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Developing an AmiBroker data plugin requires using the AmiBroker Development Kit (ADK)

, which provides the necessary C/C++ headers and sample source code to link external data feeds into the AmiBroker engine. Core Architecture

AmiBroker data plugins are standard Win32 DLLs (32-bit or 64-bit) that implement a specific set of exported functions. The engine communicates with these DLLs to request price data for specific tickers and timeframes. about.gitlab.com 1. Required Standard Exports

Every AmiBroker plugin must export these three core functions: GetPluginInfo

: Returns basic metadata like the plugin name, vendor, and a unique 4-character ID (PIDCODE). : Called when the plugin is loaded to initialize resources. : Called when the plugin is unloaded to free memory. 2. Primary Data Functions

To act as a data source, the plugin must implement functions to provide quotes: GetQuotesEx

: The modern function for fetching historical and real-time data. It receives a ticker name and periodicity and must fill a buffer with price arrays (Open, High, Low, Close, Volume, etc.). GetPluginConfig

: (Optional) Provides a custom configuration dialog for users to enter API keys or server settings.

: Used to inform AmiBroker of status changes (e.g., connection lost or new data available). AmiBroker Community Forum Key Development Resources

Optimizing Real-Time Data Plugin for Multiple Tickers - Plug-ins

AmiBroker data plugins are specialized Dynamic Link Libraries (DLLs) that bridge the software with external data sources like real-time brokers, proprietary databases, or web services. Core Architecture of a Data Plugin

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

GetPluginInfo(): The most critical entry point. AmiBroker scans the Plugins folder and ignores any DLL that does not export this function. It provides metadata like the plugin name, vendor, and version.

GetQuotesEx(): The primary function for data retrieval. It handles the actual request for price bars (OHLCV) and allows for 64-bit date/time stamps and floating-point volume.

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

GetStatus(): An optional function that reports the connection health (e.g., "Connected", "Trying to connect...") to the AmiBroker UI. Top Source Code Examples

Development typically uses C++ (via the ADK) or .NET (C#/VB.NET). ODBC/SQL Universal Data/AFL plugins - AmiBroker amibroker data plugin source code top

Developing a custom data plugin for AmiBroker allows you to stream real-time or historical market data from any source directly into the software's high-speed database. This is typically achieved using the AmiBroker Development Kit (ADK), which provides the necessary C/C++ headers and architectural guidelines. 1. Core Architecture and ADK

AmiBroker data plugins are regular Win32 Dynamic Link Libraries (.dll). To build one, you must implement specific exported functions that AmiBroker calls to communicate with your data source. Essential Exported Functions: Every plugin must include:

GetPluginInfo: Returns metadata like the plugin name, vendor, and a unique ID code to prevent conflicts.

Init() and Release(): Handle the setup and teardown of the plugin.

GetQuotesEx: The primary function for retrieving data. It handles 64-bit date/time stamps and floating-point values for volume and open interest.

Notify: Receives notifications from AmiBroker regarding database loads, unloads, or settings changes. 2. Available Source Code Templates

Developers can find starting points in several languages, depending on their expertise:

Native C/C++ (Official): The AmiBroker ADK is the standard tool. It includes a "Data_Template" project that can be compiled with Visual C++ 6.0 or newer versions like Visual Studio 2022.

C# / .NET SDK: For those preferring managed code, the AmiBroker .NET SDK on GitHub provides a wrapper that allows you to write plugins in C#.

Python Integration: While Python is often used for data scraping or "feeder" scripts (e.g., ami2py), a true data plugin typically requires a DLL bridge. 3. Implementation Patterns Modern plugins often use a two-part architecture:

A Connector: A script (often Python or Node.js) that fetches data via WebSockets or REST APIs from a broker or data provider.

The DLL Plugin: A compiled C++ or C# library that sits inside the AmiBroker/Plugins folder and feeds that data into the GetQuotesEx buffer. Starting Data plug in project - Amibroker Forum

Building a High-Performance AmiBroker Data Plugin: A Deep Dive into Source Code and Architecture

AmiBroker is renowned among quantitative traders for its blistering backtesting speed and flexibility. However, the software is only as good as the data feeding it. While many commercial vendors offer ready-made connectors, developing your own AmiBroker data plugin using the source code SDK allows for unparalleled customization—whether you’re plugging into a proprietary API, a crypto exchange, or a niche local database.

In this guide, we will explore the structural "top" tier of AmiBroker data plugin development, breaking down the C++ SDK essentials and how to optimize your source code for real-time performance. 1. The AmiBroker Development Kit (ADK)

To start, you need the AmiBroker Development Kit (ADK). This is a collection of C-style headers and sample C++ projects provided by AmiBroker's creator, Tomasz Janeczko. The ADK defines the standard interface that allows the Broker.exe process to communicate with external DLLs. Key Files in the Source: Developing an AmiBroker data plugin requires using the

Plugin.h: The core header file containing structure definitions like Quotations, StockInfo, and PluginInfo.

AmiRoot.cpp/h: Often used as the entry point for managing the connection lifecycle. 2. Core Functions Every Plugin Needs

When you look at the top-performing data plugin source codes, they all implement a specific set of exported functions. Without these, AmiBroker won't recognize your DLL. GetPluginInfo

This identifies your plugin to the system. It returns the name, vendor, and type of plugin (Data, Indicator, or Tools).

