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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
게재된 포스트 A 90% Win Rate can still loss Money:
On many Expert Advisor pages, the first number you see is the win rate: 85%, 90%, 95%. It is the easiest number to sell, because being right most of the time feels like safety. It is also the number that hides the most risk...
39
Cristian David Castillo Arrieta
I researched 39 markets for AbacuQuant Portfolio. I rejected 22 of them.
Crude oil, the S&P 500, the DAX, ten US stocks. Some looked great on paper, then failed the real-tick test or the statistical screening. AUDJPY missed one threshold by 0.004 and was rejected anyway. The rules don't bend to let a market in.
That's the point. Many EAs are sold on the best backtest that could be found. This one is built from what survived a process designed to say no.
What made it in:
• 11 validated markets: EURUSD, USDJPY, AUDUSD, EURJPY, gold and six indices (Dow Jones, Nasdaq 100, Nikkei 225, Australia 200, Euro Stoxx 50, FTSE 100)
• 71 independent configurations running from one H1 chart
• Account-level risk control: global limits on total open risk, margin and equity drawdown across every market
• Stop loss and take profit attached to every order. No martingale, no grid, no averaging, no recovery logic
The numbers (Strategy Tester, real ticks, 0.5% risk per trade, Jan 2020 – Jul 2026):
• 11,333 trades, 80.3% win rate
• Deepest equity decline: 6.4%
• Most recent period (May 2024 – Jul 2026): profit factor 1.38, 24 of 27 months positive, all 11 markets in profit, none above 14% of the total
The trade-off, stated plainly: the average loss is about 2.8x the average win. You will see losing trades. If you want an equity curve with no visible losses, this is not your EA.
Why test it: the demo is free. Run it with "Every tick based on real ticks", on your own broker, with the balance you actually plan to trade. The EA tells you at startup what balance each configuration needs. Look closely at the losing trades.
Why get the full version: it's a research framework, not just a set of presets. Manual mode gives you the same engine, and a custom optimization criterion ranks results by the win rate's margin over its own break-even level instead of net profit. New markets are added only if they pass the same tests, and you get direct support from me in English and Spanish. I have been running this engine on my own real-money account since August 2026.
Free demo, full methodology and pricing (rental from 49 USD/month):
https://www.mql5.com/en/market/product/191885
Backtests describe the past, not the future. Trading involves risk of loss.
Crude oil, the S&P 500, the DAX, ten US stocks. Some looked great on paper, then failed the real-tick test or the statistical screening. AUDJPY missed one threshold by 0.004 and was rejected anyway. The rules don't bend to let a market in.
That's the point. Many EAs are sold on the best backtest that could be found. This one is built from what survived a process designed to say no.
What made it in:
• 11 validated markets: EURUSD, USDJPY, AUDUSD, EURJPY, gold and six indices (Dow Jones, Nasdaq 100, Nikkei 225, Australia 200, Euro Stoxx 50, FTSE 100)
• 71 independent configurations running from one H1 chart
• Account-level risk control: global limits on total open risk, margin and equity drawdown across every market
• Stop loss and take profit attached to every order. No martingale, no grid, no averaging, no recovery logic
The numbers (Strategy Tester, real ticks, 0.5% risk per trade, Jan 2020 – Jul 2026):
• 11,333 trades, 80.3% win rate
• Deepest equity decline: 6.4%
• Most recent period (May 2024 – Jul 2026): profit factor 1.38, 24 of 27 months positive, all 11 markets in profit, none above 14% of the total
The trade-off, stated plainly: the average loss is about 2.8x the average win. You will see losing trades. If you want an equity curve with no visible losses, this is not your EA.
Why test it: the demo is free. Run it with "Every tick based on real ticks", on your own broker, with the balance you actually plan to trade. The EA tells you at startup what balance each configuration needs. Look closely at the losing trades.
Why get the full version: it's a research framework, not just a set of presets. Manual mode gives you the same engine, and a custom optimization criterion ranks results by the win rate's margin over its own break-even level instead of net profit. New markets are added only if they pass the same tests, and you get direct support from me in English and Spanish. I have been running this engine on my own real-money account since August 2026.
