Young Ho Seo / Perfil
- Informações
10+ anos
experiência
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62
produtos
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1178
versão demo
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4
trabalhos
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0
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assinantes
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Harmonic Pattern Indicator - Repainting + Japanese Candlestick Pattern Scanner + Automatic Channel + Many more
https://www.mql5.com/en/market/product/4488
https://www.mql5.com/en/market/product/4475
https://algotrading-investment.com/portfolio-item/harmonic-pattern-plus/
Non Repainting and Non Lagging Harmonic Pattern Indicator – Customizable Harmonic + Japanese Candlestic Pattern Scanner + Advanced Channel + Many more
https://www.mql5.com/en/market/product/41993
https://www.mql5.com/en/market/product/41992
https://algotrading-investment.com/portfolio-item/profitable-pattern-scanner/
Supply Demand Indicator – Multiple Timeframe Scanning Added + Non Repainting + Professional Indicator
https://www.mql5.com/en/market/product/40076
https://www.mql5.com/en/market/product/40075
https://algotrading-investment.com/portfolio-item/ace-supply-demand-zone/
Momentum Indicator – Path to Volume Spread Analysis
https://www.mql5.com/en/market/product/30641
https://www.mql5.com/en/market/product/30621
https://algotrading-investment.com/portfolio-item/excessive-momentum-indicator/
Elliott Wave Indicator for the Power User
https://www.mql5.com/en/market/product/16479
https://www.mql5.com/en/market/product/16472
https://algotrading-investment.com/portfolio-item/elliott-wave-trend/
Forex Prediction - Turn Support and Resistance to the Advanced Strategy
https://www.mql5.com/en/market/product/49170
https://www.mql5.com/en/market/product/49169
https://algotrading-investment.com/portfolio-item/fractal-pattern-scanner/
MetaTrader 4 and MetaTrader 5 Product Page: https://www.mql5.com/en/users/financeengineer/seller#products
Free Forex Prediction with Fibonacci Analysis: https://algotrading-investment.com/2020/10/23/forex-prediction-with-fibonacci-analysis/
Free Harmonic Pattern Signal: https://algotrading-investment.com/2020/12/17/harmonic-pattern-signal-for-forex-market/
============================================================================================================================
Here are the trading education books. We recommend reading these books if you are a trader or investor in Forex and Stock market. In the list below, we put the easy to read book on top. Try to read the easy to read book first and try to read the harder book later to improve your trading and investment.
First Link = amazon.com, Second Link = Google Play Books, Third Link = algotrading-investment.com, Fourth Link = Google Books
Technical Analysis in Forex and Stock Market (Supply Demand Analysis and Support Resistance)
https://www.amazon.com/dp/B09L55ZK4Z
https://play.google.com/store/books/details?id=pHlMEAAAQBAJ
https://algotrading-investment.com/portfolio-item/technical-analysis-in-forex-and-stock-market/
https://books.google.co.kr/books/about?id=pHlMEAAAQBAJ
Science Of Support, Resistance, Fibonacci Analysis, Harmonic Pattern, Elliott Wave and X3 Chart Pattern (In Forex and Stock Market Trading)
https://www.amazon.com/dp/B0993WZGZD
https://play.google.com/store/books/details?id=MME3EAAAQBAJ
https://algotrading-investment.com/portfolio-item/science-of-support-resistance-fibonacci-analysis-harmonic-pattern/
https://books.google.co.kr/books/about?id=MME3EAAAQBAJ
Profitable Chart Patterns in Forex and Stock Market (Fibonacci Analysis, Harmonic Pattern, Elliott Wave, and X3 Chart Pattern)
https://www.amazon.com/dp/B0B2KZH87K
https://play.google.com/store/books/details?id=7KrQDwAAQBAJ
https://algotrading-investment.com/portfolio-item/profitable-chart-patterns-in-forex-and-stock-market/
https://books.google.com/books/about?id=7KrQDwAAQBAJ
Guide to Precision Harmonic Pattern Trading (Mastering Turning Point Strategy for Financial Trading)
https://www.amazon.com/dp/B01MRI5LY6
https://play.google.com/store/books/details?id=8SbMDwAAQBAJ
http://algotrading-investment.com/portfolio-item/guide-precision-harmonic-pattern-trading/
https://books.google.com/books/about?id=8SbMDwAAQBAJ
Scientific Guide to Price Action and Pattern Trading (Wisdom of Trend, Cycle, and Fractal Wave)
https://www.amazon.com/dp/B073T3ZMBR
https://play.google.com/store/books/details?id=5prUDwAAQBAJ
https://algotrading-investment.com/portfolio-item/scientific-guide-to-price-action-and-pattern-trading/
https://books.google.com/books/about?id=5prUDwAAQBAJ
Predicting Forex and Stock Market with Fractal Pattern: Science of Price and Time
https://www.amazon.com/dp/B086YKM8BW
https://play.google.com/store/books/details?id=VJjiDwAAQBAJ
https://algotrading-investment.com/portfolio-item/predicting-forex-and-stock-market-with-fractal-pattern/
https://books.google.com/books/about?id=VJjiDwAAQBAJ
Trading Education Book 1 in Korean (Apple, Google Play Book, Google Book, Scribd, Kobo)
https://books.apple.com/us/book/id1565534211
https://play.google.com/store/books/details?id=HTgqEAAAQBAJ
https://books.google.co.kr/books/about?id=HTgqEAAAQBAJ
https://www.scribd.com/book/505583892
https://www.kobo.com/ww/en/ebook/8J-Eg58EDzKwlpUmADdp2g
Trading Education Book 2 in Korean (Apple, Google Play Book, Google Book, Scribd, Kobo)
https://books.apple.com/us/book/id1597112108
https://play.google.com/store/books/details?id=shRQEAAAQBAJ
https://books.google.co.kr/books/about?id=shRQEAAAQBAJ
https://www.scribd.com/book/542068528
https://www.kobo.com/ww/en/ebook/X8SmJdYCtDasOfQ1LQpCtg
About Young Ho Seo
Young Ho Seo is an Engineer, Financial Trader, and Quantitative Developer, working on Trading Science and Investment Engineering since 2011. He is the creator of many technical indicators, price patterns and trading strategies used in the financial market. He is also teaching the trading practice on how to use the Supply Demand Analysis, Support, Resistance, Trend line, Fibonacci Analysis, Harmonic Pattern, Elliott Wave Theory, Chart Patterns, and Probability for Forex and Stock Market. His works include developing scientific trading principle and mathematical algorithm in the work of Benjamin Graham, Everette S. Gardner, Benoit Mandelbrot, Ralph Nelson Elliott, Harold M. Gartley, Richard Shabacker, William Delbert Gann, Richard Wyckoff and Richard Dennis. You can find his dedicated works on www.algotrading-investment.com . His life mission is to connect financial traders and scientific community for better understanding of this world and crowd behaviour in the financial market. He wrote many books and articles, which are helpful for understanding the technology and application behind technical analysis, statistics, time series forecasting, fractal science, econometrics, and artificial intelligence in the financial market.
If you are interested in our software and training, just visit our main website: www.algotrading-investment.com
https://www.mql5.com/en/market/product/4488
https://www.mql5.com/en/market/product/4475
https://algotrading-investment.com/portfolio-item/harmonic-pattern-plus/
Non Repainting and Non Lagging Harmonic Pattern Indicator – Customizable Harmonic + Japanese Candlestic Pattern Scanner + Advanced Channel + Many more
https://www.mql5.com/en/market/product/41993
https://www.mql5.com/en/market/product/41992
https://algotrading-investment.com/portfolio-item/profitable-pattern-scanner/
Supply Demand Indicator – Multiple Timeframe Scanning Added + Non Repainting + Professional Indicator
https://www.mql5.com/en/market/product/40076
https://www.mql5.com/en/market/product/40075
https://algotrading-investment.com/portfolio-item/ace-supply-demand-zone/
Momentum Indicator – Path to Volume Spread Analysis
https://www.mql5.com/en/market/product/30641
https://www.mql5.com/en/market/product/30621
https://algotrading-investment.com/portfolio-item/excessive-momentum-indicator/
Elliott Wave Indicator for the Power User
https://www.mql5.com/en/market/product/16479
https://www.mql5.com/en/market/product/16472
https://algotrading-investment.com/portfolio-item/elliott-wave-trend/
Forex Prediction - Turn Support and Resistance to the Advanced Strategy
https://www.mql5.com/en/market/product/49170
https://www.mql5.com/en/market/product/49169
https://algotrading-investment.com/portfolio-item/fractal-pattern-scanner/
MetaTrader 4 and MetaTrader 5 Product Page: https://www.mql5.com/en/users/financeengineer/seller#products
Free Forex Prediction with Fibonacci Analysis: https://algotrading-investment.com/2020/10/23/forex-prediction-with-fibonacci-analysis/
Free Harmonic Pattern Signal: https://algotrading-investment.com/2020/12/17/harmonic-pattern-signal-for-forex-market/
============================================================================================================================
Here are the trading education books. We recommend reading these books if you are a trader or investor in Forex and Stock market. In the list below, we put the easy to read book on top. Try to read the easy to read book first and try to read the harder book later to improve your trading and investment.
