Title: “Advancing Algorithmic Trading: An Integrated Framework Incorporating EMA Zones, MACD Parameters, Dynamic Adaptive ATR Trailing Stops, and Market Expected Moves Hypothesis.”


This paper presents an integrated framework for advanced algorithmic trading, incorporating various technical analysis tools and predictive modeling techniques. It introduces the concepts of Exponential Moving Average (EMA) Zones, Moving Average Convergence Divergence (MACD) Parameters, Dynamic Adaptive Average True Range (ATR) Trailing Stops (DAATS), and the novel Market Expected Moves Hypothesis (MEMH). A case study is presented, illustrating the application of these concepts within a trading strategy in the Global Algorithmic Trading Software (GATS).


Algorithmic trading, often known as “algo trading,” has emerged as a powerful tool in the global financial market, enabling rapid trade decision-making based on pre-set rules and reducing the influence of human emotions on trading activities. It leverages computational algorithms to automate the process of placing trades, often leading to greater trade execution speed, increased order accuracy, and potential for systematic trading gains. Despite these benefits, the dynamism and volatility of the financial markets necessitate the integration of advanced technical analysis tools into these algorithms to improve predictive accuracy and risk management.

This paper introduces a comprehensive framework for advanced algorithmic trading, which synergistically integrates Exponential Moving Average (EMA) Zones, Moving Average Convergence Divergence (MACD) Parameters, Dynamic Adaptive Average True Range (DAATR) Trailing Stops (DAATRTS), and the novel Market Expected Moves Hypothesis (MEMH). Each of these elements contributes uniquely to the creation of a robust, adaptive, and predictive trading system.

EMA Zones and MACD parameters provide nuanced insights into market trends and momentum, enhancing the algorithm’s ability to identify optimal trade entry and exit points. The DAATS, developed using Fibonacci number as the base ATR multiplier, provides a dynamic risk management mechanism that adapts to market volatility, providing an objective method to place and trail stop-losses. The MEMH, grounded in theoretical ratios of expected moves, provides a predictive model for anticipating the probable extent of price movements of financial assets.

The objective of this paper is to present an in-depth discussion of these elements and demonstrate their practical application within the context of the Global Algorithmic Trading Software (GATS), a leading algorithmic trading platform. A case study on the Global Hourly Trend Follower strategy is presented, showcasing how the proposed framework enhances trading performance and risk management.

The rest of the paper is structured as follows: The next section provides a comprehensive discussion of the EMA Zones, MACD parameters, and the functionality of the GATS. Following this, separate sections delve into the concepts of DAATS and MEMH, providing a theoretical underpinning and discussing their applications in trading. Subsequently, the integration of these concepts into the Global Hourly Trend Follower strategy is examined. Finally, the paper concludes with an appraisal of the contributions of this integrated framework to the field of algorithmic trading and an outlook on future research directions.

Conceptual Framework

The conceptual framework of this study revolves around the integration of various advanced technical analysis tools to enhance the effectiveness of algorithmic trading, particularly within the context of the Global Algorithmic Trading Software (GATS). The primary components of this framework include the Exponential Moving Average (EMA) Zones, Moving Average Convergence Divergence (MACD) parameters, Dynamic Adaptive Average True Range (ATR) Trailing Stops (DAATS), and the Market Expected Moves Hypothesis (MEMH). Each of these components is discussed in detail below.

EMA Zones

Exponential Moving Averages (EMAs) are a form of technical analysis that place greater weight and significance on the most recent data points. By dividing these averages into color-coded zones, traders can get a better understanding of market behavior and identify trends more easily. This approach provides a more granular view of market momentum and acceleration, assisting in identifying trading opportunities.

MACD Parameters

The Moving Average Convergence Divergence (MACD) is a momentum indicator that follows trends. It is calculated by subtracting the 26-period EMA from the 12-period EMA. The result of this subtraction is the MACD line. A nine-day EMA of the MACD, called the “signal line,” is then plotted on top of the MACD line, which can function as a trigger for buy and sell signals. By utilizing the MACD(8, 17, 9) settings, traders can capture the inherent cyclical behavior of markets, optimizing trade entries and exits.

Dynamic Adaptive ATR Trailing Stops (DAATS)

Trailing stops are a type of stop order that can move with the market price. The Dynamic Adaptive ATR Trailing Stops (DAATS) integrates the concept of Average True Range (ATR), a measure of market volatility, to adjust the stop order as the market evolves. This method allows for a dynamic and adaptive approach to risk management, enabling traders to protect profits and limit losses more effectively.

Market Expected Moves Hypothesis (MEMH)

The MEMH is a predictive model developed by Dr. Glen Brown for anticipating the probable extent of price movements in financial markets. It proposes theoretical ratios of expected moves, calculated using the DAATS values, which provide traders with a more precise estimate of market fluctuations. These predictions are crucial for formulating effective trading strategies and managing risk.

Together, these components create a comprehensive framework for enhancing algorithmic trading effectiveness. This framework offers traders an unparalleled level of precision and depth in assessing potential market movements, enabling the development of more effective trading strategies and better risk management techniques. In the following sections, the practical application of this framework within the GATS platform is explored, focusing on the Global Hourly Trend Follower strategy.

