Amibroker Data | Plugin Source Code Top !!link!!

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

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

Developing a high-performance data plugin for AmiBroker requires a deep understanding of its C++ SDK and the mechanics of real-time data streaming. AmiBroker’s architecture is designed for speed, and its plugin system allows developers to feed external market data—whether from a REST API, WebSocket, or local database—directly into the software’s database engine. The Foundation of an AmiBroker Plugin

The core of any AmiBroker data plugin is a dynamic link library (DLL) written in C++. AmiBroker provides a specialized Software Development Kit (SDK) that defines the required entry points and structures. The most critical component is the PluginInfo structure, which tells AmiBroker the name of the plugin, its version, and what capabilities it supports, such as intraday data, tick-by-tick updates, or backfill functionality.

To initiate communication, the plugin must export several mandatory functions. The GetPluginInfo function is the first point of contact, providing metadata to the host application. Once the user selects the data source in AmiBroker's settings, the Init function is called to set up resources, while Release handles the cleanup when the application closes or the data source is changed. Managing Data Streams and Backfills

The true complexity of a data plugin lies in how it handles the GetQuotes and GetExtraData functions. AmiBroker operates on a "pull" or "notification" basis. When the software needs to update a chart, it calls the plugin to see if new data is available. A robust plugin must implement an efficient buffering system. Since market data often arrives in bursts, the plugin should store incoming ticks in a thread-safe queue and then flush them to AmiBroker's memory structures during the update cycle.

Backfilling is another essential feature. When a user opens a new symbol, the plugin must recognize that historical data is missing and trigger a request to the data provider's server. This is typically handled through a background thread to ensure that the AmiBroker user interface remains responsive while the historical bars are being downloaded and processed. Performance and Stability Considerations

Because AmiBroker is a 32-bit or 64-bit multi-threaded application, thread safety is paramount. Developers must use mutexes or critical sections when accessing shared data structures to prevent crashes. Furthermore, memory management must be impeccable; leaking memory in a data plugin will eventually lead to system instability, especially during long trading sessions where millions of ticks may be processed.

Optimization is also key. Using efficient data structures for symbol lookups, such as hash maps, and minimizing the overhead of string manipulations can significantly improve the speed at which the plugin feeds data to the UI. A well-coded plugin not only delivers data accurately but does so with minimal CPU footprint, allowing the user to run complex AFL (AmiBroker Formula Language) scripts without lag. Conclusion

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. amibroker data plugin source code top

To understand how AmiBroker data plugin source code works, it helps to view it through the lens of a developer building a bridge between raw financial data and a high-speed charting engine The Quest for "Total Control": A Developer’s Story

In the world of quantitative trading, data is everything. For many developers using

, the standard data feeds aren't enough—they need to connect to custom APIs, proprietary databases, or specialized brokers. This is where the AmiBroker Development Kit (ADK)

becomes the "holy grail" for those seeking "Total Control" over their data arrays. 1. Building the Foundation (The DLL) Our story begins in Microsoft Visual C++ (or even the free ). An AmiBroker data plugin is essentially a Win32 Dynamic Link Library (DLL)

. To make it talk to the main program, every plugin must expose three core functions: GetPluginInfo : Tells AmiBroker who you are (your plugin's name and ID).

: The handshake where the plugin wakes up and prepares its connections. : The graceful exit when the user closes the database. 2. The Bridge to Data A developer starts with a simple project template from the . They copy Plugin.cpp

into their workspace. In these files, they define how to handle "Quotes." The plugin acts as a translator: it takes incoming data (like a JSON stream from a WebSocket) and converts it into a format AmiBroker understands—specifically, an array of structures containing Date, Time, Open, High, Low, Close, and Volume. 3. Real-Time vs. Backfill 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:

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. The Configuration and Handshake Layer: The most fundamental

AmiBroker Custom Dev Forum: A hub for veteran coders sharing snippets for specific data formats like JSON or Protocol Buffers. Conclusion

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 official resource for AmiBroker data plugin source code is the AmiBroker Development Kit (ADK). While the source code for proprietary plugins like Interactive Brokers is not publicly available, the ADK provides full example codes for various data plugins to help you build your own. Top Official & Community Resources

AmiBroker Development Kit (ADK): The definitive starting point for writing your own plugins. It includes C++ source code examples for plugins like QuoteTracker and QP2.