First Link = amazon.com, Second Link = Google Play Books, Third Link = algotrading-investment.com, Fourth Link = Google Books
Technical Analysis in Forex and Stock Market (Supply Demand Analysis and Support Resistance)
https://www.amazon.com/dp/B09L55ZK4Z
https://play.google.com/store/books/details?id=pHlMEAAAQBAJ
https://algotrading-investment.com/portfolio-item/technical-analysis-in-forex-and-stock-market/
https://books.google.co.kr/books/about?id=pHlMEAAAQBAJ
Science Of Support, Resistance, Fibonacci Analysis, Harmonic Pattern, Elliott Wave and X3 Chart Pattern (In Forex and Stock Market Trading)
https://www.amazon.com/dp/B0993WZGZD
https://play.google.com/store/books/details?id=MME3EAAAQBAJ
https://algotrading-investment.com/portfolio-item/science-of-support-resistance-fibonacci-analysis-harmonic-pattern/
https://books.google.co.kr/books/about?id=MME3EAAAQBAJ
Profitable Chart Patterns in Forex and Stock Market (Fibonacci Analysis, Harmonic Pattern, Elliott Wave, and X3 Chart Pattern)
https://www.amazon.com/dp/B0B2KZH87K
https://play.google.com/store/books/details?id=7KrQDwAAQBAJ
https://algotrading-investment.com/portfolio-item/profitable-chart-patterns-in-forex-and-stock-market/
https://books.google.com/books/about?id=7KrQDwAAQBAJ
Guide to Precision Harmonic Pattern Trading (Mastering Turning Point Strategy for Financial Trading)
https://www.amazon.com/dp/B01MRI5LY6
https://play.google.com/store/books/details?id=8SbMDwAAQBAJ
http://algotrading-investment.com/portfolio-item/guide-precision-harmonic-pattern-trading/
https://books.google.com/books/about?id=8SbMDwAAQBAJ
Scientific Guide to Price Action and Pattern Trading (Wisdom of Trend, Cycle, and Fractal Wave)
https://www.amazon.com/dp/B073T3ZMBR
https://play.google.com/store/books/details?id=5prUDwAAQBAJ
https://algotrading-investment.com/portfolio-item/scientific-guide-to-price-action-and-pattern-trading/
https://books.google.com/books/about?id=5prUDwAAQBAJ
Predicting Forex and Stock Market with Fractal Pattern: Science of Price and Time
https://www.amazon.com/dp/B086YKM8BW
https://play.google.com/store/books/details?id=VJjiDwAAQBAJ
https://algotrading-investment.com/portfolio-item/predicting-forex-and-stock-market-with-fractal-pattern/
https://books.google.com/books/about?id=VJjiDwAAQBAJ
Trading Education Book 1 in Korean (Apple, Google Play Book, Google Book, Scribd, Kobo)
https://books.apple.com/us/book/id1565534211
https://play.google.com/store/books/details?id=HTgqEAAAQBAJ
https://books.google.co.kr/books/about?id=HTgqEAAAQBAJ
https://www.scribd.com/book/505583892
https://www.kobo.com/ww/en/ebook/8J-Eg58EDzKwlpUmADdp2g
Trading Education Book 2 in Korean (Apple, Google Play Book, Google Book, Scribd, Kobo)
https://books.apple.com/us/book/id1597112108
https://play.google.com/store/books/details?id=shRQEAAAQBAJ
https://books.google.co.kr/books/about?id=shRQEAAAQBAJ
https://www.scribd.com/book/542068528
https://www.kobo.com/ww/en/ebook/X8SmJdYCtDasOfQ1LQpCtg
About Young Ho Seo
Young Ho Seo is an Engineer, Financial Trader, and Quantitative Developer, working on Trading Science and Investment Engineering since 2011. He is the creator of many technical indicators, price patterns and trading strategies used in the financial market. He is also teaching the trading practice on how to use the Supply Demand Analysis, Support, Resistance, Trend line, Fibonacci Analysis, Harmonic Pattern, Elliott Wave Theory, Chart Patterns, and Probability for Forex and Stock Market. His works include developing scientific trading principle and mathematical algorithm in the work of Benjamin Graham, Everette S. Gardner, Benoit Mandelbrot, Ralph Nelson Elliott, Harold M. Gartley, Richard Shabacker, William Delbert Gann, Richard Wyckoff and Richard Dennis. You can find his dedicated works on www.algotrading-investment.com . His life mission is to connect financial traders and scientific community for better understanding of this world and crowd behaviour in the financial market. He wrote many books and articles, which are helpful for understanding the technology and application behind technical analysis, statistics, time series forecasting, fractal science, econometrics, and artificial intelligence in the financial market.
If you are interested in our software and training, just visit our main website: www.algotrading-investment.com
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Wave Process (Cycle)
Wave process is any cyclic patterns repeating in the fixed time interval. Two main references for Wave process can be found from Physics and Time series Analysis. Both references deal exclusively with periodic cycle of an object or a signal. However, they use different methodology to describe the periodic cycle. In physics, wave is the main term describing the periodic cycle of an object or a signal. In time series analysis, the term “Seasonality” is used to deal with seasonal fluctuations in the price series. In the Price Pattern Table (Figure 7-1), Wave Process covers both wave in Physics and seasonality in time series analysis. To better illustrate the Wave process, we provide simple description for Wave and Seasonality in this section.
In Physics, Wave can be described by three independent variables, which are frequency, wavelength and amplitude. Frequency is the number of waves passing a point in certain time interval. Scientists and engineers normally use a time of one second. Therefore, number of waves passing a point in 1 second is described by the unit called Hertz. 1 Hertz equals to one Wave per second. Wavelength is the distance from any point on one wave to the same point on the next wave along. Amplitude is the height of wave from the top of a crest to the centre line of the wave.
Figure 7-2: Description of Wave in Physics.
Normally textbook will show you a clean sine or cosine wave function to describe the property of wave. In the real world, the wave often consists of multiple cyclic components. For example, Figure 7-3 shows the synthetic multiple cycles built by adding three Sine Wave Functions: Sine (2x) + Sine (13x) + Sine (30x). Many real world signals can have more complex cyclic structure than this example. In addition, many real world signal will exhibit decreasing or increasing amplitude to make your analysis more difficult as shown in Figure 7-4.
Figure 7-3: Synthetic Cyclic Function of Sine (2x) + Sine (13x) + Sine (30x).
Figure 7-4: Synthetic Cyclic Function with increasing amplitude.
In time series analysis, seasonal fluctuation is described with smoothing factor gamma and previous seasonal level. Alternatively, one can use multiple regression with dummy variable or artificial intelligence techniques for the same task. Figure 7-5 shows typical annual seasonal fluctuations in the data. This type of series can be modelling using either additive seasonality or multiplicative seasonality.
https://algotrading-investment.com/2020/06/04/wave-process-cycle/
Wave process is any cyclic patterns repeating in the fixed time interval. Two main references for Wave process can be found from Physics and Time series Analysis. Both references deal exclusively with periodic cycle of an object or a signal. However, they use different methodology to describe the periodic cycle. In physics, wave is the main term describing the periodic cycle of an object or a signal. In time series analysis, the term “Seasonality” is used to deal with seasonal fluctuations in the price series. In the Price Pattern Table (Figure 7-1), Wave Process covers both wave in Physics and seasonality in time series analysis. To better illustrate the Wave process, we provide simple description for Wave and Seasonality in this section.
In Physics, Wave can be described by three independent variables, which are frequency, wavelength and amplitude. Frequency is the number of waves passing a point in certain time interval. Scientists and engineers normally use a time of one second. Therefore, number of waves passing a point in 1 second is described by the unit called Hertz. 1 Hertz equals to one Wave per second. Wavelength is the distance from any point on one wave to the same point on the next wave along. Amplitude is the height of wave from the top of a crest to the centre line of the wave.
Figure 7-2: Description of Wave in Physics.
Normally textbook will show you a clean sine or cosine wave function to describe the property of wave. In the real world, the wave often consists of multiple cyclic components. For example, Figure 7-3 shows the synthetic multiple cycles built by adding three Sine Wave Functions: Sine (2x) + Sine (13x) + Sine (30x). Many real world signals can have more complex cyclic structure than this example. In addition, many real world signal will exhibit decreasing or increasing amplitude to make your analysis more difficult as shown in Figure 7-4.
Figure 7-3: Synthetic Cyclic Function of Sine (2x) + Sine (13x) + Sine (30x).
Figure 7-4: Synthetic Cyclic Function with increasing amplitude.
In time series analysis, seasonal fluctuation is described with smoothing factor gamma and previous seasonal level. Alternatively, one can use multiple regression with dummy variable or artificial intelligence techniques for the same task. Figure 7-5 shows typical annual seasonal fluctuations in the data. This type of series can be modelling using either additive seasonality or multiplicative seasonality.
https://algotrading-investment.com/2020/06/04/wave-process-cycle/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Fractal-Wave Process
Fractal-Wave process is the representation of the Fractal geometry in the time dimension. Fractal geometry is made from a repeating pattern at many different scales. Simply speaking it is repeating patterns with varying size. Fractal geometry can be a self-similar pattern with the strictly same patterns across at every scale. Or if the pattern loosely matches to the past one, this can be still considered as fractal geometry. We call this as near self-similarity against the strict self-similarity. Many examples of Fractal geometry can be found in nature. Snowflakes, coastlines, Trees are the typical example of the Fractals geometry in space. Fractal-Wave is the fractal geometry generated in time dimension. Just like Fractal Geometry can be described by self-similar patterns. Fractal-Wave can be described by self-similar patterns repeating in time. The concept of Fractal-Wave can be illustrated well by Weierstrass function.
Loosely speaking, Weierstrass function is the cyclic function generated from infinite number of Cosine functions with different amplitude and wavelength. By combining infinite number of Cosine functions, it can generate a complex structure repeating self-similar patterns in different scales. This is a typical synthetic example of Fractal-Wave patterns with strict self-similar patterns. We present this function to help you to understand the properties of the self-similar process. The real world financial market shows the loose fractal geometry. They do not repeat in the identical patterns in shape and in size. The repeating patterns are similar to each other up to certain degree. Since Weierstrss function is the synthetic example for the strict fractal geometry, reader should note that Weierstrss function does not represent the real world financial market.
Figure 8-2: Weierstrass function to give you a feel for the Fractal-Wave process. Note that this is synthetic Fractal-Wave process only and this function does not represent many of real world cases.
In the real world application, Fractal-Wave process appears with the near self-similar patterns most of time. Therefore, detecting them is not easy. The Heart Beat Rate signal is one typical example of the Fractal-Wave process in nature (Figure 8-3 and Figure 8-4). If we zoom in on a subset of time series, we can see the apparent self-similar patterns. In Financial Market, Fractal-Wave process occurs frequently but the market typically shows the near self-similarity too. In terms of pattern shape, the financial market and Heart beat rate signal are different because their underlying pattern generating dynamics inside human organ and crowd behaviour are substantially different.
https://algotrading-investment.com/2020/06/04/fractal-wave-process/
Fractal-Wave process is the representation of the Fractal geometry in the time dimension. Fractal geometry is made from a repeating pattern at many different scales. Simply speaking it is repeating patterns with varying size. Fractal geometry can be a self-similar pattern with the strictly same patterns across at every scale. Or if the pattern loosely matches to the past one, this can be still considered as fractal geometry. We call this as near self-similarity against the strict self-similarity. Many examples of Fractal geometry can be found in nature. Snowflakes, coastlines, Trees are the typical example of the Fractals geometry in space. Fractal-Wave is the fractal geometry generated in time dimension. Just like Fractal Geometry can be described by self-similar patterns. Fractal-Wave can be described by self-similar patterns repeating in time. The concept of Fractal-Wave can be illustrated well by Weierstrass function.
Loosely speaking, Weierstrass function is the cyclic function generated from infinite number of Cosine functions with different amplitude and wavelength. By combining infinite number of Cosine functions, it can generate a complex structure repeating self-similar patterns in different scales. This is a typical synthetic example of Fractal-Wave patterns with strict self-similar patterns. We present this function to help you to understand the properties of the self-similar process. The real world financial market shows the loose fractal geometry. They do not repeat in the identical patterns in shape and in size. The repeating patterns are similar to each other up to certain degree. Since Weierstrss function is the synthetic example for the strict fractal geometry, reader should note that Weierstrss function does not represent the real world financial market.
Figure 8-2: Weierstrass function to give you a feel for the Fractal-Wave process. Note that this is synthetic Fractal-Wave process only and this function does not represent many of real world cases.
In the real world application, Fractal-Wave process appears with the near self-similar patterns most of time. Therefore, detecting them is not easy. The Heart Beat Rate signal is one typical example of the Fractal-Wave process in nature (Figure 8-3 and Figure 8-4). If we zoom in on a subset of time series, we can see the apparent self-similar patterns. In Financial Market, Fractal-Wave process occurs frequently but the market typically shows the near self-similarity too. In terms of pattern shape, the financial market and Heart beat rate signal are different because their underlying pattern generating dynamics inside human organ and crowd behaviour are substantially different.
https://algotrading-investment.com/2020/06/04/fractal-wave-process/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Some More Tips about Equilibrium Fractal-Wave Process in Forex Trading
The Equilibrium Fractal-Wave Process is a sophisticated concept in Forex trading that combines elements of fractal theory, wave analysis, and equilibrium states to identify trading opportunities. This approach is rooted in the idea that market prices exhibit fractal characteristics and follow wave-like patterns that tend to move towards an equilibrium state.
