Amibroker Data Plugin Source Code Top [updated]

C++ header files necessary to interact with the AmiBroker core API. Documentation on plugin architecture.

: Used by AmiBroker to signal events to the plugin, such as when a database is loaded or unloaded.

AmiBroker's user interface and calculation threads operate independently. Your plugin must handle data ingestion concurrently without locking the main application thread.

Creating a custom AmiBroker data plugin allows you to stream live ticks, historical bars, and fundamental data directly into your database. This guide details the development architecture, core structures, and implementation logic required to build a high-performance data plugin using the AmiBroker Development Kit (ADK). 1. Understanding the AmiBroker Plugin Architecture amibroker data plugin source code top

return 0;

Created by a prolific community developer, this is a modern, actively-maintained data plugin using WebSocket and JSON communication. It includes features like backfill support, bi-directional communication, and the ability to run multiple instances connected to different servers. It also provides Python example scripts, bridging the gap for developers more comfortable with Python for data fetching before feeding it into C++.

AmiBroker uses a custom 64-bit integer format to pack date and time (including seconds or milliseconds). You must convert standard UNIX timestamps or ISO strings into AmiBroker's packed format using ADK helper functions like PackDate . C++ header files necessary to interact with the

To create a functional data plugin, you must implement specific exported functions defined in the AmiBroker Development Kit (ADK) .

The main workhorse for a data plugin is GetQuotesEx . It is called by AmiBroker whenever it needs quotes for a particular symbol and date range.

Creating a top-tier AmiBroker data plugin is a bridge between raw financial data and sophisticated technical analysis. By mastering the C++ SDK, implementing reliable threading models, and ensuring efficient data throughput, a developer can create a seamless experience for traders. While the initial development curve is steep, the resulting ability to integrate any data source into AmiBroker provides a powerful competitive edge in the world of automated trading and market analysis. implementing reliable threading models

Do not use new , malloc , or dynamic STL container allocations inside GetQuotesEx . AmiBroker pre-allocates the pQuotes array buffer. Fill it directly using raw pointers or optimized indexing. Thread Synchronization

Open and create a new C++ Dynamic-Link Library (DLL) project.