DEV Community: Dragomir Dikov

From Order Book Data to Liquidity Walls: Building BASTION

Dragomir Dikov — Mon, 10 Aug 2026 13:29:25 +0000

Raw order book data is easy to collect.

Turning it into something a quantitative model, trading system, or AI application can actually reason about is a different problem.

That was one of the ideas behind BASTION — a new JungleTrade product designed to transform cryptocurrency market depth into structured liquidity-wall data.

The Problem

An order book gives us a snapshot of supply and demand across price levels:

bids below the current market price
asks above the current market price
quantities available at each level

At first glance, finding a liquidity wall might seem straightforward: find the largest orders.

But that approach quickly becomes problematic.

Liquidity is distributed differently across markets and snapshots. A quantity that is significant under one market condition may be completely ordinary under another.

There is also another problem:

Not every large visible order represents meaningful liquidity.

Orders can appear, disappear, or be repositioned. Some may represent genuine trading interest, while others may potentially be associated with spoofing behavior.

So instead of asking:

"Where are the largest orders?"

we wanted to ask:

"Where is liquidity statistically concentrated?"

A Multi-Method Approach

There is no single statistical definition of a liquidity wall.

For that reason, BASTION analyzes the same order book using four complementary approaches.

1. Peak Detection

Peak Detection searches for local concentrations of liquidity relative to neighboring price levels.

This provides a structural view of where liquidity forms noticeable local maxima within the order book.

2. Z-Score Analysis

Z-Score analysis looks at liquidity relative to its statistical distribution.

Price levels with unusually high liquidity can therefore be identified based on how far they deviate from typical observations.

3. Interquartile Range Analysis

The IQR approach provides another way of identifying unusually large liquidity concentrations without relying on the same assumptions as Z-Score analysis.

This gives us an additional perspective on potential outliers in market depth.

4. Kernel Density Estimation

KDE approaches the problem differently.

Instead of considering liquidity only at individual price levels, it allows us to examine where liquidity forms areas of concentration across the price dimension.

This is particularly useful when liquidity is distributed across several nearby levels rather than concentrated in a single order.

Why Use Multiple Methods?

Because they are answering slightly different questions.

A local peak is not necessarily a statistical outlier.

A statistical outlier is not necessarily located inside a broader area of liquidity concentration.

Rather than deciding that one definition is universally correct, BASTION exposes several analytical perspectives on the same underlying market depth.

The resulting levels can then be compared or incorporated into downstream analytical systems.

Before the Analysis: Spoofing Detection

There is another important step.

Before analyzing liquidity concentrations, BASTION applies spoofing-order detection intended to reduce the influence of potentially deceptive order activity.

This matters because simply detecting an unusually large order and calling it a liquidity wall can produce misleading results.

The objective is not to claim that every suspicious order can be identified perfectly.

Instead, spoofing detection becomes another filtering layer in the pipeline before the statistical analysis is performed.

Conceptually, the process looks like this:

Market Depth
↓
Bid / Ask Order Book
↓
Spoofing Detection
↓
Liquidity Analysis
↓
Peak | Z-Score | IQR | KDE
↓
Structured Liquidity Levels

From Liquidity to Support and Resistance

Once liquidity concentrations have been identified, they can provide additional context for potential support and resistance.

Bid-side concentrations may indicate areas where significant buying liquidity exists below the current market price.

Ask-side concentrations may indicate areas where significant selling liquidity exists above it.

But there is an important distinction:

BASTION does not treat these levels as deterministic price predictions.

They are observations about the current structure of market liquidity.

The market can change.

Orders can move.

Liquidity can disappear.

That makes the output more useful as structured market information than as a simple "price will bounce here" signal.

Making the Output Machine-Readable

This was an important design consideration for us.

The output shouldn't only be something displayed on a chart.

Once liquidity structures are represented consistently, they can become inputs for other systems:

quantitative research pipelines
algorithmic trading models
execution analysis
trading dashboards
market regime models
AI agents

This creates an interesting progression:

Raw market depth → structured liquidity → quantitative intelligence

For example, instead of only asking where liquidity walls exist at a particular moment, we can begin asking how those structures evolve through time.

Do liquidity levels persist?

Do they migrate?

Does their relationship with market price change?

Can changes in liquidity structure help identify transitions between different market regimes?

Those questions move beyond BASTION itself, but BASTION provides structured data that can be used to investigate them.

Try BASTION

We made the UI version of BASTION completely free to explore and it does not require registration.

You can experiment with the different analytical methods and inspect how they identify liquidity structures from market depth:

https://jungletrade.ai/en/marketplace/M3

For developers interested in working with the data programmatically, BASTION is also available through the JungleTrade API.

We are continuing to explore how order book structure can be transformed into useful inputs for quantitative models and AI-driven financial systems.

Feedback from developers working with market data, trading systems, quantitative finance, or market microstructure is very welcome.

How We Built Jungletrade: A Modular Market Intelligence Platform

Dragomir Dikov — Thu, 25 Jun 2026 09:38:09 +0000

Building a unified market intelligence platform for traders, analysts, researchers, and developers.

After months of development, Jungletrade is now publicly available.

The idea behind Jungletrade is simple: modern market analysis has become fragmented.

Market data, indicators, analytical models, and trading signals are often distributed across multiple platforms, forcing users to maintain several subscriptions, workflows, and dashboards just to build a complete market view.

We wanted to explore a different approach.

