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재무 및 국제 비즈니스 전문가로 재무 관리를 전공했습니다. MQL5와 Python 독학 개발자로서 알고리즘 트레이딩, 멀티에셋 포트폴리오 구축, 정량적 리스크 관리에 집중하고 있습니다.
저의 작업은 개별 전략이 아닌 조율된 포트폴리오로 작동하는 Expert Advisor의 설계, 최적화 및 검증에 중점을 두고 있습니다. 상관관계 분석, 시간대 커버리지 매핑, 자산군 분산화를 적용하여 단일 상품이나 단일 접근 방식에 의존하지 않는 시스템을 구축합니다.
현재 외환, 지수, 귀금속, 에너지, 미국 주식을 아우르는 알고리즘 포트폴리오를 관리하고 있으며, 여러 세션과 타임프레임에서 동시에 운용하고 있습니다.
이 커뮤니티에서 기술 기사와 오픈소스 도구를 통해 제 경험을 공유하고 있습니다. "개별 EA 구축"에서 "포트폴리오 엔지니어링"으로의 전환이야말로 개인 투자자의 사고와 기관 투자자의 사고를 구분짓는 경계선이라 확신하며, 이 원칙이 제가 이곳에서 발행하는 모든 것의 지침이 됩니다.Has usado 75% de tu límit
저의 작업은 개별 전략이 아닌 조율된 포트폴리오로 작동하는 Expert Advisor의 설계, 최적화 및 검증에 중점을 두고 있습니다. 상관관계 분석, 시간대 커버리지 매핑, 자산군 분산화를 적용하여 단일 상품이나 단일 접근 방식에 의존하지 않는 시스템을 구축합니다.
현재 외환, 지수, 귀금속, 에너지, 미국 주식을 아우르는 알고리즘 포트폴리오를 관리하고 있으며, 여러 세션과 타임프레임에서 동시에 운용하고 있습니다.
이 커뮤니티에서 기술 기사와 오픈소스 도구를 통해 제 경험을 공유하고 있습니다. "개별 EA 구축"에서 "포트폴리오 엔지니어링"으로의 전환이야말로 개인 투자자의 사고와 기관 투자자의 사고를 구분짓는 경계선이라 확신하며, 이 원칙이 제가 이곳에서 발행하는 모든 것의 지침이 됩니다.Has usado 75% de tu límit
Cristian David Castillo Arrieta
Why the same trailing stop breaks the moment Gold changes character
I was in a forum thread today about trailing stops on XAUUSD, and it made me put into words something I've been building my whole approach around for a while: almost every trailing method traders compare — EMA cross, Chandelier, ATR multiples, swing-structure trails — gets judged on a single backtest run over one continuous chunk of history. The "best" multiplier or ladder step that wins that test isn't actually the best method. It's the method that happened to fit whatever mix of trend and chop was sitting in that sample.
The fix I use is simple to describe and annoying to implement properly: split the history into volatility regimes first (I use ATR percentile over a rolling window, expansion vs. compression), then optimize and validate each piece of logic separately per regime instead of once over the whole dataset. A structural trail wins clearly in expansion. In compression it just gets chopped up by noise, and something tighter does better there. Neither method is "the winner" — the regime decides which one applies.
That's the same principle I ended up building AbacuQuant around, just scaled up from one exit rule to an entire portfolio. Instead of one strategy tuned to look good on one backtest, the logic behind each strategy is walk-forward tested and optimized (genetic optimization, not a single curve-fit) across different market regimes and asset classes, forex, metals, indices, energy, ETFs, individual stocks — and then combined into a portfolio specifically to keep cross-asset correlation low (the current version sits under 0.4 correlation across most pairs in the book). The idea isn't "find the one strategy that beats the market." It's "find enough structurally different, regime-validated pieces that the portfolio doesn't fall apart when one regime ends," which is exactly the failure mode people are describing in that XAUUSD thread, just at the position level instead of the portfolio level.
It also runs entirely inside your own MetaTrader account nothing custodial, your funds never move to a third party and the newer version adds the drawdown/consistency rules prop firms check for, since that's become how a lot of people are actually trading it live.
