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Senior MQL5 Expert: Risk-Free Execution Logic (40/30/30)

MQL5 エキスパート 外国為替 Python ウェブサイトへのデータのアップロード

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This is the Requirements Specification for your project, formatted to be clear, concise, and structured. This document will allow a professional MQL5/Python developer to accurately estimate the complexity, time, and cost of building your IATS.
IATS Project: Expert Advisor (MQL5) & Python Hub Specification
Job Title: AI-Validated Algorithmic Execution System Developer (MQL5/Python)
Project Goal: Build the high-speed execution engine and orchestration layer for a risk-first automated trading platform.
I. System Architecture & Environment
| Component | Responsibility | Technology | Requirements |
|---|---|---|---|
| Execution Engine | Trade Management, Data Feed, Auditing. | MQL5 (MT5) | Must be compiled for MetaTrader 5 (64-bit). |
| Orchestration Hub | AI Calls, Risk Calculation, Dashboard Server. | Python 3.10+ (with requests, Streamlit, SQLite) | Must run reliably 24/7 on a Windows VPS. |
| Communication | Bidirectional link between Python and MQL5. | Local HTTP/WebSockets (or file-based bridge) | Must ensure near-instantaneous (low-latency) command execution. |
| Risk Boundary | Max Risk Per Trade. | 1.0% of current account equity. | Non-negotiable hard limit. |
II. Module 1: MQL5 Expert Advisor (The Engine)
The MQL5 EA must perform three core functions: Execution, Management, and Data Fetching.
A. Execution and Lot Sizing
 * Instruction Input: The EA must constantly listen for and accept a single JSON command payload from the Python Hub, containing: Symbol, Type, Entry, SL, TP1, TP2, TP3, and the three pre-calculated Lot Sizes (\mathbf{40\%/30\%/30\%}).
 * Order Placement: Upon receiving a command, the EA must immediately open three separate market orders (Orders A, B, C) simultaneously for the target symbol, ensuring all orders share the same Initial Stop Loss (SL) but have unique Take Profit (TP) levels (TP1, TP2, TP3, respectively).
 * Unique Tracking: All three orders must be placed with a shared, custom Magic Number for unit tracking.
B. Mandatory Trade Manager (40%/30%/30% Protocol)
This logic must be implemented inside the OnTick() loop and use status flags to ensure rules are only executed once per trigger.
| Rule | Trigger Condition (Example: BUY Trade) | Required Action (Using PositionClosePartial and PositionModify) |
|---|---|---|
| Rule 1: Risk Elimination | Current Price (Bid) >= TP1 | 1. Close Order A (40% volume). 2. IMMEDIATELY Modify SL for the remaining \mathbf{60\%} (Orders B & C) to the Initial Entry Price (Breakeven). |
| Rule 2: Profit Securing | Current Price (Bid) >= TP2 and Rule 1 is complete. | Close Order B (30% volume). |
| Rule 3: Final Close | Current Price (Bid) >= TP3 and Rule 2 is complete. | Close Order C (30% volume), concluding the trade cycle. |
C. Data Feed and Auditing
 * Market Context Feed: The EA must calculate and package two pieces of data to send to the Python Hub upon request:
   * Trend Bias: Current price position relative to the 200-period EMA on the H4 timeframe (ABOVE or BELOW).
   * Volatility Check: Current Bid/Ask Spread in pips.
 * Audit Data Capture: Upon complete closure of the trade unit (Orders A, B, C are closed), the EA must calculate and log:
   * Final P/L (Profit/Loss).
   * Trade Duration.
   * Maximum Intrade Drawdown (MID): The largest unfavorable movement suffered by the position from the entry price before final closure.
III. Module 3: Python Orchestration Hub
The Python Hub manages external communication, the AI pipeline, and the final risk gate.
A. Core Risk and Lot Sizing
 * Maximum Risk Enforcement: The Hub must calculate the Total Lot Size based strictly on the current account balance and the \mathbf{1.0\%} risk limit, using the \text{SL} distance extracted by Agent 1.
 * Volume Allocation: The calculated Total Lot Size must be split into 40\%, 30\%, and 30\% components before being sent to the MQL5 EA.
B. Three-Stage Gemini AI Pipeline
The Hub must execute three sequential, independent calls to the Gemini 2.5 Flash API (TEXT_API_URL) for every incoming signal. The pipeline halts on any Veto signal.
| Agent | Input Data | Output Veto Rule |
|---|---|---|
| Agent 1: Translator | Raw Telegram Text | VETO: If JSON output is malformed or critical data (SL, TP) is missing. |
| Agent 2: Auditor | Raw Telegram Text | VETO: If Confidence Score is below 80 (threshold must be dynamically adjustable via the Streamlit Dashboard). |
| Agent 3: Strategist | Clean JSON from Agent 1 + Market Context Data from MQL5 | VETO: If the AI determines the trade violates a strategic rule (e.g., trading against the H4 200 EMA, or Spread > 2.0 pips). |
C. Dashboard and State Management
 * Streamlit Integration: Host the Streamlit web application on the VPS, dynamically reading and writing configuration variables (the three AI Prompts and Veto thresholds) to the SQLite database.
 * Live Data Integration: Pull and display current equity, P/L, and the logged MID/Audit data from the SQLite database onto the dashboard.
 * Gemini Dashboard Features: Implement the Auto-Refine Prompt (Text Generation API) and the TTS Weekly Recap (TTS API).
IV. Required Expertise & Timeframe
| Requirement | Level | Note |
|---|---|---|
| MQL5 Development | Expert | Deep experience required with complex order modification (PositionModify) and partial closures (PositionClosePartial). |
| Python Development | Expert | Required for multi-API orchestration (Gemini, Telegram), database management, and web framework (Streamlit) deployment. |
| Development Time | 8 Weeks | This is the minimum required timeline for clean coding and rigorous cross-platform testing of the complex bi-directional communication layer and trade management logic. |

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