Has this cycle peaked based on on-chain data?
TechFlow Selected TechFlow Selected
Has this cycle peaked based on on-chain data?
Colin believes that Bitcoin currently has the conditions for forming a top.
Navigating Web3 tides with focused insightsHost: Alex, Research Partner at Mint Ventures
Guest: Colin, Independent Trader & On-chain Data Researcher
Recording Date: 2025.2.15
Hello everyone, welcome to WEB3 Mint To Be, brought to you by Mint Ventures. Here, we continuously question and deeply reflect, aiming to clarify facts, understand realities, and seek consensus within the WEB3 world. We aim to unravel the logic behind trending topics, provide insights beyond surface-level events, and introduce diverse perspectives.
Disclaimer: The views discussed in this episode do not represent the opinions of the institutions affiliated with the guests. Mentioned projects should not be taken as investment advice.
Alex: Today's episode is a bit special. Previously, we've discussed specific sectors or projects, and exchanged cyclical narratives—like our earlier talk on memes. But today, we're focusing on on-chain data analysis, particularly Bitcoin's on-chain data. We’ll closely examine its mechanisms, key metrics, and learn its methodology. During this episode, we'll reference many metric concepts—listed at the beginning of the text version for clarity.
Data metrics and concepts mentioned in this podcast:
Glassnode: A commonly used on-chain data analytics platform requiring payment.
Realized Price: A weighted calculation based on the last price at which each Bitcoin moved on-chain. It reflects Bitcoin’s historical cost basis and is useful for assessing the market’s overall profit/loss status.
URPD (Unrealized Profit Distribution): Used to observe the price distribution of BTC holdings.
RUP (Relative Unrealized Profit): Measures the ratio of total unrealized profit across all Bitcoin holders to the current market cap.
Cointime True Market Mean Price: An on-chain average price metric from the Cointime Economics framework. By incorporating "time weighting," it aims to more accurately assess Bitcoin’s long-term value. Compared to BTC’s current market price or Realized Price, the True Market Mean Price under the Cointime system also factors in time, making it suitable for evaluating BTC over macro cycles.
Shiller ECY: A valuation metric proposed by Nobel laureate Robert Shiller, evolved from the CAPE (Cyclically Adjusted Price-to-Earnings) ratio. It evaluates long-term stock market return potential and compares equities’ attractiveness against other assets, factoring in interest rate environments.
The Motivation Behind Learning On-Chain Analysis
Alex: Our guest today is independent trader and on-chain data researcher Colin. Colin, please say hello to our listeners.
Colin: Hi everyone, thanks Alex for having me. I was actually a bit surprised when invited—I'm just an ordinary retail trader with no notable title, quietly doing my own trading. My name is Colin, and I run a Twitter account called Mr. Beggar. I mainly share tutorials on on-chain data, analyses of current market conditions, and some trading concepts. I’d describe myself in three ways: First, an event-driven trader—I often think about strategies based on market events. Second, an on-chain data analyst—that’s what I mostly share on Twitter. Third, somewhat conservatively, I consider myself an index investor—I allocate part of my funds to major U.S. stocks to reduce overall portfolio volatility through beta exposure while maintaining defensive positioning. That’s roughly how I see myself.
Alex: Thanks for the introduction, Colin. I invited you because I found your on-chain analysis of Bitcoin on Twitter very insightful—a topic we haven’t covered much before and one where I personally have gaps. After reading your series of articles, I found them logical and substantive, so I reached out. Let me remind everyone: both my views and those of our guest today are highly subjective. Information and opinions may change over time, and different people may interpret the same data differently. This content is not investment advice. Some data platforms will be mentioned purely as personal usage examples, not commercial endorsements. This show receives no sponsorship. Let’s dive into on-chain data analysis for crypto assets. You’re a trader—how did you get started learning on-chain data analysis?
Colin: I’d break this into two parts. First, anyone entering or already in financial markets—including me—primarily wants to make money to improve their lives. So my principle has always been simple: if something helps me profit, I learn it. This improves the expected value of my overall trading system. Simply put: learn what makes money. Second, I stumbled into on-chain data by accident. About six or seven years ago, I knew nothing and was just exploring randomly. When I came across interesting research theories, I wanted to learn them. I accidentally discovered the field of Bitcoin on-chain data analysis and began studying and researching it. Later, I combined knowledge from other areas—mainly quantitative trading development—and applied it to on-chain data to build trading models, eventually integrating them into my own system.
