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Experienced Python/MT5 Developer Needed to Optimize XAUUSD Signal Engine — Increase Signal Frequency Without Sacrificing Quality
4 minutes ago Python Experts Design Strategy optimization Statistics and mathematics Python
I am looking for an experienced developer specialized in Python, MetaTrader 5, algorithmic trading systems, market structure and XAUUSD, to work on an existing professional Gold/Forex AI signal platform.
The project is already developed and operational. I am NOT looking for a developer to build a new trading system from scratch.
The main objective is to improve the existing signal-generation architecture so that it can produce approximately 2 high-quality XAUUSD signals per day on average, while preserving signal quality and avoiding a simple relaxation of all existing filters.
The target is an average, not a guarantee of exactly two signals every calendar day.
Current Problem
The current engine is highly selective.
It uses multiple confirmation layers including:
Smart Money Concepts
Order Blocks
Fair Value Gaps
BOS
CHOCH
Liquidity
Volume
Multi-Timeframe analysis
Momentum
Trend Strength
Support/Resistance
News Filter
The current system can identify meaningful market movements, but many potential opportunities are rejected before becoming actionable signals.
We have already performed extensive historical and shadow testing.
Several obvious approaches have already been tested and should NOT simply be repeated, including:
Lowering the main score threshold
Loosening PHASE_B / MTF restrictions
Changing counter-trend thresholds
Removing individual confirmation requirements
Using SuperTrend + SMA as a replacement signal engine
Quality-gated direct entry
Entry-zone fallback experiments
Various alternative candidate families
Some of these increased signal quantity, but the resulting realistic performance was not sufficiently robust.
An important finding from recent realistic Tick-data testing is that entry/execution selection can create adverse selection. Therefore, I do not want a developer to solve the problem simply by generating more signals or weakening every gate.
What I Need
I need an experienced developer to diagnose and redesign the signal-generation pathway intelligently, while preserving the core philosophy of the existing system.
The goal is:
More opportunities + maintained quality + realistic execution performance.
The desired target is approximately:
≥ 2 quality XAUUSD signals/day on average
while maintaining:
Positive expectancy
Robust out-of-sample performance
Reasonable drawdown
Stable results across different years/market regimes
Realistic spread/slippage assumptions
No look-ahead bias
Closed-candle logic
No repainting
Important Requirement
Do not simply lower thresholds until two signals/day appear.
I want the developer to identify where good opportunities are currently being rejected, determine whether those rejected opportunities form a statistically valid secondary signal path, and then design the smallest defensible architectural change.
The solution may involve:
A secondary signal path
Better separation between trigger / confirmation / context
Direction-aware interpretation of existing analyzers
Better handling of mixed vs genuinely opposing evidence
Improved entry methodology
Better distinction between signal validity and entry location
A controlled secondary-quality tier
But I want these conclusions to be based on data and reproducible tests, not assumptions.
Existing Architecture
The project is a Python/FastAPI-based XAUUSD analysis platform with a React dashboard.
The Signal Engine currently uses the following 12 analyzers:
Smart Money
Order Block
Fair Value Gap
Break of Structure
CHOCH
Liquidity
Volume
Multi-Timeframe
Momentum
Trend Strength
Support/Resistance
News Filter
There is also a Signal Manager responsible for things such as duplicate/repeat protection, cooldown and active-signal management.
Development Philosophy
The developer must:
First inspect the existing architecture.
Understand the current signal flow completely.
Reproduce the current Production baseline.
Identify the real bottleneck.
Propose measurable changes.
Test changes in Shadow/Read-Only mode first.
Compare against the current Production baseline.
Use realistic spread/slippage and, where available, real MT5 Tick data.
Validate IS/OOS and yearly stability.
Only implement Production changes after approval.
No Blind Refactoring
I do not want:
A complete rewrite
A new unrelated strategy
Random threshold optimization
Curve fitting
Overfitting to one period
Look-ahead
Repainting
Future candle information
Synthetic results presented as live results
Changing multiple variables without attribution
Every meaningful modification must have a measurable reason.
Required Deliverables
The developer should provide:
1. Architecture audit
Explain exactly:
Market Data → Snapshot → Analyzers → Direction → Score → Gates → Entry → Manager → Signal
and identify where good opportunities are being lost.
2. Baseline reproduction
Reproduce the current Production results before modifying anything.
3. Bottleneck analysis
Show which gates/rejections are responsible for the majority of missed opportunities.
4. Proposed solution
Explain the proposed architecture before implementation.
5. Shadow test
Test the proposed solution without changing Production.
6. Performance comparison
At minimum:
Signals/day
Executed signals
Win Rate
AvgR
Profit Factor
Total R
Maximum Drawdown
Confidence interval
IS/OOS
Annual performance
Performance by market regime
Spread/slippage sensitivity
7. Final implementation
Only after the Shadow results are approved.
Files I Can Provide
After the developer confirms exactly what he needs, I can provide the relevant project files.
The initial review should focus on these files:
app/services/gold_analysis_service.py app/services/signal_service.py app/services/market_data_feed.py app/services/sr.py app/services/sr_quality.py app/services/scoring.py app/services/signal_state_service.py app/services/outcome_service.py app/services/engines/regime_engine.py app/tasks/scheduler.py
And the relevant API/config files if required:
app/config.py
app/api/v1/routes/analysis.py
app/api/v1/routes/signals.py
I can also provide relevant historical signal/outcome data and diagnostic reports.
If MT5 execution/entry behavior is part of the investigation, the developer can request the relevant MT5 integration files separately.
Important
The project is GoldPulsev6 only.
Do not touch GoldPulse V4.
The existing frontend is not the main focus unless a UI change is specifically required to display the new signal state.
Developer Requirements
Please apply only if you have strong experience with:
Python
FastAPI
MetaTrader 5
XAUUSD
Algorithmic trading
Market structure / SMC
Backtesting
Tick-level data
MTF analysis
Statistical validation
Avoiding look-ahead and overfitting
I am particularly interested in a developer who can diagnose an existing production system rather than simply write more code.
Please include in your proposal:
Your experience with XAUUSD signal engines.
Similar projects you have worked on.
How you would approach increasing signal frequency while preserving quality.
How you validate against overfitting.
Whether you can work with real MT5 Tick data.
Your estimated timeline.
Your proposed Phase 1 scope and price.
Phase 1 should be analysis + architecture + reproducible baseline + Shadow proposal. No Production changes without approval.
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