Key Concepts
Fractal Pattern in Forex Trading:
Fractal Pattern: These are recurring patterns that are self-similar across different scales. In Forex trading, fractal pattern are used to identify potential reversals in the market.
Wave Theory:
Elliott Wave Theory: This theory posits that market prices move in predictable wave patterns consisting of impulsive waves (trending in the direction of the main trend) and corrective waves (moving against the main trend). Traders use this to predict future price movements.
Harmonic Patterns: These are specific price patterns that align with Fibonacci ratios to predict potential price reversals. Examples include the Gartley pattern, Butterfly pattern, and Bat pattern.
Equilibrium State:
Market Equilibrium: This refers to the point where the supply and demand for a currency are balanced, leading to stable prices. In trading, equilibrium can be identified using various indicators, such as moving averages, Bollinger Bands, or support and resistance levels.
Integrating Fractals, Waves, and Equilibrium
The Equilibrium Fractal-Wave Process in Forex trading integrates these three concepts to provide a comprehensive analysis of market movements:
Identifying Fractal Pattern:
Use fractal Pattern indicators to detect recurring patterns and potential reversal points on different time frames. This helps in recognizing the underlying structure of the market.
Wave Analysis:
Apply Elliott Wave Theory or harmonic patterns to understand the larger wave structure of the market. This aids in identifying the main trend and its corrective phases.
Finding Equilibrium:
Use indicators like moving averages, Bollinger Bands, and Fibonacci retracements to identify equilibrium points where the market might stabilize before the next move. Support and resistance levels also play a crucial role in determining equilibrium.
https://algotrading-investment.com/2020/06/04/equilibrium-fractal-wave-process-trend-and-fractal-wave/
The Equilibrium Fractal-Wave Process is a sophisticated concept in Forex trading that combines elements of fractal theory, wave analysis, and equilibrium states to identify trading opportunities. This approach is rooted in the idea that market prices exhibit fractal characteristics and follow wave-like patterns that tend to move towards an equilibrium state.
Key Concepts
Fractal Pattern in Forex Trading:
Fractal Pattern: These are recurring patterns that are self-similar across different scales. In Forex trading, fractal pattern are used to identify potential reversals in the market.
Wave Theory:
Elliott Wave Theory: This theory posits that market prices move in predictable wave patterns consisting of impulsive waves (trending in the direction of the main trend) and corrective waves (moving against the main trend). Traders use this to predict future price movements.
Harmonic Patterns: These are specific price patterns that align with Fibonacci ratios to predict potential price reversals. Examples include the Gartley pattern, Butterfly pattern, and Bat pattern.
Equilibrium State:
Market Equilibrium: This refers to the point where the supply and demand for a currency are balanced, leading to stable prices. In trading, equilibrium can be identified using various indicators, such as moving averages, Bollinger Bands, or support and resistance levels.
Integrating Fractals, Waves, and Equilibrium
The Equilibrium Fractal-Wave Process in Forex trading integrates these three concepts to provide a comprehensive analysis of market movements:
Identifying Fractal Pattern:
Use fractal Pattern indicators to detect recurring patterns and potential reversal points on different time frames. This helps in recognizing the underlying structure of the market.
Wave Analysis:
Apply Elliott Wave Theory or harmonic patterns to understand the larger wave structure of the market. This aids in identifying the main trend and its corrective phases.
Finding Equilibrium:
Use indicators like moving averages, Bollinger Bands, and Fibonacci retracements to identify equilibrium points where the market might stabilize before the next move. Support and resistance levels also play a crucial role in determining equilibrium.
https://algotrading-investment.com/2020/06/04/equilibrium-fractal-wave-process-trend-and-fractal-wave/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Some More Tips about Algorithm and Prediction for Artificial Intelligence, Time Series Forecasting, and Technical Analysis
Here’s some more tips about algorithms and prediction techniques in the context of artificial intelligence (AI), time series forecasting, and technical analysis:
1. Artificial Intelligence (AI)
AI involves using algorithms to simulate human intelligence. Key aspects of AI in prediction include:
Machine Learning Algorithms
Supervised Learning: Models are trained on labeled data. Examples include:
Linear Regression: Predicts a continuous target variable.
Logistic Regression: Predicts a binary outcome.
Decision Trees and Random Forests: Tree-based methods for classification and regression.
Support Vector Machines (SVM): Classification and regression using hyperplanes.
Neural Networks: Complex models inspired by the human brain, capable of learning non-linear relationships.
Unsupervised Learning: Models find patterns in data without labeled responses. Examples include:
K-means Clustering: Groups data into clusters based on similarity.
Principal Component Analysis (PCA): Reduces dimensionality by transforming data into principal components.
Deep Learning
Convolutional Neural Networks (CNNs): Primarily used in image recognition but also applied in other domains.
Recurrent Neural Networks (RNNs): Suitable for sequential data; a specialized form, Long Short-Term Memory (LSTM) networks, are particularly useful for time series forecasting.
Transformers: Modern architectures like BERT and GPT are used for natural language processing (NLP) and time series data.
2. Time Series Forecasting
Time series forecasting involves predicting future values based on previously observed values. Key techniques include:
Statistical Methods
Autoregressive (AR) Models: Predict future values based on past values.
Moving Average (MA) Models: Model the relationship between an observation and a residual error.
ARMA and ARIMA Models: Combine AR and MA models; ARIMA includes differencing to make data stationary.
Seasonal Decomposition: Decomposes time series into trend, seasonal, and residual components.
Machine Learning Methods
Gradient Boosting Machines (GBM): Such as XGBoost and LightGBM, effective for time series with strong trends and seasonality.
Support Vector Regression (SVR): Uses SVM principles for regression tasks.
Neural Networks: Including LSTM and GRU, which are designed to handle sequential data effectively.
Advanced Techniques
Prophet: Developed by Facebook, designed for business time series forecasting with strong seasonality.
VAR and VECM: Vector autoregressive models for multivariate time series.
3. Technical Analysis
Technical analysis involves forecasting future price movements based on historical price data, primarily used in finance.
Common Techniques and Indicators
Moving Averages: Including Simple Moving Average (SMA) and Exponential Moving Average (EMA), used to smooth out price data.
Relative Strength Index (RSI): Measures the speed and change of price movements.
MACD (Moving Average Convergence Divergence): Shows the relationship between two moving averages of a security’s price.
Bollinger Bands: Uses standard deviation to plot bands above and below a moving average.
Candlestick Patterns: Visual patterns in candlestick charts indicating market sentiment.
https://algotrading-investment.com/2020/05/10/algorithm-and-prediction-for-artificial-intelligence-time-series-forecasting-and-technical-analysis/
Here’s some more tips about algorithms and prediction techniques in the context of artificial intelligence (AI), time series forecasting, and technical analysis:
1. Artificial Intelligence (AI)
AI involves using algorithms to simulate human intelligence. Key aspects of AI in prediction include:
Machine Learning Algorithms
Supervised Learning: Models are trained on labeled data. Examples include:
Linear Regression: Predicts a continuous target variable.
Logistic Regression: Predicts a binary outcome.
Decision Trees and Random Forests: Tree-based methods for classification and regression.
Support Vector Machines (SVM): Classification and regression using hyperplanes.
Neural Networks: Complex models inspired by the human brain, capable of learning non-linear relationships.
Unsupervised Learning: Models find patterns in data without labeled responses. Examples include:
K-means Clustering: Groups data into clusters based on similarity.
Principal Component Analysis (PCA): Reduces dimensionality by transforming data into principal components.
Deep Learning
Convolutional Neural Networks (CNNs): Primarily used in image recognition but also applied in other domains.
Recurrent Neural Networks (RNNs): Suitable for sequential data; a specialized form, Long Short-Term Memory (LSTM) networks, are particularly useful for time series forecasting.
Transformers: Modern architectures like BERT and GPT are used for natural language processing (NLP) and time series data.
2. Time Series Forecasting
Time series forecasting involves predicting future values based on previously observed values. Key techniques include:
Statistical Methods
Autoregressive (AR) Models: Predict future values based on past values.
Moving Average (MA) Models: Model the relationship between an observation and a residual error.
ARMA and ARIMA Models: Combine AR and MA models; ARIMA includes differencing to make data stationary.
Seasonal Decomposition: Decomposes time series into trend, seasonal, and residual components.
Machine Learning Methods
Gradient Boosting Machines (GBM): Such as XGBoost and LightGBM, effective for time series with strong trends and seasonality.
Support Vector Regression (SVR): Uses SVM principles for regression tasks.
Neural Networks: Including LSTM and GRU, which are designed to handle sequential data effectively.
Advanced Techniques
Prophet: Developed by Facebook, designed for business time series forecasting with strong seasonality.
VAR and VECM: Vector autoregressive models for multivariate time series.
3. Technical Analysis
Technical analysis involves forecasting future price movements based on historical price data, primarily used in finance.
Common Techniques and Indicators
Moving Averages: Including Simple Moving Average (SMA) and Exponential Moving Average (EMA), used to smooth out price data.
Relative Strength Index (RSI): Measures the speed and change of price movements.
MACD (Moving Average Convergence Divergence): Shows the relationship between two moving averages of a security’s price.
Bollinger Bands: Uses standard deviation to plot bands above and below a moving average.
Candlestick Patterns: Visual patterns in candlestick charts indicating market sentiment.
https://algotrading-investment.com/2020/05/10/algorithm-and-prediction-for-artificial-intelligence-time-series-forecasting-and-technical-analysis/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Must Read Books for Financial Trading in Forex and Stock Market
Must-read book on Forex trading and Stock trading covers a wide range of topics to ensure readers get a well-rounded education in trading. Here’s a detailed breakdown of the contents that should be included in such a book:
1. Introduction to Forex Trading
What is Forex?: Explanation of the Forex market, its significance, and how it operates.
Market Participants: Description of who trades Forex, including central banks, financial institutions, corporations, and retail traders.
Currency Pairs: Explanation of major, minor, and exotic currency pairs.
Trading Sessions: Overview of the major Forex trading sessions (London, New York, Tokyo).
2. Fundamentals of Forex Trading
Basic Terminology: Definitions of essential terms like pips, lots, leverage, and margin.
Order Types: Explanation of different order types (market orders, limit orders, stop-loss orders).
Forex Brokers: Guide on choosing a broker, understanding spreads, commissions, and trading platforms.
3. Technical Analysis
Charts and Time Frames: Introduction to different types of charts (line, bar, candlestick) and time frames.
Price Action: Understanding price movements and patterns.
Indicators and Oscillators: Overview of key technical indicators (Moving Averages, MACD, RSI, Stochastic Oscillator).
Chart Patterns: Detailed look at important chart patterns (Head and Shoulders, Double Top/Bottom, Triangles).
Support and Resistance: Identifying and using support and resistance levels.
Trend Analysis: How to identify and trade with trends using trendlines and channels.
4. Fundamental Analysis
Economic Indicators: Understanding key economic indicators (GDP, CPI, unemployment rates).
Interest Rates: The impact of interest rates on currency values.
Geopolitical Events: How geopolitical events and news impact the Forex market.
Central Bank Policies: The role of central banks and their impact on Forex trading.