Alex: Got it. How long have you been systematically studying and researching on-chain data since you first started?
Colin: It’s hard to define. Honestly, I’ve never truly studied it systematically. From the beginning until now, I’ve faced a problem: I’ve never seen any comprehensive teaching materials. Years ago, I noticed this field but didn’t dig deep—just read a few articles. Later, I revisited it, saw deeper content, was focused on other things at the time, but thought it was interesting, so kept going. There was no structured study—more like piecing things together bit by bit.
Alex: Understood. How long has it been since you started applying on-chain data to actual investing?
Colin: That boundary is fuzzy. Close to two Bitcoin cycles… though maybe not exactly two, depending on whether you start counting from bull or bear markets. I started around 2019–2020, but didn’t apply it practically then because I wasn’t confident yet—I was still learning.
The Value and Principles of On-Chain Data Analysis
Alex: I see. We’ll discuss many specific on-chain data concepts and indices. Which on-chain data platforms do you use regularly?
Colin: I mainly use one site: Glassnode. Briefly, it’s paid. Two tiers: a professional version costing over $800 per month, and another I forget—around $30–40 monthly. There’s a free version, but it offers very limited information. Besides Glassnode, there are others, but I chose it because it best matched my needs during initial exploration.
Alex: Understood. After seeing your content, I signed up for Glassnode myself and became a paying member. Their data is indeed rich and timely. Next question: as a trader focused on practical investment impact, what’s the core value of on-chain data analysis in your investing? What’s the underlying principle? Please explain.
Colin: Sure. First, the value and principles—I’ll combine these because they’re quite straightforward. Traditional financial markets—stocks, futures, bonds, options, real estate, commodities—differ fundamentally from Bitcoin due to blockchain technology. The most frequently cited value of this technology is transparency. All Bitcoin transfer information is public and verifiable. For example, you can directly see 300 BTC moving from one address to another via a blockchain explorer. While I don’t know who owns that address, it doesn’t matter—no single entity can influence Bitcoin’s overall price trend. Normally, when analyzing on-chain data, we look at the market as a whole—the trends, collective consensus, and behavior. Even without knowing individual addresses, aggregating all addresses allows us to analyze flow of holdings—whether profits are being taken, stop-losses triggered, profit/loss status, preferred buy-in price levels, etc. These data points are visible. This, I believe, is the biggest advantage of Bitcoin on-chain analysis over traditional markets—something impossible elsewhere.
Alex: Exactly. Just like fundamental analysis in crypto investing—similar to stocks or other products. As you said, on-chain data is transparent and observable by all. If other professional investors are using it and you’re not, you’re missing a crucial tool in your investment arsenal.
Challenges in On-Chain Data Analysis
Alex: In your practical experience with on-chain data analysis, what are the main difficulties and challenges?
Colin: Great question—I’ll split my answer into two parts. The first, easier challenge is foundational knowledge. For most people—including my past self—it’s hard to find truly systematic teaching. I haven’t checked offline for paid courses, but even if available, I probably wouldn’t buy them. Throughout my trading journey, I rarely pay for courses. I’ve never taken any formal course, so everything had to be self-discovered. There are many types of on-chain data. My approach during research was to fully understand the calculation methods and principles behind every indicator I encountered. This is extremely time-consuming—you see an indicator with a formula, but I try to reverse-engineer the reasoning: why design it this way? Once I grasp each indicator, I move to filtering. Anyone with quant strategy experience knows many indicators are highly correlated. High correlation creates noise and risks overinterpretation. For example, suppose I have a top-exit system with 10 signals (No.1 to No.10). If signals 1–4 are too correlated, a certain Bitcoin price behavior might trigger all four simultaneously—problematic. If 4 out of 10 lights turn on, you might think it’s dangerous, but it’s inevitable due to high correlation. Without cutting based on correlation, this happens easily. After understanding each indicator’s principle, I can tell just from the formula whether correlation is high, then cut accordingly—say, five are highly correlated, so I filter down to one or two.
