指定
Hello,
I have an existing XAUUSD market-analysis system called
I am looking for an experienced MQL5 developer to perform a technical review and prepare a precise architecture for rebuilding the existing SIGNAL ENGINE / ANALYSIS LOGIC in MQL5.
IMPORTANT:
This request is ONLY about the signal-analysis engine.
I am NOT requesting an Expert Advisor (EA), automated trading system, order execution, or live trading functionality.
The desired result of this project stage is:
Python Signal Engine
↓
MQL5 Signal / Analysis Engine
The MQL5 component should be a reusable analysis module capable of receiving market data and producing the same analytical outputs and final signal decisions as the existing Python implementation.
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FILES PROVIDED
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I will provide only the files related to the signal and market-analysis logic:
1.
app\services\gold_analysis_service.py
2.
app\services\market_data_feed.py
3.
app\services\sr_panel_service.py
4.
app\services\engines\base.py
5.
app\services\engines\structure.py
6.
app\services\engines\liquidity.py
7.
app\services\engines\momentum.py
8.
app\services\engines\sr.py
The complete project will NOT be provided.
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EXISTING ANALYSIS SYSTEM
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The current signal engine contains the following analysis components:
1. Smart Money Concepts (SMC)
2. Order Blocks
3. Fair Value Gaps (FVG)
4. Break of Structure (BOS)
5. Change of Character (CHOCH)
6. Liquidity / Liquidity Sweep
7. Volume
8. Multi-Timeframe Analysis
9. Momentum
10. Trend Strength
11. Support / Resistance
12. News Filter
The system also contains logic for:
- Market Structure
- Swing detection
- Direction determination
- Multi-Timeframe direction
- Counter-Trend analysis
- Weighted scoring
- Core confirmations
- Entry Zone
- Entry price
- Stop Loss
- TP1
- TP2
- TP3
- Risk/Reward calculation
- Final signal decision
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PRIMARY OBJECTIVE
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The objective is to understand the existing Python implementation and reproduce its signal-analysis behavior in MQL5 as accurately as technically possible.
This is a PORTING / REBUILDING task.
It is NOT a strategy-development task.
Please do not redesign the strategy or replace any component with your own methodology.
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STRICT REQUIREMENTS
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The MQL5 implementation must preserve the existing logic as closely as possible.
1. CLOSED CANDLES ONLY.
2. Absolutely no lookahead.
3. No future candle information may be used.
4. Preserve the existing Market Structure logic.
5. Preserve the existing Swing logic.
6. Preserve BOS and CHOCH definitions.
7. Preserve Liquidity / Liquidity Sweep logic.
8. Preserve Order Block and FVG logic.
9. Preserve Multi-Timeframe logic.
10. Preserve existing scoring logic and weights.
11. Preserve existing thresholds and gates.
12. Preserve Entry Zone logic.
13. Preserve SL calculation.
14. Preserve TP1 / TP2 / TP3 calculation.
15. Preserve RR calculation.
16. Do not add new indicators.
17. Do not remove existing analyzers.
18. Do not loosen or simplify existing conditions.
19. Do not replace the system with a conventional RSI/EMA strategy.
20. Do not introduce a new strategy.
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MULTI-TIMEFRAME
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The primary analysis timeframe is:
M15
The system also uses:
M5
M15
H1
H4
D1
depending on the specific analysis component.
Please reproduce the existing timeframe relationships and ensure that each timeframe uses the correct closed candle.
MTF synchronization must be designed specifically to prevent:
- Lookahead
- Future-bar leakage
- Incorrect candle alignment
- Using an unfinished higher-timeframe candle
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SIGNAL PIPELINE
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Please trace the complete existing pipeline:
Market Data
→ Evidence
→ 12 Analyzers
→ Direction
→ MTF
→ Score
→ Core Confirmation
→ Counter-Trend
→ Entry Zone
→ Entry
→ SL
→ TP1 / TP2 / TP3
→ RR
→ Final Signal Decision
I want the MQL5 architecture to preserve this logical flow.
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PHASE 1 — TECHNICAL REVIEW ONLY
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Before writing any implementation code, please provide a complete technical assessment.
The Phase 1 report should include:
1. Complete architecture map of the existing Python signal engine.
2. Detailed mapping of all 12 analyzers.
For each analyzer provide:
- Required market data
- Inputs
- Calculation logic
- Outputs
- Score contribution
- Gate contribution
- Dependencies
- MQL5 implementation approach
3. Python → MQL5 mapping.
Clearly identify:
- Directly portable logic
- Logic requiring reimplementation
- Logic requiring an adapter
- Logic where exact 1:1 parity may not be possible
4. Market-data requirements.
5. MTF synchronization design.
6. Closed-candle implementation.
7. No-lookahead protection.
8. Entry calculation mapping.
9. SL calculation mapping.
10. TP1 / TP2 / TP3 mapping.
11. RR calculation mapping.
12. Proposed MQL5 module/class architecture.
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SIGNAL PARITY
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A major requirement is the ability to validate:
Python Signal Engine
VS
MQL5 Signal Engine
using identical market data.
The validation framework should eventually allow comparison of:
- Timestamp
- Symbol
- Timeframe
- Direction
- Analyzer outputs
- Score
- Entry
- SL
- TP1
- TP2
- TP3
- RR
- Final signal state
Any discrepancy must be reported explicitly.
Do not silently modify calculations to make the outputs appear identical.
If exact parity is impossible for any component, document the reason and the expected difference.
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STRICTLY OUT OF SCOPE
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This project stage does NOT include:
- Expert Advisor
- Order execution
- Automated trading
- Position management
- Money management
- Martingale
- Grid
- HFT
- Trailing Stop execution
- Breakeven execution
- Account management
- Live trading
- Dashboard
- React frontend
- Support Assistant
- Telegram
- NTFY
- Payments
- Subscription system
- License system
- User management
- Admin system
- Marketing
- GoldPulse V4
- Any unrelated project functionality
Do not implement any of these.
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SECURITY / PROJECT SCOPE
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The complete project will not be shared.
I will not provide:
- .env files
- API keys
- Credentials
- Private keys
- Telegram tokens
- License secrets
- User data
- Database files
- Frontend files
- Backup files
- GoldPulse V4 files
- Any unrelated proprietary or sensitive files
The provided files are strictly for understanding and rebuilding the signal-analysis logic.
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EXPECTED FIRST DELIVERABLE
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For this first stage, I want ONLY:
READ
+
ANALYZE
+
ARCHITECTURE
Please do NOT start implementation yet.
Please first send:
1. Technical assessment
2. Complete signal-engine architecture
3. Python → MQL5 mapping
4. Dependency map
5. MTF synchronization design
6. No-lookahead design
7. Proposed MQL5 module/class structure
8. Signal parity testing methodology
9. Any technical limitations or areas where exact parity may not be possible
I will review and approve the architecture before any implementation begins.
The goal is to reproduce the existing GoldPulsev6 signal-analysis logic accurately in MQL5, not to create a different strategy.
Thank you.
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