If any of this is useful for how you're thinking about your own trailing logic or portfolio construction, happy to go deeper in the comments. And if you want to see what the regime-validated approach looks like applied across a full portfolio rather than one exit rule, it's at abacuquant.com.
Cristian David Castillo Arrieta
I was in a forum thread today about trailing stops on XAUUSD, and it made me put into words something I've been building my whole approach around for a while: almost every trailing method traders compare — EMA cross, Chandelier, ATR multiples, swing-structure trails — gets judged on a single backtest run over one continuous chunk of history. The "best" multiplier or ladder step that wins that test isn't actually the best method. It's the method that happened to fit whatever mix of trend and chop was sitting in that sample.
The fix I use is simple to describe and annoying to implement properly: split the history into volatility regimes first (I use ATR percentile over a rolling window, expansion vs. compression), then optimize and validate each piece of logic separately per regime instead of once over the whole dataset. A structural trail wins clearly in expansion. In compression it just gets chopped up by noise, and something tighter does better there. Neither method is "the winner" — the regime decides which one applies.
That's the same principle I ended up building AbacuQuant around, just scaled up from one exit rule to an entire portfolio. Instead of one strategy tuned to look good on one backtest, the logic behind each strategy is walk-forward tested and optimized (genetic optimization, not a single curve-fit) across different market regimes and asset classes, forex, metals, indices, energy, ETFs, individual stocks — and then combined into a portfolio specifically to keep cross-asset correlation low (the current version sits under 0.4 correlation across most pairs in the book). The idea isn't "find the one strategy that beats the market." It's "find enough structurally different, regime-validated pieces that the portfolio doesn't fall apart when one regime ends," which is exactly the failure mode people are describing in that XAUUSD thread, just at the position level instead of the portfolio level.
It also runs entirely inside your own MetaTrader account nothing custodial, your funds never move to a third party and the newer version adds the drawdown/consistency rules prop firms check for, since that's become how a lot of people are actually trading it live.
If any of this is useful for how you're thinking about your own trailing logic or portfolio construction, happy to go deeper in the comments. And if you want to see what the regime-validated approach looks like applied across a full portfolio rather than one exit rule, it's at abacuquant.com.
Cristian David Castillo Arrieta
Cristian David Castillo Arrieta
게재된 코드 Execution Cost Sensitivity Analyzer
A pure-MQL5 script that measures how robust a strategy's edge is to execution costs. It reads a Date,Profit,Volume CSV of closing deals and models each deal's cost as a fixed part plus a per-lot part. It prints the breakeven cost per deal, the cushion (the multiple of an assumed realistic cost at which the net profit reaches zero), the net profit and profit factor re-priced at the assumed cost, the share of winners the cost turns into losers, and a composite A+ to F cost-robustness score with recommendations. If no file is present it generates a reproducible sample and analyzes it, so the output is visible on the first run. No external libraries, no Python, no AI.
82
Cristian David Castillo Arrieta
게재된 기고글 Beyond Maximum Drawdown: Building a Drawdown DNA Analyzer in MQL5 
Maximum drawdown is one number that hides what really matters: how often an equity curve declines, how long it stays below a previous peak, and how quickly it recovers. This article builds a native MQL5 tool that reconstructs the underwater curve, breaks it into individual drawdown episodes (depth, duration, recovery time), computes the Ulcer Index, Pain Index, and Recovery Factor, and combines them into a single resilience grade with practical recommendations. No external libraries, no Python, no AI.
Cristian David Castillo Arrieta
출시돈 제품
Funded Trade Manager MT5 Most funded accounts are not lost to a bad strategy. They are lost to a single day that went too far: one oversized position, one revenge trade, one violated daily loss limit. Prop Firm Guard is a chart panel that applies the same limits your funding company applies, before the company does. What it does Tracks your daily loss limit and maximum drawdown in real time, using the same day-reset logic prop firms use (configurable server reset hour). Blocks any new trade
Cristian David Castillo Arrieta
게재된 코드 Profit Concentration Analyzer
A native MQL5 script that measures how concentrated a strategy's profit is — whether the edge is broad or rests on a few lucky trades. It reads a per-trade CSV (Date,Profit) and reports the share of net profit from the largest trades, the Gini coefficient of the winners, a concentration profile, a survival test that removes the best few trades and recomputes net profit and profit factor, and the largest single day versus a configurable consistency limit, combined into a concentration-and-consistency score (A+ to F) with recommendations. If no file is found it generates a sample set, so it runs out of the box. No external libraries, no Python, no AI. The helper ExportTrades.mq5 writes the file from your trade history.