This first part is manageable—not the main difficulty. The real challenge comes next: how do you prove your on-chain data-based viewpoint is correct—to others or yourself? Let me use a crude but clear example. I once tweeted that in quant trading, you shouldn’t “carve the boat to mark the sword” (i.e., rely on outdated methods). Suppose I have a strange trading strategy: go long if my dog barks twice and it’s raining outside. Backtesting 1,000 times shows a 95% win rate, outperforming the market. Would anyone dare use it? It sounds absurd—dog barking and rain leading to high win rates. This is called survivorship bias. Without logical support, even with sufficient samples, such a strategy isn’t reliable. Some might argue: “But backtest shows 95% win rate!” Survivorship bias means: if 1,024 people flip a coin 10 times, one will likely get 10 heads—1/1024 chance. That person becomes the “survivor,” while the other 1,023 failures go unnoticed. Returning to Alex’s question: the main difficulty lies in sample size. Bitcoin history shows only four clear cycle peaks—2013, 2017, and two in 2021—far too few. With insufficient samples, carving the boat—assuming 2024 must mirror 2013 or 2017—is unreasonable. Without adding logical reasoning, theories easily fail. The core issue: with so few historical samples, I must use deductive rather than purely inductive reasoning. After deducing a conclusion, I wait for time to prove if I’m right or wrong. If right, my logic may be sound; if wrong, I revise it. Pure induction—what most retail traders love—assumes similarity between past and present patterns implies future price surges or crashes, which is flawed. Going back to my opening point: the biggest challenge is proving my deductions correct—to others or myself. So I constantly refine my logic and assumptions, checking for flaws. Bitcoin is too young—on-chain analysis will always face insufficient samples. Thus, research must rely solely on deduction—logical inference—then wait for time to validate judgments. This is currently my biggest hurdle.
Key On-Chain Metrics to Watch
Alex: Understood—it’s very enlightening. Earlier, I shared my confusion when starting with Glassnode: with so many indicators, which should guide trading? Many have different calculation logics. I later preferred indicators whose logic made sense—like yours. I focus on whether the underlying logic is sound, not just backtested accuracy. As you said, deductive reasoning carries more weight in selecting primary indicators. Based on your experience, which on-chain metrics do you consistently monitor or consider important in daily Bitcoin analysis?
Colin: As mentioned, I filter by low correlation. I watch many on-chain metrics, so I’ll introduce three dimensions—ideally uncorrelated—for clarity.
First, I consistently prioritize URPD. It’s a chart with price on the x-axis and BTC quantity on the y-axis. If at $90K there’s a tall bar, it indicates massive accumulation at that price—holders’ cost basis. The bar height shows how many BTC were bought there. Two key observations: First, basic holding structure. If current price is ~$87K, and above it sits a huge pile—say 4.4 million BTC per last week’s data—we know heavy turnover occurred there—many bought in. Since buyers exist, consensus likely formed. Such dense zones attract prices—price tends to oscillate here. Drops often rebound quickly; rallies push higher, but lower-tier holders now in profit may sell short-term, pulling price back. Hence, oscillation is common. Second, observing distribution phases. “Distribution” means early-bear-market buyers selling cheap holdings at higher prices. I define this process as distribution. Suppose at $100K, 300K new positions appear, while 300K positions bought at $20K disappear—we infer $20K-cost holders sold ~300K BTC averaging $100K. We track whether low-cost holdings shift dramatically. Now at $90K–$100K, such shifts mean reduction, not increase—so we monitor distribution pace. This is my top-priority metric.
Second, RUP—Relative Unrealized Profit. Its sole purpose: gauge overall market profitability—how much profit holders have at current BTC price. Is it minimal, moderate, or substantial? Principle is simple: blockchain transparency lets us trace most holdings’ purchase prices. Compare purchase vs. current price. E.g., bought at $50K, now $100K—this BTC is profitable. Calculate total unrealized P&L, sum all floating gains/losses, standardize against current market cap to get a number between 0 and 1. Easy to interpret: if RUP is high—say 0.7, 0.68, 0.75—overall profit is high, potentially triggering profit-taking. Thus, high RUP often signals caution.