Free demo, full methodology and pricing (rental from 49 USD/month):
https://www.mql5.com/en/market/product/191885
Backtests describe the past, not the future. Trading involves risk of loss.
Cristian David Castillo Arrieta
Can you reproduce this equity curve?
I’m sharing the backtest setup I used with AbacuQuant Portfolio so anyone can download the demo and try to reproduce the results independently.
You can test the instruments one by one if you want to analyze each market separately.
However, if you want to reproduce the portfolio configuration shown in the backtest, set the following instruments to TRUE:
* EURUSD
* USDJPY
* AUDUSD
* XAUUSD
* EURJPY
* N225
* WS30
* NDX
* STOXX50
* UK100
All other instrument presets should remain FALSE.
Risk settings used
Risk per trade: 0.5%
General protection stop: 30%
The idea is simple: don’t just look at the equity curve — download the demo and test it yourself.
Use the same instruments and settings shown above and see what results you get on your own MT5 environment.
Different brokers, spreads, commissions and execution conditions can affect the results, so your backtest may not be identical to mine.
I would actually encourage you to challenge the results.
Test the instruments individually. Test the portfolio. Change the parameters. Stress-test it.
The goal of AbacuQuant Portfolio is not to ask you to blindly trust a backtest.
It’s to give you something you can test for yourself.
👉 Download the demo and start your own backtest.
AbacuQuant Portfolio — available on MQL5.
I’m sharing the backtest setup I used with AbacuQuant Portfolio so anyone can download the demo and try to reproduce the results independently.
You can test the instruments one by one if you want to analyze each market separately.
However, if you want to reproduce the portfolio configuration shown in the backtest, set the following instruments to TRUE:
* EURUSD
* USDJPY
* AUDUSD
* XAUUSD
* EURJPY
* N225
* WS30
* NDX
* STOXX50
* UK100
All other instrument presets should remain FALSE.
Risk settings used
Risk per trade: 0.5%
General protection stop: 30%
The idea is simple: don’t just look at the equity curve — download the demo and test it yourself.
Use the same instruments and settings shown above and see what results you get on your own MT5 environment.
Different brokers, spreads, commissions and execution conditions can affect the results, so your backtest may not be identical to mine.
I would actually encourage you to challenge the results.
Test the instruments individually. Test the portfolio. Change the parameters. Stress-test it.
The goal of AbacuQuant Portfolio is not to ask you to blindly trust a backtest.
It’s to give you something you can test for yourself.
👉 Download the demo and start your own backtest.
AbacuQuant Portfolio — available on MQL5.
Cristian David Castillo Arrieta
Anyone who has spent a few months with automated trading has seen the same pattern: a backtest with a smooth, steep equity curve, followed by a live account that behaves nothing like it. In most cases, the cause is one of three things: Loss recovery...
83
Cristian David Castillo Arrieta
게재된 코드 Edge Drift Detector
Tests whether live closing deals still match the backtest of an Expert Advisor. A bootstrap-calibrated CUSUM detector raises an alarm and estimates when the drift began, a two-window check separates a short dip from a lasting decline, and an expectancy decomposition shows whether the win rate or the trade sizes changed. The script generates demonstration data on the first run and is written in pure MQL5.
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Cristian David Castillo Arrieta
게재된 기고글 Building a Neural Loss-Pattern Auditor in MQL5 
Aggregate metrics like win rate or profit factor miss sequence-dependent behavior, such as sizing up right after a loss. This MQL5 script trains a small native neural network on closed-deal history to estimate loss probability from behavioral and market-context features. It reports accuracy uplift over a baseline, probability calibration, and permutation feature importance, then combines them into a configurable A-F grade with concise, plain-language recommendations.
