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憨厚的麦总
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Ex-VC & Banker Turned Degen. See You In The Trenches
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憨厚的麦总
12-23
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Sigh, I praised Binance too early. What kind of contracts have been listing these past few days? ZKP and IR both opened high with Alpha, then dumped the shares after listing Alpha, and then even gave them Perp positions. With this kind of operation, the project teams dump the shares first with Alpha, then dump them again after Perp, how are retail investors supposed to win? Sigh, all that hard work building up the Alpha market is gone again. I'm done, going out to have some fun.
憨厚的麦总
@Michael_Liu93
12-19
币安改了Perp的listing规则之后 项目方是骡子是驴很快就看出来了 这两天有几个上Alpha的我其实是有perp预期的 一看开盘，又是高开+毫无护盘痕迹+一大堆空投 一点买的欲望都没有 大多是国外项目方 挺好的 垃圾资产就上个Alpha就下班吧 2年白干☺️
ZKP
1.39%
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憨厚的麦总
12-23
Some people are saying it's okay to short altcoins with a -2% fee, usually just before a crash. Let me explain how fees work. After reading this, you might not dare to short Altcoin recklessly. The statement "extreme rates = impending collapse" is indeed true in certain scenarios. For example, if 50% of the spot market is about to be unlocked, and funds in the contract market are shorted first, the demand for short positions is greater than that for long positions, causing the rates to become extreme. Once the spot market is unlocked, the market will indeed collapse. However, for "monster coins," this fee rate is something that market makers can control. Let's first look at the formula for the rate: Contract fee rate % = Risk-free rate + Premium index. Premium index = (Mark price - Spot price) / Mark price. Therefore, applying the formula above, theoretically, if the premium of the contract mark price being lower than the spot price exceeds the risk-free rate, the fee rate will be negative. The contract mark price is affected by the contract's order book; theoretically, the greater the demand for short positions, the negative the fee rate, and the greater the demand for long positions, the positive the fee rate. (Important note: it's "greater demand," not "greater position size.") For example: If the spot price of a certain coin is 100 yuan, a long position in the contract places a buy order at 99.5 yuan (not daring to chase the price higher), and a short position in the contract places a sell order at 99 yuan (eager to dump the price). The actual transaction price is 99.25 yuan, which is lower than the spot price. Therefore, the fee rate is negative. This is an example of how the "high demand" for short positions leads to a negative fee rate. How does that cryptocurrency exploit short sellers using this rule? Suppose the market maker controls 99.99% of the spot market and wants to liquidate short positions. They build up enough long positions in low-priced contracts, and then push the price up. They can use a "small order book of long positions" to make the contracts fluctuate at high levels. As long as there are short positions open at the "current price", the contract price will be driven down sharply (because the order book of long positions is very thin). This will make the fees negative, and the short sellers will continue to pay the market maker's long position funding fees. Then the money earned from the funding rate is used to further drive up spot prices, thereby achieving the goal of further short selling. So what's the key point? 1. The fees aren't going to be pocketed by the exchange; they're going to your counterparties. 😅 2. The rate reflects the demand from both long and short positions, not the position size. 3. For cryptocurrencies prone to price manipulation, fee rates are not a good indicator of when they will crash. 4. The core of manipulating the market lies in absolute control of the spot market and a sufficient number of short sellers.
刁珉
@0xtroublemaker
12-23
空-2%的我觉得也没问题啊 ，基本是闪崩前夕
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憨厚的麦总
12-22
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Whenever I feel like there are no new investors to take advantage of in the industry, a trip to Binance Square fills me with hope again. On Binance Square, you can find trading advisors with the most dedicated followers, advertising 50-100 USDT per trade on pure peer-to-peer platforms; (I've recently been following about a hundred of them). You can see a bunch of retail investors short the most expensive Altcoin, focusing on only shorting the most expensive ones and not the least valuable ones. You can see retail investors shorting the market while complaining that the exchange is profiting from the funding fees, even though the fees are -2%. You can see that after being trapped, people were forced to start using CX VC coins and became part of the builder family; There is still an information gap.