Third, fair market valuation models. Many Bitcoin valuation models exist, each estimating fair value—what BTC “should” be worth. Among them, I find Cointime Price most robust. I’ve never seen a Chinese translation. Briefly, this concept emerged from a joint paper by Cathie Wood’s ARK Invest and Glassnode. Its key innovation: time-weighting in calculating fair value. Two main uses: First, buying the dip. In bear markets, if price falls below Cointime Price valuation—essentially BTC’s intrinsic value—historical backtests and logic suggest excellent buying opportunities. Every time price breached this level historically, it marked strong bottoms. Second, avoiding tops: monitor distance between current price and Cointime Price. Excessive deviation may signal market approaching peak. So, these three dimensions—holding structure, profit state, fair valuation—are the metrics and angles I share.
Handling Conflicting Data Signals
Alex: Clear explanation. Users might ask: these three metrics cover different aspects, with low correlation as you said—good for combined reference. But what if they conflict? Say Metric 1 suggests active distribution, while Metrics 2 and 3 imply price isn’t near a top yet. How do you handle conflicting data?
Colin: This isn’t unique to on-chain analysis—technical or macro analysis faces similar issues. My approach is simple: assign different weights to different layers. I prioritize holding structure—distribution progress. Profit status helps verify whether low-cost holders (e.g., those buying at $15K–$16K in bear markets) have finished distributing. A notable pattern: in every Bitcoin cycle, two large-scale distributions occur. For 2024, the clearest case was March–April last year—profit data clearly showed massive distribution. But if I see massive distribution, my next question is: are they done? All judgment hinges on this. If distribution is massive but incomplete, I stay confident the bull market continues. Last year, when BTC surged past $70K, I was excited—bull market finally arrived, new highs. Then it oscillated sideways for over half a year. Observing data then, I couldn’t conclude a bottom—only first distribution phase. Many metrics—like short-term holder average cost—didn’t match true bull-market-end patterns. So I stayed calm. When data conflicts—saying “distribution is happening”—do I exit? Not necessarily. The core question remains: is distribution complete? Using this as a filter for each metric makes resolving conflicts straightforward—even with large-scale distribution, judging completion suffices.
Alex: Let’s set a scenario: suppose URPD shows two distribution phases—like you described: one in March–April last year, another peak December–January. Assume distribution occurred, but the other two valuation metrics aren’t high. In such cases, you mentioned assigning different weights. Do you reduce position proportionally based on weights, or unify all three indicators, ignoring weights, making one or two key decisions at critical moments?
Colin: I prefer the former. No one knows the exact top—no one can perfectly time the peak. If someone could, they’d be legendary—I’d want to meet them. Tops, I believe, form gradually. On a daily chart it looks fast, but living through it—say at $69K, the prior cycle top—you don’t feel it’s the top. We can only judge if top-forming conditions exist. Given that, I stage exits. When I sense top conditions maturing, any indicator giving a warning—like the RUP divergence I shared on Twitter—triggers partial profit-taking. The reduction amount must be predefined—can’t arbitrarily decide when divergence appears. I plan ahead: e.g., divide position into four parts. Upon certain warning signs, sell one part; second sign, another part. Also plan a final exit: last portion must exit regardless. E.g., bear market confirmed over, but no other warnings—need an extreme “last escape” strategy as filter.
Alex: Understood—gradual exit or position reduction triggered by different warnings.
Colin: Yes.
Assessment of BTC’s Current Cycle Position and Basis
Alex: Understood. I’ve followed your Twitter recently—you apply these metrics and their underlying philosophies in your trading. Currently, BTC has oscillated between $91K–$109K for nearly three months. Market views on this range are highly divided. Unlike December–January, when optimism ran high—BTC won’t stop at $150K, $200K, even $300K—now opinions vary widely. Some believe BTC’s current cycle top is near $100K; others think the cycle hasn’t peaked, expecting a major uptrend in 2025. Based on your comprehensive assessment, what’s your view? Where is BTC in this macro cycle? And what data supports your view?
