Gamuchirai Zororo Ndawana / プロファイル

Preprocessing is a powerful yet quickly overlooked tuning parameter. It lives in the shadows of its bigger brothers: optimizers and shiny model architectures. Small percentage improvements here can have disproportionately large, compounding effects on profitability and risk. Too often, this largely unexplored science is boiled down to a simple routine, seen only as a means to an end, when in reality it is where signal can be directly amplified, or just as easily destroyed.

This article takes a fresh perspective on a hidden, geometric source of error that quietly shapes every prediction your models make. By rethinking how we measure and apply machine learning forecasts in trading, we reveal how this overlooked perspective can unlock sharper decisions, stronger returns, and a more intelligent way to work with models we thought we already understood.

Financial markets are unpredictable, and trading strategies that look profitable in the past often collapse in real market conditions. This happens because most strategies are fixed once deployed and cannot adapt or learn from their mistakes. By borrowing ideas from control theory, we can use feedback controllers to observe how our strategies interact with markets and adjust their behavior toward profitability. Our results show that adding a feedback controller to a simple moving average strategy improved profits, reduced risk, and increased efficiency, proving that this approach has strong potential for trading applications.

Human traders had long participated in financial markets before the rise of computers, developing rules of thumb that guided their decisions. In this article, we revisit a well-known breakout strategy to test whether such market logic, learned through experience, can hold its own against systematic methods. Our findings show that while the original strategy produced high accuracy, it suffered from instability and poor risk control. By refining the approach, we demonstrate how discretionary insights can be adapted into more robust, algorithmic trading strategies.

This article explores the powerful role of matrix factorization in algorithmic trading, specifically within MQL5 applications. From regression models to multi-target classifiers, we walk through practical examples that demonstrate how easily these techniques can be integrated using built-in MQL5 functions. Whether you're predicting price direction or modeling indicator behavior, this guide lays a strong foundation for building intelligent trading systems using matrix methods.

In this discussion, we will set the foundation for using powerful linear, algebra tools that are implemented in the MQL5 matrix and vector API. For us to make proficient use of this API, we need to have a firm understanding of the principles in linear algebra that govern intelligent use of these methods. This article aims to get the reader an intuitive level of understanding of some of the most important rules of linear algebra that we, as algorithmic traders in MQL5 need,to get started, taking advantage of this powerful library.

Factorization is a mathematical process used to gain insights into the attributes of data. When we apply factorization to large sets of market data—organized in rows and columns—we can uncover patterns and characteristics of the market. Factorization is a powerful tool, and this article will show how you can use it within the MetaTrader 5 terminal, through the MQL5 API, to gain more profound insights into your market data.

In this article, we continue our exploration of building an ensemble of trading strategies and using the MT5 genetic optimizer to tune the strategy parameters. Today, we analyzed the data in Python, showing our model could better predict which strategy would outperform, achieving higher accuracy than forecasting market returns directly. However, when we tested our application with its statistical models, our performance levels fell dismally. We subsequently discovered that the genetic optimizer unfortunately favored highly correlated strategies, prompting us to revise our method to keep vote weights fixed and focus optimization on indicator settings instead.

This article outlines the design of a double moving average crossover strategy that uses signals from a higher timeframe (D1) to guide entries on a lower timeframe (M15), with stop-loss levels calculated from an intermediate risk timeframe (H4). It introduces system constants, custom enumerations, and logic for trend-following and mean-reverting modes, while emphasizing modularity and future optimization using a genetic algorithm. The approach allows for flexible entry and exit conditions, aiming to reduce signal lag and improve trade timing by aligning lower-timeframe entries with higher-timeframe trends.

This article explores how determining the optimal number of strategies in an ensemble can be a complex task that is easier to solve through the use of the MetaTrader 5 genetic optimizer. The MQL5 Cloud is also employed as a key resource for accelerating backtesting and optimization. All in all, our discussion here sets the stage for developing statistical models to evaluate and improve trading strategies based on our initial ensemble results.

Join us for our follow-up discussion, where we will merge our first two trading strategies into an ensemble trading strategy. We shall demonstrate the different schemes possible for combining multiple strategies and also how to exercise control over the parameter space, to ensure that effective optimization remains possible even as our parameter size grows.

