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Various Risks in Trading and Investment


Trading and investment carry risk. The opportunities in trading and investment without risk rarely exits except some arbitrage opportunities, which will not be discussed in this book. In theory, you could develop several classes of risks for trading and investment. For example, risk in trading and investment can be classified as Macro and Micro risks depending on where they are originated. Macro and Micro risks can be subdivided further into smaller categories like the market risk, operational risk, liquidity risk, credit risk, political risk, etc. Since this book is not the theoretical textbook, we only describe some examples of Macro and Micro risks in Table 10-1 for your trading. However, this list is definitely not the exhausted one.

Risk Factors Description Examples Exposure on
Trader/broker

Nature
Market Risk Risk of changing the fundamentals of the underlying security due to the competitive market environment. Microsoft Window is losing its market share due to the wide popularity of android OS developed by Google. Trader: Yes
Broker: No

Marco
Political risk Risk associated with the possibility of unfavourable government action or social changes resulting in a loss of the security value. Large change in the currency value and stock prices after the presidential election. Trader: Yes
Broker: Yes

Macro
Interest rate risk Risk that an investment’s value will change due to a change in the absolute level of interest rate. If interest rate increase, bond prices fall. When interest rates fall, then bon price rise. In addition, interest rate change cause huge spikes on Forex market too. Trader: Yes
Broker: Yes

Macro
Operational Risk Risk that originates from the mistake of the operator or the company during its trading and investment process. You have executed your order with wrong stop loss size or wrong contract size. Trader: Yes
Broker: Yes

Micro
Liquidity risk Risk that refers to the difficulty of converting the assets to cash at the fair value. You want to sell your 10 million shares of Google but your broker cannot find buyer of your shares because of the large volume. Trader: Yes
Broker: Yes

Micro
Credit risk Risk or possibility that the operator or company can go bankrupt. Your broker gone bankrupt so your trading account is suspended from trading. Trader: Yes
Broker: Yes

Micro
Table 10-1: Common risks for your trading and investment.

Trader and investor are exposed on both Marco risks and Micro risks every day. Macro risks like the market risk, political risk and interest rate risk are caused by the external factors outside your trading operation. Most of time, these external factors are not controllable by us. In fact, some of the technical and fundamental analysis might be used to protect traders from these Macro risks. However, some of the risky event can not be warned at all even using any technical or fundamental analysis. For example, trader can make some educated guess on the possible depreciation or appreciation of the currency by looking at some Macro-economic data and technical analysis. Likewise, by studying the company balance sheets and by applying many technical analyses, we can guess that if the company is increasing their market share from its competitors. On the other hands, guessing when the government will increase or decrease the corporation tax is impossible with any technical or fundamental analysis. Macro risks can contribute to the predictable and non-predictable parts of the market. In fact, many technical and fundamental analyses are there for you to reduce the Macro risks for your trading. Charting techniques and technical indicators can help you to identify the short-term or long-term price movement up to some degree. Besides the technical analysis, monitoring the important news can reduce the market risks too. For example, trader need to watch out any news about the taxes or labour laws, trade tariff change, environmental regulation or reformation in the national economy because they can change the entire market dynamics.

Some Micro risks like operational risk and credit risk can be originated from trader or from broker internally. In 2009, trader at UBS, the Swiss banking giant, placed a $22 billion of Capcom bonds in mistake while trying to buy just £220,000. In 2012, Knight Capital lost nearly $440 million in just 30 minutes because their trading software sent erroneous orders. These types of fat finger mistakes are the typical operational risk in trading. Operational risk can be made by anyone or by any algorithm. Sometimes, some trading platforms have many protective systems to prevent some common operational risk but not all of them can be prevented. You can still send wrong contract size or wrong stop loss size to your broker anytime. Especially the erroneous automated trading system can send the erroneous orders at high speed. The penalty from the mistake is always 100% yours. If a book was accidently dropped on your keyboard and hit the enter key sending the market order with 10 million contracts to your forex broker, you will lose a lot of money on commission even if you close the order immediately. You can not blame other people for this accident. To prevent the operational risk, trader needs to be highly cautious in their trading. It is better to avoid trading when you are not set for the trading. If you are working in a team, it is important to monitor each other to prevent such silly mistake. If you have to build the automated trading algorithm, the operation of the algorithm must be fully tested in the paper account first.