Colin: Before answering, a disclaimer: I’m very bearish on 2025. I believe BTC already meets conditions for a top formation. I know many—including peers—performed poorly in 2024’s “special” bull run, as market behavior differed from past cycles—most notably, no altcoin season. This hurt many, including non-professional trader friends who entered and suffered altcoin losses. Why? Briefly reviewing 2024: one altcoin rally early year, another in November—Trump’s election. Both lacked persistence compared to prior cycles. Especially Nov–Dec: alts didn’t rise broadly—it was clear sector rotation: DeFi led, then old coins like XRP, Litecoin. Rotation was evident. Thus, 2024’s bull market—if we call it that—differs greatly from history. Some claim a bull market can’t end without an altcoin season. I disagree—no strong correlation. Can’t use its absence to judge bull market end. Earlier, I noted on-chain analysis’s inherent flaw: perpetually insufficient samples. Simply extrapolating history to today’s market is carving the boat—unwise. If you insist: 2013, 2017, 2021 tops appeared around year-end—timing-wise.
I believe current conditions already meet top-formation criteria. Reasons are complex—I use multiple indicators. Briefly: First, holding structure—URPD chart. Low-cost holdings accumulated in 2022–2023—massive BTC bought cheap—have since undergone significant distribution. Plainly: they’ve sold, exited. Some may wonder: why care if they sold? Key point: nearly every past bull market ended when low-cost holders finished distributing. Counterintuitively, it’s not that they dumped causing the end—rather, rising prices let them sell gradually until done, then prices stalled, ending the bull run. This isn’t baseless—it has logic. If all current BTC holdings are high-cost—say bought above $90K—and low-cost ones (bought at $50K, $20K, $30K) have exited, then unless a strong, sustained breakout occurs—even mild wide-range oscillation (like last year’s $50K–$70K, or current $90K–$109K) puts immense pressure on high-cost holders. At ~$95K–$96K, a drop to $89K—under 10%—creates huge stress. Many are short-term traders; under pressure, they sell, driving further drops, prompting other high-cost holders to capitulate—chain reaction. This, per URPD, confirms massive low-cost distribution.
Second, RUP—market profit status. If interested, check it—it’s fascinating: plotting RUP line with price shows extremely high correlation—they almost move together. Makes sense: higher price → higher unrealized profit → lines align. So higher price → higher RUP; lower price → lower RUP. Simple. But when RUP diverges—meaning price makes higher highs while RUP makes lower highs—it signals market change. Example: BTC rises to $90K, pulls back, rallies to $100K (higher high), but RUP at $100K is lower than at $90K—RUP down, price up. Why? Only logical explanation: RUP measures unrealized profit, primarily contributed by low-cost holders. E.g., bought at $16K, now $96K—unrealized gain $80K per BTC. Bought at $86K, now $96K—only $10K gain. So low-cost holders dominate unrealized profit. If price rises but RUP falls, it means many low-cost holders sold earlier, converting unrealized to realized profit—thus invisible in RUP, lowering it, creating divergence. This validates—via RUP—actual low-cost holder exit.
Third, beyond on-chain data: I offer a unique perspective—U.S. stock market. Those familiar know equities have valuation metrics—P/E ratio. Many variants exist. I reference Shiller ECY, from Yale’s Prof. Shiller. It measures stock yield relative to bonds, introduced in a 2020 pandemic-era paper. He felt his prior model—Shiller PE (CAPE)—became less effective post-pandemic due to structural market changes, so created Shiller ECY, which better predicts market turns. In short: current U.S. stock valuations are somewhat elevated. Clarify: high valuation doesn’t mean imminent crash—can go higher. But it’s like a spectrum—approaching danger zone. Current level feels relatively risky. Stock valuations now driven mainly by hot topics—AI. Recently, DeepSeek emerged unexpectedly, triggering a valuation correction in U.S. stocks. On this, I’m medium-term bearish. Though DeepSeek long-term benefits AI, short-term valuation effects won’t vanish quickly—downside revision space remains. If U.S. stocks weaken, Bitcoin—as junior partner—won’t fare well. But these are my personal biases—take as reference.