__declspec(dllexport) int GetPluginInfo(struct PluginInfo *pInfo) pInfo->Name = "Custom SQL Connector"; pInfo->Vendor = "YourName Quant Lab"; pInfo->Type = 1; // 1 for Data Plugin return 1; Use code with caution. GetQuotes

This is the "engine room." When AmiBroker needs data for a chart, it calls GetQuotes. A high-performance plugin source code should implement intelligent caching here. Instead of hitting your API every time a user scrolls, the plugin should store data in a local buffer. 3. Real-Time Streaming vs. Backfill

The "top" tier of plugins are those that handle both historical backfill and real-time "tick" data seamlessly.

Historical Backfill: Uses a loop to populate the Quotations array. Efficiency here depends on how you handle memory allocation—pre-allocating the array size based on the expected date range is a common optimization.

Real-Time Streaming: Requires a multi-threaded approach. Your source code should have a background thread listening to a WebSocket or Socket connection, pushing new ticks into a thread-safe queue that GetQuotesEx can then drain. 4. Best Practices for Professional Source Code

If you are searching for "top" source code examples, look for these architectural patterns:

Thread Safety: Since AmiBroker may request data for multiple charts simultaneously, your internal data structures (like a std::map of symbols) must be protected by Mutexes or Critical Sections.

Error Logging: Implement a robust logging system that writes to the AmiBroker "Log" window using SiteContext->LogMessage(). This makes debugging connection drops much easier.

Adaptive Polling: Top-tier plugins adjust their request frequency based on whether a symbol is currently being viewed or if it's just being updated in the background. 5. Where to Find Source Code Examples?

While the official ADK includes a "Universal Data Plug-in" sample, it is quite basic. For more advanced implementations, developers often look toward:

GitHub Repositories: Search for "AmiBroker Plugin C++" to find wrappers for modern APIs like Interactive Brokers (IBKR) or IQFeed.

AmiBroker Custom Dev Forum: A hub for veteran coders sharing snippets for specific data formats like JSON or Protocol Buffers. Conclusion Part 7: The Future – 64-bit Only &

Writing an AmiBroker data plugin is a rite of passage for serious systems traders. By mastering the ADK and focusing on thread-safe, cached data delivery, you can build a connector that matches the speed of the software it feeds.

The most authoritative "paper" and resource for AmiBroker data plugin source code is the AmiBroker Development Kit (ADK). It provides the official C/C++ header files, source code samples, and documentation necessary to interface with AmiBroker's internal structures. Official AmiBroker Development Kit (ADK)

The ADK is the primary reference for creating data plugins. It includes updated documentation and samples for 64-bit date/time resolution and floating-point volume fields.

Documentation & Headers: Contains the PluginInfo and Quotation structures required for data handling.

Key Functions: Focuses on functions like GetQuotesEx() for handling real-time and historical data streams. Download Links: Official EXE: ADK.exe Official ZIP: ADK.zip Git Mirror: AmiBroker Development Kit on GitLab Modern SDK Alternatives

If you prefer higher-level languages like C#, several open-source wrappers provide pre-built templates: Amibroker Data Plugin Source Code Top _hot_

It sounds like you are looking for top-tier features to include in an Amibroker data plugin (real-time or historical feed), specifically if you are writing or evaluating source code for one.

Below is a ranked list of must-have, advanced, and competitive features to implement in high-quality Amibroker data plugin source code.

Part 7: The Future – 64-bit Only & JSON APIs

The "top" source code of 2025 will abandon old COM interfaces entirely. Modern plugins are:

• Compiled with /MT (static runtime) for zero dependency.
• Using nlohmann/json or simdjson for parsing crypto exchange order books.
• Implementing asynchronous I/O via IOCP (Windows Completion Ports) to handle 10,000+ symbols.

Example of a modern symbol request (pseudo-code):

// In GetQuotesEx, for ACTION_REFRESH (real-time)
async_http_get("https://api.exchange.com/ticker?symbol=" + symbol, [&](json data) 
    pQuotes[0].fLast = data["price"];
    pQuotes[0].nVolume = data["vol"];
    pQuotes[0].fOpen = prevOpen; // Carry over
);

7. API / Source Code Quality (for Developers)

If you are selling or sharing the source code, add these:

• Clean separation – Data source transport layer (network) vs. Amibroker plugin interface.
• Example implementation – e.g., plugin that reads from a simple CSV file or WebSocket demo.
• Build scripts – CMake or Visual Studio project files for both x86 and x64.
• Documentation – Inline comments explaining the Amibroker SDK (especially tricky parts like GetQuotesEx flags).
• Thread safety – Proper mutex usage; Amibroker can call plugin functions from multiple threads.

Data Flow in 64-bit vs 32-bit

Modern Amibroker (v6.0+) strongly prefers 64-bit plugins. The top source code repositories avoid the legacy 32-bit __stdcall conventions in favor of __fastcall for speed.

Memory Management Warning: A common bug in third-party source code is improper handling of QuoteEx arrays. The plugin must fill pQuote->fOpen, fHigh, fLow, fClose, nVolume atomically to avoid chart glitches.

Part 1: Why Build or Reverse-Engineer a Data Plugin?

Before diving into source code, we must understand the hierarchy. Amibroker uses a Plugin SDK (Software Development Kit) to interface with external data sources. The "top" plugins (like those for Interactive Brokers, eSignal, or custom WebSocket feeds) share three traits:

1. Low latency (microsecond tick resolution).
2. Bidirectional sync (symbol discovery + real-time updates).
3. Memory efficiency (no garbage collection stalls during market hours).

Building your own plugin gives you absolute control: you can connect to proprietary APIs, crypto exchanges, or DDE servers without paying monthly data fees.