Figure 13-16: Fibonacci expansion chart object placed on Point 1, 2 and 3

Typically, the charting platform will provide the default ratios for Fibonacci

Expansion. However, you can customize the ratios according to your

preferences. To do so, double click on Fibonacci Expansion in Chart. Click

on Properties of Fibonacci Expansion.

Figure 13-17: Pop-up menu for Fibonacci expansion chart object

From the property window of Fibonacci expansion (Figure 13-18), you can

change, add, and remove ratios. At the same time, you can also change their

look including the line colour, line thickness, and line style.

Figure 13-18: Property window of Fibonacci expansion chart object

In MetaTrader 5, instead of using left click and hold move, you will make

two clicks on the first and second points in your chart. Then you will need to

drag the third snap point to the Point 3. In Optimum Chart, you will make

three clicks on first, second and third points. No drag or hold moves are

involved. Sometimes, the platform developer can change the mouse

behaviour with chart object. Once, you have some basics, it is not so difficult

to adapt new approach. This tutorial is applicable for the time being of

writing this book. In summary, we have provided the simple tutorials for you

to get familiar with Fibonacci price patterns. Use this tutorial before you

study Harmonic Patterns, Elliott Wave patterns and X3 patterns.

14. Special Chapter: Algorithm and Prediction for Artificial

Intelligence, Time Series Forecasting, and Technical Analysis

Explaining the computational algorithm behind the technical analysis or

pattern scanner to non-technical people is not an easy task. If we are an

occasional user or if the algorithm is used for the light application with small

or no monetary risk, then there is no need to understand how the algorithm

works. However, if the usage of algorithm, for example, technical indicators,

involves with some or high monetary risk, as in forex and stock market

trading, you should have at least some basic understanding on how algorithm

works. Having such a knowledge is highly advantageous for your trading.

When we want to create a technical indicator, pattern scanner, or other

technical analysis tool, two main components are the concept and the

implementation. Often most of people think about the concept only. They

skip to think about the implementation. Until you can implement the concept

using the computer algorithm, the concept is just concept. In fact, it could be

concept forever. It is the same for the trading strategy or market prediction

technique. Sometimes, your trading strategy or market prediction technique

might be not feasible at all. Sometimes, your trading strategy or market

prediction technique might be implementable to the computer algorithm but

the implementation might be not feasible within reasonable timeframe and

cost. If you think about not all the car design in the concept stage is feasible

to be manufactured within the reasonable cost and time, this is not hard to

understand. In case of the computer algorithm development, many people

tend to forget the feasibility issue because the computer algorithm is not

physical like a car or airplane. However, this is not right. The algorithm

development using the programming language like C++, C Sharp, VBA,

MQL4 or MQL5 could be one of the labour intensive work depending on

what you want to implement.

Another important consideration on using algorithm is the amount of

computational work required to get the predictive value from technical

indicator or the predictive tool. The computational work for the algorithm is

another non-tangible part. When we drive a car, we can tell how much work

the car has done easily. The works of CPU and Memory on the motherboard

are not visible most of time. However, we need to understand that there are

heavy algorithm and light algorithm. Sometimes, it is inevitable to avoid

heavy algorithm unfortunately. Most of common technical indicators are

probably not the case. However, more and more serious mathematics are

employed to build unique and powerful technical indicator, analysis tools,

and predictive models. For example, artificial intelligence or advanced

pattern scanner often requires notably heavy computation.

As a trader, you probably do not require to learn the programming language.

However, having some knowledge on how algorithm works is definitely

beneficial for your trading. Therefore, in this article, we try to provide you

some practical knowledge and practical tutorial. We hope this article is

helpful for you to understand the algorithm working behind your trading

strategy. We will explain this by comparing three different industrial

applications including Artificial Intelligence, Time series Forecasting and

Technical Analysis.

The common goal of Artificial Intelligence, Time series forecasting and

Technical Analysis is to predict something. However, in spite of the common

goal, you will be surprised how the different industry handles the algorithm

in different ways. In fact, they are different dramatically. To explain this, we

will be looking at three areas including data, prediction output, and

requirement for optimization.