Alex: Understood. Let’s recap Colin’s detailed view: he believes current levels meet many past top-formation conditions—citing distribution patterns, unrealized profit ratios, and referencing traditional finance’s Shiller ECY metric—all suggesting topping signs.
How to Start Learning On-Chain Data Analysis
Alex: We’ve covered on-chain analysis principles, observation methods, and practical applications. Many listeners may be new to this. Suppose a beginner asks: “Colin, your talk intrigued me—I want to learn from scratch to aid my BTC investing.” What learning advice would you give to start this journey?
Colin: I’ve received dozens of DMs asking this. My advice is consistent. I have two strengths: on-chain data and—self-assessed—technical analysis. Most who ask show me charts—chart patterns or indicators like MACD, RSI—and ask how to integrate with on-chain views. I must advise: I strongly discourage beginners from starting with technical analysis. Reason: too many schools, many lacking scientific rigor. They rely purely on induction—no underlying logic—risking the “dog bark + rain” survivorship bias trap. Beginners lack tools to distinguish valid strategies from mere luck. My advice: on-chain data is ideal for beginners. I’ll explain how. Why suitable? First, most retail traders aren’t full-time—many are students or professionals with day jobs. If you can’t spend hours monitoring screens, on-chain trading suits you. As discussed, on-chain observations operate on large scales—daily minimum. Thus, signals trigger infrequent actions—buy/sell maybe 4–5 times yearly. Fits student or worker schedules. Spend 30–60 mins daily reviewing preset alerts and data changes. Second, learning method: I’ve never seen a free, systematic tutorial. Tutorials exist—but not structured. One article details 1–2 indicators thoroughly—great, but lacks zero-to-one framework. Learning is painful: this indicator looks powerful—learn it? Next one too—where to start? My approach: brute force. Initially unaware of quality, I learned all. Unpacked each—principle, formula design rationale, intended insight, effectiveness. Time-consuming. After reviewing all, filtered. For beginners, this demands patience—study slowly one by one. Trading isn’t easy. Currently, Chinese-language resources—both simplified and traditional—are scarce. My advice: if researching an indicator, find the original author’s paper—best source. Avoid secondary interpretations—the author understands it best. If unavailable, at least study the formula. Glassnode’s site has a column—Weekly Onchain—publishing weekly analyses using various (non-fixed) indicators to explain current market views. Studying these gives vast learning material. I’ve posted tutorials on Twitter—not systematic, but interested parties can review.
Alex: Still quite systematic—I’ve followed your updates, now over ten posts, each covering one metric concept. Worth checking out. Another question: you identify first as a trader. We spent much time on on-chain data’s role in trading. But in actual trading, besides on-chain indicators, do you consider other factors? Macro trends? Fundamental events—like U.S. states or federal government accumulating Bitcoin? Beyond on-chain analysis, what weights do other indicators carry in your overall trading decisions?
Colin: Good, profound question. In my system, on-chain data forms an independent module for position sizing. I maintain a long-tail spot allocation—sometimes adding slight leverage in bear-market bottoms—say 1.3x–1.5x. This system relies primarily on on-chain data. It provides a broad directional framework—identifying market phase: early, mid, late; bull or bear—offering macro guidance. Other components: I mentioned technical analysis—my other strength. Hard to elaborate—it’s complex, with many schools and assumptions needing clarification to avoid misleading. In my system, TA aids short-to-medium-term trades. Its main role: refining entry points. Suppose I confirm a trade opportunity—TA helps pinpoint optimal entry. Example (not financial advice): ETH tradable between $2K–$2.6K, I believe it’ll rise. If I were omniscient, I’d just buy. But I’m not—so I use TA to find a more satisfactory entry. Exact levels require case-by-case assessment—no fixed number—but I have evaluation benchmarks. Macro-wise, I monitor global supply chains and U.S. Fed policy—given America’s outsized financial influence. Rate hike/cut expectations heavily impact risk markets. E.g., poor CPI data triggers repricing—markets front-run expectations, rising before cuts, falling before hikes. Futures and options traders price in these views. So I monitor this, though less deeply than TA or on-chain—my relative weakness. Lastly, news/fundamentals—like strategic reserves, as Alex mentioned. This ties back to my interest in designing event-driven trading strategies—targeting high-probability opportunities around specific events. Example: late May last year, Bloomberg’s senior ETF analyst Eric—widely followed—posted at 3 AM Beijing time raising ETH ETF approval odds to 75%. Market previously expected rejection. Within 24 hours, ETH surged 20%, outperforming Solana. My immediate thought: prepare for event-driven trade—go long Solana, short ETH. Logic: world knows ETH ETF approval is huge bullish—ETH pumps. But next target? At that time, Litecoin or Doge didn’t have Solana’s momentum. I targeted Solana. About a week later, I positioned Solana vs. ETH long-short—going long Solana, short ETH, betting on their exchange rate rise. I believed Solana was next hype target—ETH already priced in. If ETH approved, Solana would catch a wave. Might counter: is this idea robust? Not 100%, but precedent: January 2024—when did Bitcoin ETF approve? ETH spiked—ETH/BTC rate jumped ~30 points in 24 hours. Why? Next hype was ETH. This exemplifies event-driven trading. Back to Alex’s question: news/fundamental analysis is hard to quantify. I prefer designing event-driven strategies to exploit potential market mispricing inefficiencies.
