Analysis of Trader Behavior Prediction Capabilities in DEX Based on Identification and Modeling

Decentralized exchanges (DEX) represent a unique ecosystem where all transactions are recorded on the blockchain, providing an unprecedented level of transparency. This opens up opportunities for identifying market participants, predicting their behavior, and detecting manipulations. Let's examine how data from a video about market manipulations can be applied to the concept of a "desire orderbook" in the context of DEX.

DEX Trader Identification Network Visualizing the DEX ecosystem: Translating anonymous blockchain transactions into detailed trader profiles and behavioral vectors.

Trader Identification in DEX

Unlike traditional exchanges, on DEX each participant has a unique wallet address, allowing their activity to be tracked with high precision. Research shows that even based on a limited set of data (about 100 transactions), it's possible to create "embeddings" or vector representations that accurately characterize a specific participant's trading style¹.

This identification approach has several advantages:

Ability to recognize traders with up to 84% accuracy among 100,000 candidates¹
Creation of dense vector representations reflecting a unique trading style
Scalability of the identification method with an increase in the candidate pool

Behavior Prediction and the "Desire Orderbook" Concept

Based on identified trader behavior patterns, a predictive model can be built that assesses the probability of certain actions. This allows for the creation of a "desire orderbook" concept – an order book that reflects not only current but also potential future orders.

Desire Orderbook Prediction Model The Desire Orderbook: A holographic projection of predicted market liquidity, combining existing orders with probabilistic models of future trader actions.

Components of the prediction model:

Analysis of historical patterns - studying a trader's typical behavior in various market conditions
Assessment of current portfolio state - analysis of asset balance and distribution
Contextual factors - considering time of day, day of week, market trends
Behavioral triggers - identifying events that usually precede a decision to sell

Such a model allows not only predicting the behavior of individual traders but also aggregating these predictions to create a more complete picture of potential supply and demand.

Number of Active Traders as a Market Asset

An interesting concept considers the number of active traders as an independent market asset. In traditional economics, liquidity is often measured by trading volume, but in the context of DEX, the number of unique active participants may be an equally important indicator.

Advantages of this approach:

Market health indicator - a large number of independent participants usually indicates a healthy market
Resistance to manipulation - the more independent participants, the harder it is to manipulate the market
Predictor of future liquidity - growth in the number of participants often precedes growth in trading volumes

Detection of Manipulations on DEX

The transparency of DEX creates unique opportunities for identifying various types of manipulations that were described in the video:

DEX Manipulation Detection Anomaly Detection in Action: Uncovering repetitive self-trading patterns and wash-trading cycles buried within the chaotic stream of onchain data.

1. Self-Trading

In DEX, self-trading is particularly noticeable since all transactions are recorded on the blockchain. The DEFIRANGER system, described in a research paper, is capable of detecting such manipulations by analyzing Cash Flow Tree (CFT) and identifying patterns characteristic of self-trading².

Signs of self-trading on DEX:

Transactions between related addresses
Unusually large, overlapping buy/sell orders at almost identical prices
Repetitive transaction cycles without economic sense

2. Price-Setting

In DEX, price-setting occurs through interaction with the orderbook or liquidity pools. Manipulators may try to influence the price by placing large orders they don't plan to fully execute.

Detection methods:

Analysis of anomalies in order curves compared to simulations of fair markets³
Identification of symmetry violations or recurring irregular behaviors
Standardization of order patterns and their comparison with reference models

3. Pump-and-Dump

The "pump and dump" scheme in cryptocurrencies includes four phases: pre-launch, launch, pump, and dump. On DEX, these phases may be more noticeable due to transaction transparency.

Signs of Pump-and-Dump on DEX:

Accumulation phase: quiet purchase of a large quantity of tokens
Pump phase: artificial price increase using self-trading or aggressive orders
Dump phase: mass selling at artificially inflated prices

Technical Solutions for Implementation

Implementing the described concepts will require a combination of several technologies:

Machine learning systems for creating trader embeddings and predicting their behavior
Transaction graph analysis for identifying related addresses and self-trading patterns
Market simulation models for creating reference patterns and detecting anomalies
Real-time monitoring systems for prompt detection of suspicious activity

Limitations and Ethical Considerations

Despite the potential benefits, the proposed approach has a number of limitations:

Privacy issues - although the blockchain is pseudonymous, detailed behavior analysis may violate users' privacy expectations
False positives - legitimate trading strategies may be erroneously classified as manipulations
Adaptation of manipulators - awareness of detection methods may lead to the development of more sophisticated manipulation schemes

Conclusion

The concept of identifying traders on DEX and predicting their behavior to create a "desire orderbook" represents an innovative approach to market analysis. The transparency of DEX creates unique opportunities for detecting manipulations and creating a fairer trading environment.

However, implementing such a system requires a careful balance between the effectiveness of manipulation detection and user privacy protection. Additionally, it's necessary to consider that even the most sophisticated prediction algorithms have limitations, especially in conditions of high volatility and uncertainty characteristic of cryptocurrency markets.

Overall, the integration of machine learning methods, transaction graph analysis, and simulation models can significantly increase the transparency and efficiency of DEX, creating a fairer and more manipulation-resistant trading environment.

Citation

@software{soloviov2025analysistraderpredictiondex,
  author = {Soloviov, Eugen},
  title = {Analysis of Trader Behavior Prediction Capabilities in DEX Based on Identification and Modeling},
  year = {2025},
  url = {https://marketmaker.cc/en/blog/post/analysis-trader-prediction-dex},
  version = {0.1.0},
  description = {How DEX transparency and modern identification methods allow predicting trader behavior, detecting manipulations, and building a desire orderbook.}
}

Footnotes

DEX Trader Identification Network Visualizing the DEX ecosystem: Translating anonymous blockchain transactions into detailed trader profiles and behavioral vectors.

Trader Identification in DEX

This identification approach has several advantages:

Ability to recognize traders with up to 84% accuracy among 100,000 candidates¹
Creation of dense vector representations reflecting a unique trading style
Scalability of the identification method with an increase in the candidate pool

Behavior Prediction and the "Desire Orderbook" Concept

Desire Orderbook Prediction Model The Desire Orderbook: A holographic projection of predicted market liquidity, combining existing orders with probabilistic models of future trader actions.