Credit risk is another Micro risk, on which both trader and broker are heavily exposed. Simply speaking, credit risk is the chance of experiencing the bankruptcy for the business organization. Any business organization can go bankrupt. Trader, broker or any liquidity provider can face the bankruptcy. The insolvency of the Alpari UK, currency broker, due to the Swiss franc turmoil in 2015 was a good example of the credit risk exposed by the currency brokers. From the trader’s point of view, trader can always lose their entire capital or nearly entire capital from their trading. If the operational risk can be considered as a mistake, credit risk often happens because traders are not educated or not experienced. Except that your account blowing was experimentally carried out on the small account for some educational purpose, this experience can cause serious damage to your finance. For traders, the credit risk is normally originated from the lack of understanding on the market volatility and position sizing.

Consider the aggressive trading example in Table 10-2, where the credit risk is amplified to blow your account. Your starting balance is 10,000 US dollar and pip value for EURUSD is 10 dollar per pip in this example. In this trading example, a trader used the aggressive trading volume for each trade. Luckily, he got the two winning trades increasing his account to 30,000 US dollar initially. Then his luck was run out losing all his account in next three trades. Can you imagine how he would feel in his first two trades? Can you imagine how he would feel after he lost all his account? In this trading example, his obvious mistake is to use the excessively large trading volume. This sort of mistake typically happens to starters who ignore to learn how the pip value and contract size relate the market movement to the profit and loss on his holding positions.


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Introduction to Technical Analysis


Designing a successful strategy is an intellectually challenging process. It requires extensive research and testing. The research in trading is always followed by the immediate real world outcome. The trading strategy based on the bad system or methodology will be falsified extremely fast in the real world trading. Naturally, a trader with the scientific mind set can learn the great deal of knowledge about this world from testing various trading methodology with the financial market. Science or scientific methodology plays an important role in trading and investment.

Technical and fundamental analyses are the two main schools of thoughts for financial trading and investment. Technical analysis assumes that price discounts for everything. For example, technical analyst believe that at a given time a stock’s price reflects everything that could affect the company including company’s fundamental factors, economic factors and market psychological factors. Technical analyst also believes that history tends to repeat itself. Therefore, they can predict the future. Technical analysis only leaves the price as the main subject to study. For fundamental analysis, traders study the intrinsic value of the company. For example, they make their trading decision based on growth potential of the security. They are more concerned with basis like sales, earnings and management of the company. In general, fundamental traders are considered as the long term investors whereas the technical traders are considered as the short term investors. However, there are short-term fundamental traders too. For example, some news traders do not hold their position too long. On the other hands, there are technical traders basing their trading decision on monthly timeframe. Those technical traders can hold their position for several months to few years too.

The origin of technical analysis could be traced back to the trading of Japanese rice in Osaka in late 1600. This is the period when the Japanese candlestick technique was developed. With the development of high capacity computers and internet, the development of technical analysis has been accelerated even fast. In this book, we are only interested in the technical analysis in terms of the methodological point of view. Do not confuse the technical analysis with technical indicators. Technical analysis is the comprehensive methodology that covers broad scientific and mathematical methods. Technical indicator is the mathematical transformation of the price series to extract smoothed price trajectory or oscillating motion of the price like Simple Moving average or Relative Strength Index. Of course, technical indicator is a part of technical analysis but it is much smaller concept comparing to technical analysis. To give you some ideas about technical analysis, we will present five important categories for technical analysis. The five categories include charting, pattern analysis, technical indicator, mathematical method and artificial intelligence. We list some of the sub elements of the five categories in Table 1-1.

Table 1-1: Five main categories of Technical Analysis.