Firstly, they expect different amount of data. Typically, Artificial intelligence

like Neural Networks and Deep Machine Learning require the larger data set

comparing to the other two. Often, using small data set less than 100 data

points in Neutral Networks and Deep Machine Learning might not provide

any valid predictive model. In contrast to this, Time series forecasting and

Technical analysis can handle less than 100 data points typically. However,

there are cases, few hundreds data points are not enough for time series

forecasting and technical analysis too. For example, when there is seasonality

of 24 hours or 48 hours, it is better to have larger data set for time series

forecasting. In case of technical analysis, the pattern detection algorithm

often requires more than just several hundred data points. The implication of

large data set is that they use more memory on your computer. Hence,

processing larger data set could increase the processing time of the predictive

algorithm. Practically, you might find a lot of frustration with some artificial

intelligence models or other heavy algorithm because their computation does

not seem to progress at all on some computers.

Secondly, the different industrial applications have different prediction

output. In Artificial intelligence, binary prediction (i.e. classification) and

clustering are the common output. In time series forecasting, the forecasting

model tries to extrapolate the trend or seasonal patterns outside the data set.

In technical analysis, price level forecasting, overbought, and oversold area

confirmation, and geometric pattern recognition are the common output.

They are geared up to identify buy or sell timing for our trading. Although

these are the general view, there are some different cases. For example, in

Neural Networks and Deep Machine Learning, it is possible to extrapolate

the trend and seasonal patterns as in the time series forecasting. This is

considered as the autoregressive network model. However, this extrapolation

ability is the native application to the time series forecasting definitely. For

example, time series forecasting provides the ability to define prediction

interval at each prediction without too much cost. Probably, this is not

impossible in Artificial intelligence too but it is not straight forward as in the

time series forecasting. Another deviated usage example is to use binary

prediction or clustering in technical analysis. Even in the technical analysis,

binary prediction or clustering could be useful. However, many technical

analysis tools are designed to help the trader for the fast reaction to the price

change. This is often done through the visual confirmation in chart. In this

end, the binary prediction or clustering might provide the limited benefits in

our trading. Many artificial intelligence practitioners agree that price

dynamics in the financial market are less optimal for binary prediction (i.e.

classification) due to non-stationary assumption. For example, say that we are

developing Artificial intelligence algorithm to classify a dog in the photo.

The visual properties of a dog do not change over time. We can say that the

visual properties of a dog are stationary. When we train the neural networks

to recognize 1000 different photos with a dog, the trained neural networks

can go online and start to find the photos with a dog automatically. However,

in financial market, the statistical properties of price data are not stationary

most of time. Hence, building good binary prediction machine for financial

market is not an easy task. Another important consideration of the algorithm

in the technical analysis is their ability to calculate the predictive value when

new tick arrives. For example, moving average indicator or relative strength

indicator dynamically update their latest value according to latest tick data.

With the dynamic update, trader can find the cross over between moving

average lines between 12 and 20 periods upon the arrival of the latest tick.

Likewise, with dynamic update, we can find if latest RSI value entered the

overbought area above 70 or it entered the oversold area below 30. Especially

this sort of the intra bar action (i.e. intra-day or intra-hour) is an important

decision making tools for Price Action trader.

Thirdly, the requirement for optimization is another important difference

among the different industrial applications. Optimization is typically used to

find the best parameter sets for the model. In Artificial intelligence, the most

of supervised learning require the optimization to estimate the weights (i.e.

parameters) of the model. In time series forecasting, this is the case too.

Exponential smoothing and ARIMA models require optimization too. In

general, any technical indicator or technical analysis tools do not require

optimization. Requirement for optimization can increase the computation

time for algorithm dramatically. Literarily, optimization involves many

repetitive computations over the same algorithm until the satisfactory

parameters are found. Typically, convex optimization or global optimization

can be used. Convex optimization like conjugate gradient methods is more

popular than global optimization. Genetic algorithm and simulated annealing

are the good examples of global optimization techniques. In Artificial

intelligence, not only the model parameters (i.e. weights) are required to be

optimized, but also the model architecture is required to be optimized too.