VC
5.55%
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憨厚的麦总
12-20
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I closed 80% of my short positions in the past few days (leaving only a few short positions in newly listed coins with high FDV that couldn't be controlled). Let me talk about my scenario for the future. It's unlikely to be accurate, as no one in the world can accurately predict the market. But my trading habit has always been to have a pre-set scenario in mind first, and then take it one step at a time during the trading process. The scenario: First, a small new low is reached to wipe out those who buy the dips the dip > A rebound begins in February or March > In March or April, the 94,000 resistance level is broken, and the market starts looking for new highs again > A new round of decline begins in April or May. During this phase, I will not open any positions on the major blue-chip stocks, especially breakout trades. If you look back at January to April 2022, you will understand. In the last round, this position was drawn horizontally for more than 100 days. Anyone who dared to play short-term with slightly higher leverage was ground to death. Bear markets are especially bad for frequent trading because they are full of trading patterns. I have also emphasized this point in my previous bear market survival guide. To survive in a bear market, the first thing is to control your hands.
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憨厚的麦总
12-17
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A few days ago, I discussed the topic of "How big is the AI market bubble?" with a friend. I'd like to share our discussion and then do some calculations together. First, let's roughly estimate the market size. Taking Chatgpt, which has the largest customer base, as an example, it currently has around 800 million monthly active users globally (covering 10% of the global population). Of these, 35-40 million are paying users. Let's take 37.5 million as the median, with most paying $20 per month. Assuming an average monthly revenue of $25, Chatgpt's annual revenue from its consumer-facing (C-end) market is $11.25 billion. Assuming that each of these 37.5 million customers pays for at least 3 AI services on average, the total C-end market size is $33.75 billion. (I think an average of 3 is a very optimistic assumption. As a heavy AI user myself, I currently pay for only 3-4 AI products, around $100 per month.) So, what is the market capitalization of the companies serving these AI C-end markets? Let's exclude the primary market (roughly estimated at around $1 trillion) and focus only on the seven major tech giants in the secondary market: Nvidia $4.4 trillion, Apple $3.9 trillion, Microsoft $3.7 trillion, Amazon $2.4 trillion, Google $2.4 trillion, Facebook $1.7 trillion, and Tesla $1.3 trillion. Except for Nvidia's $4.4 trillion market capitalization, which is purely AI-driven, the others' valuations are more or less not centered on AI. Assuming we only use 25% as the weighting for their AI market capitalization (I believe the actual premium for AI in the capital market is much higher than 25%, but for conservative estimates of a bubble, we'll use 25%), the result is $8.25 trillion (of course, there are many, many AI companies not included in this calculation, such as AMD, Palantir, Qualcomm, Oracle, etc.). $8.25 trillion divided by $33.75 billion = 244 times P/S. How do we understand this 244 times P/S ratio? We can refer to the P/S valuations of leading companies on March 24, 2000, the peak of the dot-com bubble. Amazon: 19x Cisco: 35x Qualcomm: 22x Microsoft: 26x IBM: 3x Oracle: 27x Intel: 16x After the dot-com bubble, most of these companies, even giants like Amazon and Microsoft, took 10-15 years to recover their 2000 bubble peaks. Of course, a P/S ratio of 244x doesn't necessarily indicate a large bubble in the AI sector. Currently, most AI revenue isn't from the $33.75 billion consumer market, but rather from "spending money on GPUs and infrastructure." Ultimately, these companies' investment in GPUs and infrastructure serves the consumer market, which, at least for now, isn't as large as many believe. The current dilemma facing the AI sector is that even though AI is quite usable, it remains a "production tool," not "productivity" itself. Therefore, AI service companies are still only paid for the "tool," but the capital market is valuing them based on the "productivity" narrative. What does this mean? If you ask a consumer or business owner to spend $50-$100 per month to equip their employees with AI, they're likely willing to spend that money, and that AI might indeed save the company the cost of one or two junior employees. But if you ask them to spend $2000-$3000, they're unlikely to be willing to spend that much. So even if the consumer market penetration rate increases tenfold to 375 million people (more than the US population of 350 million), a P/S valuation of 24.4x is still expensive in any industry. But who knows? Perhaps one day AGI will emerge, and AI will become productivity itself, and these AI companies won't be expensive anymore. Just like when we look back at 2000, were companies like Amazon, Apple, and Microsoft expensive? It's certainly much cheaper than it is now, but if you bought it in 2000, it would take 10-15 years to break even. And we don't know when AGI will arrive, or even if it will arrive at all. Let's just wait and see.
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