Charting techniques are the first requirement for trading. Simply speaking traders cannot trade without any chart. The value for good visualization technique is a prime importance for traders. Important attributes in the modern charting technique is that they must allow the instant recognition of important patterns and trend from the price series. In addition, market volatility should be also easily gleaned from the chart too. Some commonly used charting techniques are line chart, OHLC bar char and candlestick chart. In modern trading software, these three types of charts are essentially provided in their basic package. Some more sophisticated software offers Renko chart, Point & Figure chart and Tick chart for advanced users. Traders tend to have their preferences for the choice of the charts. For traders using Japanese candlestick patterns, they will stick with candlestick chart over OHLC bar chart. If traders are looking for breakout patterns, then they will prefer Renko chart or Point & Figure chart.



Figure 1-1: Candlestick chart of EURUSD Daily series with tick volume.

The objective of the technical indicator is to measure the strength of trend, volatility and momentum of the price series. Technical indicators are mostly derived from the price series. Sometime technical indicator uses open, high, low and close price. Sometimes the technical indicator only uses close price for computation. The advantage of technical indicators is ease of use. For example, most of technical indicators can be displayed simultaneously together with the original price series in a convenient way. Therefore, traders can easily incorporate alerting system for his trading. The disadvantage is that most of time technical indicators are lagging behind the actual price series. In modern trading platform, technical indicators and charting facilities are the basic requirements for trading. Many of the software vendors provide over 100 technical indicators with their trading platform. There are some of the vendors claiming that they are offering over 3000 different technical indicators unofficially. Most common mistake for traders is that they tend to apply the same technical indicator across every market. The different market can have different market dynamics. Therefore, before blindly applying any technical indicators, you should ask the question like “Is this technical indicator right one for this market?”. For example, for the stock exhibiting strong growth patterns, it is not good idea to look for the trend reversal opportunities using the relative strength indicator.



Figure 1-2: Candlestick chart of EURUSD Daily series (top) with Relative Strength Indicator (middle) and Average Directional Movement Index (bottom).

Besides technical indicators, pattern analysis is another important tool for traders. Pattern analysis concerns about the price levels and the geometry of the price series. Support & resistance, Japanese candlestick pattern and Fibonacci retracement are the popular pattern recognition techniques for traders. Support and Resistance represents key price levels where the force of supply and demand meets (Figure 1-3). Normally support and resistance levels are detected by connecting frequently tested level from your chart. Support is the price level at which demand is strong to prevent the price from declining further. Resistance is the price level at which selling is strong to prevent the price from rising further. Some textbook might teach you the support and resistance level as the reversal level but this may be not true. Practically speaking, support and resistance level can act as the breakout level too. For example, when the price penetrates through resistance level, more buying momentum can build up for strong bullish movement. Likewise, when the price penetrates the supply level, more selling momentum can build up for strong bearish movement. However, what is always true is that there are strong volatility around the support and resistance area. Price will either penetrate hard or bounce back hard at support and resistance level. When the resistance level is penetrated, then the resistance level becomes support level. Likewise, when the support level is penetrated, the support level becomes resistance level. Traders should get habit of making note for the important levels always for their trading.


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Introduction to Charting Techniques


For the Price Action and Pattern Analysis, it is important to have good visualization tools. Since we want to find important patterns for our trading, we will need a good size monitor and good visualization software. Of course, you should invest on them as much as you can afford. No single visualization techniques are perfect. They always possess some advantages as well as some disadvantages. Firstly, line chart is the most basic visualization technique for traders. Line is simply drawn by connecting each session’s closing price. For example, 1-hour line chart is simply drawn by connecting the closing price of 1-hour candle. As line chart are produced by connecting two points at the fixed time interval, they can provide a great insight about some regularities in the price series. For this reason, not only traders use the line chart but also many mathematicians use them to visualize the price series data. Line chart is useful when we want to exam some cyclic behaviour like seasonality or any cyclic patterns made up from sine or cosine function. Line chart is also useful when you want to compare multiple price series in one chart. On the other hands, the disadvantage of the line chart is that it does not provide the trading range of each session. In addition, due to the continuously drawn line, it is difficult to see any gap between sessions. In addition, line chart miss some important attributes like highest and lowest prices of each session.