Alex: Understood. Thanks, Colin, for your logical, structured explanations—clarifying thought processes behind each strategy and applicable scenarios. Clearly, you possess a rich toolkit and know precisely which tool to use when—not making vague, gut-based decisions.
Daily Routine of an On-Chain Data Researcher
Alex: Final question: as a trader and on-chain analyst, what does a typical workday look like? Beyond on-chain data, what info or tools do you consult?
Colin: Interesting question—my days are actually dull and boring. My schedule isn’t regular, but I try to stay awake during U.S. market open—simple reason: when U.S. equities open, crypto liquidity is usually highest. If energy permits, I hunt short-term opportunities then. This habit formed years ago. If exhausted daytime, I nap—daytime missed moves are less costly; nighttime misses matter more—monitoring more valuable. Notice weekends or Asian daytime—often dull, flat, low volume, poor liquidity. Hence, staying awake nights. After waking, morning routine: besides checking on-chain data for changes—as Alex said—I observe and record extra data. Beyond K-charts, I routinely scan all watched assets. Manually track net inflows/outflows of U.S. Bitcoin and Ethereum ETFs, market volatility, fear & greed index—quantified sentiment gauge. Also monitor derivatives market open interest. During extreme moves—sharp rises/falls—I may check liquidation volumes. I record all—very sensitive to these numbers. Then check for unexpected events—prompting data rechecks. Regularly tracked: derivatives open interest, market volatility, fear & greed, ETF flows. Also love watching Coinbase vs. major exchanges—Binance, OKX—premium/discount on contracts. I see this as a quantifiable sentiment proxy—reflecting U.S. capital mood. E.g., clear Coinbase premium suggests stronger U.S. demand—very evident during Trump’s election. I daily watch these numbers—maintain sensitivity—once spotting anomalies, I ponder: random or trading opportunity? Beyond data logging, I monitor charts—mentioned TA is one of my few bragging rights. Spend a few hours daily—watching if planned trades reach target zones. If nearing or hitting, I focus intensely on charts and data. Or check for plan deviations needing adjustment. I use two screens—one for charts, the other for Twitter—running my Mr. Beggar account. Outside trading, life is dull—I occasionally run, not frequently—just to move, avoid sedentary lifestyle. Rest time mainly for family. So my days are uneventful—nothing flashy. Trading is my job—like office workers or students—mostly working, off-duty, eating, sleeping—basically that’s it.
Alex: Understood. Colin described his daily workflow—high information and cognitive load, but he’s standardized and modularized it—so daily operations run smoothly without mental warm-up—data tracking, etc. His habits ensure efficient task execution. We also observe Colin’s curiosity toward trading, investing, and business—he gains not just money, but genuine enjoyment. This mindset, I believe, is a vital trait for great traders and investors. Thanks, Colin, for sharing so much—on on-chain analysis, investing, trading—your thoughtful, systematic insights. Hope to invite you again for future episodes on other topics. Thank you, Colin.
Colin: Too kind, Alex—just sharing personal views. Thank you.
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