A Comprehensive Guide to Improving the Accuracy of "Buy Low, Sell High"
TechFlow Selected TechFlow Selected
A Comprehensive Guide to Improving the Accuracy of "Buy Low, Sell High"
This article shares how to increase the probability and accuracy of "buying low and selling high" through four parts.
Navigating Web3 tides with focused insightsPreface
Previously, articles on ordi, eths, pepe, and similar topics accurately predicted project developments and price trends to some extent, with detailed reasoning paths shared in the content.
Today, I’m sharing a deeper methodological tutorial—what’s called “underlying logic” on platforms like Douyin. Using several case studies, I’ll walk you through the logical framework step by step, using clear examples (🌰) to help everyone understand the principles and applications in a simple, direct way.
The methods discussed here are evident in many recent hot projects such as “Ketai Coin, X, sats, atom, pipe, ibtc…” These apply not only to inscription projects but also to token primary markets, inscription markets, NFT markets, and secondary markets. They’re also applicable to web2 internet marketing strategies.
"Buy low, sell high" is the dream of every investor in financial products, yet reality often falls short. This article breaks down how to increase the probability and accuracy of successfully doing so into four parts.
Part 1: 🌰 A short story to reveal the core issue
Part 2: 🛜 The规律 of传播 effects
Part 3: 📈 God’s-eye view: understanding pump mechanics
Part 4: 💡 Teaching methods to improve buy-low-sell-high accuracy
The following content is summarized by ALD (@daji_357 on Twitter) after months of practical experience across dozens of projects. Please credit the source when reposting.
The theme combines characteristics of new media传播 with those of financial markets. There's currently no established category for this type of content; personally, I refer to it temporarily as "New Media Financial Propagation Phenomenon."
🌰｜Opening with an example
In栗子 Town, a buffet restaurant named Wai Bo San Buffet opened, charging 299 per person. It was extremely popular, packed from day one. After research, some said the food was high quality and great value; others claimed ingredients were average, no different than other buffets.
Seeing such booming business, some people got excited, tried it themselves, then decided to copy the exact same concept to compete—same scale of decor, same quality supply chain.
The result was tragic: either they operated for a long time, had good sales, but closed due to unsustainable ingredient costs; or they opened with almost no customers and went bankrupt quickly.
Strange, isn’t it? Let’s switch to the owner’s perspective:
As the restaurant owner, your main goal is profit. You know the renovation cost was 3 million, and monthly fixed expenses excluding ingredients are 400,000. To ensure ROI and stay ahead of competitors, you designed a unique operating system.
During the first month after opening, your main promotion method was handing out flyers, targeting nearby residents (within 3–5 km). At this stage, you used premium ingredients, nearly operating at zero profit per customer just to build reputation.
After a month, word-of-mouth started spreading—each satisfied guest influenced more people to come visit (within 10–20 km). For the next two months, you switched to mid-to-high-tier ingredients, achieving about 20% profit per order.
After these two months, you downgraded again to mid-tier ingredients—no better than other 200-per-person buffets. Now your profit margin reached 50–60%. Thanks to peer-to-peer referrals, your restaurant became widely known, attracting visitors from up to 50–100 km away, keeping foot traffic consistently strong.
You ran like this for about half a year, then noticed that reputation maintenance costs peaked and reservations began declining. You realized you’d already converted 90% of your target market in the city. Just then, during Yangcheng Lake hairy crab season, you launched a new offer: unlimited Yangcheng Lake crabs for only 499 per person…
End of story.
Characteristic of New Media: “Accelerates the Speed of Wave Propagation”
Bring this story back into the web3 world, and you’ll notice close connections between word-of-mouth among users and increases in project holdings data.
In the media world, information spreads in waves—like a drop hitting a still lake. Depending on duration and the weight of the drop, ripples expand outward across the entire surface at varying speeds.
In past timelines, things moved slowly. Time horizons were stretched—positive news in stock markets didn't instantly reflect full impact. It took significant time to spread, manifesting through gradual fluctuations.
Just like the restaurant—it took one month for initial 3-km locals to influence people within 5–10 km (mid-circle), then another two months to validate and further propagate to those within 50–100 km (outer circle). Those outer-circle users sustained six months of explosive revenue, even if negative reviews emerged—they wouldn’t cause a cliff-like collapse. (Yes, you absolutely need to flood review platforms with positive ratings—or don’t open at all.)
With the rise of new media, information gaps have been shattered. Reaction times are no longer measured in days or months—but in hours or minutes.
To put it vividly: someone hit fast-forward on that ripple effect by 100x.
Characteristic of New Media: “The Public Never Cares About Truth”
Now you might ask: over half a year, won’t negative feedback eventually cancel out the hype?
Actually, under today’s new media landscape, it rarely does. Early口碑 from the first 3-km users has already created a viral phenomenon. From day one, it tops search results, and ongoing traffic ensures its continued “traffic monopoly.”
Even when new negative reviews appear, they’re quickly drowned out by the overwhelming volume of prior positive content. Only a few meticulous individuals will dig into recent reviews to verify authenticity—but they’re a minority among the masses.
Moreover, the speed of dissemination isn’t exclusive to users. As mentioned earlier, when businesses realize rising costs of maintaining reputation, they pivot to harvest profits—businesses must invest in reputation management too.
Alongside genuine user-generated content, there are also disguised shills pretending to be regular users—their role is to confuse perception and boost conversion rates.