Cristian David Castillo Arrieta
게재된 기고글 Did Your Scale Outs Actually Help? A Scale Out Value Analyzer in MQL5 
The article presents an MQL5 tool that tests whether scaling out improved results rather than only appearing disciplined. It reconstructs positions from closing-deal history and reprices the full volume at the first, last, and best exit rates actually achieved, producing a Value-Add Ratio, a Scale-Out Win Rate, and an Efficiency measure. A single-trade dependence check and a configurable A+ to F grade turn these into clear, decision-ready feedback.
Cristian David Castillo Arrieta
These remarkable optimization results were generated using the AbacuQuant Portfolio EA, available here:
What makes this interesting is that this is not a single strategy being optimized over and over. AbacuQuant Portfolio is designed as a portfolio engine, capable of running multiple independent configurations across different instruments and market conditions from a single MT5 chart.
The process starts by testing different combinations of strategies, trading conditions, parameters, stop/target structures and risk settings. Instead of simply looking for the configuration with the highest profit, AbacuQuant uses a custom optimization criterion designed to evaluate whether the historical win rate provides a meaningful margin above the break-even level implied by the stop and target structure.
The resulting configurations are then combined into a portfolio, allowing different strategies and instruments to operate during different market sessions while a shared risk engine controls total exposure at the account level.
The objective is not to find one “perfect” setup. It is to build a collection of independent configurations that complement each other and create a more robust portfolio.
This image shows one of those optimization and portfolio-development results. The robot used to generate it is the AbacuQuant Portfolio EA.
🔗 https://www.mql5.com/en/market/product/191885
What makes this interesting is that this is not a single strategy being optimized over and over. AbacuQuant Portfolio is designed as a portfolio engine, capable of running multiple independent configurations across different instruments and market conditions from a single MT5 chart.
The process starts by testing different combinations of strategies, trading conditions, parameters, stop/target structures and risk settings. Instead of simply looking for the configuration with the highest profit, AbacuQuant uses a custom optimization criterion designed to evaluate whether the historical win rate provides a meaningful margin above the break-even level implied by the stop and target structure.
The resulting configurations are then combined into a portfolio, allowing different strategies and instruments to operate during different market sessions while a shared risk engine controls total exposure at the account level.
The objective is not to find one “perfect” setup. It is to build a collection of independent configurations that complement each other and create a more robust portfolio.
This image shows one of those optimization and portfolio-development results. The robot used to generate it is the AbacuQuant Portfolio EA.
🔗 https://www.mql5.com/en/market/product/191885
Cristian David Castillo Arrieta
게재된 코드 Neural Loss-Pattern Auditor
Neural Loss-Pattern Auditor는 네이티브 MQL5로 처음부터 직접 작성된 소규모 피드포워드 신경망을 과거 체결된 거래 내역을 기반으로 학습시켜, 행동 및 시장 맥락 특성이 어떤 거래가 손실을 입을 가능성이 더 높은지 예측할 수 있는지 테스트합니다. 이 도구는 단순한 기준 모델, 확률 보정 테이블, 순열 특징 중요도 순위, 그리고 권장 사항이 포함된 구성 가능한 A-F 복합 등급에 비해 정확도가 향상되었음을 보여줍니다. 첫 실행 시 내장된 합성 데모 데이터를 사용하므로 별도의 설정 없이도 결과를 즉시 확인할 수 있습니다. 대신 실제 계좌 내역을 분석하려면 입력값 중 하나를 InpUseDemoData=false로 설정하십시오. 순수 MQL5: 외부 라이브러리, 파이썬, 어떠한 종류의 AI 서비스도 사용하지 않습니다.
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163
Cristian David Castillo Arrieta
Introduction AbacuQuant Portfolio is a multi-configuration Expert Advisor for MetaTrader 5. Instead of running one strategy with one set of parameters on one instrument, it runs up to 48 independent configurations at the same time, across EURUSD, GBPUSD, USDJPY and AUDUSD, from a single H1 chart...
102
Cristian David Castillo Arrieta
After months of research, I've published AbacuQuant Portfolio — a multi-configuration Expert Advisor for MetaTrader 5, and I wanted to share it here first.
