Python Workspace

MQL5-Python Translation Workspace Skill

Seamless MQL5 indicator translation to Python with autonomous validation and self-correction.

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Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.

When to Use This Skill

Use this skill when the user wants to:

• Export market data or indicator values from MetaTrader 5
• Translate MQL5 indicators to Python implementations
• Validate Python indicator accuracy against MQL5 reference
• Understand MQL5-Python workflow capabilities and limitations
• Troubleshoot common translation issues

Activation Phrases: "MQL5", "MetaTrader", "indicator translation", "Python validation", "export data", "mql5-crossover workspace"

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Core Mission

Main Theme: Make MQL5-Python translation as seamless as possible through:

1. Autonomous workflows (headless export, CLI compilation, automated validation) 1. Validation-driven iteration (>=0.999 correlation gates all work) 1. Self-correction (documented failures prevent future mistakes) 1. Clear boundaries (what works vs what doesn't, with alternatives)

Project Root: ~/Library/Application Support/CrossOver/Bottles/MetaTrader 5/drive_c

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Workspace Capabilities Matrix

WHAT THIS WORKSPACE CAN DO

1. Automated Headless Market Data Export (v3.0.0)

Status: PRODUCTION (0.999920 correlation validated)

What It Does:

• Fetches OHLCV data + built-in indicators (RSI, SMA) from any symbol/timeframe
• True headless via Wine Python + MetaTrader5 API
• No GUI initialization required (cold start supported)
• Execution time: 6-8 seconds for 5000 bars

Command Example:

CX_BOTTLE="MetaTrader 5" \
WINEPREFIX="$HOME/Library/Application Support/CrossOver/Bottles/MetaTrader 5" \
wine "C:\\Program Files\\Python312\\python.exe" \
  "C:\\users\\crossover\\export_aligned.py" \
  --symbol EURUSD --period M1 --bars 5000

Use When: User needs automated market data exports without GUI interaction

Limitations: Cannot access custom indicator buffers (API restriction)

Reference: /docs/guides/WINE_PYTHON_EXECUTION.md

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Reference Documentation

For detailed information, see:

• Capabilities Detailed - In-depth capability documentation
• Complete Workflows - End-to-end user workflows
• Troubleshooting & Errors - Requirements, assumptions, error patterns
• Validation Metrics - Success metrics and version history

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Troubleshooting

Post-Execution Reflection

After this skill completes, check before closing:

1. Did the command succeed? — If not, fix the instruction or error table that caused the failure. 2. Did parameters or output change? — If the underlying tool's interface drifted, update Usage examples and Parameters table to match. 3. Was a workaround needed? — If you had to improvise (different flags, extra steps), update this SKILL.md so the next invocation doesn't need the same workaround.

Only update if the issue is real and reproducible — not speculative.

Skill: MQL5→Python Translation Workspace Skill

2. GUI-Based Custom Indicator Export (v4.0.0)

Status: ✅ PRODUCTION (file-based config system complete)

What It Does:

• Exports custom indicator values via file-based configuration
• 13 configurable parameters (symbol, timeframe, bars, indicator flags)
• Flexible parameter changes without code editing
• Execution time: 20-30 seconds (manual drag-and-drop required)

Workflow:

# Step 1: Generate config
python generate_export_config.py \
  --symbol EURUSD --timeframe M1 --bars 5000 \
  --laguerre-rsi --output custom_export.txt

# Step 2: Drag ExportAligned.ex5 to chart in MT5 GUI, click OK
# Step 3: CSV exported to MQL5/Files/

Use When: User needs custom indicator values (Laguerre RSI, proprietary indicators)

Limitations: Requires GUI interaction (not fully headless)

Reference: /docs/guides/V4_FILE_BASED_CONFIG_WORKFLOW.md

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3. Rigorous Validation Framework

Status: ✅ PRODUCTION (1.000000 correlation achieved for Laguerre RSI)

What It Does:

• Validates Python implementations against MQL5 reference exports
• Calculates 4 metrics: Pearson correlation, MAE, RMSE, max difference
• Stores historical validation runs in DuckDB for regression detection
• 32-test comprehensive suite (P0-P3 priorities)

Quality Gates:

• Correlation: MUST be ≥0.999 (not 0.95 "good enough")
• MAE: MUST be \<0.001
• NaN Count: MUST be 0 (after warmup period)
• Historical Warmup: MUST use 5000+ bars for adaptive indicators

