An Integrated Approach for Market Predictions: Expanding Dr. Glen Brown’s Market Expected Moves Hypothesis (MEMH), Dynamic Adaptive ATR Trailing Stops (DAATS) with Fibonacci Scaling Factors and Break-Even Point Analysis

An Integrated Approach for Market Predictions: Expanding Dr. Glen Brown’s Market Expected Moves Hypothesis (MEMH), Dynamic Adaptive ATR Trailing Stops (DAATS) with Fibonacci Scaling Factors and Break-Even Point Analysis

Abstract:

This research presents a comprehensive and sophisticated integrated approach that expands upon Dr. Glen Brown’s esteemed Market Expected Moves Hypothesis (MEMH). By incorporating Fibonacci factors and break-even point analysis, along with the utilization of Dynamic Adaptive ATR Trailing Stops (DAATS), the enhanced MEMH offers an advanced predictive model for estimating price movements in the financial market. This novel framework provides traders and investors with an enhanced level of precision, detail, and confidence in making informed decisions.

Introduction:

The accurate prediction of price movements in the dynamic and complex financial market remains a formidable challenge. Dr. Glen Brown’s MEMH has gained recognition for its efficacy in forecasting market trends by employing the concept of expected moves utilizing DAATS. To further refine and augment MEMH, this study introduces an innovative integrated approach that incorporates Fibonacci factors and break-even point analysis. By integrating these components, the enhanced MEMH introduces a novel methodology for anticipating market fluctuations with heightened accuracy.

The Market Expected Moves Hypothesis:

Dr. Glen Brown’s MEMH serves as the foundation for our integrated approach. The hypothesis leverages the adaptive nature of DAATS, which adjusts to market volatility, to estimate the likely extent of price fluctuations. The MEMH formula is expressed as follows:

Market Daily Average Expected Moves (MDAEM) = 0.6375 * Average Dynamic Adaptive ATR Trailing Stops (DAATS) on M1440.

We can also applied this approach to other trading timeframe by calculating the Dynamic Adaptive ATR Trailing Stops (DAATS) for this timeframe.

Dr. Glen Brown current preference is to use a 252-Period Average True Range(ATR) for each trading timeframe. There are approximately 252 trading days within the year and hence he is using this as the standard period for all timeframes.

A tangible aspect of the DAATS system is its implementation using specific ATR multipliers derived from Fibonacci sequences. According to Dr. Glen Brown the standard Based Multiplier is given by the square root of the ATR Period. Therefore in this case the Standard Based Multiplier is given by the square root of 252 divided by the Fibonacci ratio of 0.786 which is approximately 21 rounded up. Hence we can generate a set of ATR multipliers that provide a harmonized relationship across different timeframes.

According to Dr. Glen Brown  methodology  the ATR multiplier for each timeframe are as follows:

  • ATR multiplier the 1-Minute Trading Timeframe is: 21. Hence Dynamic Adaptive ATR Trailing Stops (DAATS) = 21ATR252
  • ATR multiplier the 5-Minute Trading Timeframe is: 16. Hence Dynamic Adaptive ATR Trailing Stops (DAATS) = 16ATR252
  • ATR multiplier the 15-Minute Trading Timeframe is: 12. Hence Dynamic Adaptive ATR Trailing Stops (DAATS) = 12ATR252
  • ATR multiplier the 30-Minute Trading Timeframe is: 10. Hence Dynamic Adaptive ATR Trailing Stops (DAATS) = 10ATR252
  • ATR multiplier the 60-Minute Trading Timeframe is: 8. Hence Dynamic Adaptive ATR Trailing Stops (DAATS) = 8ATR252
  • ATR multiplier the 240-Minute Trading Timeframe is: 6. Hence Dynamic Adaptive ATR Trailing Stops (DAATS) = 6ATR252
  • ATR multiplier the 1440-Minute Trading Timeframe is: 5. Hence Dynamic Adaptive ATR Trailing Stops (DAATS) = 5ATR252
  • ATR multiplier the 10080-Minute Trading Timeframe is: 4. Hence Dynamic Adaptive ATR Trailing Stops (DAATS) = 4ATR252
  • ATR multiplier the 43200-Minute Trading Timeframe is: 3. Hence Dynamic Adaptive ATR Trailing Stops (DAATS) = 3ATR252

These formulas represent theoretical percentages of expected moves, forming the core of the MEMH.