Why it's different
Most EAs run one strategy with one set of parameters on one instrument. This one runs up to 48 independent configurations at once, across EURUSD, GBPUSD, USDJPY and AUDUSD, from a single chart. Each configuration owns its own trading hours, its own combination of ten built-in strategies, and its own ATR-based stop and target. A configuration built for the London session simply doesn't trade outside it — and risk is measured across the whole account, not per configuration, so running four instruments together doesn't quietly multiply your exposure.
And it's built the honest way: no martingale, no grid, no averaging into a losing position, no hidden loss-recovery logic. Every stop is attached the moment the trade opens. If you've been burned by an EA with a beautiful equity curve that fell apart the first time the market disagreed with it, this was built specifically against that failure mode.
How it decides a trade
Each active configuration waits for its window, reads only completed H1 bars (never the forming candle), requires several of its enabled strategies to agree, and sizes the position from the stop distance rather than a fixed lot — so a wider stop always means a smaller position, and the money at risk stays constant. Every configuration passed the same acceptance procedure on real-tick data before it shipped: statistical significance over its own break-even line, a minimum number of losing trades to actually measure it, and stops that were genuinely reached rather than just theorized.
Try it before you take my word for it
The demo runs in full inside the Strategy Tester — same presets, same logic, no time limit. Turn on the validated instruments, select "Every tick based on real ticks," and look at the trade list yourself: the stops, the position sizing, the risk ceilings holding across instruments. I'd rather you find out in the tester, for free, than after committing real money.
Link to the product page: https://www.mql5.com/en/market/product/191885
Why it's different
Most EAs run one strategy with one set of parameters on one instrument. This one runs up to 48 independent configurations at once, across EURUSD, GBPUSD, USDJPY and AUDUSD, from a single chart. Each configuration owns its own trading hours, its own combination of ten built-in strategies, and its own ATR-based stop and target. A configuration built for the London session simply doesn't trade outside it — and risk is measured across the whole account, not per configuration, so running four instruments together doesn't quietly multiply your exposure.
And it's built the honest way: no martingale, no grid, no averaging into a losing position, no hidden loss-recovery logic. Every stop is attached the moment the trade opens. If you've been burned by an EA with a beautiful equity curve that fell apart the first time the market disagreed with it, this was built specifically against that failure mode.
How it decides a trade
Each active configuration waits for its window, reads only completed H1 bars (never the forming candle), requires several of its enabled strategies to agree, and sizes the position from the stop distance rather than a fixed lot — so a wider stop always means a smaller position, and the money at risk stays constant. Every configuration passed the same acceptance procedure on real-tick data before it shipped: statistical significance over its own break-even line, a minimum number of losing trades to actually measure it, and stops that were genuinely reached rather than just theorized.
Try it before you take my word for it
The demo runs in full inside the Strategy Tester — same presets, same logic, no time limit. Turn on the validated instruments, select "Every tick based on real ticks," and look at the trade list yourself: the stops, the position sizing, the risk ceilings holding across instruments. I'd rather you find out in the tester, for free, than after committing real money.
Link to the product page: https://www.mql5.com/en/market/product/191885
Cristian David Castillo Arrieta
출시돈 제품
이 Expert Advisor를 위해 39개 시장을 조사했습니다. 22개는 탈락했고, 11개가 최종 선정되었습니다. AbacuQuant Portfolio는 검증을 통과한 11개 시장에서 거래합니다. 3개의 주요 통화쌍, 유로-엔 교차 통화쌍, 금, 그리고 도쿄와 시드니에서 런던, 프랑크푸르트, 뉴욕으로 이어지는 거래일을 따라가는 6개 주가지수입니다. H1 차트 하나. 서로 독립된 71개 설정. 거래 하나씩이 아니라 계좌 전체를 측정하는 하나의 리스크 엔진. 모든 시장은 같은 연구 과정을 거쳤고, 그 과정은 검토한 대상의 대부분을 탈락시켰습니다. 이 비율이 이 제품에 대해 가장 먼저 알아야 할 점입니다. 승인보다 탈락이 많은 과정은 보기 좋은 백테스트를 만들기 위한 것이 아닙니다. 버티지 못하는 것을 걸러내기 위한 것입니다. 제공 내용 바로 사용할 수 있는 11개의 검증된 프리셋. EURUSD, USDJPY, AUDUSD, EURJPY, XAUUSD, 다우존스 (WS30), 나스닥
Cristian David Castillo Arrieta
게재된 기고글 Building a Hidden Risk of Ruin Auditor in MQL5 
Aggregate metrics alone do not reveal how a trade sequence manages risk. This MQL5 tool analyzes closed positions to flag four structural patterns: post-loss volume escalation, overlapping same-direction entries, asymmetric payoffs, and a classical risk-of-ruin figure. The results are merged into a configurable A-F grade with concise recommendations to guide further review.