Command Example:

python validate_indicator.py \
  --csv Export_EURUSD_PERIOD_M1.csv \
  --indicator laguerre_rsi \
  --threshold 0.999

Use When: User needs to verify Python indicator accuracy

Critical Requirement: 5000-bar warmup (NOT 100 or 500 bars)

Reference: /docs/guides/INDICATOR_VALIDATION_METHODOLOGY.md

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4. Complete MQL5→Python Migration Workflow (7 Phases)

Status: ✅ PRODUCTION (2-4 hours first time, 1-2 hours subsequently)

What It Does:

• Phase 1: Locate & analyze MQL5 indicator (40% automated)
• Phase 2: Modify MQL5 to expose buffers (30% automated)
• Phase 3: CLI compile (~1 second, 90% automated)
• Phase 4: Fetch historical data (95% automated)
• Phase 5: Implement Python indicator (20% automated)
• Phase 6: Validate with warmup (95% automated)
• Phase 7: Document lessons (40% automated)

Overall Automation: 60-70% (strategic automation at integration points)

Self-Correction Mechanisms:

1. Validation-driven re-implementation loop (correlation threshold) 1. Multi-level compilation verification (4 checks) 1. Wine Python MT5 API error handling (actionable messages) 1. DuckDB historical tracking (regression detection) 1. Comprehensive test suite (32 automated tests)

Use When: User wants to migrate a complete indicator from MQL5 to Python

Time Investment: 2-4 hours first indicator, faster for subsequent indicators

Reference: /docs/guides/MQL5_TO_PYTHON_MIGRATION_GUIDE.md

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5. Lessons Learned Knowledge Base (185+ Hours Captured)

Status: ✅ COMPREHENSIVE (8 critical gotchas, 6 validation pitfalls)

What It Contains:

• 8 Critical Gotchas: /inc parameter trap, path spaces, warmup requirement, pandas mismatches, array indexing, shared state, parameter passing, temporal assumptions
• 6 Validation Pitfalls: Cold start comparison, pandas rolling windows, off-by-one errors, series vs iloc, NaN handling, correlation thresholds
• 70+ Legacy Items: Documented as NOT VIABLE to prevent retesting
• Time Savings: 30-50 hours per developer by reading first

Use When: User encounters a bug or wants to avoid common mistakes

Critical Reading: /docs/guides/LESSONS_LEARNED_PLAYBOOK.md (read BEFORE starting work)

Reference: /docs/guides/LESSONS_LEARNED_PLAYBOOK.md

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❌ WHAT THIS WORKSPACE CANNOT DO

1. Custom Indicator Headless Automation

Limitation: Python MetaTrader5 API cannot access custom indicator buffers

Why: API design limitation - no copy_buffer() function for custom indicators

Evidence:

• /archive/experiments/spike_1_mt5_indicator_access.py (confirmed via testing)
• Official MetaQuotes statement: "Python API unable to access indicators"

Alternative:

• Use v4.0.0 GUI mode for custom indicator exports
• OR reimplement indicator logic in Python directly

Time Saved by Knowing: 2+ hours (don't waste time trying API approach)

Reference: /docs/guides/EXTERNAL_RESEARCH_BREAKTHROUGHS.md (Research B)

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2. Reliable Startup.ini Parameter Passing

Limitation: MT5 does NOT support named sections or ScriptParameters reliably

Why: Fundamental MT5 bugs documented in 30+ community sources (2015-2025)

Failed Approaches (v2.1.0 - ALL NOT VIABLE):

1. Named sections [ScriptName] - ignored by MT5 1. ScriptParameters directive - blocks execution silently 1. .set preset files - strict requirements + silent failures

Evidence:

• /archive/plans/HEADLESS_MQL5_SCRIPT_SOLUTION_A.NOT_VIABLE.md (22 KB research)
• Full day of testing, comprehensive community research

Alternative:

• Use v3.0.0 Python API (no startup.ini needed)
• OR use v4.0.0 file-based config (MQL5/Files/export_config.txt)

Time Saved by Knowing: 6-8 hours (approach is research-confirmed broken)

Reference: /docs/guides/SCRIPT_PARAMETER_PASSING_RESEARCH.md

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3. Pandas Rolling Windows for MQL5 ATR

Limitation: Pandas rolling().mean() does NOT match MQL5 expanding window behavior

Why: Different denominator logic

• MQL5: sum(bars 0-5) / 32 (divide by period, even if partial)
• Pandas: sum(bars 0-5) / 6 (divide by available bars)

