Some time ago,Bybit once again emphasized the importance of risk management and trading behavior monitoring in the platform announcement, reminding users to trade rationally and pay attention to position control. The announcement itself is a reminder of risks in a volatile market environment, but from an operational perspective, it also releases a signal—the differences in trading behavior are becoming increasingly obvious.

Some people only experience contract trading occasionally, while others operate frequently almost every day. For teams doing user stratification, cooperative promotion or strategic docking, distinguish“Newbie traders” and “high-frequency traders” become particularly important.

If these two types of users cannot be accurately distinguished, the marketing direction will be easily misaligned. Strategies for high-frequency traders will naturally have limited effect if promoted to novices who have just registered.

1. Typical characteristics of novice traders

From a behavioral perspective, novice traders usually have the following characteristics:

First, there are fewer transactions.

If you try to place an order several times after just registering, the interval may be longer.

Second, the capital scale is relatively small.

They mostly test the waters with small amounts and are more sensitive to risks.

Third, trading time is concentrated.

People often only participate when there are hot market fluctuations.

This type of user is more suitable for basic educational content or low-threshold activities, rather than complex strategic promotion.

2. Behavioral characteristics of high-frequency traders

Unlike novice traders, high-frequency traders often exhibit consistent behavior.

First, the trading frequency is stable.

There are relatively regular transaction records on a daily or weekly basis.

Second, the rhythm of holding and closing positions is clear.

Able to continuously adjust positions according to market changes.

Third, the scale of funds is relatively high.

There are frequent fund flows in the account.

High-frequency traders pay more attention to strategy efficiency, fee optimization or in-depth tool services. if doingBybit cannot identify this group of people when filtering its trading users, and the promotion effect will be greatly reduced.

3. The significance of screening different types of users

Distinguishing novices from high-frequency traders is not simply about labeling them, but to improve matching.

For example:

lFor novice users, introductory courses or trial gold activities can be provided.

lFor high-frequency traders, strategy tools or high-end services can be pushed.

If the layering is clear, the marketing information will be more targeted and the conversion will naturally be higher.

The core value of Bybit trading user screening lies in establishing this hierarchical structure.

4. Simple differentiation ideas

In actual operation, judgment can be made from three dimensions.

First, look at the number of transactions.

High-frequency traders usually have dense trading records, while newbies tend to have sporadic trading records.

Second, look at the trading cycle.

Long-term continuous trading has more reference significance than short-term bursts.

Third, look at changes in funds.

Stable capital flow usually means more mature trading habits.

When the data scale is large, manually counting these behaviors can be very time-consuming. At this time, you can use system tools to perform batch identification.

digital planet as global244 national social AI number screening platforms not only support basic account detection, but also identify and hierarchically manage active transaction behaviors through algorithm models. When it comes to Bybit trading user screening, it can help quickly distinguish novice users from high-frequency traders and improve screening efficiency.

The platform supports free trials. You can import some data for testing first to verify whether the filtering logic meets actual business needs.

5. Suggestions to avoid misjudgment

It should be noted that the screening criteria cannot be too single.

Just seeing a large transaction once does not qualify you as a high-frequency user;

It is impossible to judge whether it is stable just by looking at the number of transactions.

Stratification should be based on ongoing behavior, not short-term data fluctuations.

whenBybit's trading user screening logic is clear, and user stratification is naturally more accurate. Whether it is strategic promotion, cooperation sharing, or user operations, a clear structure will make decisions more based.

Instead of adjusting the promotion direction in the later stage, it is better to stratify users in the early stage. Through systematic screening to distinguish novices from high-frequency traders, the marketing matching degree will naturally increase.

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