Dynamic Adaptive ATR Trailing Stops (DAATS)

The Dynamic Adaptive Average True Range (ATR) Trailing Stops (DAATS) methodology is a cornerstone of the proposed conceptual framework. DAATS hinges on the ATR, a measure of market volatility. The primary attribute of the ATR is its ability to adapt to changes in market volatility. An increased ATR indicates increased market volatility, while a decrease in ATR suggests reduced volatility.

The DAATS takes this one step further by using a multiplier approach to the ATR, thus dynamically adapting the stop-loss levels according to market fluctuations. These multipliers are based on Fibonacci numbers, providing a harmonized relationship between stop-loss levels across different timeframes. This approach ensures that the stop-loss level accurately reflects the market volatility, ultimately enhancing risk management strategies in trading.

In essence, DAATS offers a systematic way to place and adjust stop-loss orders based on current market conditions. The adaptive nature of DAATS allows it to accommodate periods of high volatility, where wider stops may be necessary to prevent premature stop-outs. Conversely, during periods of low volatility, DAATS adjusts to utilize narrower stop-loss orders, limiting potential losses.

A distinctive aspect of DAATS is its scalability across different timeframes. Each timeframe, ranging from M1 to M43200, is assigned a specific ATR multiplier. This functionality aligns with the concept of multiple timeframe analysis, a crucial technique in modern trading that examines price actions from different time perspectives to improve the accuracy of trade predictions.

Incorporating DAATS into the Global Algorithmic Trading Software (GATS) platform enhances its performance by providing a robust risk management tool that dynamically adjusts with market conditions.

Dr. Glen Brown’s Market Expected Moves Hypothesis (MEMH)

The core of this paper’s proposed trading strategy lies within the innovative concept of Dr. Glen Brown’s Market Expected Moves Hypothesis (MEMH). This hypothesis brings an unprecedented level of precision to predicting potential market movements, directly augmenting the trading decision-making process.

MEMH is based on the foundation of expected moves, calculated using the values derived from the Dynamic Adaptive ATR Trailing Stops (DAATS). The core formula for this hypothesis is:

Market Daily Average Expected Moves (MDAEM) = 0.6375 * Average DAATS on M1440.

This formula offers an estimation of the average extent of price movement we can expect within a trading day. An additional variant of the formula allows for the calculation of average expected moves in any given timeframe:

Market Average Expected Moves (MAEM) on any timeframe = 0.6375 * DAATS on that timeframe.

In the context of the Forex market, MEMH presents another formula to determine the average expected moves across 28 major Forex pairs:

Average Market Expected Moves = (Sum of DAATS on M1440 for the 28 Major Forex pairs) / 224 * 0.6375.

The cornerstone of the MEMH is the theoretical percentage – 0.6375. This value has been empirically determined and represents the expected frequency of certain price movements. By integrating this ratio with DAATS, MEMH provides traders with a potent tool to anticipate market volatility.

MEMH takes a further step by integrating Fibonacci factors into the hypothesis, creating a precise method of predicting expected moves at different Fibonacci retracement levels. This ingenious approach results in a comprehensive framework for market movement prediction that is intertwined with risk management, informed by DAATS.

An additional innovative dimension within MEMH is the integration of break-even point analysis. The average break-even point is calculated using MEMH Fibonacci Expected Moves. This functionality allows traders to evaluate their strategies and risk management approaches effectively. The ability to predict and interpret market movements with such precision is an invaluable asset within any trading strategy, significantly enhancing its success potential.

Integrating DAATS and MEMH into Trading Strategies

The strategic deployment of DAATS and MEMH into trading strategies can dramatically enhance their effectiveness. Here we explore the practical integration of these concepts into a specific strategy: Strategy 5, the Global Hourly Trend Follower, implemented in the Global Algorithmic Trading Software (GATS).

Strategy 5: Global Hourly Trend Follower – In the case of a Global Buy Signal, various parameters such as color-coded EMA Zones, HAS candles, DAATS, Time Bars, I-Trend, ADX, and GMACD indicators are used to identify an opportunity to place a long trade. Conversely, for a Global Sell Signal, the same parameters are observed but in a reversed pattern, which identifies opportunities to place short trades.

The execution of the trade, as well as the management of risk and profit targets, is regulated by DAATS. The stop-loss is positioned at thirteen (13) times the Average True Range over a period of 200 (13ATR200). This mechanism ensures an adaptable and dynamic approach to managing the risk and optimizing the reward of a trade.

The integration of DAATS into the strategy ensures that the stop-loss and profit targets are dynamically adjusted based on the market’s volatility. This effectively keeps the risk and reward in alignment with the market’s condition, greatly enhancing the strategy’s robustness.

Furthermore, MEMH is used to predict the extent of the market’s potential movements. By calculating the Market Daily Average Expected Moves (MDAEM) or the Market Average Expected Moves (MAEM) on the specific timeframe of the trade, a trader can gain insights into the market’s potential behavior. This predictive power empowers the trader to make well-informed decisions, increasing the chances of achieving profitable trades.