📊 The Problem

Most market platforms focus on a specific layer of the analytical stack:

Raw data
Technical indicators
Quantitative models
Trading signals

Each layer provides value, but users are frequently required to move between multiple tools to connect the pieces.

Our goal was to create a modular ecosystem where these layers can coexist within a single platform.

🧭 The Jungletrade Ecosystem

Today, Jungletrade provides four product categories:

📦 Data

Structured datasets for market research and discovery.

🧠 Models

Analytical frameworks designed to identify patterns and relationships within market data.

📈 Indicators

Tools that transform raw information into actionable insights.

⚡ Triggers

Event-driven signals designed to highlight potential market opportunities.

🔍 Built for Transparency

One design decision was particularly important to us: every product should explain itself.

Each product includes:

Product description
Key features
Use cases
Interpretation guidelines
Methodology overview

The objective is not simply to provide charts but to explain the problem being solved and how the underlying analysis works.

🔌 API First

All products available through the platform are also accessible through API endpoints.

Developers interested in integrating Jungletrade data into their own applications, dashboards, or research pipelines can request a demo API key through the platform.

🏗️ Architecture

Jungletrade is built using a modular, service-oriented architecture designed to support both high-performance market data delivery and computationally intensive analytical workloads.

The user interface is built with React, Next.js, and Node.js, providing a modern, responsive platform for accessing market intelligence products across the ecosystem.

At the core of the platform, .NET services are responsible for orchestration, business logic, data processing pipelines, and scalable multithreaded operations that power the underlying infrastructure.

For advanced quantitative research and analytical processing, Jungletrade leverages Python-based services responsible for statistical modeling, machine learning workflows, data science pipelines, and market intelligence calculations.

Communication between services is handled through cross-language gRPC, enabling efficient interoperability between .NET and Python components while maintaining a clean separation of responsibilities.

This architecture allows us to combine the performance and scalability of .NET, the flexibility of the modern JavaScript ecosystem, and the extensive scientific computing capabilities available in Python.

As the platform evolves, new products can be integrated as independent modules while continuing to operate within the same unified ecosystem.

🌍 Free Access

All products are currently available through the Jungletrade interface without registration.

We want users to be able to evaluate the platform and the underlying methodologies before committing to any integration.

🚀 Architecture and Roadmap

Jungletrade is built on a modular architecture that allows new products to be introduced without disrupting the existing ecosystem.

Future development will focus on:

Additional datasets
New analytical models
Enhanced indicators
Expanded trigger frameworks
API improvements

🌱 Looking Forward

This public launch is the first step.

We're interested in feedback from traders, analysts, researchers, and developers.

If you have ideas, questions, or suggestions, we'd love to hear them.

Visit

https://jungletrade.ai

AI-Powered • Data Driven • Built For Traders

How We Built JungleTrade: A Modular Market Intelligence Platform

Dragomir Dikov — Thu, 25 Jun 2026 09:20:31 +0000

Building a unified market intelligence platform for traders, analysts, researchers, and developers.

After months of development, Jungletrade is now publicly available.

The idea behind Jungletrade is simple: modern market analysis has become fragmented.

We wanted to explore a different approach.

📊 The Problem

Most market platforms focus on a specific layer of the analytical stack:

Raw data
Technical indicators
Quantitative models
Trading signals

Each layer provides value, but users are frequently required to move between multiple tools to connect the pieces.

Our goal was to create a modular ecosystem where these layers can coexist within a single platform.

🧭 The Jungletrade Ecosystem

Today, JungleTrade provides four product categories:

📦 Data

Structured datasets for market research and discovery.

🧠 Models

Analytical frameworks designed to identify patterns and relationships within market data.

📈 Indicators

Tools that transform raw information into actionable insights.

⚡ Triggers

Event-driven signals designed to highlight potential market opportunities.

🔍 Built for Transparency

One design decision was particularly important to us: every product should explain itself.

Each product includes:

Product description
Key features
Use cases
Interpretation guidelines
Methodology overview

The objective is not simply to provide charts but to explain the problem being solved and how the underlying analysis works.

🔌 API First

All products available through the platform are also accessible through API endpoints.

Developers interested in integrating JungleTrade data into their own applications, dashboards, or research pipelines can request a demo API key through the platform.

🏗️ Architecture

JungleTrade is built using a modular, service-oriented architecture designed to support both high-performance market data delivery and computationally intensive analytical workloads.

The user interface is built with React, Next.js, and Node.js, providing a modern, responsive platform for accessing market intelligence products across the ecosystem.

For advanced quantitative research and analytical processing, JungleTrade leverages Python-based services responsible for statistical modeling, machine learning workflows, data science pipelines, and market intelligence calculations.

Communication between services is handled through cross-language gRPC, enabling efficient interoperability between .NET and Python components while maintaining a clean separation of responsibilities.

As the platform evolves, new products can be integrated as independent modules while continuing to operate within the same unified ecosystem.

🌍 Free Access

All products are currently available through the Jungletrade interface without registration.

We want users to be able to evaluate the platform and the underlying methodologies before committing to any integration.

🚀 Architecture and Roadmap

Jungletrade is built on a modular architecture that allows new products to be introduced without disrupting the existing ecosystem.

Future development will focus on:

Additional datasets
New analytical models
Enhanced indicators
Expanded trigger frameworks
API improvements

🌱 Looking Forward

This public launch is the first step.

We're interested in feedback from traders, analysts, researchers, and developers.

If you have ideas, questions, or suggestions, we'd love to hear them.

Visit

https://jungletrade.ai

AI-Powered • Data Driven • Built For Traders