Figure 2-1: Line chart for EURUSD from 1 September 2016 to 16 January 2017

Candlestick chart provides some additional attributes, which line chart misses. Figure 2-2 presents the anatomy of the candlestick chart. Candlestick chart provides three important information. Firstly, the bottom and top of the box represents the opening and closing price of the session. Secondly, each candlestick shows the trading range between high and low for each session. Thirdly, candlestick shows the direction of movement for each session. In Figure 2-2, the green candle reveals the upward movement for the session immediately whereas the red candle shows the downward movement. From Figure 2-3, we can feel how richer information candlestick chart provide for each session comparing to the Line chart. As shown in Figure 2-3, Candlestick chart is useful to spot the gaps in between sessions. This is very useful property of the candlestick chart since Line chart or any other chart is difficult to spot the gaps. One of the drawbacks of the candlestick chart is that it does not provide the sequence of high and low price but this is the common problem for other visualization techniques too. It is simply because the sequence of high and low price was not collected traditionally by the Financial Institutions. If anyone starts to provide the historical sequence of high and low prices for each session, then this would reveal a lot of information on the psychology of the financial market. All they have to put some simple identifier which price comes first between high and low prices during the session. For example, one can put the letter “h” to highlight that high price comes first before low price. Therefore, storing cost is no more than just a letter for this crucial information. This might be cheap but useful alternative to the expensive tick history data, which often require enormous hard drive space. In addition, the candlestick chart is the basis for the popular Japanese candlestick patterns. Although the Japanese candlestick pattern alone does not provide the perfect trading entry, many traders uses them as the confirming tool for their entry or exit.



Figure 2-2: Anatomy of the Candlestick chart.



Figure 2-3: Candlestick chart for EURUSD from 1 September 2016 to 16 January 2017.

OHLC Bar chart is another popular form of visualization techniques. The OHLC bar chart has some improvement over the line chart. It provides all of the same data including open, close, range and direction to the candlestick chart. However, OHLC bar chart is not visually easy to follow like candlestick chart. In addition, spotting the gap between sessions is not easy with the OHLC bar chart. However, many traders still not given up to use OHLC bar chart over the candlestick and line chart.



Figure 2-4: Anatomy of the Range Bar.

So far, we have introduced the visualization techniques with the fixed time interval. For example, line chart, candlestick chart and the OHLC bar chart uses the information collected in each session. The common time interval for the session is 1 hour, 4 hour, 1 day, 1 week and 1 month. Instead of using the fixed time interval, several techniques do not use the fixed time interval to construct the chart. For example, tick chart record the open, high, low and close prices during the fixed tick arrival intervals. Therefore, all the bars in the Tick chart have the same tick volumes. For example, 100 Tick chart will record the open, high, low and close price during 100 tick arrivals. All the bars in 100 Tick chart will have 100 tick volumes. One can construct line, candlestick chart and OHLC bar chart with Tick chart too. Tick chart will look like normal chart except that every bar has the identical tick volume. In Tick chart, during busy market hours, one candlestick can be formed fast but during slow market hours, one candlestick can be formed slowly. The tick chart is useful to replace the normal candlestick chart with lower timeframe when the candlestick chart produces the poor visual representation of the market with standard time interval. This is not always the case but when there is low interest in the market, this can happen. For example, Figure 2-5 shows the broken 1-minute candlestick chart for NZDSGD currency pairs. In this case, instead of using the candlestick chart with 1-minute chart, trader can use 100 tick chart. Because each candle is completed with 100 tick arrivals every time (Figure 2-6), we naturally have smoother looking chart in comparison to the broken chart in Figure 2-5. Once traders become familiar with tick chart, they tend to stick with them even for the higher timeframe. For example, you can use 500 tick chart or 1000 tick chart for your trading. Disadvantage of the tick chart is that tick is generally much heavier to store in the hard drive in terms of size. Therefore, not many trading package offer the capability of using tick chart for the time of writing this book. Just for your information, one-year worth of tick data can take up over some serous gigabytes of the space on your hard drives. In addition, Tick chart does not provide volatility information since every bar has identical tick volume. However, if programmatically doable, one can store time duration it takes to form the bar in the place of the tick volume. This would provide different insight, which the fixed time interval chart can’t provide.