For example, you might need to try the model building with different number

of neurons and hidden layers. Sometimes you have to do countless trials to

find one satisfactory model. Hence, the production of good Artificial

intelligence model is a tedious task.

We have covered three important points about the algorithms used in the

different industries. Of course, saying is not enough. Hence, we will provide

you some hands on tutorials. We hope that you will be able to build some

concrete understanding on the algorithm used in the financial market after

this tutorial. For this tutorial, we provide the Market Forecasting Algorithm

built in Excel spreadsheet. Spreadsheet is often considered as the good

educational materials for the algorithm. However, spreadsheet might have

some limitations too. For example, a spreadsheet with VBA could be slower

than applications built in using C++ or C Sharp. For example, if the same

application were created using VBA, C++, and C Sharp, VBA would be the

slowest. In addition, the Excel spreadsheet is distributed for general purpose.

Hence, the graphical user interface within the Excel is not dedicated for the

specific purpose. Since we provide this article for the educational purpose,

we do not mind to use Excel with VBA application. If you want to use this

Market Forecasting Algorithm for your trading, you can still use them for

many different purposes.

To start this tutorial, firstly, download Market Forecasting Algorithm Excel

file from our website. Below is the download link to the zip file.

www.algotrading-

investment.com/FreeStuffs/MarketForecastingAlgorithmRelease.zip

Unzip the Excel file and run the Excel file. In the Market Forecasting

Algorithm, we provide the algorithms for the time series forecasting and the

technical analysis. We do not provide the Artificial intelligence algorithm.

There are some reasons for this. Model building practice for artificial

intelligence is notoriously subjective. As we have mentioned in the third

point, the artificial intelligence model requires the tedious optimization for

both the weights and the architecture. The choice of final model tends to be

highly subjective per user. We have learnt that high subjectivity is bad for our

trading. For the day by day practical trading, its benefit might be limited in

comparing to its downside like complexity and slow computation. If you

really want to educate yourself with artificial intelligence, then we

recommend starting with some free artificial intelligence applications. There

are some free ones on the internet. For example, Weka and TensorFlow are

the free artificial intelligence application.

Now let us start our tutorial with the downloaded Excel file. In your

decompressed folder, run the Market Forecasting Algorithm Excel file.

Figure 14-1: Run the Excel file in the decompressed folder

In the Excel file, you can load the graphical user interface of the time series

forecasting and technical analysis algorithm by clicking the two buttons in

the main worksheet.

Figure 14-2: How to access the time series forecasting and technical analysis

You can also access the algorithm from the Excel Macro directly. This

approach is useful when you want to use the time series forecasting and

technical analysis in the worksheet with no buttons.

Figure 14-3: Access the algorithm without buttons

In the Macro list, choose Lunch_AM1 to use the technical analysis. Choose

Lunch_AM2 to use the time series forecasting.

Figure 14-4: Macro List in Excel file

Before you start any computation with these algorithms, you need to add

Three Add-Ins including Solver, Technical Analysis and Technical Analysis

Add-In in your Add-Ins list. If you do not add them in your Add-Ins list, then

you can not use both time series forecasting and technical analysis. To add

these Add-Ins, go to File >> Options >> Add-Ins in Excel 2013. Later

version of Excel, this configuration might be different. On the bottom of

Add-Ins page, find “Manage: Excel Add-Ins”. Click on “Go” button to see

the Add-Ins list below.

Figure 14-5: Configuration of the three Add-Ins in Excel file

To add TechnicalAnalysis.xll file, click on “Browse” button. Choose

TechnicalAnalysis.xll file. Repeat the same step for TechnicalAnalysisAdd-

In.xll file too. To access the Technical Analysis algorithm, you need to add

both TechnicalAnalysis.xll and TechnicalAnalysisAdd-In.xll. Adding Solver

is only required if you need to use Time Series Forecasting methods. This

situation might change in the future because technical analysis can utilize the

solver in their algorithm. However, at least, this condition stays true for the

version 3.2 of Market Forecasting Algorithm.