Impact: 0.95 correlation (FAILED validation) instead of 1.000000

Required Fix: Manual loops (10x slower, but correct)

for i in range(len(tr)):
    if i < period:
        atr.iloc[i] = tr.iloc[:i+1].sum() / period  # NOT pandas rolling
    else:
        atr.iloc[i] = tr.iloc[i-period+1:i+1].mean()

Project Philosophy: "Correctness > Speed for validation"

Time Saved by Knowing: 30-45 minutes debugging NaN values

Reference: /docs/guides/LESSONS_LEARNED_PLAYBOOK.md (Gotcha #4)

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4. Cold Start Validation (\<5000 Bars)

Limitation: Cannot validate adaptive indicators without sufficient historical warmup

Why: ATR requires 32-bar lookback, Adaptive Period requires 64-bar warmup

Evidence:

• 100 bars → 0.951 correlation (FAILED)
• 5000 bars → 1.000000 correlation (PASSED)

Mental Model:

MQL5: [....4900 bars warmup....][100 bars exported]
Python: [100 bars CSV] ← ZERO context (WRONG!)

Correct: Fetch 5000, calculate on ALL, compare last N

Required Workflow: Two-stage validation (fetch 5000, calculate all, compare subset)

Time Saved by Knowing: 2-3 hours debugging correlation failures

Reference: /docs/guides/PYTHON_INDICATOR_VALIDATION_FAILURES.md (Failure #5)

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5. Accept 0.95 Correlation as "Good Enough"

Limitation: 0.95 correlation indicates systematic bias, NOT "95% accurate"

Why: Small errors compound in live trading

Production Requirement: ≥0.999 (99.9% minimum)

Diagnostic Pattern:

• 0.95-0.97: Missing historical warmup
• 0.85-0.95: NaN handling mismatch
• 0.70-0.85: Algorithm mismatch
• \<0.70: Fundamental implementation error

Time Saved by Knowing: Don't waste time on "good enough" - fix the root cause

Reference: /docs/guides/LESSONS_LEARNED_PLAYBOOK.md (Bug Pattern #1)

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6. Wine/CrossOver Compilation with Spaces in Paths

Limitation: Paths with spaces break Wine compilation SILENTLY

Symptom: Exit code 0 (success!) but NO .ex5 file created

Required Workflow: Copy-Compile-Verify-Move (4 steps)

# Step 1: Copy to simple path
cp "Complex (Name).mq5" "C:/Temp.mq5"

# Step 2: Compile
metaeditor64.exe /compile:"C:/Temp.mq5"

# Step 3: Verify (.ex5 exists AND log shows 0 errors)
ls -lh "C:/Temp.ex5"

# Step 4: Move to destination
cp "C:/Temp.ex5" "C:/Program Files/.../Script.ex5"

Time Saved by Knowing: 3+ hours debugging silent failures

Reference: /docs/guides/LESSONS_LEARNED_PLAYBOOK.md (Gotcha #2)

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7. Use /inc Parameter for Standard Compilation

Limitation: /inc parameter OVERRIDES (not augments) default include paths

Common Mistake:

# WRONG (causes 102 errors):
metaeditor64.exe /compile:"C:/Program Files/MT5/MQL5/Scripts/Script.mq5" \
  /inc:"C:/Program Files/MT5/MQL5"  # Redundant + breaks

# RIGHT (no /inc needed):
metaeditor64.exe /compile:"C:/Program Files/MT5/MQL5/Scripts/Script.mq5"

When to Actually Use `/inc`: ONLY when compiling from EXTERNAL directory

Time Saved by Knowing: 4+ hours debugging compilation errors

Reference: /docs/guides/EXTERNAL_RESEARCH_BREAKTHROUGHS.md (Research A)

Evolution Log

Convention: Reverse chronological order (newest on top, oldest at bottom). Prepend new entries.

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2026-02-26: Initial Evolution Log

Status: Skill is in use and maintained. Track improvements here.