5. Trading Strategies
Day Trading: Strategies and techniques for short-term trading.
Swing Trading: Approaches for medium-term trading.
Position Trading: Long-term trading strategies.
Scalping: High-frequency trading techniques.
Algorithmic Trading: Introduction to automated trading systems and how they work.
https://algotrading-investment.com/2020/05/19/must-read-books-for-financial-trading-in-forex-and-stock-market/
Must-read book on Forex trading and Stock trading covers a wide range of topics to ensure readers get a well-rounded education in trading. Here’s a detailed breakdown of the contents that should be included in such a book:
1. Introduction to Forex Trading
What is Forex?: Explanation of the Forex market, its significance, and how it operates.
Market Participants: Description of who trades Forex, including central banks, financial institutions, corporations, and retail traders.
Currency Pairs: Explanation of major, minor, and exotic currency pairs.
Trading Sessions: Overview of the major Forex trading sessions (London, New York, Tokyo).
2. Fundamentals of Forex Trading
Basic Terminology: Definitions of essential terms like pips, lots, leverage, and margin.
Order Types: Explanation of different order types (market orders, limit orders, stop-loss orders).
Forex Brokers: Guide on choosing a broker, understanding spreads, commissions, and trading platforms.
3. Technical Analysis
Charts and Time Frames: Introduction to different types of charts (line, bar, candlestick) and time frames.
Price Action: Understanding price movements and patterns.
Indicators and Oscillators: Overview of key technical indicators (Moving Averages, MACD, RSI, Stochastic Oscillator).
Chart Patterns: Detailed look at important chart patterns (Head and Shoulders, Double Top/Bottom, Triangles).
Support and Resistance: Identifying and using support and resistance levels.
Trend Analysis: How to identify and trade with trends using trendlines and channels.
4. Fundamental Analysis
Economic Indicators: Understanding key economic indicators (GDP, CPI, unemployment rates).
Interest Rates: The impact of interest rates on currency values.
Geopolitical Events: How geopolitical events and news impact the Forex market.
Central Bank Policies: The role of central banks and their impact on Forex trading.
5. Trading Strategies
Day Trading: Strategies and techniques for short-term trading.
Swing Trading: Approaches for medium-term trading.
Position Trading: Long-term trading strategies.
Scalping: High-frequency trading techniques.
Algorithmic Trading: Introduction to automated trading systems and how they work.
https://algotrading-investment.com/2020/05/19/must-read-books-for-financial-trading-in-forex-and-stock-market/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Fractal Pattern – Trading Operation
Fractal Patterns are important in Forex trading because they help traders detect certain predictive patterns in price movements and make specific decisions to profit in Forex and Stock trading. Fractal patterns refer to a recurring or repeating pattern that occurs at different scales with chaotic price movements. It is important to understand the Fractal defined by Bill Williams or Williams’s Fractal Indicator is merely one type of Fractal Pattern. However, I have seen a lot of traders think fractal pattern can be learned by learning Williams’s Fractal Indicator. Definitely not. It is entirely wrong to frame the Williams’s Fractal Indicator for the entire Fractal Pattern just many search engines do. Fractal or Fractal pattern have very strong theoretical background stemmed from the pinoeer scientist, Benoit Mandelbrot, who created the term, “Fractal” and dedicated himself in the discovery of Fractal Pattern in the real world application. https://en.wikipedia.org/wiki/Benoit_Mandelbrot
The usable Fractal pattern for Forex trader include many other repeating patterns like Elliott Wave, Harmonic Pattern, W and M pattern, Support and Resistance, Supply and Demand zone, etc. Lately we have also found that Fractal Pattern in Forex and Stock market are more relevant or more predictive when we view them as Fractal Wave instead. After we looked into Fractal Wave, we have concluded that Fractal Wave is the ultimate tool to model Stochastic Cycle presents in Forex and Stock market. Hence, we built Fractal Pattern Scanner to help trader to extract the stochastic cycles so that they can enter the right timing with higher success rate.
In this short article, we will explain the trading operation with turning point probability for Fractal Pattern Scanner in MetaTrader and in Optimum Chart. Fractal Pattern Scanner is the powerful tool to predict the Forex and Stock market. It is the highly predictive tool that quantifies the price action at every wave in your chart. Fractal Pattern Scanner provides three main features. The three features include:
1. Turning Point Probability Measurement (Essential feature that you can use everyday)
2. Breakout and Reversal Trading Signal Detection with Mother Wave detection
3. Automatic Gann Angles (Bonus feature and optional use only)
The turning point probability measurement is the essential to your daily trading. The turning point probability basically quantifies the price action in your chart. Quantifying the price action will provide you the another level of trading experience. Capability to access the turning point probability in one button click is really handy feature for every day trader on the earth. In addition, Fractal Pattern Scanner provides the probability scanning capability across all timeframe. You can also switch on and off the multiple timeframe pattern detection. When you set Detect Mother Wave = false, you can run the Fractal Pattern Scanner as the pure probability machine.
The turning point probability is a powerful tool that you can use it as both reversal trading or breakout trading within your technical analysis. You can watch this YouTube video titled as “Breakout Trading vs Reversal Trading (Turn Support & Resistance to Killer Strategy)” in this link below to get some hands on practice with breakout and reversal trading opportunity with Support and Resistance Technical Analysis.
YouTube Video Link: https://youtu.be/UbORmOacKIQ
Mother wave pattern detection can be considered as the statistical representation of the Elliott Wave Theory. For example, Mother wave pattern detect the pattern inside pattern structure, where small patterns are jagged inside big pattern, like the Elliott Wave pattern. We can use this pattern inside pattern to trade both reversal and breakout trading. Fractal Pattern Scanner does the excellent job in detecting these signals automatically. When you set Detect Mother Wave = true, then Fractal Pattern Scanner will detect trading signal using Mother wave pattern detection. Even in that, you can also perform both breakout and reversal trading automatically.
If you want to use Breakout Trading Mode, then set Detect Breakout Opportunity = true.
If you want to use Reversal Trading Mode, then set Detect Breakout Opportunity = false.
You can also watch the YouTube Video titled as “Breakout Trading Signal Explained” to understand the breakout trading with mother wave detection.
YouTube video link: https://youtu.be/4XGuMIMaV6w
https://algotrading-investment.com/2020/05/24/fractal-pattern-scanner-trading-operation-tips/
Fractal Patterns are important in Forex trading because they help traders detect certain predictive patterns in price movements and make specific decisions to profit in Forex and Stock trading. Fractal patterns refer to a recurring or repeating pattern that occurs at different scales with chaotic price movements. It is important to understand the Fractal defined by Bill Williams or Williams’s Fractal Indicator is merely one type of Fractal Pattern. However, I have seen a lot of traders think fractal pattern can be learned by learning Williams’s Fractal Indicator. Definitely not. It is entirely wrong to frame the Williams’s Fractal Indicator for the entire Fractal Pattern just many search engines do. Fractal or Fractal pattern have very strong theoretical background stemmed from the pinoeer scientist, Benoit Mandelbrot, who created the term, “Fractal” and dedicated himself in the discovery of Fractal Pattern in the real world application. https://en.wikipedia.org/wiki/Benoit_Mandelbrot
The usable Fractal pattern for Forex trader include many other repeating patterns like Elliott Wave, Harmonic Pattern, W and M pattern, Support and Resistance, Supply and Demand zone, etc. Lately we have also found that Fractal Pattern in Forex and Stock market are more relevant or more predictive when we view them as Fractal Wave instead. After we looked into Fractal Wave, we have concluded that Fractal Wave is the ultimate tool to model Stochastic Cycle presents in Forex and Stock market. Hence, we built Fractal Pattern Scanner to help trader to extract the stochastic cycles so that they can enter the right timing with higher success rate.
In this short article, we will explain the trading operation with turning point probability for Fractal Pattern Scanner in MetaTrader and in Optimum Chart. Fractal Pattern Scanner is the powerful tool to predict the Forex and Stock market. It is the highly predictive tool that quantifies the price action at every wave in your chart. Fractal Pattern Scanner provides three main features. The three features include:
1. Turning Point Probability Measurement (Essential feature that you can use everyday)
2. Breakout and Reversal Trading Signal Detection with Mother Wave detection
3. Automatic Gann Angles (Bonus feature and optional use only)
The turning point probability measurement is the essential to your daily trading. The turning point probability basically quantifies the price action in your chart. Quantifying the price action will provide you the another level of trading experience. Capability to access the turning point probability in one button click is really handy feature for every day trader on the earth. In addition, Fractal Pattern Scanner provides the probability scanning capability across all timeframe. You can also switch on and off the multiple timeframe pattern detection. When you set Detect Mother Wave = false, you can run the Fractal Pattern Scanner as the pure probability machine.
The turning point probability is a powerful tool that you can use it as both reversal trading or breakout trading within your technical analysis. You can watch this YouTube video titled as “Breakout Trading vs Reversal Trading (Turn Support & Resistance to Killer Strategy)” in this link below to get some hands on practice with breakout and reversal trading opportunity with Support and Resistance Technical Analysis.
YouTube Video Link: https://youtu.be/UbORmOacKIQ
Mother wave pattern detection can be considered as the statistical representation of the Elliott Wave Theory. For example, Mother wave pattern detect the pattern inside pattern structure, where small patterns are jagged inside big pattern, like the Elliott Wave pattern. We can use this pattern inside pattern to trade both reversal and breakout trading. Fractal Pattern Scanner does the excellent job in detecting these signals automatically. When you set Detect Mother Wave = true, then Fractal Pattern Scanner will detect trading signal using Mother wave pattern detection. Even in that, you can also perform both breakout and reversal trading automatically.
If you want to use Breakout Trading Mode, then set Detect Breakout Opportunity = true.
If you want to use Reversal Trading Mode, then set Detect Breakout Opportunity = false.
You can also watch the YouTube Video titled as “Breakout Trading Signal Explained” to understand the breakout trading with mother wave detection.
YouTube video link: https://youtu.be/4XGuMIMaV6w
https://algotrading-investment.com/2020/05/24/fractal-pattern-scanner-trading-operation-tips/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Fractal Pattern Strategy Guide
Fractal Pattern is the technical analysis with high predictive power when it comes to Forex and Stock trading rather than any other subjects. It explain the evolution of the price movement behind stock and forex market. Once we have transformed the price series into triangles using Peak Trough Analysis, we are almost ready to study the price patterns using the ratio and size variable. However, before we do that, you need to understand these triangles generated from Peak Trough Analysis are Fractal Pattern. In Fractal Pattern, the same or similar geometric shape is repeating infinitely in different scales. In another words, with Fractal pattern, we are likely to see the same or similar pattern when we magnify the part of pattern. In the Romanesco Broccoli, the smaller piece, if they are broken off from the bigger piece, do look like the big piece. Like this, in Fractal Pattern, the same or similar patterns are occupying the entire structure of an object.
There are few different types of Fractal Patterns. When the same geometric pattern is repeating, we call this as a strict self-similarity. For example, Sierpinski triangle is good example of strict self-similarity in Fractal pattern. In Sierpinski triangle, the triangle is continuously expanding to build bigger triangle. As we magnify any part of Sierpinski triangle, we see the identical triangles. On the other hands, fractal pattern can have a loose self-similarity structure. For example, the simplest form of loose self-similarity can be found in coastline. As we magnify a segment of coastline, we do not expect to see an identical copy of original coastline but rather similar shapes. Instead, we can observe that the same statistical properties are preserved across scales. In nature, loose self-similarity is more common.
