Cristian David Castillo Arrieta
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2 年
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財務・国際ビジネスの専門家であり、財務管理を専門としています。MQL5およびPythonの独学開発者として、アルゴリズム取引、マルチアセットポートフォリオの構築、定量的リスク管理に注力しています。
私の仕事の核心は、孤立した戦略ではなく、協調的なポートフォリオとして機能するエキスパートアドバイザーの設計、最適化、検証にあります。相関分析、時間カバレッジマッピング、資産クラスの分散化を活用し、単一の商品や単一のアプローチに依存しないシステムを構築しています。
現在、外国為替、株価指数、貴金属、エネルギー、米国株式にまたがるアルゴリズムポートフォリオを管理しており、複数のセッションと時間軸で同時に運用しています。
このコミュニティでは、技術記事やオープンソースツールを通じて自身の経験を共有しています。「個々のEAを構築する」ことから「ポートフォリオをエンジニアリングする」ことへの転換こそが、リテールと機関投資家の思考を分ける境界線であると確信しており、その信念が私がここで公開するすべてのものの指針となっています。
私の仕事の核心は、孤立した戦略ではなく、協調的なポートフォリオとして機能するエキスパートアドバイザーの設計、最適化、検証にあります。相関分析、時間カバレッジマッピング、資産クラスの分散化を活用し、単一の商品や単一のアプローチに依存しないシステムを構築しています。
現在、外国為替、株価指数、貴金属、エネルギー、米国株式にまたがるアルゴリズムポートフォリオを管理しており、複数のセッションと時間軸で同時に運用しています。
このコミュニティでは、技術記事やオープンソースツールを通じて自身の経験を共有しています。「個々のEAを構築する」ことから「ポートフォリオをエンジニアリングする」ことへの転換こそが、リテールと機関投資家の思考を分ける境界線であると確信しており、その信念が私がここで公開するすべてのものの指針となっています。
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
パブリッシュされたコードNeural Loss-Pattern Auditor
Neural Loss-Pattern Auditor trains a small feed-forward neural network, written from scratch in native MQL5, on closed-deal history to test whether behavioral and market-context features predict which trades are more likely to lose. It reports an accuracy uplift over a naive baseline, a probability-calibration table, a permutation feature-importance ranking, and a configurable A-F composite grade with recommendations. On first run it uses a built-in synthetic demo, so the output is visible immediately with no setup; switch one input to InpUseDemoData=false to analyze real account history instead. Pure MQL5: no external libraries, no Python, and no AI service of any kind.
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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
パブリッシュされたプロダクト
AbacuQuant Portfolio：マルチ構成型 Expert Advisor 最大48個の独立したトレード構成。複数の取引銘柄。1つのチャート。口座全体で共有されるリスク管理。 MetaTrader 5向けのマルチ通貨対応Expert Advisorです。1つのH1チャートからEURUSD、GBPUSD、USDJPY、AUDUSDを取引し、検証済みのプリセット、完全な手動モードを備え、システム内にマーチンゲールやグリッド戦略は一切使用していません。 多くのExpert Advisorは、1つの戦略、1つのパラメータセット、1つの銘柄を中心に構築されています。しかし、1日の中で市場環境が変化すると、その単一構成が一部の時間帯では機能しても、それ以外では適さなくなる可能性があります。 AbacuQuant Portfolioは、複数の独立した構成を同時に実行します。それぞれが特定の市場環境と取引時間帯の組み合わせを対象として設計されています。各構成には独自の取引時間帯、戦略の組み合わせ、インジケーター期間、ATRベースのStop LossとTake
Cristian David Castillo Arrieta
パブリッシュされた記事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
パブリッシュされたコード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