140
Cristian David Castillo Arrieta
게재된 코드 Drawdown DNA Analyzer
A native MQL5 script that analyzes the structure of an account's drawdowns, not just the single "maximum drawdown" figure. It reads a daily equity curve (Date,DailyPnL CSV), rebuilds the underwater curve, and splits it into individual drawdown episodes with their depth, duration and recovery time. It then reports the Ulcer Index, Pain Index, Recovery Factor and time spent underwater, and combines them into a single resilience score (A+ to F) with recommendations, printed in the Experts tab. No external libraries; if no file is found it generates a sample curve so it runs out of the box.
113
Cristian David Castillo Arrieta
Introduction: The Context-Blind Expert Advisor Problem A carefully optimized Expert Advisor completes six months of profitable forward testing. The equity curve is smooth, the drawdown is bounded, and the trade distribution looks healthy. On the first Friday of the seventh month, the EA opens a 0...
228
Cristian David Castillo Arrieta
게재된 기고글 Building a Correlation-Aware Multi-EA Portfolio Scorer in MQL5 
Most algo traders optimize Expert Advisors individually but never measure how they behave together on a single account. Correlated strategies amplify drawdowns instead of reducing them, and coverage gaps leave portfolios blind during entire trading sessions. This article builds a complete portfolio scorer in MQL5 that reads daily P&L from backtest CSV files, computes a full Pearson correlation matrix, maps trading activity by hour and weekday, evaluates asset class diversity, and outputs a composite grade from A+ to F. All source code is included; no external libraries are required.
1
Cristian David Castillo Arrieta
출시돈 제품
ABQ Portfolio Correlation Scorer: Inteligencia Artificial de Grado Institucional para la Gestión de Riesgo La mayoría de los traders no fracasan por una mala estrategia de entrada, sino por una falla invisible en la arquitectura de su portafolio . El error más común es la sobreexposición por correlación: abrir múltiples posiciones pensando que se está diversificando, cuando en realidad se está multiplicando el riesgo sobre un mismo factor. ABQ Portfolio Correlation Scorer es un Copiloto de
· 2
Cristian David Castillo Arrieta
ABQ Visual Risk Sizer - Official User Manual Author: Cristian Castillo | AbacuQuant Version: 1.60 Platform: MetaTrader 5 (MT5...
· 2
78
Cristian David Castillo Arrieta
출시돈 제품
ABQ Visual Risk Sizer - Institutional Risk & Trade Execution 카테고리: 유틸리티 / 리스크 관리 수동 로트 계산은 시간과 비용을 낭비하게 합니다. 현대 트레이딩, 특히 프랍 펌(Prop Firms) 계좌를 운영할 때 로트 계산 오류나 단 5초의 주문 지연은 일일 드로다운(Daily Drawdown) 규정 위반으로 이어지거나 최적의 진입 가격을 놓치는 원인이 됩니다. ABQ Visual Risk Sizer 는 이 문제를 완벽하게 해결하기 위해 설계된 기관급 도구입니다. 복잡한 리스크 수학 계산을 MetaTrader 5 차트 위에서 직접적이고 빠르며 100% 정확한 시각적 경험으로 변화시킵니다. 이제 외부 계산기나 엑셀 시트는 잊으십시오. 차트 위의 라인을 드래그하고 클릭 한 번으로 거래를 실행하기만 하면 됩니다. 왜 ABQ Visual Risk Sizer가 필요한가요? 프랍 펌을 위한 완벽한 보호: 모든 거래에서 사용자가 설정한
· 2
Cristian David Castillo Arrieta
게재된 코드 Portfolio Scorer — Multi-EA Correlation and Coverage Analyzer
Portfolio Scorer is a standalone MQL5 script that evaluates the quality of a multi-EA portfolio across three critical dimensions that most algo traders overlook.