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The Five Regularities in the Financial Market


The Financial Market is the place where different investors are trading securities like equities, bonds, currencies, and derivatives. It is the market place to facilitate the exchange of securities between buyers and sellers. Loosely speaking, the financial market works like the auction market where buyers enter competitive bids and where sellers enter competitive offers at the same time. However, unlike auction market, in the financial market securities are often traded without delivering actual physical goods. Although some companies can use financial market to hedge their physical positions, in this book, we will assume that you are more of speculator who wants to profit from the market dynamics. Various buyers and sellers with different attributes, different geographic location, different purchasing power and different financial goals, forms the daily transactions of the financial market. Therefore, the dynamics of financial market can be represented as the crowd behaviour. It is not necessarily perfectly rational place but the fundamentals play some important role behind the market dynamics up to some degree.

For traders and investors, it is important to develop the right trading strategy for specific market. Good trading strategy never comes blindly. Understanding the underlying dynamics for the financial market is the key requirement to build a solid trading strategy. Then, what is the underlying dynamics for the financial market and how can we study them to benefit our trading and investment? Scientists had a strong interest in the dynamics of the financial market for many decades. They have extensively studied the dynamics of the price series in the Stocks and Forex market. The simplest but most effective way to study the dynamics might be the decomposition approach. In decomposition, literarily we are breaking down some complex system into the simple and digestible bits. Then we use this decomposed bits to predict the behaviour of the complex system.

When we apply the decomposing technique, the price series can be decomposed into several sub price patterns. In fact, the sub price patterns are the regularities that constitute the dynamics of the financial price series (Figure 3-1). For trading and investment, we make use of the knowledge of these regularities to predict up or down movement of the financial market. All the known trading strategies, including simple and complex ones, are based on some of these regularities existing in the price series. Remember that none of trading strategies is merely created to offer you just some luck or based on some random theory.



Figure 3-1: The concept of the decomposition for the financial price series.

In Figure 3-1, what could be the pattern 1, pattern 2, pattern 3 and pattern N making up the real world financial price series? Yet, many different version of explanations exist to describe the price patterns in the financial market. Among them, Gardner’s version considers the trend and seasonality as the main patterns of the price series (Gardner, 1987, p175). Many traders are already familiar with trend in the financial markets. For example, many technical indicators like exponential moving average and MACD were developed to visualize trend. Seasonality is literally seasonal fluctuations in the market. It is also used by many traders. For example, because the sales of Ice Creams increase during summer, stock price for Ice Creams Company can go up due to the increased profits during summer. This sort of patterns will make up the seasonal fluctuations. The Gardner’s framework is intuitive and easy to understand because trend and seasonality are the backbone of many techniques used for the univariate price series in many scientific fields. Although Gardner’s framework does not mention about random process explicitly, his framework already assumed that any price series include some random process.

Depending on their underlying dynamics, the price series can show the multitude of behaviours because real world price series are made up from different magnitude of each price pattern. For example, sometimes, the price series can exhibit strong trend without seasonality and vice versa. Sometimes the price series can exhibit some trend with some seasonality. In the Gardner’s trend-seasonality framework, we can generate twelve different behaviour of the price series by combining the basic trend and seasonal patterns as shown in Figure 3-2. Scientist uses this framework to categorize many real world price series for prediction purpose. Then, what is the real value of the Price Pattern Table in Figure 3-2 for traders? As a trader, we can develop trading strategies to capture these price patterns for profiting purpose. These patterns in price series are regularities, which help us to predict the price series into the future. Financial trading is based on our prediction for the future market. We buy EURUSD because we predict that EURUSD have the high chance to go up. We sell EURUSD because we predict that EURUSD have the high chance to go down. If we understand the regularities of the financial market better, then we can make better trading and investment decision too.