Access the ADK on GitLab for the base source files and documentation.

AmiBroker .NET SDK: If you prefer working with C# or .NET rather than native C++, this open-source SDK allows you to create data plugins more easily.

Explore the kriasoft AmiBroker .NET SDK on GitHub for full implementation details, including the DataSource.cs template.

WsRtd WebSocket Data Plugin: A modern, high-performance plugin that uses WebSocket-JSON communication for real-time data streaming.

Find the Rtd_Ws_AB_plugin source code on GitHub, which is broker-agnostic and supports historical backfilling.

Q2Ami Plugin: An open-source project on GitHub that provides headers like Plugin.h detailing the AmiBroker plugin interface structure. Quick Implementation Steps

Download the SDK: Start with either the official C++ ADK or the community .NET SDK.

Define Plugin Info: Update the GetPluginInfo() method with your plugin's metadata.

Implement Data Logic: Add your quote-fetching logic inside methods like GetQuotesEx() or the equivalent in your chosen language.

Install: Compile your code into a .dll and place it in the C:\Program Files\AmiBroker\Plugins directory.

Do you need help debugging a specific error in your plugin's C++ or .NET implementation?

Download the Ib.dll (data plugin) source code - Plug-ins - AmiBroker Community Forum

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

, which provides the necessary C/C++ headers and sample source code to interface with the AmiBroker core engine.

Below is a structured "paper" or guide on setting up and coding a data plugin from scratch. 1. Getting Started: The AmiBroker Development Kit (ADK)

The ADK is the official package for C/C++ developers to build custom indicator or data plugin DLLs. Get the latest ADK from the AmiBroker Download Page Essential Files: The kit includes

, which contains the required data structures and function prototypes for the plugin interface. about.gitlab.com 2. Development Environment Setup You can use standard C++ environments like Visual Studio or even the free about.gitlab.com Project Type: Create a new Win32 Dynamic-Link Library (DLL) Configuration: Set the project to build a to your project's include path. Ensure the calling convention is for exported functions. about.gitlab.com 3. Key Functions to Implement

A data plugin must export specific functions that AmiBroker calls to retrieve quotes and configuration details. GetPluginInfo

Returns the plugin's name, version, and type (Data/Indicator).

The core function that provides price data (OHLCV) to AmiBroker when requested. SetTimeFrame Notifies the plugin of the current chart's time interval.

Handles events like database opening, closing, or workspace changes.

(Optional) Opens a dialog for user-specific settings like API keys or server addresses. 4. Basic Source Code Structure The most efficient way to start is using the Data_Template provided in the ADK archive. about.gitlab.com // Sample skeleton for GetPluginInfo GetPluginInfo( PluginInfo *pInfo ) { pInfo->nStructSize = PluginInfo ); pInfo->nType = // 1 for Data Plugin pInfo->nVersion = // version 1.0.0 strcpy( pInfo->szID, "MY_DATA_SOURCE" ); strcpy( pInfo->szName, "My Custom Data Feed" Use code with caution. Copied to clipboard 5. Installation and Testing Data Plugin creation - Plug-ins - AmiBroker Community Forum The Request/Response Handler: This is the engine room

Title: Deconstructing the Architecture: A Guide to Amibroker Data Plugin Source Code

Introduction

In the ecosystem of technical analysis software, Amibroker stands out as a preferred platform for algorithmic traders due to its high-speed backtesting engine and flexible coding environment. However, the engine is only as good as the fuel it receives. This "fuel" comes in the form of market data, supplied via plugins. For developers and trading firms, accessing and understanding the "top" or most critical aspects of Amibroker data plugin source code is essential for creating custom data feeds, integrating with proprietary APIs, and ensuring low-latency execution. This essay explores the architecture, critical components, and significance of the source code behind Amibroker data plugins.