Purpose

This evolution log tracks updates to the skill. Each entry should note:

• What changed (content, structure, tooling)
• Why it changed (bug fix, feature request, best practice)
• Files affected

How to Use

1. When updating SKILL.md or references, add an entry here with the date 2. Keep entries reverse-chronological (newest first) 3. Link to ADRs or GitHub issues when relevant 4. Reference specific line changes when helpful

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Skill: MQL5→Python Translation Workspace Skill

Critical Requirements & Assumptions

Required Assumptions:

1. ✅ MT5 Terminal Running: API approaches require logged-in terminal 1. ✅ Wine/CrossOver Installed: No native macOS MT5 support 1. ✅ Python 3.12+ in Wine: Required for MetaTrader5 package 1. ✅ NumPy 1.26.4: MUST use this version (not 2.x - Wine incompatible) 1. ✅ 5000+ Bar Warmup: Required for validation (not 100 or 500 bars) 1. ✅ Manual Loops for ATR: Cannot use pandas rolling windows 1. ✅ ≥0.999 Correlation: Strict threshold (not 0.95 "good enough") 1. ✅ Copy-Compile-Move: Required for paths with spaces in Wine

Incorrect Assumptions:

1. ❌ startup.ini parameter passing works reliably 1. ❌ Python API can access custom indicator buffers 1. ❌ Pandas operations match MQL5 behavior automatically 1. ❌ 0.95 correlation is "good enough" 1. ❌ 100 bars is sufficient for validation 1. ❌ /inc parameter helps with standard compilation 1. ❌ Paths with spaces work in Wine compilation 1. ❌ NumPy 2.x works with MetaTrader5 package

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Common User Workflows

1. Quick Market Data Export (Beginner - 10-15 seconds)

Use Case: User wants EURUSD M1 data with RSI

Workflow:

# One-liner (v3.0.0 headless)
CX_BOTTLE="MetaTrader 5" \
WINEPREFIX="$HOME/Library/Application Support/CrossOver/Bottles/MetaTrader 5" \
wine "C:\\Program Files\\Python312\\python.exe" \
  "C:\\users\\crossover\\export_aligned.py" \
  --symbol EURUSD --period M1 --bars 5000

Output: CSV with OHLCV + RSI_14 at users/crossover/exports/

Reference: /docs/guides/V4_FILE_BASED_CONFIG_WORKFLOW.md (Quick Start)

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2. Custom Laguerre RSI Export (Intermediate - 20-30 seconds)

Use Case: User wants Laguerre RSI indicator values

Workflow:

# Step 1: Generate config
python generate_export_config.py --symbol XAUUSD --timeframe M1 \
  --bars 5000 --laguerre-rsi --output laguerre_export.txt

# Step 2: Open MT5 GUI, drag ExportAligned.ex5 to XAUUSD M1 chart, click OK

# Step 3: CSV at MQL5/Files/Export_XAUUSD_M1_Laguerre.csv

Output: CSV with OHLCV + Laguerre_RSI + ATR + Adaptive_Period

Reference: /docs/guides/V4_FILE_BASED_CONFIG_WORKFLOW.md (Example 3)

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3. Validate Python Indicator (Intermediate - 5-10 minutes)

Use Case: User wrote Python Laguerre RSI, needs to verify accuracy

Workflow:

# Step 1: Fetch 5000 bars from MT5 (v3.0.0 OR v4.0.0)

# Step 2: Calculate Python indicator on ALL 5000 bars

# Step 3: Validate
python validate_indicator.py \
  --csv Export_EURUSD_PERIOD_M1.csv \
  --indicator laguerre_rsi \
  --threshold 0.999

# Output:
# [PASS] Laguerre_RSI: correlation=1.000000
# [PASS] ATR: correlation=0.999987
# Status: PASS - All buffers meet threshold

Success Criteria: All buffers ≥0.999 correlation

Reference: /docs/guides/INDICATOR_VALIDATION_METHODOLOGY.md

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4. Complete Indicator Migration (Advanced - 2-4 hours)

Use Case: User wants to translate new MQL5 indicator to Python

Workflow: 7-phase checklist-driven process

Checklist: /docs/templates/INDICATOR_MIGRATION_CHECKLIST.md (copy-paste ready)

Key Phases:

1. Locate & analyze (bash commands + manual review) 1. Modify MQL5 (expose hidden buffers) 1. CLI compile (~1 second) 1. Fetch 5000 bars (automated) 1. Implement Python (manual + pandas patterns) 1. Validate ≥0.999 (automated) 1. Document lessons (manual + git)

Time Investment: 2-4 hours first time, 1-2 hours subsequently

Reference: /docs/guides/MQL5_TO_PYTHON_MIGRATION_GUIDE.md

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Documentation Hub (Single Source of Truth)

Quick Start (35-45 minutes)

• New Users: /docs/guides/MQL5_TO_PYTHON_MIGRATION_GUIDE.md (7-phase workflow)
• Critical Gotchas: /docs/guides/LESSONS_LEARNED_PLAYBOOK.md (read FIRST)
• Copy-Paste Checklist: /docs/templates/INDICATOR_MIGRATION_CHECKLIST.md