As you might guess, financial market represents fractal pattern with the loose self-similarity. For example, when we apply the Peak Trough Analysis, the triangles are not identical but they are rather similar. Each triangle is not strictly identical but they are triangles with different angles. Typically, fractal patterns in nature expand its geometry in two or three dimension in space. In the financial market, the fractal patterns are expanding in the two dimension of price and time. Hence, fractal pattern in the financial market can be considered as fractal wave. In another words, it is some sort of cycles. This inclusion of time dimension makes the fractal pattern in financial market special. What this means is that to predict financial market, we need to consider size and ratio in time dimension too as well as price dimension.
To give you better idea of fractal wave in financial market, consider Peak Trough Analysis with grid spacing of 2. In this fine grid spacing, we can observe many triangles in the smaller scale. As we increase our grid spacing to 4, we can see less number of bigger triangles in comparing to grid spacing 2. Now, we can tell that scale of triangles is bigger with grid spacing 4. However, are these big triangles new? No, these big triangles are made up from small triangles in the grid spacing of 2. If we keep increasing the grid spacing, then eventually we will see one big triangle spans from the beginning to the end of price series. Grid spacing example implies that combination of several small fractal wave can form the bigger fractal wave. The grid spacing example shows that there is mother wave and child wave relationship existing between fractal waves in different scales. For example, consider a fractal wave with following swing high and swing low properties:
https://algotrading-investment.com/2020/04/07/fractal-pattern-indicator-manuals-and-strategy-guide/
Fractal Pattern is the technical analysis with high predictive power when it comes to Forex and Stock trading rather than any other subjects. It explain the evolution of the price movement behind stock and forex market. Once we have transformed the price series into triangles using Peak Trough Analysis, we are almost ready to study the price patterns using the ratio and size variable. However, before we do that, you need to understand these triangles generated from Peak Trough Analysis are Fractal Pattern. In Fractal Pattern, the same or similar geometric shape is repeating infinitely in different scales. In another words, with Fractal pattern, we are likely to see the same or similar pattern when we magnify the part of pattern. In the Romanesco Broccoli, the smaller piece, if they are broken off from the bigger piece, do look like the big piece. Like this, in Fractal Pattern, the same or similar patterns are occupying the entire structure of an object.
There are few different types of Fractal Patterns. When the same geometric pattern is repeating, we call this as a strict self-similarity. For example, Sierpinski triangle is good example of strict self-similarity in Fractal pattern. In Sierpinski triangle, the triangle is continuously expanding to build bigger triangle. As we magnify any part of Sierpinski triangle, we see the identical triangles. On the other hands, fractal pattern can have a loose self-similarity structure. For example, the simplest form of loose self-similarity can be found in coastline. As we magnify a segment of coastline, we do not expect to see an identical copy of original coastline but rather similar shapes. Instead, we can observe that the same statistical properties are preserved across scales. In nature, loose self-similarity is more common.
As you might guess, financial market represents fractal pattern with the loose self-similarity. For example, when we apply the Peak Trough Analysis, the triangles are not identical but they are rather similar. Each triangle is not strictly identical but they are triangles with different angles. Typically, fractal patterns in nature expand its geometry in two or three dimension in space. In the financial market, the fractal patterns are expanding in the two dimension of price and time. Hence, fractal pattern in the financial market can be considered as fractal wave. In another words, it is some sort of cycles. This inclusion of time dimension makes the fractal pattern in financial market special. What this means is that to predict financial market, we need to consider size and ratio in time dimension too as well as price dimension.
To give you better idea of fractal wave in financial market, consider Peak Trough Analysis with grid spacing of 2. In this fine grid spacing, we can observe many triangles in the smaller scale. As we increase our grid spacing to 4, we can see less number of bigger triangles in comparing to grid spacing 2. Now, we can tell that scale of triangles is bigger with grid spacing 4. However, are these big triangles new? No, these big triangles are made up from small triangles in the grid spacing of 2. If we keep increasing the grid spacing, then eventually we will see one big triangle spans from the beginning to the end of price series. Grid spacing example implies that combination of several small fractal wave can form the bigger fractal wave. The grid spacing example shows that there is mother wave and child wave relationship existing between fractal waves in different scales. For example, consider a fractal wave with following swing high and swing low properties:
https://algotrading-investment.com/2020/04/07/fractal-pattern-indicator-manuals-and-strategy-guide/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Reversal Trading with Fractal Wave and Stochastic Cycles
In the previous chapters, we demonstrated the practical application of Geometric Prediction in the financial trading. In doing so, we have shown several different ways to detect geometric regularity. Then, we tried to combine them with turning point probability (i.e. statistical regularity) to fulfill the technical version of Geometric Prediction.
Geometric Prediction (General version) = statistical regularity + geometric regularity + knowledge around object
Geometric Prediction (Technical version) = statistical regularity + geometric regularity
The approaches to detect the geometric regularity include Support, Resistance, Harmonic Pattern, Falling Wedge pattern, Rising Wedge pattern, and Gann Angles. This is not necessarily the exhausted list of the approaches to detect the geometric regularity. With these approaches, our decision making process for buy and sell decision-making included two steps like below.
Step 1: Detect geometric regularity in price series
Step 2: Confirm the geometric regularity with statistical regularity
For example, in the case of using Harmonic Pattern, we detect Harmonic pattern (Step 1). Then we use the turning point probability in price and time to confirm the timing provided from the Harmonic Pattern (Step 2). Likewise, the rest of approaches will follow more or less the similar steps. In this chapter, we will be showing how to use above two steps in the reverse order. Hence, we will be making our trading decision like below:
Step 1: Detect statistical regularity in price series
Step 2: Confirm the statistical regularity with geometric regularity
To demonstrate the two reversed steps, we will use the confluence pattern of mother wave and child wave with high turning point probability (Step 1). Then we will use Fibonacci Expansion to get the fine tune of the possible turning point (Step 2).
The importance of the confluence pattern of mother wave and child wave was discussed heavily in many parts of this book. The confluence price pattern can be used to predict the multiple stochastic cycles present in fractal waves. Anatomy of the confluence price pattern is shown in Figure 4.8-1. By definition, the confluence price pattern is the mother wave pattern made up from several child fractal triangles. Some example of fractal triangles are shown in Figure 4.8-2. In fact, we can have various combination of child fractal triangles to form the mother wave.
https://algotrading-investment.com/2020/04/22/reversal-trading-with-fractal-wave-and-stochastic-cycles/
In the previous chapters, we demonstrated the practical application of Geometric Prediction in the financial trading. In doing so, we have shown several different ways to detect geometric regularity. Then, we tried to combine them with turning point probability (i.e. statistical regularity) to fulfill the technical version of Geometric Prediction.
Geometric Prediction (General version) = statistical regularity + geometric regularity + knowledge around object
Geometric Prediction (Technical version) = statistical regularity + geometric regularity
The approaches to detect the geometric regularity include Support, Resistance, Harmonic Pattern, Falling Wedge pattern, Rising Wedge pattern, and Gann Angles. This is not necessarily the exhausted list of the approaches to detect the geometric regularity. With these approaches, our decision making process for buy and sell decision-making included two steps like below.
Step 1: Detect geometric regularity in price series
Step 2: Confirm the geometric regularity with statistical regularity
For example, in the case of using Harmonic Pattern, we detect Harmonic pattern (Step 1). Then we use the turning point probability in price and time to confirm the timing provided from the Harmonic Pattern (Step 2). Likewise, the rest of approaches will follow more or less the similar steps. In this chapter, we will be showing how to use above two steps in the reverse order. Hence, we will be making our trading decision like below:
Step 1: Detect statistical regularity in price series
Step 2: Confirm the statistical regularity with geometric regularity
To demonstrate the two reversed steps, we will use the confluence pattern of mother wave and child wave with high turning point probability (Step 1). Then we will use Fibonacci Expansion to get the fine tune of the possible turning point (Step 2).
The importance of the confluence pattern of mother wave and child wave was discussed heavily in many parts of this book. The confluence price pattern can be used to predict the multiple stochastic cycles present in fractal waves. Anatomy of the confluence price pattern is shown in Figure 4.8-1. By definition, the confluence price pattern is the mother wave pattern made up from several child fractal triangles. Some example of fractal triangles are shown in Figure 4.8-2. In fact, we can have various combination of child fractal triangles to form the mother wave.
https://algotrading-investment.com/2020/04/22/reversal-trading-with-fractal-wave-and-stochastic-cycles/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Price Action and Pattern
Price action and patterns are fundamental concepts in technical analysis that traders use to analyze market movements and make trading decisions. Here’s how they apply to various methodologies like Harmonic Patterns, Chart Patterns, Elliott Wave Theory, and Support and Resistance:
Price Action:
Definition: Price action refers to the movement of a security’s price over time, as depicted on a chart. It reflects the collective behavior of market participants, including buyers and sellers, and encompasses all factors influencing price, such as supply and demand dynamics, market sentiment, and fundamental news.
Application: Traders analyze price action to identify trends, reversals, and significant price levels.
Price action analysis involves studying candlestick patterns, chart patterns, and trendlines to gauge market sentiment and make trading decisions. By understanding price action, traders can anticipate potential support and resistance levels, breakout and reversal patterns, and market trends.
Harmonic Patterns:
Definition: Harmonic patterns are specific price formations that adhere to Fibonacci ratios and geometric patterns. They include patterns such as the Gartley, Butterfly, Bat, and Crab patterns.
Application:Traders use harmonic patterns to identify potential reversal or continuation points in the market.
Harmonic patterns help traders anticipate turning points based on the convergence of Fibonacci retracement and extension levels. By recognizing harmonic patterns, traders can enter trades with defined risk and reward parameters and manage their positions accordingly.
Chart Patterns:
Definition: Chart patterns are visual representations of price movements on a trading chart. They include patterns such as head and shoulders, triangles, flags, and pennants.
Application:Traders use chart patterns to identify potential breakout or reversal points in the market.
Chart patterns provide visual cues about market sentiment and price dynamics, helping traders anticipate future price movements. By recognizing chart patterns, traders can establish entry and exit points, set stop-loss orders, and manage their trades effectively.
Elliott Wave Theory:
Definition: Elliott Wave Theory proposes that market movements unfold in repetitive wave patterns, consisting of impulse waves (trend-following) and corrective waves (counter-trend).
Application:Traders use Elliott Wave Theory to analyze market cycles and forecast future price movements.
Elliott Wave patterns help traders identify potential turning points and anticipate the direction of the next market move. By understanding Elliott Wave patterns, traders can develop trading strategies based on wave counts, Fibonacci ratios, and wave relationships.
Support and Resistance:
Definition: Support and resistance levels are price levels where buying and selling pressure converge, creating barriers to price movement. Support is a price level where buying interest exceeds selling pressure, while resistance is a price level where selling interest exceeds buying pressure.
Application:Traders use support and resistance levels to identify potential entry and exit points, as well as to set stop-loss and take-profit orders.
Support and resistance levels help traders gauge market sentiment and anticipate potential turning points in the market. By recognizing key support and resistance levels, traders can adjust their trading strategies and manage their risk effectively.