Cristian David Castillo Arrieta
게재된 코드 Scale Out Value Analyzer
A native MQL5 tool that reconstructs closed positions from deal-level history, flags the ones closed through more than one exit, and reprices each one at its own first, last, and best exit rates to measure whether scaling out actually added value. Reports a Value-Add Ratio, a Scale Out Win Rate, an Efficiency figure, and a single-trade dependence check, combined into an A+ to F score with recommendations. Runs out of the box against a built-in demonstration data set; a companion script exports the real input file from your own account history. Pure MQL5, no external libraries.
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Cristian David Castillo Arrieta
게재된 코드 Hidden Risk of Ruin Auditor
청산된 포지션의 거래 내역(CSV 파일 또는 동반 스크립트인 RuinExport.mq5가 현재 계좌의 거래 내역에서 자동으로 생성한 파일)을 읽어들이고, 다음 네 가지 독립적인 위험 지표를 보고합니다: 손실 발생 후 거래량 증가, 평균 가격이 더 나빠지는 중복된 동일 방향 포지션, 수익과 손실 간의 수익 비대칭성, 그리고 거래당 지정된 위험 수준에 따른 고전적인 파산 위험 추정치입니다. 이 네 가지 점수는 하나의 A~F 등급으로 통합되며, 평이한 언어로 된 권장 사항이 함께 제공됩니다. CSV 파일이 발견되지 않으면 스크립트가 재현 가능한 데모 거래 내역을 자동으로 생성하므로, 첫 실행 시에도 보고서를 확인할 수 있습니다.
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Cristian David Castillo Arrieta
게재된 기고글 Execution Cost and Slippage Sensitivity Analyzer 
Backtests often understate spread, commission, and slippage. This MQL5 analyzer loads closing deals and simulates rising execution costs to measure robustness. It computes the breakeven cost per deal, the cushion over an assumed cost, the net profit and profit factor at that cost, and how many winners turn into losers, then summarizes the result with an A+ to F grade and targeted guidance.
Cristian David Castillo Arrieta
게재된 기고글 Creating a Profit Concentration Analyzer in MQL5 
Net profit and win rate tell you how much a strategy made, not how the result is distributed. This article builds a native MQL5 script that reads your closed trades and measures profit concentration: the top-N trade share, the Gini coefficient of the winners, an outlier-dependence stress test that removes the best few winners, and the largest day against a prop-firm consistency limit. It combines these into one A+ to F score with recommendations, running inside MetaTrader 5.
Cristian David Castillo Arrieta
게재된 코드 Portfolio Correlation and Margin Risk Calculator
임의의 상품 세트와 해당 상품들을 모두 동시에 보유하기 위해 계좌에 필요한 총 마진 간의 과거 피어슨 상관계수를, 순자산 대비 백분율로 계산합니다. 외부 라이브러리, Python, AI 없이 MetaTrader 5에서 네이티브로 실행됩니다. 심볼 목록과 랏 크기를 입력값으로 설정하면 ‘전문가’ 탭과 차트에 전체 행렬이 표시되며, 타이머에 따라 자동으로 갱신됩니다.
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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