The latest articles on DEV Community by Dragomir Dikov (@dragomir_dikov). https://dev.to/dragomir_dikov https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3977141%2F62fc982d-6733-46d2-afbb-cdf942ccbcf7.jpg https://dev.to/dragomir_dikov en Dragomir Dikov Thu, 25 Jun 2026 09:38:09 +0000 https://dev.to/jlabsco/how-we-built-jungletrade-a-modular-market-intelligence-platform-3onl https://dev.to/jlabsco/how-we-built-jungletrade-a-modular-market-intelligence-platform-3onl <blockquote> <p>Building a unified market intelligence platform for traders, analysts, researchers, and developers.</p> </blockquote> <p>After months of development, Jungletrade is now publicly available.</p> <p>The idea behind Jungletrade is simple: modern market analysis has become fragmented.</p> <p>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.</p> <p>We wanted to explore a different approach.</p> <h2> 📊 The Problem </h2> <p>Most market platforms focus on a specific layer of the analytical stack:</p> <ul> <li>Raw data</li> <li>Technical indicators</li> <li>Quantitative models</li> <li>Trading signals</li> </ul> <p>Each layer provides value, but users are frequently required to move between multiple tools to connect the pieces.</p> <p>Our goal was to create a modular ecosystem where these layers can coexist within a single platform.</p> <h2> 🧭 The Jungletrade Ecosystem </h2> <p>Today, Jungletrade provides four product categories:</p> <h3> 📦 Data </h3> <p>Structured datasets for market research and discovery.</p> <h3> 🧠 Models </h3> <p>Analytical frameworks designed to identify patterns and relationships within market data.</p> <h3> 📈 Indicators </h3> <p>Tools that transform raw information into actionable insights.</p> <h3> ⚡ Triggers </h3> <p>Event-driven signals designed to highlight potential market opportunities.</p> <h2> 🔍 Built for Transparency </h2> <p>One design decision was particularly important to us: every product should explain itself.</p> <p>Each product includes:</p> <ul> <li>Product description</li> <li>Key features</li> <li>Use cases</li> <li>Interpretation guidelines</li> <li>Methodology overview</li> </ul> <p>The objective is not simply to provide charts but to explain the problem being solved and how the underlying analysis works.</p> <h2> 🔌 API First </h2> <p>All products available through the platform are also accessible through API endpoints.</p> <p>Developers interested in integrating Jungletrade data into their own applications, dashboards, or research pipelines can request a demo API key through the platform.</p> <h2> 🏗️ Architecture </h2> <p>Jungletrade is built using a modular, service-oriented architecture designed to support both high-performance market data delivery and computationally intensive analytical workloads.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>Communication between services is handled through cross-language gRPC, enabling efficient interoperability between .NET and Python components while maintaining a clean separation of responsibilities.</p> <p>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.</p> <p>As the platform evolves, new products can be integrated as independent modules while continuing to operate within the same unified ecosystem.</p> <h2> 🌍 Free Access </h2> <p>All products are currently available through the Jungletrade interface without registration.</p> <p>We want users to be able to evaluate the platform and the underlying methodologies before committing to any integration.</p> <h2> 🚀 Architecture and Roadmap </h2> <p>Jungletrade is built on a modular architecture that allows new products to be introduced without disrupting the existing ecosystem.</p> <p>Future development will focus on:</p> <ul> <li>Additional datasets</li> <li>New analytical models</li> <li>Enhanced indicators</li> <li>Expanded trigger frameworks</li> <li>API improvements</li> </ul> <h2> 🌱 Looking Forward </h2> <p>This public launch is the first step.</p> <p>We're interested in feedback from traders, analysts, researchers, and developers.</p> <p>If you have ideas, questions, or suggestions, we'd love to hear them.</p> <h3> Visit </h3> <p><a href="https://jungletrade.ai" rel="noopener noreferrer">https://jungletrade.ai</a></p> <p><strong>AI-Powered • Data Driven • Built For Traders</strong></p> ai api architecture python Dragomir Dikov Thu, 25 Jun 2026 09:20:31 +0000 https://dev.to/dragomir_dikov/how-we-built-jungletrade-a-modular-market-intelligence-platform-17h https://dev.to/dragomir_dikov/how-we-built-jungletrade-a-modular-market-intelligence-platform-17h <blockquote> <p>Building a unified market intelligence platform for traders, analysts, researchers, and developers.</p> </blockquote> <p>After months of development, Jungletrade is now publicly available.</p> <p>The idea behind Jungletrade is simple: modern market analysis has become fragmented.</p> <p>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.</p> <p>We wanted to explore a different approach.</p> <h2> 📊 The Problem </h2> <p>Most market platforms focus on a specific layer of the analytical stack:</p> <ul> <li>Raw data</li> <li>Technical indicators</li> <li>Quantitative models</li> <li>Trading signals</li> </ul> <p>Each layer provides value, but users are frequently required to move between multiple tools to connect the pieces.</p> <p>Our goal was to create a modular ecosystem where these layers can coexist within a single platform.</p> <h2> 🧭 The Jungletrade Ecosystem </h2> <p>Today, JungleTrade provides four product categories:</p> <h3> 📦 Data </h3> <p>Structured datasets for market research and discovery.</p> <h3> 🧠 Models </h3> <p>Analytical frameworks designed to identify patterns and relationships within market data.</p> <h3> 📈 Indicators </h3> <p>Tools that transform raw information into actionable insights.</p> <h3> ⚡ Triggers </h3> <p>Event-driven signals designed to highlight potential market opportunities.</p> <h2> 🔍 Built for Transparency </h2> <p>One design decision was particularly important to us: every product should explain itself.</p> <p>Each product includes:</p> <ul> <li>Product description</li> <li>Key features</li> <li>Use cases</li> <li>Interpretation guidelines</li> <li>Methodology overview</li> </ul> <p>The objective is not simply to provide charts but to explain the problem being solved and how the underlying analysis works.</p> <h2> 🔌 API First </h2> <p>All products available through the platform are also accessible through API endpoints.</p> <p>Developers interested in integrating JungleTrade data into their own applications, dashboards, or research pipelines can request a demo API key through the platform.</p> <h2> 🏗️ Architecture </h2> <p>JungleTrade is built using a modular, service-oriented architecture designed to support both high-performance market data delivery and computationally intensive analytical workloads.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>Communication between services is handled through cross-language gRPC, enabling efficient interoperability between .NET and Python components while maintaining a clean separation of responsibilities.</p> <p>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.</p> <p>As the platform evolves, new products can be integrated as independent modules while continuing to operate within the same unified ecosystem.</p> <h2> 🌍 Free Access </h2> <p>All products are currently available through the Jungletrade interface without registration.</p> <p>We want users to be able to evaluate the platform and the underlying methodologies before committing to any integration.</p> <h2> 🚀 Architecture and Roadmap </h2> <p>Jungletrade is built on a modular architecture that allows new products to be introduced without disrupting the existing ecosystem.</p> <p>Future development will focus on:</p> <ul> <li>Additional datasets</li> <li>New analytical models</li> <li>Enhanced indicators</li> <li>Expanded trigger frameworks</li> <li>API improvements</li> </ul> <h2> 🌱 Looking Forward </h2> <p>This public launch is the first step.</p> <p>We're interested in feedback from traders, analysts, researchers, and developers.</p> <p>If you have ideas, questions, or suggestions, we'd love to hear them.</p> <h3> Visit </h3> <p><a href="https://jungletrade.ai" rel="noopener noreferrer">https://jungletrade.ai</a></p> <p><strong>AI-Powered • Data Driven • Built For Traders</strong></p> ai python api architecture