Therefore, in this era where anyone can publish news, the ability to filter and discern information becomes a key determinant of life quality. After all, when facing something unfamiliar, what’s the harm in paying a little to try?
📈｜What If This Happened in the Stock Market?
🌰 Let’s use new media propagation规律 to take a god’s-eye view 🌰
A project team plans to pump their token price. They prepare bullish news in advance, quietly accumulate positions, and discreetly share the news with 10 close partners, strictly instructing them not to leak it.
Each of these 10 shares it with 10 trusted contacts and 3 private groups, repeating instructions not to spread it further. The team monitors buying pressure and growth in new holder addresses, confirming the message has passed from Tier 1 to Tier 2.
At this point, high-quality communities and small groups begin discussing the project’s bullish outlook. A few posts start appearing on public platforms (Twitter, Weibo, etc.). As time passes, the team observes data indicating Tier 2 coverage is nearly complete—and then gently initiates a slight price rise.
As prices climb, sentiment in Tier 3 ignites rapidly. Major groups start buzzing. Even dead groups revive with questions. Content on public platforms explodes. Watching slowing growth in holder count and weakening buy-side data, the team realizes outreach has reached ~100%, nearing saturation.
They call Akun: “Dump everything.” The market wails in despair.
When a project’s bullish news emerges, Tier 1 benefits first—those closest to the core (lowest cost). Influence radiates outward to Tier 2 (mid-cost), and as inner-circle adoption grows, reaches Tier 3 (highest cost)—by now, everyone knows.
[Here I broadly categorize people into three tiers. Further细分 is possible, but varies by individual—won’t elaborate here; future articles will cover this.]
From the moment Tier 3 begins to be reached until conversion completes, nearly everyone in the market becomes aware.
Eventually, those willing to buy have already paid. Hesitant ones will only act if “project teams/institutions” inject real capital to drive prices higher. At this point, turnover rate and trading data reflect this shift—aligning with what technical analysts describe as k-line changes.
Unlike the restaurant:
In financial markets, entry price peaks when Tier 3 joins, while restaurant ingredient costs hit their lowest.
The restaurant took six months to convert Tier 3; financial markets may do it in 5–10 minutes.
Thus, in finance, identifying who belongs to Tier 1, Tier 2, or Tier 3 is crucial.
Only by doing so can you effectively reduce decision time, lower holding costs, sell at peaks, and maximize returns.
💡｜Applying Buy-Low-Sell-High Methods
Switching back to the user’s perspective: crypto social circles are static, and trading behaviors are habitual. Simply put, everyone’s daily routines and preferred Douyin content are fixed.
So, when I reviewed propagation paths of various projects, I started labeling communities and individuals in my WeChat networks. After extensive tagging, certain people accumulated more and more Tier 1 labels; others gathered increasing Tier 3 tags. Same for communities.
I tested this with several projects: bought when Tier 1 started talking, sold decisively when Tier 3 began discussing.
Data showed overall returns reached as high as 70%, with peak gains hitting 12,000%. Yes, this method may seem shady, but it’s undeniably effective.
As individuals accumulate more Tier 1 tags, their return rates stabilize. Their yield becomes a direct reflection of their传播 power.
As individuals collect more Tier 3 tags, their sell signals get closer to price peaks—their identity increasingly represents the crypto fringe. Oh right, some lose all funds and quit. Don’t rely on just one—use multiple references.
Therefore, tagging users, communities, and accounts on public platforms is a crude but effective way to assess information. However, it requires long-term data accumulation to achieve high accuracy.
It’s also a useful method for beginners to evaluate community quality.
* No matter which community you join, without independent judgment or info-discrimination skills, you’ll remain stuck in Tier 3.
Of course, you can build your own community/account to influence others—that’s the fastest way to escape Tier 3 fate. But personally, I believe strengthening internal capabilities leads to more stable and lasting results.
Future content will include tutorials on earning in web3 via community building, becoming a KOL, and related topics.
As mentioned above, joining communities to reach Tier 1 is a dependent survival strategy—you can’t escape being exploited. That’s inevitable.
Learning to distinguish, analyze, and develop your own systematic methodology enhances survivability in financial markets—that’s true freedom.
Beyond the tagging method above, some technically skilled friends use data models to track “smart wallets,” rank them by win rate, and set automated buy/sell scripts to follow “smart money” for instant entry and exit to capture gains.
Others use specific techniques to boost their visibility, gather more information, and then sort and filter it accordingly.
The above content explains the entire process and规律 of wave propagation. Currently, projects entering secondary markets aren’t driven by just one single “wave.” For instance, “BTC, ETH, eths, sats, ordi, atom, pipe, floki, pepe…” are all composed of multiple overlapping waves.
Next time, I’ll use real cases to teach how to judge the number of waves in a project (a key metric for assessing long-term investment value).
Final Thoughts
Compared to zero-sum battles in existing markets, I personally admire actions that expand overall influence—whether art-focused NFTs, Stenp-style GameFi, or celebrity-driven phenomena like BAYC, the Chinese billionaire, Buffett’s dinner, or Silicon Valley’s Iron Man with his 42069...
Setting aside price data, viewing these objectively from a traffic perspective, each case truly generated breakout-level explosive growth under their respective spatial, contextual, and conditional settings. In a sense, they created a fourth tier of participants—generating collective benefit.
Of course, perspectives vary—every event has multiple sides. I look forward to seeing more breakout initiatives emerge.
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
@Daji_357