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Random Process


Most of time, the financial price series will exhibit some Random fluctuations. Therefore, you have to assume that some random process exists in the real world financial price series always. Random fluctuation is literarily independent from any causality and therefore they are not the predictable component in the price series. Randomness is an opposite component to the regularities we are looking to capture in the price series. Therefore, if the price series have strong randomness, it is bad for us. It is always better for traders to assume that any real world price series possess some randomness because they really do. Such a random process in the financial market data might be either white noise or something else. When the random process exists on their own, they are simple in terms of modelling and analysing because you can only describe the random series with mean and standard deviation. In real world, the financial market data possess the mixed price patterns between randomness and regularities. For simplicity, just imagine that we have isolated the randomness from our price series into a container in our laboratory. Then they will look like as in Figure 4-1.



Figure 4-1: White Noise series with fixed mean and average.

In fact, the best predictive model or trading strategy is those separating randomness perfectly from regularities. However, the perfect isolating of randomness from the regularities is almost impossible because the perfect quantification of randomness is not possible. Normally the daily return series for Stocks and Forex market data is considered as white noise random process. If they are white noise process, then the return series will have the fixed mean and standard deviation. The mean of the return series for a particular stock can be positive or negative rather than zero. If the mean of the return series is positive, you can buy and hold the stock for long run. If the mean of the return series is negative, you can sell and hold.

If the return series have zero mean, then you will lose money either buying or selling because of the commission you have to pay for. Buy and hold or sell and hold strategies are the typical long run passive strategies and this type of passive strategy need to be reinforced with modern portfolio theory. Otherwise, the long run strategy might suffer from the long period of drawdown.

If we synthetically generate a random price series by summing up the value of the previous random price series, this series is called the Random Walk series. In contrast to the White noise series, we cannot recognize the fluctuation around the fixed mean any more for Random Walk series (Figure 4-2). Instead, they look like they are moving upwards or downwards. Sometimes, the Random Walk series move as if they will never come back to their origin any more. Since the Random Walk series are generated from summing unpredictable white noise series, the Random Walk series are also unpredictable too. In general, Random Walk series look like real world stocks and forex market price series but it will not show any regularities like trend or cyclic behaviour. Many people blindly assume that the price series are the perfect random walk series and they are not predictable. However, remember that there are the fundamentals moving the market. Traders and investors are not perfectly rational but they will also make their trading and investment decision based on the market fundamentals. For example, if USA increases the interest rate, then US dollar will be appreciated by pushing US dollar high against Euro. If the company director is involved with some serious sex scandals risking his director’s position, then the share price can be depreciated or this might cause the increased volatility of that share price at least. Of course, you might find many instances where fundamentals did not move the market. Even in such a case, it is better to assume that some unknown factors cancelled out the fundamental effect rather than assuming the market is totally Random Walk. You should assume the random walk process only if you have gathered strong evidence with the price series.



Figure 4-2: Synthetic Random Walk series.



Trading Strategy Note

If the price series is the pure Random Walk series, traders and investors have a very few choices for his strategy. If the return of the price series has the mean of zero, then there is no point to trade. However, with some positive return, you can construct portfolio of many assets according to the Modern Portfolio theory (Harry Markowitz, 1952). This is a systematic approach to reduce the risk dramatically across many different assets. However, this strategy is limited to the investors with large capitals since one has to split his investment over the reasonable number of assets. In addition, this strategy requires to solve the optimization problem to calculate weights for the capital allocation for the given correlation matrix between assets. Therefore, one will require a specialized software package to construct the optimal portfolio using this methodology. In addition, there are some fund management company make use of skewness in the return series for their investment strategy. This information is probably worth to note for your strategy development. Skewness can be readily obtained in many analytical tools like MS-Excel and MatLab.