In summary, price action and patterns play a crucial role in technical analysis across various trading methodologies. Whether traders are analyzing harmonic patterns, chart patterns, Elliott Wave Theory, or support and resistance levels, understanding price action and patterns helps them make informed trading decisions and navigate the complexities of the financial markets.
https://algotrading-investment.com/2020/03/22/price-action-and-pattern-trading/
Price action and patterns are fundamental concepts in technical analysis that traders use to analyze market movements and make trading decisions. Here’s how they apply to various methodologies like Harmonic Patterns, Chart Patterns, Elliott Wave Theory, and Support and Resistance:
Price Action:
Definition: Price action refers to the movement of a security’s price over time, as depicted on a chart. It reflects the collective behavior of market participants, including buyers and sellers, and encompasses all factors influencing price, such as supply and demand dynamics, market sentiment, and fundamental news.
Application: Traders analyze price action to identify trends, reversals, and significant price levels.
Price action analysis involves studying candlestick patterns, chart patterns, and trendlines to gauge market sentiment and make trading decisions. By understanding price action, traders can anticipate potential support and resistance levels, breakout and reversal patterns, and market trends.
Harmonic Patterns:
Definition: Harmonic patterns are specific price formations that adhere to Fibonacci ratios and geometric patterns. They include patterns such as the Gartley, Butterfly, Bat, and Crab patterns.
Application:Traders use harmonic patterns to identify potential reversal or continuation points in the market.
Harmonic patterns help traders anticipate turning points based on the convergence of Fibonacci retracement and extension levels. By recognizing harmonic patterns, traders can enter trades with defined risk and reward parameters and manage their positions accordingly.
Chart Patterns:
Definition: Chart patterns are visual representations of price movements on a trading chart. They include patterns such as head and shoulders, triangles, flags, and pennants.
Application:Traders use chart patterns to identify potential breakout or reversal points in the market.
Chart patterns provide visual cues about market sentiment and price dynamics, helping traders anticipate future price movements. By recognizing chart patterns, traders can establish entry and exit points, set stop-loss orders, and manage their trades effectively.
Elliott Wave Theory:
Definition: Elliott Wave Theory proposes that market movements unfold in repetitive wave patterns, consisting of impulse waves (trend-following) and corrective waves (counter-trend).
Application:Traders use Elliott Wave Theory to analyze market cycles and forecast future price movements.
Elliott Wave patterns help traders identify potential turning points and anticipate the direction of the next market move. By understanding Elliott Wave patterns, traders can develop trading strategies based on wave counts, Fibonacci ratios, and wave relationships.
Support and Resistance:
Definition: Support and resistance levels are price levels where buying and selling pressure converge, creating barriers to price movement. Support is a price level where buying interest exceeds selling pressure, while resistance is a price level where selling interest exceeds buying pressure.
Application:Traders use support and resistance levels to identify potential entry and exit points, as well as to set stop-loss and take-profit orders.
Support and resistance levels help traders gauge market sentiment and anticipate potential turning points in the market. By recognizing key support and resistance levels, traders can adjust their trading strategies and manage their risk effectively.
In summary, price action and patterns play a crucial role in technical analysis across various trading methodologies. Whether traders are analyzing harmonic patterns, chart patterns, Elliott Wave Theory, or support and resistance levels, understanding price action and patterns helps them make informed trading decisions and navigate the complexities of the financial markets.
https://algotrading-investment.com/2020/03/22/price-action-and-pattern-trading/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Geometric Prediction, the Bible for Successful Trading
Order is present not only in the universe but in human world too. We can find order everywhere including fashion, music, architecture, financial market, arts, and disease. Order can be present in our world as the different form of regularity. For example, they could be present as symmetry, balance, structure, repeating, cycle, or trend. Recognition of these regularities in our world makes our life meaningful and provides us enhancing experience. Sometimes, recognizing regularity brings us new scientific discovery and technological development leading to better life. For example, once we can model the blueprint of virus gene, we can create vaccine for virus by changing the blueprint. Hence, recognizing genetic structures of deadly virus can save millions of our life each year from measles, mumps, Corona Virus, and influenza. Another important but interesting example is recognizing geometric shapes in objects. This ability is used almost everywhere in our life from designing house, creating CAD model of a car, drawing portrait, and creating 3D animation. Regularity is attractive. We love symmetry and balance. Hence, from child hood we are often attracted to play with the geometric shapes like triangle, rectangle, and circle. When we grow up, some of us are capable to apply these skills to more complex task as in architecture, arts, and even in the financial trading.
Although the recognition skills can vary from individual to individual, we can tell that almost everyone has the skills. Let us take some example of this in ourselves. Now imagine that you are a single adult male at the age of late twenties. Say that you are looking for marriage. In selecting your marriage partner, you can consider multiple of factors. Physical attractiveness is one of them. She might be a colleague from the same company or a friend from the same school or a friend from the same town. You might know her for a short time or for a long time. During the date with her, you will spend a lot of time to talk to her. Voluntarily and involuntarily, you will use your recognition in many different ways to find out some attractiveness from her. For example, you might use your recognition in finding attractiveness in her face.
What is the classic wisdom behind the face attractiveness? In his writings on human anatomy, Leonardo da Vinci described that face can be divided into three equal thirds. What is this equal thirds means? It tells the proportion in your face in horizontal dimension as shown in Figure 1.1-1.
In vertical dimension, face can be divided into equal fifth. The width of eyes will represent two fifth and width of nose will represent one fifth. Figure 1.1-2 represents the proportion in vertical dimension.
Most of time, this sort of recognition is taking part involuntarily in our brain. However, face attractiveness is one of the most frequently used recognition when we fall in love with others. Hence, we can tell that one of the important factor for face attractiveness is proportion within face or ratio between facial features. We use proportion and ratio to find regularity in our face, for example, symmetry or balance.
Next thing we can consider in the face attractiveness is the size of certain facial features. For example, size of eyes or lips can play an important role. Some man love a girl with big eyes. Plastic surgery to make eyes bigger is one of the most frequently performed procedure in the developed countries like USA, Japan and UK.
https://algotrading-investment.com/2020/04/05/geometric-prediction-the-bible-for-successful-trading/
Order is present not only in the universe but in human world too. We can find order everywhere including fashion, music, architecture, financial market, arts, and disease. Order can be present in our world as the different form of regularity. For example, they could be present as symmetry, balance, structure, repeating, cycle, or trend. Recognition of these regularities in our world makes our life meaningful and provides us enhancing experience. Sometimes, recognizing regularity brings us new scientific discovery and technological development leading to better life. For example, once we can model the blueprint of virus gene, we can create vaccine for virus by changing the blueprint. Hence, recognizing genetic structures of deadly virus can save millions of our life each year from measles, mumps, Corona Virus, and influenza. Another important but interesting example is recognizing geometric shapes in objects. This ability is used almost everywhere in our life from designing house, creating CAD model of a car, drawing portrait, and creating 3D animation. Regularity is attractive. We love symmetry and balance. Hence, from child hood we are often attracted to play with the geometric shapes like triangle, rectangle, and circle. When we grow up, some of us are capable to apply these skills to more complex task as in architecture, arts, and even in the financial trading.
Although the recognition skills can vary from individual to individual, we can tell that almost everyone has the skills. Let us take some example of this in ourselves. Now imagine that you are a single adult male at the age of late twenties. Say that you are looking for marriage. In selecting your marriage partner, you can consider multiple of factors. Physical attractiveness is one of them. She might be a colleague from the same company or a friend from the same school or a friend from the same town. You might know her for a short time or for a long time. During the date with her, you will spend a lot of time to talk to her. Voluntarily and involuntarily, you will use your recognition in many different ways to find out some attractiveness from her. For example, you might use your recognition in finding attractiveness in her face.
What is the classic wisdom behind the face attractiveness? In his writings on human anatomy, Leonardo da Vinci described that face can be divided into three equal thirds. What is this equal thirds means? It tells the proportion in your face in horizontal dimension as shown in Figure 1.1-1.
In vertical dimension, face can be divided into equal fifth. The width of eyes will represent two fifth and width of nose will represent one fifth. Figure 1.1-2 represents the proportion in vertical dimension.
Most of time, this sort of recognition is taking part involuntarily in our brain. However, face attractiveness is one of the most frequently used recognition when we fall in love with others. Hence, we can tell that one of the important factor for face attractiveness is proportion within face or ratio between facial features. We use proportion and ratio to find regularity in our face, for example, symmetry or balance.
Next thing we can consider in the face attractiveness is the size of certain facial features. For example, size of eyes or lips can play an important role. Some man love a girl with big eyes. Plastic surgery to make eyes bigger is one of the most frequently performed procedure in the developed countries like USA, Japan and UK.
https://algotrading-investment.com/2020/04/05/geometric-prediction-the-bible-for-successful-trading/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Turning Point, Peak, Trough, Swing High, Swing Low, and ZigZag
To recognize the geometric shape in our chart, we need a measurable object. In financial market, the geometric recognition is done in the two dimension of price and time. Price is usually placed in Y axis and time is usually placed in X axis. In financial market, we recognize the geometric shapes in the candlestick chart. Some might use range bar chart. In this book, we will be using candlestick chart most of time because they are easy to read.
When we look at the plain chart, we can recognize almost nothing. It is because we do not know which object we will be studying in the continuous price series. For an object to be measurable, the object requires boundary or outline. Hence, to recognize any geometric shapes in our chart, we need a good logic of dividing the continuous price series into some measurable objects. Typically, it is best to start with Peak Trough Analysis.
In Peak Trough Analysis, we define peak and trough in an alternating manner. The analysis will start from oldest price data to latest price data. If we start with peak, then we will detect trough, peak, trough, peak, and so on to the right. If we start with trough, then we will detect peak, trough, peak, trough, and so on to the right. Each peak and trough requires having the certain distance in price and time as shown in Figure 1.2-2. Doing this manually could be quite tedious. Fortunately, there is a Zigzag indicator available everywhere to do this task automatically. At the same time, we provide Peak Trough Analysis free for audience of this book. Please refer to the downloading guide for Peak Trough Analysis tool in this book.
https://algotrading-investment.com/2020/04/06/turning-point-peak-trough-swing-high-swing-low-and-zigzag/
To recognize the geometric shape in our chart, we need a measurable object. In financial market, the geometric recognition is done in the two dimension of price and time. Price is usually placed in Y axis and time is usually placed in X axis. In financial market, we recognize the geometric shapes in the candlestick chart. Some might use range bar chart. In this book, we will be using candlestick chart most of time because they are easy to read.
When we look at the plain chart, we can recognize almost nothing. It is because we do not know which object we will be studying in the continuous price series. For an object to be measurable, the object requires boundary or outline. Hence, to recognize any geometric shapes in our chart, we need a good logic of dividing the continuous price series into some measurable objects. Typically, it is best to start with Peak Trough Analysis.