Execution Workflows

• Headless Export: /docs/guides/WINE_PYTHON_EXECUTION.md (v3.0.0)
• GUI Export: /docs/guides/V4_FILE_BASED_CONFIG_WORKFLOW.md (v4.0.0)
• Validation: /docs/guides/INDICATOR_VALIDATION_METHODOLOGY.md

Critical References

• Lessons Learned: /docs/guides/LESSONS_LEARNED_PLAYBOOK.md (8 gotchas)
• Validation Failures: /docs/guides/PYTHON_INDICATOR_VALIDATION_FAILURES.md (3-hour journey)
• External Research: /docs/guides/EXTERNAL_RESEARCH_BREAKTHROUGHS.md (game-changers)
• Legacy Assessment: /docs/reports/LEGACY_CODE_ASSESSMENT.md (what NOT to retry)

Architecture & Tools

• Environment Setup: /docs/guides/CROSSOVER_MQ5.md (Wine/CrossOver)
• File Locations: /docs/guides/MT5_FILE_LOCATIONS.md (paths reference)
• CLI Compilation: /docs/guides/MQL5_CLI_COMPILATION_SUCCESS.md (~1s compile)

Navigation

• Task Navigator: /docs/MT5_REFERENCE_HUB.md (decision trees, canonical map)
• Project Memory: /CLAUDE.md (hub-and-spoke architecture)
• Documentation Index: /docs/README.md (complete guide catalog)

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Skill Activation Guidelines

When to Activate This Skill

Activate when user mentions:

• "MQL5" or "MetaTrader 5" or "MT5"
• "indicator translation" or "export data"
• "Python validation" or "correlation check"
• "CrossOver bottle" or "Wine Python"
• "Laguerre RSI", "ATR", "technical indicators"
• File paths containing MetaTrader 5/drive_c

How to Guide Users

1. Understand Intent First

• What do they want to export? (market data vs custom indicator)
• What's their experience level? (beginner vs advanced)
• What's their time constraint? (quick export vs full migration)

2. Recommend Appropriate Workflow

• Headless automation → v3.0.0 (built-in indicators only)
• Custom indicators → v4.0.0 (GUI mode)
• Validation → Universal framework (≥0.999 threshold)
• Full migration → 7-phase workflow (2-4 hours)

3. Set Clear Expectations

• What CAN be done (with confidence)
• What CANNOT be done (with alternatives)
• Time investment (realistic estimates)
• Quality gates (≥0.999 correlation non-negotiable)

4. Prevent Common Mistakes

• Read Lessons Learned Playbook FIRST (saves 8-12 hours)
• Use 5000 bars for validation (not 100 or 500)
• Don't retry NOT VIABLE approaches (30-50 hours saved)
• Respect "Correctness > Speed" philosophy

5. Reference Documentation Frequently

• This workspace has 95/100 documentation readiness score
• Every failure documented with solutions
• Hub-and-spoke architecture (single source of truth per topic)

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Error Handling Patterns

Common Errors & Solutions

Error: correlation=0.951 (threshold 0.999) - FAILED Diagnosis: Missing historical warmup Solution: Fetch 5000 bars, calculate on ALL, compare last N Time: 2-3 hours if not known upfront

Error: No module named 'MetaTrader5' Diagnosis: Running in macOS Python (not Wine Python) Solution: Use Wine Python: wine "C:\\...\\python.exe" Time: 5-10 minutes

Error: Exit code 0 but no .ex5 file created Diagnosis: Path has spaces, Wine compilation silent failure Solution: Copy-Compile-Verify-Move (4-step pattern) Time: 3+ hours if not known upfront

Error: 102 compilation errors Diagnosis: /inc parameter overrides defaults Solution: Remove /inc parameter entirely Time: 4+ hours if not known upfront

Error: 99 NaN values in indicator output Diagnosis: Using pandas rolling windows (returns NaN until full window) Solution: Use manual loops for expanding window logic Time: 30-45 minutes

Skill: MQL5→Python Translation Workspace Skill

Success Metrics

Validated Indicators (Production-Ready)

Laguerre RSI v1.0.0:

• ✅ Correlation: 1.000000 (all 3 buffers)
• ✅ Temporal leakage audit: CLEAN
• ✅ Documentation: Complete (analysis + validation + audit)
• ✅ Test coverage: Comprehensive validation suite
• Status: PRODUCTION READY

