The 'Desire Orderbook' Concept: An Innovative Approach to Market Behavior Prediction

The "desire orderbook" represents a revolutionary concept in market structure analysis, based on predicting potential actions of market participants before their actual execution. Unlike a standard orderbook that reflects current buy and sell orders, the "desire orderbook" is built on assumptions about traders' future intentions.

Theoretical Foundation of the Concept

The "desire orderbook" concept is based on the assumption that most traders have an internal model of targeted position realization, which is often not reflected in current market orders. This model assumes that traders:

• Enter positions with a specific exit plan
• Have flexible realization strategies depending on market conditions
• Are psychologically attached to certain price levels (round numbers, entry levels, breakeven points)
• Tend to distribute the realization of large positions through a series of smaller orders

Gradient Approach to Order Modeling

The key innovation of the concept lies in presenting each potential order not as a point event, but as a gradient or "ladder" of probabilities:

P(sell|price, trader) = f(price, entry_price, position_size, market_conditions, trader_history)

Where:

• P(sell|price, trader) - probability that a trader will place a sell order at a certain price
• f - a complex function taking into account various factors

Methodology for Building a Desire Orderbook

1. Trader Profiling

The first step is creating trader profiles based on their historical behavior:

• Strategic profile: tendency towards short-term or long-term trading
• Risk profile: loss tolerance and profit taking
• Behavioral profile: reaction to market movements and news
• Temporal profile: preferred time of activity

2. Analysis of Open Positions

For each identified trader in DEX, the following are analyzed:

• Current open positions (size, entry price)
• Historical patterns of closing similar positions
• Average position holding period

3. Building Probability Gradients

For each open position, a realization probability gradient is built:

Realization gradient = {(price₁, probability₁), (price₂, probability₂), ... (priceₙ, probabilityₙ)}

This gradient reflects not just a single order, but a distribution of probabilities for placing orders at different price levels. For example:

• A trader with a long position may have a high probability of selling at levels +10%, +20%, +50% from the entry price
• A trader with a short position may have a high probability of closing when certain support levels are reached

4. Aggregation of Gradients into a Single Desire Orderbook

The final step is overlaying thousands of individual gradients to create a single "desire orderbook":

Desire_Orderbook(price) = ∑ᵢ Realization_gradient_trader_i(price)

Visually, this can be represented as a heat map of potential supply and demand at various price levels.

Application in the DEX Context

Decentralized exchanges provide unique opportunities for implementing the "desire orderbook" concept due to:

1. Complete transaction transparency - ability to track historical patterns of each address
2. Immutability of records - historical data cannot be deleted or modified
3. Ability to identify related addresses through transaction graph analysis

Practical Example of Use

Let's consider a specific scenario of using the "desire orderbook":

1. Identification of the 1000 largest holders of token X
2. Analysis of historical selling patterns after similar market events
3. Building selling probability gradients for each holder
4. Aggregation into a single "desire orderbook"
5. Identification of potential sell or buy "walls" at certain price levels

Advantages Over Traditional Orderbook

The "desire orderbook" has several significant advantages:

1. Predictive value - shows potential orders before they are placed
2. Revealing hidden levels of supply/demand - identifies levels where significant pressure may arise
3. Strategic planning - allows traders to optimize their entries and exits considering the likely behavior of other participants
4. Manipulation detection - anomalous patterns in the "desire orderbook" may indicate coordinated manipulative actions

Machine Learning Model for Building Gradients

For effective construction of individual gradients, a multi-level machine learning model can be used:

Level 1: Trader Type Classification

• Input data: historical transactions, position sizes, holding time
• Output data: trader category (speculator, investor, market maker, etc.)

Level 2: Realization Probability Prediction

• Input data: trader type, current position, market conditions
• Output data: probability distribution for various price levels

Level 3: Temporal Analysis

• Input data: realization probabilities, temporal activity patterns
• Output data: probability distribution by time and price

Technical Challenges and Solutions

Implementation of the "desire orderbook" comes with several technical challenges:

1. Computational complexity - overlaying thousands of gradients requires significant computational resources

   • Solution: use of distributed computing, optimized aggregation algorithms

2. User privacy - detailed behavior analysis may raise privacy concerns

   • Solution: data anonymization, aggregation at the level of user groups

3. Dynamic updating - market conditions and trader intentions change rapidly

   • Solution: incremental model updating in real-time

Visualization of the Desire Orderbook

Visualization of the "desire orderbook" can take various forms:

1. Heat map - color intensity reflects the probability of orders appearing at a given price level
2. Volume profile - three-dimensional representation where the third dimension is probability
3. Gradient ribbon - continuous gradient showing smooth probability changes

Conclusion

The "desire orderbook" concept represents a revolutionary approach to market structure analysis, especially in the context of decentralized finance. The transition from a deterministic orderbook to a probabilistic one allows for a substantially expanded understanding of potential market dynamics.

By representing orders not as point events but as probability gradients, this model more accurately reflects real trader behavior, who often distribute their buy or sell decisions across various price levels. The overlay of thousands of such gradients creates a multidimensional map of potential market activity, revealing hidden levels of support and resistance.

In an era where data is becoming the new oil, the "desire orderbook" concept represents an innovative way to extract valuable insights from existing blockchain data, potentially revolutionizing approaches to market analysis and trading strategies.

Citation

@software{soloviov2025desireorderbook,
  author = {Soloviov, Eugen},
  title = {The 'Desire Orderbook' Concept: An Innovative Approach to Market Behavior Prediction},
  year = {2025},
  url = {https://marketmaker.cc/en/blog/post/desire-orderbook},
  version = {0.1.0},
  description = {Desire orderbook — a revolutionary concept of market structure analysis based on predicting potential actions of market participants before their actual execution.}
}