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Image of ROBOPRO’s AI operation engine analyzing market data
AI Portfolio Engine

Algorithms capture market fluctuations and adjust allocation according to risk tolerance

ROBOPRO is an AI operation engine that continuously analyzes crypto asset market data and automatically adjusts asset allocation according to each customer's risk tolerance. We emphasize judgments that are not influenced by emotion and processes that can be checked.

Request for information See how it works
Issue recognition

In the crypto asset market, prices fluctuate significantly over a short period of time.

The amount of data needed to make decisions continues to increase every day, including price information, trading volumes, and the flow of funds throughout the market. It is not easy for individuals to keep track of all this and make decisions without emotion. ROBOPRO is built on the design philosophy of offloading this burden to algorithms.

core technology

Be able to understand complex decisions without relying on technical terms

ROBOPRO operations team checking data

Mechanism for learning risk tolerance

In addition to the answers to your questions during registration, we continually estimate your unique risk tolerance based on your history of configuration changes after operation begins. Rather than being fixed at a value once set, it is a mechanism that updates over time.

The estimation is a combination of multiple prediction models and does not depend on a single indicator. When the market suddenly changes, there is a process to review the weighting of each model.

Dynamic asset rebalancing

Asset allocation is not fixed; it adjusts as market conditions change. If the distribution is skewed beyond a predetermined tolerance, the system will automatically make adjustments.

The frequency and extent of adjustments depends on your risk tolerance settings. Conservative settings are designed to keep the adjustment threshold narrow and avoid excessive replacement.

You can review the allocation trends in quarterly reports.

operational process

Flow from data analysis to portfolio optimization

STEP 01

data collection

We collect data from multiple sources, including exchange price and volume data, on-chain metrics, and fund flows across markets.

STEP 02

Estimating risk tolerance

We calculate your specific risk tolerance based on initial questions and behavioral data during operation.

STEP 03

Portfolio optimization

Calculate allocation candidates by combining the output of the predictive model and risk tolerance.

STEP 04

Execution and monitoring

We implement allocation changes and continuously monitor changes in market conditions.

The types of data used for analysis and an overview of the calculation process are provided in the explanatory materials provided after requesting materials. Prediction models are not indicators that guarantee specific price movements, but are used as information to assist in making decisions.

Concept of risk management

Multiple safety devices premised on careful operation

Introduction example

How does the allocation logic change depending on risk tolerance?

Profile 01

conservative type

This is a setting that keeps the amount of change in allocation small even when prices fluctuate, and maintains a high cash ratio. During sharp declines, we prioritize reducing the proportion of assets with high volatility.

Profile 02

Standard type

The allocation will be adjusted depending on the market environment, but the frequency of changes will be on a monthly basis. Different adjustment logics are applied during uptrends and downtrends.

Profile 03

proactive type

This setting allows for a wide range of changes in allocation and emphasizes responding to market opportunities. However, the capping and pausing mechanisms apply the same way.

first step

First, please check the information on how to operate it in the documentation.

We will send you a free document summarizing ROBOPRO's analysis process and risk management mechanism. If you do not wish to be contacted by a sales representative, please indicate so.

Crypto asset investment involves price fluctuation risk. Past performance does not guarantee future results. Customers must make their own final investment decisions.