Quality Standards

• Correlation: ≥0.999 (not 0.95)
• MAE: \<0.001
• NaN Count: 0 (after warmup)
• Historical Warmup: 5000+ bars
• Documentation: Algorithm analysis + validation report + temporal audit

Validation Runs

• DuckDB Tracking: All validation runs stored permanently
• Regression Detection: Historical comparison enabled
• Bar-Level Debugging: Top 100 largest differences stored
• Reproducibility: All parameters stored

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Version History

v1.0.0 (2025-10-27)

• Initial skill creation based on 5-agent parallel research
• Comprehensive boundary definition (CAN vs CANNOT)
• 7-phase workflow documentation
• 185+ hours of debugging captured
• Production-ready validation framework (1.000000 correlation)

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Skill Maintenance

When to Update This Skill

• New indicator validated (add to production-ready list)
• New NOT VIABLE approach discovered (add to limitations)
• New gotcha documented (add to lessons learned reference)
• Workflow optimization (update automation percentages)

Health Check

Run comprehensive validation suite:

python comprehensive_validation.py --priority ALL --verbose

Target: 30/32 PASS (2 expected failures: duckdb/numpy missing in macOS Python)

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Skill Status: ✅ PRODUCTION READY Last Updated: 2025-10-27 Maintenance: Update when new indicators validated or limitations discovered

Skill: MQL5→Python Translation Workspace Skill

1. Quick Market Data Export (Beginner - 10-15 seconds)

Use Case: User wants EURUSD M1 data with RSI

Workflow:

# One-liner (v3.0.0 headless)
CX_BOTTLE="MetaTrader 5" \
WINEPREFIX="$HOME/Library/Application Support/CrossOver/Bottles/MetaTrader 5" \
wine "C:\\Program Files\\Python312\\python.exe" \
  "C:\\users\\crossover\\export_aligned.py" \
  --symbol EURUSD --period M1 --bars 5000

Output: CSV with OHLCV + RSI_14 at users/crossover/exports/

Reference: /docs/guides/V4_FILE_BASED_CONFIG_WORKFLOW.md (Quick Start)

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2. Custom Laguerre RSI Export (Intermediate - 20-30 seconds)

Use Case: User wants Laguerre RSI indicator values

Workflow:

# Step 1: Generate config
python generate_export_config.py --symbol XAUUSD --timeframe M1 \
  --bars 5000 --laguerre-rsi --output laguerre_export.txt

# Step 2: Open MT5 GUI, drag ExportAligned.ex5 to XAUUSD M1 chart, click OK

# Step 3: CSV at MQL5/Files/Export_XAUUSD_M1_Laguerre.csv

Output: CSV with OHLCV + Laguerre_RSI + ATR + Adaptive_Period

Reference: /docs/guides/V4_FILE_BASED_CONFIG_WORKFLOW.md (Example 3)

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3. Validate Python Indicator (Intermediate - 5-10 minutes)

Use Case: User wrote Python Laguerre RSI, needs to verify accuracy

Workflow:

# Step 1: Fetch 5000 bars from MT5 (v3.0.0 OR v4.0.0)

# Step 2: Calculate Python indicator on ALL 5000 bars

# Step 3: Validate
python validate_indicator.py \
  --csv Export_EURUSD_PERIOD_M1.csv \
  --indicator laguerre_rsi \
  --threshold 0.999

# Output:
# [PASS] Laguerre_RSI: correlation=1.000000
# [PASS] ATR: correlation=0.999987
# Status: PASS - All buffers meet threshold

Success Criteria: All buffers ≥0.999 correlation

Reference: /docs/guides/INDICATOR_VALIDATION_METHODOLOGY.md

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4. Complete Indicator Migration (Advanced - 2-4 hours)

Use Case: User wants to translate new MQL5 indicator to Python

Workflow: 7-phase checklist-driven process

Checklist: /docs/templates/INDICATOR_MIGRATION_CHECKLIST.md (copy-paste ready)

Key Phases:

1. Locate & analyze (bash commands + manual review) 1. Modify MQL5 (expose hidden buffers) 1. CLI compile (~1 second) 1. Fetch 5000 bars (automated) 1. Implement Python (manual + pandas patterns) 1. Validate ≥0.999 (automated) 1. Document lessons (manual + git)

Time Investment: 2-4 hours first time, 1-2 hours subsequently

Reference: /docs/guides/MQL5_TO_PYTHON_MIGRATION_GUIDE.md