In Peak Trough Analysis, we define peak and trough in an alternating manner. The analysis will start from oldest price data to latest price data. If we start with peak, then we will detect trough, peak, trough, peak, and so on to the right. If we start with trough, then we will detect peak, trough, peak, trough, and so on to the right. Each peak and trough requires having the certain distance in price and time as shown in Figure 1.2-2. Doing this manually could be quite tedious. Fortunately, there is a Zigzag indicator available everywhere to do this task automatically. At the same time, we provide Peak Trough Analysis free for audience of this book. Please refer to the downloading guide for Peak Trough Analysis tool in this book.
https://algotrading-investment.com/2020/04/06/turning-point-peak-trough-swing-high-swing-low-and-zigzag/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Fractal Wave, Mother Wave, and Child Wave
Once we have transformed the price series into triangles using Peak Trough Analysis, we are almost ready to study the price patterns using the ratio and size variable. However, before we do that, you need to understand these triangles generated from Peak Trough Analysis are Fractal Pattern. In Fractal Pattern, the same or similar geometric shape is repeating infinitely in different scales. In another words, with Fractal pattern, we are likely to see the same or similar pattern when we magnify the part of pattern. In the Romanesco Broccoli, the smaller piece, if they are broken off from the bigger piece, do look like the big piece. Like this, in Fractal Pattern, the same or similar patterns are occupying the entire structure of an object.
There are few different types of Fractal Patterns. When the same geometric pattern is repeating, we call this as a strict self-similarity. For example, Sierpinski triangle is good example of strict self-similarity in Fractal pattern. In Sierpinski triangle, the triangle is continuously expanding to build bigger triangle. As we magnify any part of Sierpinski triangle, we see the identical triangles.
On the other hands, fractal pattern can have a loose self-similarity structure. For example, the simplest form of loose self-similarity can be found in coastline. As we magnify a segment of coastline, we do not expect to see an identical copy of original coastline but rather similar shapes. Instead, we can observe that the same statistical properties are preserved across scales. In nature, loose self-similarity is more common.
As you might guess, financial market represents fractal pattern with the loose self-similarity. For example, when we apply the Peak Trough Analysis, the triangles are not identical but they are rather similar. As you can see from Figure 1.3-4, each triangle is not strictly identical but they are triangles with different angles. Typically, fractal patterns in nature expand its geometry in two or three dimension in space. In the financial market, the fractal patterns are expanding in the two dimension of price and time. Hence, fractal pattern in the financial market can be considered as fractal wave. In another words, it is some sort of cycles. This inclusion of time dimension makes the fractal pattern in financial market special. What this means is that to predict financial market, we need to consider size and ratio in time dimension too as well as price dimension.
https://algotrading-investment.com/2020/04/06/fractal-wave-mother-wave-and-child-wave/
Once we have transformed the price series into triangles using Peak Trough Analysis, we are almost ready to study the price patterns using the ratio and size variable. However, before we do that, you need to understand these triangles generated from Peak Trough Analysis are Fractal Pattern. In Fractal Pattern, the same or similar geometric shape is repeating infinitely in different scales. In another words, with Fractal pattern, we are likely to see the same or similar pattern when we magnify the part of pattern. In the Romanesco Broccoli, the smaller piece, if they are broken off from the bigger piece, do look like the big piece. Like this, in Fractal Pattern, the same or similar patterns are occupying the entire structure of an object.
There are few different types of Fractal Patterns. When the same geometric pattern is repeating, we call this as a strict self-similarity. For example, Sierpinski triangle is good example of strict self-similarity in Fractal pattern. In Sierpinski triangle, the triangle is continuously expanding to build bigger triangle. As we magnify any part of Sierpinski triangle, we see the identical triangles.
On the other hands, fractal pattern can have a loose self-similarity structure. For example, the simplest form of loose self-similarity can be found in coastline. As we magnify a segment of coastline, we do not expect to see an identical copy of original coastline but rather similar shapes. Instead, we can observe that the same statistical properties are preserved across scales. In nature, loose self-similarity is more common.
As you might guess, financial market represents fractal pattern with the loose self-similarity. For example, when we apply the Peak Trough Analysis, the triangles are not identical but they are rather similar. As you can see from Figure 1.3-4, each triangle is not strictly identical but they are triangles with different angles. Typically, fractal patterns in nature expand its geometry in two or three dimension in space. In the financial market, the fractal patterns are expanding in the two dimension of price and time. Hence, fractal pattern in the financial market can be considered as fractal wave. In another words, it is some sort of cycles. This inclusion of time dimension makes the fractal pattern in financial market special. What this means is that to predict financial market, we need to consider size and ratio in time dimension too as well as price dimension.
https://algotrading-investment.com/2020/04/06/fractal-wave-mother-wave-and-child-wave/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Fibonacci Probability Graph
In the first chapter of this book, we have introduced three prediction equations. The good market prediction is the one that takes account for both statistical regularity and geometric regularity together. On top of that, if you can add surrounding knowledge around the market, this could be the most complete form of prediction. Surrounding knowledge around market is the information that describes the current market condition outside statistical and geometric regularity. This can include fundamental and political knowledge. The topic of fundamental and political knowledge is comprehensive. We will not cover them here. In this book, we will focus on the technical version of Geometric Prediction equation mostly. You can heavily resource the technical prediction taught in this book for your trading. However, you should never block yourself from the latest information source for the market news and fundamental data release in any case. Below is the three version of the geometric prediction equations.
Geometric Prediction = size + ratio + surrounding knowledge around object where size and ratio is measured from geometric shape.
Geometric Prediction (General version) = statistical regularity + geometric regularity + surrounding knowledge around object
Geometric Prediction (Technical version) = statistical regularity + geometric regularity
Geometric market prediction requires recognizing statistical regularity and geometric regularity in chart. To aid your market prediction, it is possible to devise a tool. We can create the most intuitive tool by combining the functionality of Fibonacci retracement with probability. The idea is that probability will help us to recognize the statistical regularity whereas Fibonacci retracement will help us to recognize the geometric regularity. We can call this tool as Fibonacci Probability Graph.
In the devised Fibonacci Probability Graph, Y axis indicates the amplitude of fractal wave in price dimension and X axis indicates the wavelength of fractal wave in time dimension. In another words, Y axis indicates the turning point probability in price dimension and X axis indicates the turning point probability in time dimension.
https://algotrading-investment.com/2020/04/06/fibonacci-probability-graph/
In the first chapter of this book, we have introduced three prediction equations. The good market prediction is the one that takes account for both statistical regularity and geometric regularity together. On top of that, if you can add surrounding knowledge around the market, this could be the most complete form of prediction. Surrounding knowledge around market is the information that describes the current market condition outside statistical and geometric regularity. This can include fundamental and political knowledge. The topic of fundamental and political knowledge is comprehensive. We will not cover them here. In this book, we will focus on the technical version of Geometric Prediction equation mostly. You can heavily resource the technical prediction taught in this book for your trading. However, you should never block yourself from the latest information source for the market news and fundamental data release in any case. Below is the three version of the geometric prediction equations.
Geometric Prediction = size + ratio + surrounding knowledge around object where size and ratio is measured from geometric shape.
Geometric Prediction (General version) = statistical regularity + geometric regularity + surrounding knowledge around object
Geometric Prediction (Technical version) = statistical regularity + geometric regularity
Geometric market prediction requires recognizing statistical regularity and geometric regularity in chart. To aid your market prediction, it is possible to devise a tool. We can create the most intuitive tool by combining the functionality of Fibonacci retracement with probability. The idea is that probability will help us to recognize the statistical regularity whereas Fibonacci retracement will help us to recognize the geometric regularity. We can call this tool as Fibonacci Probability Graph.
In the devised Fibonacci Probability Graph, Y axis indicates the amplitude of fractal wave in price dimension and X axis indicates the wavelength of fractal wave in time dimension. In another words, Y axis indicates the turning point probability in price dimension and X axis indicates the turning point probability in time dimension.
https://algotrading-investment.com/2020/04/06/fibonacci-probability-graph/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Mother Wave and Child Wave with Joint Probability
When we try to apply the geometric prediction equation, we need to remember one specific point about financial market. Price dynamics in the financial market are fractal. By definition, in fractal pattern, the same or similar geometric shape is repeating infinitely in different scales. We need to pay attention to the fact that we could have a confluence between different scales. It is like two rivers meeting to form a bigger river in concept. In such a case, we can expect stronger market movement (Figure 4.2-1).
In fact, this concept is not new. We have already touched the concept through the deterministic cycles in previous chapter. For example, mathematically we can find the global turning point, where the turning point of small and large cycles meets together (Figure 4.2-2). Unfortunately, in stochastic cycles, we do not have fixed wavelength and amplitude. We can only estimate the turning point probability based on current amplitude and wavelength.
We can only tell if current amplitude and wavelength could become a turning point with some measured certainty or uncertainty. However, we can never be sure for 100%. If each cycle only provides the probability to become peak or trough, then how can we estimate the confluence between small and large cycle of stochastic cycles? Probably, one way to do is calculating the joint probability between small and large cycles.
To visualize this joint probability concept in our trading, consider we have one small cycle and one big cycle. Say that three fractal triangles in the small cycle are the child waves of one fractal triangle in the big cycle. In such case, we can calculate two turning point probability, one for child wave (Triangle 0), and the other one for mother wave (Triangle M0). Firstly, we can calculate the turning point probability for the Triangle 0, the child wave, based on its amplitude and wavelength (Figure 4.2-4).
https://algotrading-investment.com/2020/04/07/mother-wave-and-child-wave-with-joint-probability/
When we try to apply the geometric prediction equation, we need to remember one specific point about financial market. Price dynamics in the financial market are fractal. By definition, in fractal pattern, the same or similar geometric shape is repeating infinitely in different scales. We need to pay attention to the fact that we could have a confluence between different scales. It is like two rivers meeting to form a bigger river in concept. In such a case, we can expect stronger market movement (Figure 4.2-1).
In fact, this concept is not new. We have already touched the concept through the deterministic cycles in previous chapter. For example, mathematically we can find the global turning point, where the turning point of small and large cycles meets together (Figure 4.2-2). Unfortunately, in stochastic cycles, we do not have fixed wavelength and amplitude. We can only estimate the turning point probability based on current amplitude and wavelength.
We can only tell if current amplitude and wavelength could become a turning point with some measured certainty or uncertainty. However, we can never be sure for 100%. If each cycle only provides the probability to become peak or trough, then how can we estimate the confluence between small and large cycle of stochastic cycles? Probably, one way to do is calculating the joint probability between small and large cycles.
To visualize this joint probability concept in our trading, consider we have one small cycle and one big cycle. Say that three fractal triangles in the small cycle are the child waves of one fractal triangle in the big cycle. In such case, we can calculate two turning point probability, one for child wave (Triangle 0), and the other one for mother wave (Triangle M0). Firstly, we can calculate the turning point probability for the Triangle 0, the child wave, based on its amplitude and wavelength (Figure 4.2-4).
https://algotrading-investment.com/2020/04/07/mother-wave-and-child-wave-with-joint-probability/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Predicting Volatility with Turning Point Probability
One important advantage of using turning point probability in financial market is that we can also predict the volatility of the price movement. This is useful when we need to decide our stop loss and take profit targets in our trading.
We can consider three different cases. Firstly, we can expect neutral market sentiment, where sell and buy pressure is nearly equal. Secondly, we can expect the market with bullish sentiment, where often amplitude of bearish movement could be smaller than the amplitude of bullish movement. Thirdly, we can expect the market with bearish sentiment, where often amplitude of bullish movement could be smaller than the amplitude of bearish movement. Any of these three cases can happen when we make either buy or sell entry. Hence, we will analyse these three different cases respectively for buy and sell entry.