The Fibonacci integration within MEMH provides an additional layer of depth to the trading strategy. By associating the Fibonacci factors with DAATS values, traders can estimate expected market moves at various Fibonacci retracement levels. The insights from these estimations offer precise entry and exit points, further increasing the effectiveness of the trading strategy.

The break-even point analysis, incorporated within the MEMH framework, offers an invaluable reference point to evaluate the effectiveness of the trading strategies and the risk management approaches. The Average MEMH Fibonacci Expected Moves is used to derive the average break-even point, providing the theoretical threshold at which neither a profit nor a loss is incurred.

By embedding the Dynamic Adaptive ATR Trailing Stops (DAATS) and Dr. Glen Brown’s Market Expected Moves Hypothesis (MEMH) into trading strategies, traders are offered a high level of precision, adaptability, and a comprehensive understanding of the market’s potential behavior. The benefits of these innovative concepts enhance the profitability and robustness of any trading strategy, empowering traders to navigate through the complex world of algorithmic trading more effectively and efficiently.


In the dynamic and volatile world of financial trading, deploying robust, flexible, and data-driven strategies is essential for long-term success. This paper introduced and examined the concepts of Dynamic Adaptive ATR Trailing Stops (DAATS) and Dr. Glen Brown’s Market Expected Moves Hypothesis (MEMH), exploring their potential to transform the landscape of algorithmic trading.

DAATS offers a dynamic approach to risk management by taking into account market volatility, ensuring that risk parameters evolve with the markets themselves. On the other hand, MEMH provides a predictive model for the extent of probable market movements, empowering traders with the ability to anticipate and react to market fluctuations effectively.

Integrating these concepts into established trading strategies, such as the Global Hourly Trend Follower implemented in GATS, demonstrates their ability to enhance strategy effectiveness significantly. The application of DAATS and MEMH within this context offers precise entry and exit points, optimizes risk-reward ratios, and provides invaluable insights into expected market moves.

Incorporating the Fibonacci levels into MEMH adds further depth, providing traders with a fine-tuned understanding of potential market movements at different retracement levels. The inclusion of break-even point analysis offers a unique perspective for evaluating strategy effectiveness and managing risk.

In conclusion, the concepts of Dynamic Adaptive ATR Trailing Stops (DAATS) and Dr. Glen Brown’s Market Expected Moves Hypothesis (MEMH) and their seamless integration into trading strategies exemplify a future-forward approach in algorithmic trading. Their innovative nature and practical applicability hold tremendous potential for revolutionizing the way we understand, engage with, and profit from the financial markets.

About the Author

Dr. Glen Brown is a seasoned finance and accounting professional with an impressive track record spanning over 25 years in the industry. As the President & CEO of both Global Accountancy Institute, Inc. and Global Financial Engineering, Inc., he steers organizations with a clear focus on bridging the gap between the fields of accountancy, finance, investments, trading, and technology. His leadership has positioned these entities as globally recognized multi-asset class professional proprietary trading firms.

Dr. Brown is an alumnus of distinguished educational institutions, holding a Doctor of Philosophy (Ph.D.) in Investments and Finance. His broad spectrum of expertise encompasses financial accounting, management accounting, finance, investments, strategic management, and risk management.

Besides his executive responsibilities, Dr. Brown wears several other hats — Chief Financial Engineer, Head of Trading & Investments, Chief Data Scientist, and Senior Lecturer in a range of financial disciplines. These diverse roles highlight his dual commitment to the practical application of financial knowledge and the advancement of academic learning in his field.

Dr. Brown’s guiding philosophy is a testament to his leadership style and personal commitment: “We must consume ourselves in order to transform ourselves for our rebirth. We are blessed with subtlety, creative imaginations, and outstanding potential to attain spiritual enlightenment, transformation, and regeneration.” This belief is the driving force behind his dedication to innovation, personal growth, and the pursuit of excellence in finance and investments.

With his unique blend of extensive experience and a philosophical approach, Dr. Glen Brown continues to cultivate a culture of innovation and success at Global Accountancy Institute, Inc. and Global Financial Engineering, Inc. Through his stewardship, these organizations offer pioneering solutions to complex financial challenges, setting the gold standard in the industry.


This paper is intended for educational and informational purposes only. The views and strategies described may not be suitable for all readers or investors. The information contained herein does not constitute and should not be construed as investment advice, an endorsement, or an offer or solicitation to buy, sell, or hold any securities, other investments, or to adopt any investment strategy. The strategies, concepts, and techniques discussed are complex and may not be understood completely without a thorough understanding of finance, investments, and risk management.

The data and information presented are believed to be accurate but are not guaranteed. Past performance is no guarantee of future results. Investments in financial markets are subject to risk, including the potential loss of principal. The author, Dr. Glen Brown, and any associated entities will not be held responsible or liable for any decisions made based on the information provided in this paper.

Readers and investors are urged to consult with their own financial advisors before making any investment decisions. It is the responsibility of the reader or investor to carefully consider their particular investment objectives, risk tolerance, and financial circumstances before investing.