Analytical Note

ARIMA (Autoregressive Integrated Moving Average) model is a popular econometric model used to study the different properties of the price series data in Finance and Economics. A white noise series can be modelled using ARIMA (0, 0, 0) since white noise is assumed to be stationary. At the same time, Random walk can be modelled using ARIMA (0, 1, 0) involving one order of the difference term. Therefore, we can clearly see that the difference between White Noise and Random Walk process is the presence of stationary process. To confirm the white noise process, the price series must be free from the serial correlation in the data. The distribution is assumed as the normal Gaussian distribution too.

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Some More Tips about Stationary Process in Forex Trading

In Forex trading, a stationary process refers to a time series whose statistical properties, such as mean, variance, and autocorrelation, remain constant over time. Understanding and identifying stationary processes is crucial because many statistical and machine learning models used for forecasting and analysis assume that the underlying data is stationary. When a time series is not stationary, it can lead to inaccurate predictions and misinterpretations of market behavior.


Key Concepts of Stationary Processes



Definition of Stationarity :

A time series is stationary if its statistical properties do not change over time. This means the mean, variance, and autocorrelation structure are constant.


Types of Stationarity :

Strict Stationarity : The joint distribution of any set of observations is identical regardless of time shifts.
Weak (or Second-Order) Stationarity : Only the first two moments (mean and variance) and autocovariance structure are constant over time.


Non-Stationary Processes :

A non-stationary time series has properties that change over time. Common examples include trends, seasonality, and varying volatility.



Importance in Forex Trading



Model Assumptions :

Many predictive models, such as ARIMA (AutoRegressive Integrated Moving Average), assume the data is stationary. Using non-stationary data with these models can lead to unreliable forecasts.


Statistical Inference :

Stationary processes allow for consistent and reliable statistical inference, making it easier to identify underlying patterns and relationships in the data.




Identifying Stationarity

Visual Inspection :

Plot the time series and look for constant mean and variance. Trends or patterns indicate non-stationarity.


Statistical Tests :

Augmented Dickey-Fuller (ADF) Test : Tests the null hypothesis that a unit root is present in the time series (i.e., the series is non-stationary).
Phillips-Perron (PP) Test : Similar to ADF but accounts for serial correlation and heteroskedasticity in the error terms.
Kwiatkowski-Phillips-Schmidt-Shin (KPSS) Test : Tests the null hypothesis that the series is stationary.


Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) :

Examine the ACF and PACF plots. A slow decay indicates non-stationarity, while a quick drop suggests stationarity.




Transforming Non-Stationary Data to Stationary



Differencing :

Subtract the previous observation from the current observation. This process can be repeated (first difference, second difference) until stationarity is achieved.


Log Transformation :

Apply a logarithm to stabilize the variance.


De-trending :

Remove the trend component from the series.


Seasonal Adjustment :

Remove seasonal effects using methods like Seasonal Decomposition of Time Series (STL).




Practical Application in Forex Trading

Forecasting Models :

ARIMA Model : Combines autoregression, differencing, and moving average components. It requires the time series to be stationary. The ARIMA model is commonly used for predicting future currency prices.
GARCH Model : Generalized Autoregressive Conditional Heteroskedasticity model is used for modeling volatility. It also assumes stationarity in the variance of the time series.


Technical Analysis :

Stationary processes are used in various technical indicators. For example, moving averages and Bollinger Bands assume some form of stationarity to provide meaningful insights.


Algorithmic Trading :

Algorithms rely on stationary data for backtesting and optimization. Ensuring stationarity improves the robustness of trading strategies.




Examples



Stationary Series :

A currency pair's daily returns might exhibit stationarity if the returns' mean and variance are constant over time.


Non-Stationary Series :

The actual price level of a currency pair is often non-stationary due to trends and changing economic conditions.




Advantages and Limitations

Advantages :


Reliable Predictions : Stationary series provide a stable basis for forecasting models, leading to more accurate predictions.
Statistical Validity : Ensures the validity of statistical tests and inferences.


