid war or turmoil, cross-border capital seeks portable, secure stores—gold and Bitcoin. This drives sharp price rallies.
But in stable times, their roles diverge. Due to extreme volatility, Bitcoin acts more like a risk asset—its price moves closely with equities, amplified by leverage and speculative participation.
In calm periods, investors favor stability. They allocate cautiously to proven assets like gold, usually capped at 5% of portfolios due to its long-standing reliability.
Additionally, both assets are swayed by expectations. In flush liquidity regimes, investors chase yield; during tightening, they retreat to havens.
Ultimately, Bitcoin’s and gold’s status as safe havens depends on macro regime and cycle phase. Under certain conditions, they exhibit defensive traits; otherwise, they behave as high-beta instruments.
What Are the Levers of Macro Analysis?
Zheng@LUCIDA:
Now let’s discuss data sources: what datasets do you rely on for macro analysis? Do you collect your own data? Any unconventional tools or proprietary sources worth sharing?
HighFreedom:
I built a dashboard on TradingView using custom scripts to monitor macro liquidity continuously. The data itself isn’t exclusive—it’s sourced from Fed and Treasury websites—but consolidating it into one interface enables efficient tracking.
I also follow several analysts on Twitter, especially a Taiwanese commentator who aggregates unique metrics like exchange-specific lending rates. These reveal positioning differences between whales and retail, including lead-lag relationships. I find them valuable, though I haven’t fully integrated his tools yet.
I’m actively searching for daily net issuance data on Treasuries—especially short-, mid-, and long-dated bonds (new issuance minus redemptions). This is vital for assessing current and near-term liquidity. Currently, I manually download and process this from TreasuryDirect.gov. If anyone knows better sources, please reach out!
Albert:
Let me add some data sources we watch. Since I focus on risk commodities, I use Spotgamma Menthor Q, which offers comprehensive data on U.S. equities, bonds, and commodity options.
For U.S. equities, GR provides affordable real-time data. For deeper insights—say on gold or interbank markets—we often depend on proprietary or insider resources.
For crypto, Amberdata Derivatives is highly recommended—especially for options data, with clear advantages. They also provide real-time feeds from CME and other exchanges.
Exchange data matters too—especially venues with high institutional volume. Deribit, for example, sees ~80% institutional and 20% pro-retail activity. Their order flow reflects institutional sentiment and heavily influences pricing.
Exchanges like Bitfinex resemble the interbank market for crypto—their short-term lending rates approximate the risk-free rate, crucial for calculating risk premia.
Compliant platforms like Coinbase—and particularly their whale trade and dark pool data—can also move markets.
Overall, while vast data exists, ultimate success depends on risk discipline. Our goal: avoid losses or earn small gains most of the time, then capture outsized returns in rare favorable setups.
Review and Outlook for This Cycle
Zheng@LUCIDA:
Finally, let’s turn to forward-looking views.
I’ll start: consensus expects Fed rate cuts in late 2024 or 2025 to unleash massive liquidity, combined with Bitcoin halving catalysts—many anticipate a rerun of 2021’s bull market. Yet I’m deeply skeptical of this widespread optimism, as historically, extreme consensus often precedes major risks.
Even public funds and institutional investors—though long-term holders—adjust allocations based on market conditions. They won’t blindly “chase tops.”
HighFreedom:
My view aligns closely. The main uptrend began last November, accelerating after January’s spot ETF launch, bringing notable volatility. Q1 saw rising liquidity and penetration—but mostly retail-driven. Institutions haven’t entered en masse. For example, ~80–85% of ETF inflows came from retail. Q2 saw liquidity dip and penetration stall. For Q3–Q4, I hope liquidity stabilizes while penetration improves with deeper institutional involvement.
Rate hikes/cuts don’t immediately alter liquidity—they shift expectations about future liquidity. My concern: will we see another 2021-style combo of loose fiscal and monetary policy? Unlikely. So I remain cautious about overly optimistic outlooks.
Near-term, little may change. Market expects 4–5 rate cuts over the next 15 months—providing stable expectations. But actual liquidity release will be gradual, not explosive. Without severe recession or crisis, another broad easing regime is improbable.
Absent deep downturn, this rate-cut cycle may be “asymmetric.” Past hikes and cuts were symmetric—e.g., ~1 year to hike from 0–0.25% to 5–5.25% (Mar 2022–May 2023), followed by ~1 year to cut back. This time, cuts may proceed slowly and steadily.
Vivienna:
My conclusion is simple and aligned: from mid-2024 through year-end, liquidity may remain challenging. Even if one rate cut occurs, it boosts sentiment but doesn’t transform reality.
The economy isn’t in recession; equities may keep rising. Daily life feels unaffected—deposits and dividends still support household spending. But if inflation persists, stagflation trade ideas may emerge. Should high rates continue into 2025—or require further hikes—without fiscal easing, liquidity could tighten further. For liquidity-dependent assets like Bitcoin, that’s clearly negative.
As for hopes of mass institutional entry—this feels aspirational, not realistic. With weak base liquidity, limited crypto understanding, and high volatility, institutions won’t aggressively accumulate. Such expectations may be too idealistic; reality likely differs.
Albert:
Current short-term sentiment leans bearish, especially toward Bitcoin. Despite increased volatility around July options expiry, long-term asset allocation trends could push prices up. Still, any rally likely hinges on two factors: significant institutional allocation and rising investor sentiment. But sentiment-driven rallies may prove fragile—high funding costs and volatility are unsustainable over time.
I expect a slow grind higher, as market participation builds gradually. Derivatives and leverage alone won’t fuel rapid surges. In this context, market maker behavior may become pivotal. Macro forces may primarily influence dealers’ hedging needs rather than directly shaping prices—making markets appear less predictable and increasing difficulty for CTA-type strategies.
Overall, no imminent crash or parabolic rise. Instead, expect a prolonged, gradual evolution. Liquidity won’t swing wildly unless a serious recession hits. Investors should stay cautious, monitoring institutional flows and sentiment shifts carefully.
Join TechFlow official community to stay tuned
Telegram:https://t.me/TechFlowDaily
X (Twitter):https://x.com/TechFlowPost
X (Twitter) EN:https://x.com/BlockFlow_News
@lucidafund