Let us start with the case when we make the buy entry. When we try to make buy entry, the market could be surrounded with neutral sentiment. In this case, the sensible profit target could be the mean amplitude. In theory, mean amplitude is the price movement, at which we have 50% turning point probability. If you believe the neutral sentiment after your buy entry, then it is good to limit your take profit target around the mean amplitude. You can measure the amplitude from Fibonacci Probability Graph as shown in Figure 4.3-1.
https://algotrading-investment.com/2020/04/07/predicting-volatility-with-turning-point-probability/
One important advantage of using turning point probability in financial market is that we can also predict the volatility of the price movement. This is useful when we need to decide our stop loss and take profit targets in our trading.
We can consider three different cases. Firstly, we can expect neutral market sentiment, where sell and buy pressure is nearly equal. Secondly, we can expect the market with bullish sentiment, where often amplitude of bearish movement could be smaller than the amplitude of bullish movement. Thirdly, we can expect the market with bearish sentiment, where often amplitude of bullish movement could be smaller than the amplitude of bearish movement. Any of these three cases can happen when we make either buy or sell entry. Hence, we will analyse these three different cases respectively for buy and sell entry.
Let us start with the case when we make the buy entry. When we try to make buy entry, the market could be surrounded with neutral sentiment. In this case, the sensible profit target could be the mean amplitude. In theory, mean amplitude is the price movement, at which we have 50% turning point probability. If you believe the neutral sentiment after your buy entry, then it is good to limit your take profit target around the mean amplitude. You can measure the amplitude from Fibonacci Probability Graph as shown in Figure 4.3-1.
https://algotrading-investment.com/2020/04/07/predicting-volatility-with-turning-point-probability/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Support and Resistance Trading with Turning Point Probability
In this chapter, we will show you how to combine statistical regularity and geometric regularity to trade with the most popular trading technique, the support and resistance. In the support and resistance, the geometric regularity is recognized when several peaks (or troughs) are aligned within one straight line. Especially, when three or more peaks (or troughs) are hitting the path of one straight line, it is hard to take this as the random event. When we add the statistical regularity in our decision-making process, we can even reduce the uncertainty further. Hence, we can improve our accuracy.
There are two different cases for our trading. Firstly, we can pick up the continuation pattern for trading. In the continuation pattern, we trade in the same direction of the slope of geometric support and resistance line. Continuation pattern is useful when we anticipate the continued momentum in the current trend direction. Secondly, we can try to pick up the reversal pattern. In the reversal pattern, we trade in the opposite direction of the slope of the geometric support and resistance line. Reversal pattern is useful when we believe that current market trend stayed for the prolonged period and new trend should be born in the opposite direction.
https://algotrading-investment.com/2020/04/07/support-and-resistance-trading-with-turning-point-probability/
In this chapter, we will show you how to combine statistical regularity and geometric regularity to trade with the most popular trading technique, the support and resistance. In the support and resistance, the geometric regularity is recognized when several peaks (or troughs) are aligned within one straight line. Especially, when three or more peaks (or troughs) are hitting the path of one straight line, it is hard to take this as the random event. When we add the statistical regularity in our decision-making process, we can even reduce the uncertainty further. Hence, we can improve our accuracy.
There are two different cases for our trading. Firstly, we can pick up the continuation pattern for trading. In the continuation pattern, we trade in the same direction of the slope of geometric support and resistance line. Continuation pattern is useful when we anticipate the continued momentum in the current trend direction. Secondly, we can try to pick up the reversal pattern. In the reversal pattern, we trade in the opposite direction of the slope of the geometric support and resistance line. Reversal pattern is useful when we believe that current market trend stayed for the prolonged period and new trend should be born in the opposite direction.
https://algotrading-investment.com/2020/04/07/support-and-resistance-trading-with-turning-point-probability/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Harmonic Pattern Trading with Turning Point Probability
In this chapter, we will show you how to combine statistical regularity and geometric regularity to trade with Harmonic patterns. In Harmonic Pattern trading, the geometric regularity is recognized with the ratio when several neighbouring fractal triangles are form the certain structure. Once, geometric regularity is recognized by detecting Harmonic Pattern, we detect the statistical regularity with Fibonacci Probability graph to reduce the uncertainty further. Hence, we can improve our accuracy for our trading.
As in the support and resistance, we can consider continuation pattern and reversal pattern for harmonic pattern trading. In continuation pattern, we try to find the harmonic pattern giving us the trading entry in the current trend direction. As you might expect, in the case of continuation pattern, the counter trend movement (i.e. correction) is weak. This will result in the small probability in the joint probability matrix. Hence, we have less conservative criteria in choosing our entry. For example, we will consider the probability 50% as the minimum cut for the continuation pattern.
https://algotrading-investment.com/2020/04/07/harmonic-pattern-trading-with-turning-point-probability/
In this chapter, we will show you how to combine statistical regularity and geometric regularity to trade with Harmonic patterns. In Harmonic Pattern trading, the geometric regularity is recognized with the ratio when several neighbouring fractal triangles are form the certain structure. Once, geometric regularity is recognized by detecting Harmonic Pattern, we detect the statistical regularity with Fibonacci Probability graph to reduce the uncertainty further. Hence, we can improve our accuracy for our trading.
As in the support and resistance, we can consider continuation pattern and reversal pattern for harmonic pattern trading. In continuation pattern, we try to find the harmonic pattern giving us the trading entry in the current trend direction. As you might expect, in the case of continuation pattern, the counter trend movement (i.e. correction) is weak. This will result in the small probability in the joint probability matrix. Hence, we have less conservative criteria in choosing our entry. For example, we will consider the probability 50% as the minimum cut for the continuation pattern.
https://algotrading-investment.com/2020/04/07/harmonic-pattern-trading-with-turning-point-probability/
![Young Ho Seo](https://c.mql5.com/avatar/2014/1/52E29441-6BDD.jpg)
Young Ho Seo
Here are some more tips about Falling Wedge Pattern and Rising Wedge Pattern to trade in Forex market in regards to the turning point. The Falling Wedge Pattern and Rising Wedge Pattern are two common chart patterns used in technical analysis to predict potential price movements. Let’s delve into each pattern and discuss their turning point probabilities:
Falling Wedge Pattern:
Description: The Falling Wedge Pattern is a bullish continuation pattern characterized by a series of lower highs and lower lows, converging toward a point. It resembles a narrowing triangle pointing downwards.
Turning Point Probability: The Falling Wedge Pattern typically appears during a downtrend and signals that the downtrend may be losing momentum. Traders anticipate a potential reversal in the direction of the prevailing trend when the price breaks out above the upper trendline of the wedge. The turning point probability is higher when the breakout is accompanied by increased volume, confirming the strength of the reversal.
https://algotrading-investment.com/2020/04/07/falling-wedge-pattern-and-rising-wedge-pattern-with-turning-point-probability/
Falling Wedge Pattern:
Description: The Falling Wedge Pattern is a bullish continuation pattern characterized by a series of lower highs and lower lows, converging toward a point. It resembles a narrowing triangle pointing downwards.
Turning Point Probability: The Falling Wedge Pattern typically appears during a downtrend and signals that the downtrend may be losing momentum. Traders anticipate a potential reversal in the direction of the prevailing trend when the price breaks out above the upper trendline of the wedge. The turning point probability is higher when the breakout is accompanied by increased volume, confirming the strength of the reversal.
https://algotrading-investment.com/2020/04/07/falling-wedge-pattern-and-rising-wedge-pattern-with-turning-point-probability/
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Young Ho Seo
The “profitable chart pattern” refers to specific formations or configurations observed on price charts that have historically demonstrated a higher probability of resulting in profitable trading opportunities. These patterns are identified through technical analysis, which involves studying historical price movements and patterns to forecast future price direction.
A profitable chart pattern typically exhibits certain characteristics:
Reliability: Profitable chart patterns have a track record of accurately predicting future price movements more often than not. Traders rely on the consistency of these patterns across different time frames and market conditions.
Clear Signal: A profitable chart pattern provides a clear indication of when to enter or exit a trade. It usually has defined criteria for identification, making it easy for traders to recognize and act upon.
Risk-to-Reward Ratio: Successful chart patterns often offer a favorable risk-to-reward ratio, meaning the potential profit from a winning trade outweighs the potential loss from a losing trade. This helps traders manage risk effectively.
Confirmation: Many profitable chart patterns are confirmed by other technical indicators or market conditions, adding to their reliability. Confirmation may come from volume analysis, momentum indicators, or trend analysis.
Applicability Across Assets: Some chart patterns are applicable across different financial instruments, such as stocks, forex, commodities, and cryptocurrencies. This universality increases their appeal to a wide range of traders.
https://algotrading-investment.com/2020/02/16/profitable-chart-patterns-in-forex-and-stock-market-on-google-play-book-and-google-book-now/
A profitable chart pattern typically exhibits certain characteristics:
Reliability: Profitable chart patterns have a track record of accurately predicting future price movements more often than not. Traders rely on the consistency of these patterns across different time frames and market conditions.
Clear Signal: A profitable chart pattern provides a clear indication of when to enter or exit a trade. It usually has defined criteria for identification, making it easy for traders to recognize and act upon.
Risk-to-Reward Ratio: Successful chart patterns often offer a favorable risk-to-reward ratio, meaning the potential profit from a winning trade outweighs the potential loss from a losing trade. This helps traders manage risk effectively.
Confirmation: Many profitable chart patterns are confirmed by other technical indicators or market conditions, adding to their reliability. Confirmation may come from volume analysis, momentum indicators, or trend analysis.
Applicability Across Assets: Some chart patterns are applicable across different financial instruments, such as stocks, forex, commodities, and cryptocurrencies. This universality increases their appeal to a wide range of traders.
https://algotrading-investment.com/2020/02/16/profitable-chart-patterns-in-forex-and-stock-market-on-google-play-book-and-google-book-now/
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Young Ho Seo
Turning Point Probability (TPP) is a concept used in technical analysis to assess the likelihood of a price reversal at a certain point in a financial market, which can be used with Harmonic Pattern, Elliott Wave pattern, and X3 pattern. It’s often utilized in conjunction with various patterns like Harmonic Patterns, Elliott Wave Patterns, and X3 Patterns to identify potential turning points in price movements.
Harmonic Patterns: Harmonic patterns are geometric price patterns that use Fibonacci ratios to identify potential reversal points in the market. Common harmonic patterns include the Gartley, Butterfly, Bat, and Crab patterns. Turning Point Probability in harmonic patterns involves analyzing key Fibonacci retracement levels, confluence zones, and the symmetry of the pattern to gauge the likelihood of a price reversal occurring at those levels.
https://algotrading-investment.com/2020/02/21/trading-harmonic-pattern-elliott-wave-pattern-and-x3-pattern-with-turning-point-probability/
Harmonic Patterns: Harmonic patterns are geometric price patterns that use Fibonacci ratios to identify potential reversal points in the market. Common harmonic patterns include the Gartley, Butterfly, Bat, and Crab patterns. Turning Point Probability in harmonic patterns involves analyzing key Fibonacci retracement levels, confluence zones, and the symmetry of the pattern to gauge the likelihood of a price reversal occurring at those levels.
https://algotrading-investment.com/2020/02/21/trading-harmonic-pattern-elliott-wave-pattern-and-x3-pattern-with-turning-point-probability/
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