The Architecture of Connectivity

To understand the source code, one must first understand the interface. Amibroker does not simply read raw text files; it utilizes a plugin architecture based on a standardized interface definition (often utilizing C++). This allows third-party developers to create Dynamic Link Libraries (DLLs) that act as a bridge between a data vendor’s API and the Amibroker charting engine.

The "top" of the source code hierarchy refers to the entry points and the structural headers that define how the plugin communicates with the host application. The source code is typically structured around a set of callback functions and exported methods that Amibroker calls during its runtime cycle. These functions handle everything from the initial handshake (identifying the plugin name and version) to the granular retrieval of price ticks.

Critical Components of the Source Code

When examining the source code of a high-quality Amibroker plugin, three distinct layers of code stand out as the "top" priorities for developers:

1. The Configuration and Handshake Layer: The most fundamental part of the source code manages the connection logic. This involves the GetPluginInfo and Init functions. In the source code, this section defines the plugin’s unique ID, the supported data types (e.g., EOD, Intraday, Tick), and the status of the connection. Robust source code in this layer includes error handling to manage disconnections, ensuring that the plugin can auto-reconnect to the data vendor’s server without crashing the Amibroker application.

2. The Request/Response Handler: This is the engine room of the plugin. The source code here defines how Amibroker requests historical or real-time data. It typically involves mapping Amibroker’s internal request structures (asking for a specific symbol, timeframe, or date range) to the external data vendor’s API queries. Efficient source code in this section utilizes asynchronous programming techniques. Since network requests can be slow, "top-tier" source code avoids blocking the main Amibroker thread, ensuring the user interface remains responsive while data loads in the background.

3. The Data Mapping and Transformation Layer: Data vendors rarely send data in the exact format Amibroker requires. The source code must therefore contain logic to transform incoming packets. This involves mapping vendor-specific price structures to the Amibroker Quotation structure—translating fields like Open, High, Low, Close, and Volume. Furthermore, advanced source code handles time-zone conversions and corporate actions (splits and dividends), ensuring that the data visualized in the chart is accurate and adjusted correctly.

Significance of Source Code Transparency

The availability or development of custom source code offers significant advantages over off-the-shelf, "black-box" plugins.

Firstly, it allows for latency optimization. For high-frequency traders, the speed at which a tick arrives from the exchange to the chart is paramount. By accessing the source code, developers can strip away unnecessary logging or validation layers found in generic plugins, optimizing the "copy" operations in memory to achieve microsecond-level efficiencies.

Secondly, it enables proprietary integration. Many institutional traders use bespoke execution systems or internal data lakes. Standard plugins do not exist for these private systems. Writing the source code allows a firm to bridge their proprietary database directly into Amibroker, leveraging its analytical power without changing their data infrastructure.

Challenges and Best Practices

While the benefits are substantial, writing this source code is not without challenges. The primary difficulty lies in thread safety. Amibroker is multi-threaded, meaning it can request data for multiple symbols simultaneously. If the source code is not written with thread-safe logic (using mutexes or critical sections), race conditions can occur, leading to corrupted data or software crashes. Therefore, the "top" concern for any developer is ensuring that global variables and connection handles are managed safely across concurrent threads.

Conclusion

The "top" of Amibroker data plugin source code is not merely a collection of syntax; it is the architectural blueprint that dictates the fidelity and speed of market data analysis. By mastering the handshake layer, optimizing the request handlers, and ensuring precise data mapping, developers can transform Amibroker from a standard charting tool into a powerful, custom-tailored trading terminal. Whether for reducing latency or integrating proprietary data streams, the ability to navigate and engineer this source code remains a cornerstone of advanced quantitative trading.

C. The DDE / Excel Bridge Plugin (Legacy but Robust)

• Use case: Piping data from Bloomberg or Reuters terminals.
• Source code tip: The top implementations use WM_COPYDATA or shared memory segments alongside the DDE callback to avoid cross-thread marshalling costs.