The script reads daily profit and loss data from CSV files (one per Expert Advisor), computes a full Pearson correlation matrix between every strategy pair, maps trading activity by UTC hour and weekday, detects asset class diversity, and produces a weighted composite score with a letter grade from A+ to F.
How it works:
The tool runs in four sequential stages. First, the Data Loader reads and validates CSV files containing daily returns for each EA in the portfolio. Second, the Correlation Engine calculates the complete NxN Pearson correlation matrix and flags pairs that exceed a configurable threshold. Third, the Coverage Analyzer maps which hours and weekdays have active trading and identifies blind spots. Fourth, the Scoring Function combines all three dimensions into a single composite score using adjustable weights.
· 1
153
Cristian David Castillo Arrieta
It was late 2022. I had spent six months building what I believed was the perfect Expert Advisor. The backtest was gorgeous: Profit Factor 3.2, maximum drawdown under 5%, a Sharpe ratio that would make a hedge fund manager jealous . I had optimized every parameter. Every indicator period...
· 1
182
Cristian David Castillo Arrieta
AbacuQuant 3.7 — The Complete User Manual: From Installation to Live Portfolio A comprehensive, practical guide to configuring, optimizing, and deploying AbacuQuant — the institutional-grade algorithmic trading system for MetaTrader 5...
· 1
159
Cristian David Castillo Arrieta
AbacuQuant 4.10 — Complete User Manual & Setup Guide The definitive reference for installation, configuration, prop-firm compliance, local AI setup, portfolio analysis, and live deployment. LIVE SIGNAL: CLICK HERE Table of Contents Introduction — What's New in v4...
· 1
165
Cristian David Castillo Arrieta
MetaTrader 5 시그널 발표됨
AbacuQuant: Systematic Trading with Institutional Discipline Why most traders fail—and why AbacuQuant is different. The financial markets punish emotion. Fear triggers early exits. Greed extends losing positions. Fatigue leads to missed opportunities. Over 90% of retail traders lose money not because they lack intelligence, but because they lack discipline. AbacuQuant eliminates the human factor entirely. A Portfolio-Based Approach to Algorithmic Trading AbacuQuant is not a single strategy
· 1
Cristian David Castillo Arrieta
🔍 The One Question Every Trader Asks — And Why Most Never Find the Answer
“Are my settings actually working… or am I just lucky?”
Be honest. You’ve been there.
You find a strategy that looks promising. You tweak a moving average here, adjust a stop loss there, maybe change the timeframe. It works for a week. Then the market shifts — and everything falls apart.
So you start over. Again.
This isn’t a failure of discipline. It’s a failure of process. Manual optimization is a guessing game disguised as analysis. You’re testing a handful of combinations out of thousands — maybe millions — of possibilities. The odds of landing on the best parameters by hand? Almost zero.
What If Your EA Could Find the Answer for You?
This is exactly the problem AbacuQuant was built to solve.
AbacuQuant doesn’t just give you a strategy. It gives you a framework to discover the optimal version of that strategy — for any instrument, on any timeframe, under real market conditions.
Here’s how:
→ One EA. 10+ Built-In Strategies.
From Fibonacci confluences and imbalance detection to Bollinger reversals and candlestick pattern recognition — AbacuQuant packs institutional-grade strategies into a single Expert Advisor. You don’t need ten different EAs. You need one that adapts.
→ Full Optimization Power Across Any Market.
EURUSD on H1? Gold on M15? NASDAQ on H4? It doesn’t matter. AbacuQuant’s architecture is instrument-agnostic and timeframe-flexible. Run the MetaTrader 5 Strategy Tester, and let the optimization engine do what no human can: test thousands of parameter combinations systematically to surface the configurations that actually perform — not the ones that just look good on a single backtest.
→ AI-Assisted Analysis to Confirm What the Data Shows.
Here’s where it gets powerful. AbacuQuant integrates directly with AI models (OpenAI, Google Gemini, DeepSeek) to provide a second layer of confirmation. Before entering a trade, the AI analyzes current market context — trend structure, volatility regime, key levels — and validates whether the signal aligns with broader conditions. It’s like having a senior analyst reviewing every setup before execution.