Join us in our discussion as we look for additional improvements to make to our moving-average cross over strategy to reduce the lag in our trading strategy to more reliable levels by leveraging our skills in data science. It is a well-studied fact that projecting your data to higher dimensions can at times improve the performance of your machine learning models. We will demonstrate what this practically means for you as a trader, and illustrate how you can weaponize this powerful principle using your MetaTrader 5 Terminal.

How best can we combine multiple strategies to create a powerful ensemble strategy? Join us in this discussion as we look to fit together three different strategies into our trading application. Traders often employ specialized strategies for opening and closing positions, and we want to know if our machines can perform this task better. For our opening discussion, we will get familiar with the faculties of the strategy tester and the principles of OOP we will need for this task.

本連載記事では、テクニカル指標を使用する際の最適な期間を特定するためのさまざまな方法を検討してきました。本記事では、読者に対して逆のロジックを示します。すなわち、単一の最適期間を選ぶのではなく、利用可能なすべての期間を効果的に活用する方法を示します。このアプローチにより廃棄されるデータ量が減少し、通常の価格予測以外に機械学習アルゴリズムを活用する方法も得られます。

本記事では、同一の戦略と金融銘柄を用いても、ブローカーによって取引結果が大きく異なる理由について探ります。その背景には、価格が分散的に形成されていることや、データの不一致があるためです。本記事は、MQL5開発者がMQL5マーケットプレイスで自らの製品に対して賛否両論の評価を受ける理由を理解し、透明性が高く再現可能な成果を確保するためには、特定のブローカーに合わせたアプローチを取る必要があることを示唆しています。この取り組みが広く受け入れられれば、コミュニティにとって重要な実務上のベストプラクティスへと発展する可能性があります。

私たちのコミュニティがAIをあらゆる形態で活用した信頼性の高い取引戦略を構築しようとする努力を、静かに蝕んでいる強力で広範な力があります。本稿では、私たちが直面している問題の一部は、「ベストプラクティス」に盲目的に従うことに根ざしていることを明らかにします。読者に対して、実際の市場に基づくシンプルな証拠を提供することで、なぜそのような行動を避け、むしろドメイン固有のベストプラクティスを採用すべきかを論理的に示します。これによって、私たちのコミュニティがAIの潜在的な可能性を回復するチャンスを少しでも持てるようになるのです。

高確率セットアップ(high probability setups)は、私たちの取引コミュニティではよく知られていますが、残念ながら明確には定義されていません。この記事では、「高確率セットアップ」とは具体的に何かを、経験的かつアルゴリズム的な方法で定義し、それを特定して活用することを目指します。勾配ブースティング木を用いることで、任意の取引戦略のパフォーマンスを向上させる方法、そしてコンピュータに対して「何をすべきか」をより明確かつ意味のある形で伝える手段を、読者に示します。

次元削減手法は、機械学習モデルのパフォーマンスを向上させるために広く用いられています。ここでは、UMAP (Uniform Manifold Approximation and Projection)という比較的新しい手法について説明します。UMAPは、古い手法に見られるデータの歪みや人工的な構造といった欠点を明確に克服することを目的として開発されました。UMAPは非常に強力な次元削減技術であり、似たローソク足を新たに効果的にグループ化できるため、アウトオブサンプル（未知データ）に対する誤差率を低減し、取引パフォーマンスを向上させることができます。

本記事では、より良い売買シグナルを得るために、RSIのレベルと期間を最適化する方法を探ります。最適なRSI値を推定する手法や、グリッドサーチと統計モデルを用いた期間選定の自動化について紹介します。最後に、Pythonによる分析を活用しながら、MQL5でソリューションを実装します。私たちのアプローチは、複雑になりがちな問題をシンプルに解決することを目指した、実用的かつ分かりやすいものです。

トレーダーは、誤ったシグナルによるドローダウンに直面することが多い一方で、確認を待ちすぎることで、有望な機会を逃すこともあります。本稿では、ドル建て銀価格(XAGUSD)、ユーロ建て銀価格(XAGEUR)、およびEURUSD為替レートを用いた三角裁定取引戦略を紹介し、市場のノイズをフィルタリングする方法を解説します。市場間の相関関係を活用することで、隠れた市場センチメントをリアルタイムで捉え、エントリータイミングをより洗練させることが可能になります。