1. The "CSV/File Plugin" – Best for Learning (Top Simplicity)

• Structure: Reads from a memory-mapped file updated by an external script.
• Top Feature: Uses ReadDirectoryChangesW to detect new data without polling.
• Key file: FileWatcher.cpp

WebSocket Integration (Using libwebsockets)

// Real-time WebSocket thread
DWORD WINAPI WebSocketThread(LPVOID lpParam)
struct lws_context_creation_info info;
    memset(&info, 0, sizeof(info));
    info.port = CONTEXT_PORT_NO_LISTEN;
    info.protocols = protocols;
struct lws_context *context = lws_create_context(&info);
while(!g_shutdown)
lws_service(context, 50); // 50ms timeout
// Parse incoming JSON tick
    if(new_tick_arrived)
QuoteEx q;
        q.dDateTime = current_time;
        q.dOpen = json_tick["price"];
        q.dHigh = json_tick["price"];
        q.dLow = json_tick["price"];
        q.dClose = json_tick["price"];
        q.ulVolume = json_tick["volume"];
// Push to AmiBroker via callback
        g_pDataSite->AddRealTimeQuote(&q);

Why this represents "top" source: It uses asynchronous lws_service, not blocking recv(). This ensures AmiBroker can request data simultaneously while the plugin ingests ticks.

4. Symbol Handling & Metadata

• Dynamic symbol discovery – Provide a symbol list from the data source (e.g., from a broker API or CSV config).
• Symbol mapping – Map external symbol names (e.g., AAPL from broker) to Amibroker symbols.
• Market center / exchange filter – Allow user to enable/disable specific exchanges.
• Watchdog for symbol status – Detect delisted or suspended symbols and notify Amibroker.

1. Core Protocol Features (Base Requirements)

These are non-negotiable for any plugin source to function correctly.

• Full 32-bit & 64-bit Support – Amibroker 6.0+ requires 64-bit plugins; older versions need 32-bit. The source should compile to both.
• Plugin_ID and Plugin_Version correctly implemented – Amibroker uses these to manage multiple plugins.
• Proper callback functions – GetQuotesEx, GetQuotes2, GetHistorical, GetExtraData.
• Reliable field mapping – O/H/L/C/V, OpenInt, etc., mapped to Amibroker’s internal QuoteField enum.

Part 6: Common Pitfalls in Public Source Code

When evaluating "top" source code you find online, watch for these red flags:

1. Missing Thread Safety: Many amateur plugins crash because GetQuotesEx is called from Amibroker’s UI thread AND a background timer. Use std::mutex or EnterCriticalSection.
2. String Encoding: Amibroker expects LPCTSTR (which compiles to LPCWSTR in Unicode builds). Top source code uses TCHAR macros everywhere. Hardcoded ASCII strings cause symbol corruption.
3. Volume Overflows: Using int for volume. Use double or __int64 as shown in the SDK's QUOTE struct.

2. High-Performance Data Streaming (Real-time)

This is what separates basic plugins from "top" ones.

• Asynchronous quote processing – Never block Amibroker’s UI thread. Use separate threads for network I/O.
• Tick aggregation – Convert incoming ticks to 1-minute or user-defined interval bars inside the plugin (efficiently).
• Dynamic buffer management – Avoid memory reallocation during high tick rates. Pre-allocate circular buffers.
• Minimal latency path – Use lightweight parsing (e.g., hand-written decoders for binary protocols like WebSocket binary or FIX).

Top-Tier Practice (Stack Allocation or Object Pool):

QuoteEx quote; // Stack allocated - auto cleanup
quote.dClose = 100.50;
g_pDataSite->AddRealTimeQuote("msft", "e); // Plugin site copies data

Or, for high-frequency scenarios:

// Static pre-allocated pool
QuoteEx* GetQuoteFromPool()
static QuoteEx pool[10000];
    static int idx = 0;
    return &pool[idx++ % 10000];