The Real Edge Isn’t the Strategy. It’s the Process.
Most traders spend years searching for the “perfect” strategy. The truth? No single set of parameters works forever. Markets evolve. What separates profitable traders from the rest is the ability to re-optimize, re-validate, and re-deploy — quickly and confidently.
With AbacuQuant, that process looks like this:
1. Select your instrument and timeframe.
2. Run optimization using the MT5 Strategy Tester with your preferred criteria.
3. Analyze the results — filter by profit factor, drawdown, recovery factor.
4. Enable AI confirmation to add a layer of contextual intelligence.
5. Deploy with confidence, knowing your parameters are data-driven, not gut-driven.
Whether you’re building a single-instrument setup or constructing a diversified multi-asset portfolio, AbacuQuant scales with you.
Don’t Take Our Word for It. Test It Yourself.
AbacuQuant is available for free download in demo mode on the MQL5 Market. No commitment. No risk. Just results you can verify on your own charts, with your own instruments, on your own terms.
👉 Download the demo now and run your first optimization today.
Because the best time to stop guessing was yesterday. The second best time is right now.
“Are my settings actually working… or am I just lucky?”
Be honest. You’ve been there.
You find a strategy that looks promising. You tweak a moving average here, adjust a stop loss there, maybe change the timeframe. It works for a week. Then the market shifts — and everything falls apart.
So you start over. Again.
This isn’t a failure of discipline. It’s a failure of process. Manual optimization is a guessing game disguised as analysis. You’re testing a handful of combinations out of thousands — maybe millions — of possibilities. The odds of landing on the best parameters by hand? Almost zero.
What If Your EA Could Find the Answer for You?
This is exactly the problem AbacuQuant was built to solve.
AbacuQuant doesn’t just give you a strategy. It gives you a framework to discover the optimal version of that strategy — for any instrument, on any timeframe, under real market conditions.
Here’s how:
→ One EA. 10+ Built-In Strategies.
From Fibonacci confluences and imbalance detection to Bollinger reversals and candlestick pattern recognition — AbacuQuant packs institutional-grade strategies into a single Expert Advisor. You don’t need ten different EAs. You need one that adapts.
→ Full Optimization Power Across Any Market.
EURUSD on H1? Gold on M15? NASDAQ on H4? It doesn’t matter. AbacuQuant’s architecture is instrument-agnostic and timeframe-flexible. Run the MetaTrader 5 Strategy Tester, and let the optimization engine do what no human can: test thousands of parameter combinations systematically to surface the configurations that actually perform — not the ones that just look good on a single backtest.
→ AI-Assisted Analysis to Confirm What the Data Shows.
Here’s where it gets powerful. AbacuQuant integrates directly with AI models (OpenAI, Google Gemini, DeepSeek) to provide a second layer of confirmation. Before entering a trade, the AI analyzes current market context — trend structure, volatility regime, key levels — and validates whether the signal aligns with broader conditions. It’s like having a senior analyst reviewing every setup before execution.
The Real Edge Isn’t the Strategy. It’s the Process.
Most traders spend years searching for the “perfect” strategy. The truth? No single set of parameters works forever. Markets evolve. What separates profitable traders from the rest is the ability to re-optimize, re-validate, and re-deploy — quickly and confidently.
With AbacuQuant, that process looks like this:
1. Select your instrument and timeframe.
2. Run optimization using the MT5 Strategy Tester with your preferred criteria.
3. Analyze the results — filter by profit factor, drawdown, recovery factor.
4. Enable AI confirmation to add a layer of contextual intelligence.
5. Deploy with confidence, knowing your parameters are data-driven, not gut-driven.
Whether you’re building a single-instrument setup or constructing a diversified multi-asset portfolio, AbacuQuant scales with you.
Don’t Take Our Word for It. Test It Yourself.
AbacuQuant is available for free download in demo mode on the MQL5 Market. No commitment. No risk. Just results you can verify on your own charts, with your own instruments, on your own terms.
👉 Download the demo now and run your first optimization today.
Because the best time to stop guessing was yesterday. The second best time is right now.