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7.6 KiB
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194 lines
No EOL
7.6 KiB
Markdown
# Article-23893-Building-AIPowered-Trading-Systems-MQL5-Part-12
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This repository is an article-derived reference project based on the original MQL5 article. It does not claim to reproduce the full original source code unless files are explicitly attached.
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## Overview
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This repository documents and references the MQL5 article **“Building AI-Powered Trading Systems in MQL5 (Part 12): Giving the Assistant Chart Vision and Tool Access.”** It is an article-derived MQL5 project focused on extending an AI trading assistant with two major capabilities:
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- chart vision through screenshots and image attachments
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- tool access for live terminal, market, account, and calendar data
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The article describes how an Expert Advisor-based assistant can move beyond plain text chat by sending chart images to a multimodal model and by allowing the model to request specific live data through a controlled tool-calling workflow.
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## Original Article
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- **Article ID:** 23893
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- **Title:** Building AI-Powered Trading Systems in MQL5 (Part 12): Giving the Assistant Chart Vision and Tool Access
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- **Author:** Allan Munene Mutiiria
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- **Publication date:** Not available
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- **Categories:** Trading Systems, Integration, Expert Advisors, Statistics
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- **Article URL:** https://www.mql5.com/en/articles/23893
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- **Author URL:** https://www.mql5.com/en/users/29210372
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## Repository Purpose
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This repository exists to preserve the structure and technical ideas presented in the original article in a reusable repository format.
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A reader can use this repository to:
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- understand how chart screenshots can be captured and sent to an AI model from MQL5
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- study a multimodal message flow that combines text and image content
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- learn how tool-calling can expose controlled terminal data to an assistant
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- reuse the article’s architectural ideas for MQL5 dashboard panels, image rendering, and request orchestration
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- inspect the project file layout explicitly listed in the article attachments
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Because this repository is article-derived, its completeness depends on the files actually attached or otherwise provided by the author.
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## Key Concepts
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- Trading Systems
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- Integration
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- Expert Advisors
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- Statistics
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- Multimodal AI messaging
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- Chart screenshot capture
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- Base64 image encoding
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- Tool calling
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- Live market context injection
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- Chart object inspection
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- Trade history summarization
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- Economic calendar access
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- Canvas-based UI rendering
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## Algorithm / Architecture Summary
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The article presents a combined UI, image-processing, and AI-integration workflow for an MQL5 assistant.
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1. **Capture chart imagery**
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- The assistant captures the current chart as a screenshot.
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- The panel UI is temporarily hidden so the screenshot contains only the chart.
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- PNG is used for request payload/archive purposes, while BMP is used for local panel display.
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2. **Encode the image for transport**
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- The PNG bytes are converted to base64.
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- Line breaks are removed so the encoded image can be embedded safely into JSON.
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3. **Stage the attachment before sending**
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- A pending attachment state stores:
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- image base name
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- width and height
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- base64 payload
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- The user can attach or remove the staged screenshot before sending the message.
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4. **Build multimodal request content**
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- The outgoing message becomes a content array rather than plain text.
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- It includes:
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- a text item for the prompt
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- an image item using a base64 data URL
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- This allows the model to reason about actual chart visuals instead of typed descriptions only.
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5. **Render image previews inside the panel**
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- The local BMP copy is decoded for display.
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- Thumbnails are cached using a bounded cache strategy.
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- Inline thumbnails and a compose-chip preview are drawn into the dashboard.
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6. **Open a full-image viewer**
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- Clicking a thumbnail opens a native-resolution viewer.
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- The viewer supports:
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- full-resolution image display
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- vertical and horizontal scrolling
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- grab-pan interaction
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- close controls
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- This is handled through dedicated viewer state and interaction routing.
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7. **Clean up image files**
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- Stored chat history references image markers.
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- When a chat is deleted, related PNG/BMP files are also deleted.
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8. **Define callable tools for the model**
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- The article defines a JSON tool menu for the model with functions for:
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- indicator retrieval
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- open positions
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- chart objects
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- trade history
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- economic calendar events
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9. **Dispatch tool calls inside MQL5**
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- Tool-call arguments are parsed from JSON.
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- A dispatcher routes the request to the appropriate MQL5 function.
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- Tool results are returned as readable text rather than raw binary or complex structures.
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10. **Attach automatic market context**
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- A live market snapshot can be appended automatically to each message.
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- This context includes current symbol/timeframe details, prices, recent candles, positions, and account status.
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11. **Run a tool-call loop during send**
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- The assistant sends the request with optional vision content and tool definitions.
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- If the model responds with tool calls:
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- the terminal executes each requested tool
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- tool outputs are appended to the conversation
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- the request loop continues until the model returns final text or the round limit is reached
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Overall, the architecture combines a chart-facing UI layer, an image handling pipeline, a tool exposure layer, and a controlled model interaction loop.
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## Mentioned or Attached Files
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### Explicitly attached files
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The article explicitly lists these attached files:
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- `AI Canvas Theme.mqh`
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- `AI Canvas Primitives.mqh`
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- `AI JSON FILE.mqh`
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- `AI Canvas State.mqh`
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- `AI Canvas Scrollbar.mqh`
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- `AI Canvas Editor.mqh`
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- `AI Canvas Render.mqh`
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- `AI Logic.mqh`
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- `AI Canvas Interact.mqh`
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- `AI Canvas Shell.mqh`
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- `AI EA PART 12.mq5`
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- `MQL5.zip`
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### ZIP attachment
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An attached ZIP archive is available in the processed input:
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- `https://www.mql5.com/en/articles/download/23893_271278.zip?s=0cee6d030d20a2b0f90ab6d2f4d447bafd07f9c216585854de7d946f8ee1b136&t=1790945720`
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### Files mentioned in text
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The article also mentions internal image storage outputs used by the implementation:
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- PNG screenshot copies
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- BMP screenshot copies
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## Statistics
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- **Word count:** Not available
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- **Reading time:** Not available
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- **Image count:** Not available
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- **Code block count:** Not available
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- **File count:** Not available
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## Tags
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- mql5
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- metatrader-5
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- trading-systems
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- integration
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- expert-advisors
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- statistics
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- difficulty-advanced
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## Difficulty
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**Advanced**
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Reason: the article combines MQL5 UI rendering, chart screenshot handling, base64 encoding, JSON request construction, multimodal AI messaging, tool schema design, live terminal data extraction, and iterative tool-call orchestration.
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## Limitations
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- This repository is derived from the article and may not reproduce the full original project unless the attached files are included here.
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- The implementation depends on files actually attached or provided by the author.
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- The README is based on article metadata and article content analysis, not on verification of every attached source file inside the ZIP archive.
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- Internal ZIP contents were described in the article, but repository completeness still depends on what was actually extracted and committed.
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- The project should not be assumed production-ready unless explicitly stated by the original author, which was not established in the processed input.
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## Reference
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- Original article: https://www.mql5.com/en/articles/23893
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- Author profile: https://www.mql5.com/en/users/29210372 |