Limitations :


Data Transformation : Converting non-stationary data to stationary can sometimes lead to loss of information or added complexity.
Assumption Constraints : Some market behaviors might be inherently non-stationary, challenging the applicability of certain models.


Conclusion
Understanding stationary processes in Forex trading is crucial for building reliable predictive models and making informed trading decisions. By identifying and transforming non-stationary data, traders can ensure their analysis and models are robust, leading to more accurate forecasts and better risk management. Tools like the ADF test, differencing, and log transformation are essential for working with time series data in the dynamic Forex market. https://algotrading-investment.com/2020/06/04/stationary-process-no-trend/

Equilibrium Process (Trend)


Figure 6-1: Equilibrium process is corresponding to price pattern (2, 1), (3, 1) and (4, 1) in the table.

Simply speaking, Equilibrium process is conceptually similar to trend. It is the market force moving the price series in one direction. Equilibrium process is not different from the equilibrium concept found in the classic supply-demand economic theory. Literally, it is the force to move price to release the unbalance between supply and demand. There are many types of Equilibrium process (trend). They might be linear trend like Pattern (2, 1) in Figure 6-2 or they might be nonlinear trend like Pattern (3, 1) and (4, 1) in Figure 6-2. The three trend patterns, that are linear, exponential and damped, are common. Exponential trend (3, 1) represents the strong trend with increasing momentum. Damped trend (4, 1) represents the trend with reducing momentum. Figure 6-2 presents the six trend patterns for both bullish and bearish markets. These six trend patterns are found most frequently in the financial market comparing to other trend patterns.



Figure 6-2: Trend Patterns for bullish and bearish market.

Equilibrium process is highly related to market fundamentals. Equilibrium process is the direct representation of the supply demand balance in the financial market. Assuming there is the fixed number of shares for Google, if more people want to buy a stock than sell it, then the share price for Google will move up. We call this as a bullish trend. Likewise, if more people want to sell a stock than buy it, there would be greater supply than demand and the price would fall. We call this as a bearish trend. When there is the imbalance in supply and demand force, then the price will try to move towards equilibrium to release the imbalance. The supply demand imbalance is often caused by the fundamental change in the market. For example, change in the taxation or interest rate can change the quantity of supply and demand force dramatically. Natural disasters like Earthquake or Hurricanes or other serious transmissible diseases can influence the dynamics of supply demand too in the financial markets. War, terror, or other political corruption can reduce the demand of the financial assets affected by them.



Figure 6-3: Economic Supply Demand curve.

If the Equilibrium process (trend) is overwhelmingly dominating in the price series, prediction is easier. However, the strength of Equilibrium process differs for different financial price series. It is also not easy to quantify or to isolate the portion of Equilibrium process from rest of regularities perfectly. In reality, Equilibrium process can be caused by multiple information sources introduced in different time (Figure 6-4). Each information source will be diffusing across the market in time. Some of them are slowly and some of them are quickly. Some information source will introduce bullish Equilibrium process, which increase demand and reduce supply. Some other information source will introduce bearish Equilibrium process, which reduce demand and increase supply. Since each trader and investor will react differently to different information sources, the diffusion process of each information source can vary dramatically. Modelling or visualization of diffusion process of multiple information sources is complex. If there are many bullish and bearish Equilibrium process acts together in the price series, the simplest way of guessing the market direction is calculating the net of bullish and bearish equilibrium process in the market. However, with simple netting, you will still miss the time factor because each information source will be diffused in different speed. Another difficulty comes from quantifying bullish and bearish strength of each source. Typically, to quantify them correctly, we need good amount of historical data. Especially if you need the accuracy for trading quality, you will need much more data than just doing some academic research. To most of average trader, this is not accessible.


https://algotrading-investment.com/2020/06/04/equilibrium-process-trend/

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/

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/

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/

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/

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/

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 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/

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/

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/

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/

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/

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/

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/