Cristian David Castillo Arrieta
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2 years
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Finance and International Business professional with a specialization in Financial Management. Self-taught MQL5 and Python developer focused on algorithmic trading, multi-asset portfolio construction, and quantitative risk management.
My work centers on designing, optimizing, and validating Expert Advisors that operate as a coordinated portfolio rather than isolated strategies. I apply correlation analysis, temporal coverage mapping, and asset class diversification to build systems that do not rely on a single instrument or a single approach.
I currently manage algorithmic portfolios spanning forex, indices, metals, energy, and US equities, operating across multiple sessions and timeframes simultaneously.
I share my experience through technical articles and open-source tools in this community. I believe the shift from "building individual EAs" to "engineering portfolios" is the transition that separates retail from institutional thinking, and that principle guides everything I publish here.
My work centers on designing, optimizing, and validating Expert Advisors that operate as a coordinated portfolio rather than isolated strategies. I apply correlation analysis, temporal coverage mapping, and asset class diversification to build systems that do not rely on a single instrument or a single approach.
I currently manage algorithmic portfolios spanning forex, indices, metals, energy, and US equities, operating across multiple sessions and timeframes simultaneously.
I share my experience through technical articles and open-source tools in this community. I believe the shift from "building individual EAs" to "engineering portfolios" is the transition that separates retail from institutional thinking, and that principle guides everything I publish here.
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...
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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
Published product
AbacuQuant Portfolio: a multi-configuration Expert Advisor Up to 48 independent trading configurations. Multiple instruments. One chart. Shared account-level risk control. A multicurrency Expert Advisor for MetaTrader 5 trading EURUSD, GBPUSD, USDJPY and AUDUSD from a single H1 chart, with validated presets included, a full manual mode, and no martingale or grid anywhere in it. Most Expert Advisors are built around one strategy, one set of parameters and one instrument. When the market changes
Cristian David Castillo Arrieta
Published article 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.
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Cristian David Castillo Arrieta
Published code 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
Published code Hidden Risk of Ruin Auditor
Reads a closed-position trade history (a CSV file, or one generated automatically from the current account's deal history by the companion RuinExport.mq5 script) and reports four independent risk fingerprints: volume escalation after a loss, overlapping same-direction exposure that averages into a worse price, payoff asymmetry between wins and losses, and a classical risk-of-ruin estimate at a stated risk per trade. The four scores combine into a single A-to-F grade with plain-language recommendations. If no CSV is found, the script generates a reproducible demonstration book automatically, so the report is visible on the first run.
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Cristian David Castillo Arrieta
Published article 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.
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Cristian David Castillo Arrieta
Published article 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.
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Cristian David Castillo Arrieta
Published code Portfolio Correlation and Margin Risk Calculator
Computes the historical Pearson correlation between any set of instruments and the combined margin your account would need to hold all of them at once, as a percentage of your equity. Runs natively in MetaTrader 5 with no external libraries, no Python, and no AI — set your symbol list and lot sizes as inputs and it reports the full matrix in the Experts tab and on the chart, refreshing on a timer.
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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
Cristian David Castillo Arrieta
Added topic How do you handle drawdown rules across a portfolio of EAs (prop firm style)?
I've been running several EAs together on the same account instead of one single strategy, mainly to smooth the equity curve, but it's forcing me to rethink risk management completely. A single trailing stop or fixed lot size per EA doesn't work
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Patrick Murimi Njoroge
2026.07.13
It is an extremely involved process. You need to know how many concurrent trades are open for each EA, the confidence of the signals from each, and how they interact with your prop firm limits and when each other so that you can apply appropriate bet-sizing. Look up Machine Learning for Asset Managers by Marcos Lopez de Prado.
Cristian David Castillo Arrieta
Published code 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.
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Cristian David Castillo Arrieta
Published article 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.
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Cristian David Castillo Arrieta
Published product
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
Published code 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.
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Cristian David Castillo Arrieta
Published code 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.
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Cristian David Castillo Arrieta
Added topic Warning: Beware of the "Partnership & Profit Split" Scam out of MQL5.com
Hello everyone, While this topic may have been discussed before, I believe it's crucial to bring it up as often as necessary to protect our community. As creators and developers, our hard work, coding, and system optimization deserve to be rewarded