  • Market Average Expected Moves (MAEM) For M1 = 0.6375 *  Dynamic Adaptive ATR Trailing Stops (DAATS) For  M1 = 0.6375 x 20ATR252
  • Market  Average Expected Moves (MAEM) For M5 = 0.6375 *  Dynamic Adaptive ATR Trailing Stops (DAATS) For  M5 = 0.6375 x 16ATR252
  • Market  Average Expected Moves (MAEM) For M15 = 0.6375 *  Dynamic Adaptive ATR Trailing Stops (DAATS) For  M15 = 0.6375 x 12ATR252
  • Market  Average Expected Moves (MAEM) For M30 = 0.6375 *  Dynamic Adaptive ATR Trailing Stops (DAATS) For  M30 = 0.6375 x 10ATR252 
  • Market  Average Expected Moves (MAEM) For M60 = 0.6375 *  Dynamic Adaptive ATR Trailing Stops (DAATS) For  M60 = 0.6375 x 8ATR252
  • Market  Average Expected Moves (MAEM) For M240 = 0.6375 *  Dynamic Adaptive ATR Trailing Stops (DAATS) For  M240 = 0.6375 x 6ATR252 
  • Market  Average Expected Moves (MAEM) For M1440 = 0.6375 *  Dynamic Adaptive ATR Trailing Stops (DAATS) For  M1440 = 0.6375 x 5ATR252
  • Market  Average Expected Moves (MAEM) For M10080 = 0.6375 *  Dynamic Adaptive ATR Trailing Stops (DAATS) For  M10080 = 0.6375 x 4ATR252 
  • Market  Average Expected Moves (MAEM) For M43200 = 0.6375 *  Dynamic Adaptive ATR Trailing Stops (DAATS) For  M43200 = 0.6375 x 3ATR252

Integrating Fibonacci Factors:

To enhance MEMH, Fibonacci factors are seamlessly integrated into the model. Fibonacci retracement levels, including 23.6%, 38.2%, 50.0%, 61.8%, and 78.6%, are assigned corresponding factors. These factors are derived by multiplying the MEMH Fib Factor (0.6375) with each respective Fibonacci level:

  • 23.6%: MEMH Fib Factor = 0.6375 * 23.6%
  • 38.2%: MEMH Fib Factor = 0.6375 * 38.2%
  • 50.0%: MEMH Fib Factor = 0.6375 * 50.0%
  • 61.8%: MEMH Fib Factor = 0.6375 * 61.8%
  • 78.6%: MEMH Fib Factor = 0.6375 * 78.6%

These constants represent the calculated values obtained by multiplying 0.6375 with the respective Fibonacci levels.

23.6%: MEMH Fib Factor = 0.6375 * 23.6% = 0.15042

38.2%: MEMH Fib Factor = 0.6375 * 38.2% = 0.24355

50.0%: MEMH Fib Factor = 0.6375 * 50.0% = 0.31875

61.8%: MEMH Fib Factor = 0.6375 * 61.8% = 0.393885

78.6%: MEMH Fib Factor = 0.6375 * 78.6% = 0.501015

These Fibonacci factors are then applied to the DAATS values, enabling the estimation of expected moves at different Fibonacci retracement levels. This integration empowers traders with an unparalleled level of precision and depth in assessing potential market movements.

Break-Even Point Analysis:

In addition to Fibonacci factors, Break-Even Point(BEP) analysis is incorporated into the integrated MEMH framework. The average break-even point is derived from the average MEMH Fibonacci Expected Moves. It represents the theoretical threshold at which neither profit nor loss is incurred.

We can also set our Standard Break-Even Point(BEP)  at  50% the Market  Average Expected Moves (MAEM) = 50% x 0.6375x MAEM = 0.31875MAEM

Our Fixed Normal Trailing Stop is also set at 0.31875MAEM

The integration of break-even point analysis enhances the model by providing traders with a reference point to evaluate the effectiveness of their trading strategies and risk management approaches.

Conclusion:

This research has proposed an advanced integrated approach that expands upon Dr. Glen Brown’s MEMH by incorporating Fibonacci factors and break-even point analysis. The resulting enhanced MEMH framework offers traders and investors a sophisticated and comprehensive methodology for predicting price movements in the financial market. By integrating these components, market participants can gain an unprecedented level of precision, detail, and confidence in their decision-making processes. It is crucial to acknowledge that the integrated MEMH should be utilized alongside other analysis techniques and risk management strategies to form a comprehensive and robust trading strategy.

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.

Disclaimer

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.



Leave a Reply

Layer 1