パブリッシュされたコードHidden Risk of Ruin Auditor
決済済みの取引履歴（CSVファイル、または付属のRuinsExport.mq5スクリプトによって現在の口座の取引履歴から自動的に生成されたもの）を読み取り、4つの独立したリスク指標を報告します： 損失後の取引量の増加、平均価格が不利になる同方向のエクスポージャーの重複、勝ちと負けにおけるペイオフの非対称性、および1取引あたりの指定リスクに基づく古典的な破綻リスクの推定値。 これら4つのスコアは、平易な言葉で書かれた推奨事項とともに、A～Fの単一の評価に統合されます。CSVファイルが見つからない場合、スクリプトは再現可能なデモ用取引記録を自動的に生成するため、初回実行時からレポートを確認することができます。
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Cristian David Castillo Arrieta
パブリッシュされた記事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
パブリッシュされた記事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
パブリッシュされたコードPortfolio Correlation and Margin Risk Calculator
任意の銘柄セットと、それらをすべて同時に保有するために口座が必要とする合計証拠金との間の過去のピアソン相関を、純資産に対する割合として算出します。 外部ライブラリ、Python、AIを一切使用せずにMetaTrader 5上でネイティブに動作します。銘柄リストとロット数を入力として設定するだけで、「エキスパート」タブおよびチャート上に完全な行列が表示され、タイマーに基づいて自動的に更新されます。
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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
パブリッシュされたコードExecution Cost Sensitivity Analyzer
戦略の優位性が執行コストに対してどれほど堅牢であるかを測定する、純粋なMQL5スクリプトです。このスクリプトは、決済済み取引の「日付、利益、取引量」を記載したCSVファイルを読み込み、各取引のコストを「固定部分」と「ロット単位の費用」の合計としてモデル化します。 本スクリプトは、取引ごとの損益分岐コスト、クッション（想定される現実的なコストの何倍で純利益がゼロになるかを示す倍率）、想定コストで再評価した純利益と利益率、コストによって勝ちトレードが負けトレードに転じる割合、および推奨事項付きのA+～Fの複合コスト耐性スコアを出力します。 ファイルが存在しない場合は、再現可能なサンプルを生成して分析するため、初回実行時にも出力を確認できます。外部ライブラリ、Python、AIは一切使用していません。
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Cristian David Castillo Arrieta
パブリッシュされた記事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
パブリッシュされたプロダクト
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
パブリッシュされたコードProfit Concentration Analyzer
戦略の利益がどれほど集中しているか、つまり優位性が広範囲に及んでいるのか、それとも少数の幸運な取引に依存しているのかを測定する、ネイティブのMQL5スクリプトです。このスクリプトは、取引ごとのCSVファイル（日付、利益）を読み込み、最大規模の取引による純利益の割合、 勝ちトレードのジニ係数、集中度プロファイル、上位数件のトレードを除外して純利益と利益率を再計算するサバイバルテスト、および設定可能な一貫性限界値に対する最大単日利益を算出し、これらを「集中度・一貫性スコア」（A+～F）として統合し、推奨事項を提示します。 ファイルが見つからない場合はサンプルセットを生成するため、インストール後すぐに実行可能です。外部ライブラリ、Python、AIは一切不要です。ヘルパーのExportTrades.mq5が、取引履歴からファイルを書き出します。
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Cristian David Castillo Arrieta
パブリッシュされたコードDrawdown DNA Analyzer
単なる「最大ドローダウン」という数値だけでなく、口座のドローダウンの構造を分析するネイティブのMQL5スクリプトです。日次のエクイティカーブ（Date,DailyPnL CSV）を読み込み、アンダーウォーターカーブを再構築し、それを個々のドローダウンエピソードに分割して、その深さ、継続期間、回復時間を算出します。 その後、「ウルサー指数」、「ペイン指数」、「回復係数」、およびアンダーウォーター状態の継続時間を算出し、これらを組み合わせて単一のレジリエンススコア（A+～F）と推奨事項を生成し、「エキスパート」タブに表示します。外部ライブラリは不要です。ファイルが見つからない場合はサンプルカーブを生成するため、インストール後すぐに実行可能です。
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