OKX Trading Signal Platform
A superstar-level Key Opinion Leader (KOL) on the OKX trading bot platform has caused a community discussion and debate due to its strategy’s 30-day backtest annualized return of -95%. What should investors pay attention to when using strategy services that claim to help with easy trading?
Table of Contents
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Superstar KOL’s Strategy Trading Hits a Wall! 30-day Backtest Annualized Return -95%
What is a Signal Strategy?
Profit Model of Signal Strategy?
Renowned Researcher: Asymmetric Risk in Strategy Follow Order
How to Solve Asymmetric Risk?
Renowned Entrepreneur: Rule Design Needs Improvement
“Half-life Sharing” Mechanism
Discussion: Will anyone play when KOL has more responsibility?
The aforementioned superstar-level KOL’s signal on OKX resulted in a total negative return (in USDT) of -930,000 USD, with a 30-day backtest annualized return of -95.86%. This signal only focuses on two trading pairs: BTCUSDT perpetual and ETHUSDT perpetual.
Due to the poor performance of following specific signals, many people have started discussing whether individuals understand the risks associated with such products, as well as the transparency and responsibility of signal creators.
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Signal strategies allow traders to showcase their customized digital currency trading strategies on the platform. Traders have full control over their designed algorithms, and the strategies will be executed in real-time with high performance and reliability.
OKX’s signal strategy platform is divided into free, monthly fee, and revenue sharing models, allowing traders to share their strategies while having the opportunity to earn profits. Taking the aforementioned superstar-level KOL as an example, they adopt the revenue sharing model for subscriptions. If subscribers use their signal creation strategy and make profits, they will receive a 20% revenue share.
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Researcher Yu Zhe’an expresses a more conservative view on trading functions such as “strategy follow order” and other replication behaviors.
He believes that if the strategy provider does not have a vested interest in subscribers and does not bear the risk of their losses, they should not make decisions for others.
He uses the book “Skin in the Game: Hidden Asymmetries in Daily Life” as an example to illustrate situations of symmetric and asymmetric risks.
Example of symmetric risk: “In most cases, the final payment of a project is usually tied to acceptance. Various products usually have warranties or liability insurance, which is a rule design that tends to make risks symmetric.”
Example of asymmetric risk: “Celebrities who have children and assets overseas and can escape at any time advocating and supporting wars.”
In his view, “strategy follow order” is a design with asymmetric risk. It is understood that since people do not know how much position the strategy signal creator themselves are trading, there may be a situation of asymmetric risk when the position taken by subscribers far exceeds that of the strategy signal creator, as subscribers bear greater financial risks.
Researcher Yu Zhe’an believes that making the interests of strategy providers and subscribers aligned may be a solution.
“In principle, if the strategy provider makes money, they should share the profits, and if they lose money, they should naturally bear the losses, or even more.”
He gives an example: If the total amount of subscribed strategy is one million US dollars, the proportion of the strategy provider’s own investment must be 10% of the total amount in order to qualify for a 20% revenue share from users, and it can increase proportionally.
In addition, the profit withdrawal by the strategy creator should be subject to a time limit. Subscribed earnings should be executed in the strategy for at least six months before gradually being withdrawn. He even believes that the problem lies in the fact that even if there is no asymmetric risk, users will still buy in, so improving the quality of users should be promoted to drive improvements in trading platforms.
Entrepreneur Fenix Hsu also agrees with the problem of misalignment of interests between strategy providers and users, extending it to the issue of APR distortion in the DeFi world.
For example, in UniSwap v3’s AMM strategy, because the strategy provider does not specify whether their APR is the actual APR or FeeAPR, users often put funds in based on the high displayed APR, which ultimately leads to loss of principal. On the other hand, the strategy provider only focuses on the high annualized fee returns, leading to a misalignment of interests.
Fenix Hsu proposes two improvement suggestions: one is the calculation method for the actual APR mentioned above, and the other is to establish a reasonable revenue sharing mechanism for strategy providers.
Fenix Hsu states that in general hedge funds, the commonly used “High water mark” revenue sharing mechanism requires that the fund’s net asset value exceeds the historical high before revenue sharing can occur. However, a prerequisite for this mechanism to work easily is that users must enter and exit at the same time and have consistent calculations. However, in the Web3 world where users can enter and exit at any time and there may be various equity tokens, this mechanism becomes difficult to calculate.
Fenix Hsu uses his own entrepreneurial project, Teahouse Finance, as an example and suggests the “half-life sharing” mechanism to extend the time for strategy providers to receive revenue sharing. When the strategy performs poorly, the previous revenue sharing can be used to compensate for losses, holding the strategy provider accountable for the long term. This is the only way to effectively bind the interests of both parties.
Although blockchain financial services bring many innovative ways to play due to their freedom, their essence remains unchanged. However, in traditional finance, mature financial regulations have imposed limitations on many things.
The cryptocurrency investment field is still in a period of regulatory arbitrage, where many things are not defined and not easily regulated. Therefore, investment funds and investment advisors, which have similar nature and roles, have not been regulated by existing laws.
As a result, the trading products and services of virtual assets have flourished, and exchanges and KOLs with followers have become mutually beneficial roles. Exchanges can earn transaction fees from users’ high-frequency trading, while KOLs essentially act as investment advisors or even fund managers, sharing revenue from user transactions and earning commission fees.
When their performance is excellent, the community treats KOLs as superstars. When their performance is poor, they criticize them and demand stricter systems to protect investors. However, when KOLs realize that they are accountable for their performance and may even have their profits restricted, will they still want to participate? As the rule maker, will the exchange actively discourage KOLs?
“Do we want the cryptocurrency community to grow up or let everyone continue to stay in Neverland?” – Fenix Hsu
OKX
Yu Zhe’an
Signal Strategy
Asymmetric Risk
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Further Reading:
Grayscale Sends 588 Million Magnesium Bitcoin to Coinbase, BTC Falls to 38K, ETH Breaks 2.2K
Yu Zhe’an’s Viewpoint | Bitcoin Spot ETF = Bull Market? Understanding the Blind Spots and Actual Impact of Bitcoin ETF