Changtui Exploring Feasible Strategies to ‘Awaken’ the Bull Market
Changtui seeks strategies to revive the bull market
Note: This original post is a long tweet by @thecryptoskanda.
Want the bull market to come back? Just click. We don’t need flashy buzz words. What we need are new issuance mechanisms, trading mechanisms, and market-making mechanisms to bring in dumb money.
So where do we find potential new issuance mechanisms? Low liquidity asset markets.
- OP Stack explicitly states that Sequencer should be shared. Why sho...
- Does the Ethereum Foundation provide purposeful guidance on Layer2?
- Besides the recently popular Friend.Tech, what other Web3 social ap...
From Numerai, BRC20, Friend, NFTs, gambling models, and lessons from @blur_io, let’s find the formula for new issuance mechanisms and markets.
Let’s dive in.
The crypto audience is the most adept at learning and has the strongest gambling nature in the world. Their gambling is different from the classic “casino gamblers” because they: – Don’t need fixed odds;
– Believe in information asymmetry rather than long-term training skills (cheating) or the law of large numbers.
Therefore, they don’t like to spend time studying how to beat the 0.5% house edge of the blackjack dealer, but rather look for hidden alpha in new targets and exit before the market catches on.
They need targets that are not widely known, so traditional markets and mainstream coins do not meet the requirements. The “cost” of seeking something “new” is a low liquidity, high-risk target market:
1. High trading efficiency (can be quickly executed)
2. High volatility (small trading volume leads to large fluctuations and rapid increases)
3. Minimization of non-trading mechanism advantages (can gamble and lose but not be rug pulled)
Only targets like these meet their needs for early discovery, precise exits, and the ability to exit.
Similarly, dealers make trade-offs in three dimensions, but their goal is to maximize overall profit ROI, regardless of how:
– Fairness (whether there are non-trading advantages)
– Trading efficiency (whether both parties can quickly execute trades)
– Volatility (whether they can manipulate at low cost)
Unless they are philanthropists, these three dimensions are an impossible triangle from the perspective of dealers. When designing, dealers consider how to balance player preferences and maximize ROI.
Whether it’s the old electronic trading platform for postage stamps, Numerai, AMM mechanisms for meme coins, NFT listing trading, BRC20/ETHS fair inscription listings, or the Friend curve, they are essentially the same thing. Different ratios, but they all need to attract users with:
– Wealth effect (flagship projects with exponential ROI)
– Adequate targets (later entrants have more projects to speculate on)
– Controllable perceived customer loss
Doesn’t it resemble the GGR kill rate model?
Let’s compare non-free mint PFP NFTs, BRC-20, and Numerai from the perspective of dealers.
BRC-20 is the least economical: no dealer advantage, low efficiency in the listing trading mode. The advantage is that it doesn’t require a lot of capital to pump up the price when sentiment is positive, but the premise is that the dealers obtain sufficient fair inscriptions.
NFT ≈ BRC-20, the advantage of the project party is greater, and there is a possibility of reservation for smashing the market and rug pulling.
Shu Cang is completely black box, with almost no usable public market, basically a game between the house and the player.
If we use the GGR kill rate model, we find that:
The kill rate of Shu Cang is the most controllable, followed by NFT, and BRC-20 is completely uncontrollable.
If you are the house, what would you do? That’s why iBox’s level is still difficult to reach in the NFT market.
Similarly, BRC-20 project parties have no successors and have a much shorter lifespan than NFT. It is not surprising to see this apart from cultural attributes. “Fairness” can bring temporary popularity and wealth effects, but ultimately the needs of the operators determine everything.
So, let’s compare @Friendtech, Shu Cang, and NFT:
We will find that FT is more brutal because the users only compete with the platform, and the transactions are conducted using an automated market-making algorithm. The algorithm is positively correlated with the net purchase times, and the only cost is 5% given to KOL.
In the same pump, FT actually only needs to pay 5% of the current price, while NFT needs to pay 100% of the floor price of the circulating supply in the market.
Even in the price decline range, if the market needs to be protected, FT is still equivalent to constantly conducting LBP issuance, while NFT can only continuously absorb the selling pressure through sales revenue and royalties.
Imagine, does FriendTech’s undisclosed voting plan have the possibility of only rewarding buying and punishing selling?
Speaking of this, we have to mention another project that LianGuaradigm did wrong, @blur_io. It clearly uses the same trading mining and cashback model.
The problem with Blur lies in subsidizing the wrong target. Blur let its users go to the gambling table and provide liquidity as bid walls for whom? For the blue-chip NFT holders.
The holders sold to the users, causing losses. Who took the customer loss? It’s the holders. Blur didn’t get anything. Instead, it had to airdrop to users based on trading volume. It’s like a gambler washing their own bets and getting a 1.2% commission, but losing 80% to buy chips. And the casino didn’t make any money.
What is the correct approach? Blur only allows its own NFT targets to participate in trading mining, compete against bid walls, and turn customer losses into Blur’s GGR. It uses a portion of the pump and subsidizes user buying behavior through trading mining, ultimately controlling the kill rate within a certain range.
Next, continuously absorb excellent NFT teams, join trading mining on the premise of using Blur as the market maker, become the Curve of NFT, and even dominate NFT.
Actually, when we see this, I believe the answer is already obvious:
– Discover a (practical) low fairness, high volatility, and as high transaction efficiency as possible;
– Establish a transaction platform with transaction mining subsidy mechanism;
– Subsidize the purchase behavior through the transaction mining mechanism to fully control the target;
– Ensure that the total customer loss is slightly higher than the total subsidy (including the cost of pulling the target);
– Package the mining mechanism with market-making terms to expand the selection scope of the project;
Activate the target.
Of course, I only adopted the perspective of the house bank, the POV of LianGuairadigm. However, in fact, we are mostly 10,000 steps away from LianGuairadigm (the ability to mobilize the entire network’s big V is not something that can be learned).
It is difficult to say how the market will respond, whether the timing, location, and people are in place. But with Ordinal pearls in front, the Bot system will come from behind, Why Not?
There will definitely be the next (group) hero on the chain.