Figure 14-6: Include Technical Analysis Add-In

After the Three Add-Ins are included, you are ready to use both time series

forecasting and technical analysis. When you click on the button for the

technical analysis, the graphical user interface for technical analysis will be

loaded. Then you will follow these five steps to compute the technical

indicators for your data.

Step 1: Choose technical indicator from the list

Step 2: Edit indicator settings and click “Add Indicator” button

Step 3: Choose data range in your worksheet

Step 4: Check your data column arrangement in your data

Step 5: Finally, click on “Calculate” button

Figure 14-7: Graphical user interface for the technical analysis

Let us say that you want to apply Bollinger Bands with 50 period and RSI

indicator with 20 period in your chart. The indicator setup will look like

below.

Figure 14-8: Steps to add technical indicators

Then click the range button to select the data range in your worksheet.

Figure 14-9: Select data range in the worksheet

In this example, we will use Microsoft stock data. Start the first cell in the top

left corner in your data and then drag your mouse until you include the last

cell in the bottom right corner.

Figure 14-10: Drag your mouse from top left to bottom right of your data

After the data is selected, tick on the box “Heading included in the range”.

Then check if you have chosen the right data format. For example, choose

“DOHLCV” if your data is arranged in the order of Date, Open, High, Low,

Close, and Volume. Finally, click on “Calculate” button. Now everything is

done.

Figure 14-11: Click Calculate button

Now you will see the Bollinger Bands and RSI computed for Microsoft stock

data. Now you might ask the question what happens if the data format is

wrong. If you feed the data with wrong format, then the calculated value

could be wrong too. Sometimes, it can throw error. In any case, we

recommend keeping the data with the following order of Date, Open, High,

Low, Close, and Volume (i.e. DOHLCV format). When volume is not

available, you might just use Date, Open, High, Low, and Close (i.e. DOHLC

format). However, some technical indicators can throw some errors if they

require volume data in their calculation.

Figure 14-12: Chart with Bollinger Bands and Relative Strength Index

Market Forecasting Algorithm also provides the ZigZag algorithm in two

different indicators. Firstly, you can use ZigZag indicator for the ZigZag line

chart. Secondly, you can use the Peak Trough indicator for the peak trough

detection. Both are based on the ZigZag algorithm. However, ZigZag

indicator outputs the indicator values in one single column and peak trough

indicator outputs the indicator values in two different columns, respectively

for peaks and troughs. You can choose any of these two according to your

needs.

Figure 14-13: ZigZag indicator and Peak Trough Indicator in the indicator

List

As we have emphasized in many chapters of this book, Peak Trough analysis

is the important gateway through many advanced techniques like Fibonacci

Price Pattern, Elliott Wave Pattern, Harmonic Pattern, X3 Price Pattern,

Support and Resistance. Without the Peak Trough Analysis, you are not able

to start with these techniques, as the pattern detection process gets too

complicated. Hence, you can use the Market Forecasting Algorithm to train

yourself with these advanced techniques if you trade in either Forex or Stock

market.

Figure 14-14: Peak Trough Indicator applied to S&P 500 index

As soon as you realized that market repeats themselves, you would appreciate

the concept of Fractal Pattern and Fractal Wave for your trading. At that

stage, you will notice that all the advanced trading techniques are merely

derived from the concept of Fractal Pattern and Fractal Wave. Unfortunately,

there is no shortcut to profitable trading. Practice and knowledge are the only

viable option. Hence, we provide you the tools.

Figure 14-15: ZigZag Indicator applied to Microsoft Cooperation chart

Now, we covered the basics on how to use the Market Forecasting

Algorithm. Here is four tasks you should perform to complete this tutorial.

For each task, measure the time taken to complete the computation with

stopwatch. Compare the computation time for these four tasks.

Task 1: Calculate Bollinger Bands with 200 data sets.

Task 2: Calculate Bollinger Bands with 800 data sets.

Task 3: Calculate Bollinger Banks, RSI, and CCI with 200 data set.

Task 4: Calculate Bollinger Banks, RSI, and CCI with 800 data set.

The Market Forecasting Algorithm Excel file provides more than 20 technical

indicators. We added the 20 most popular technical indicators including

Moving Average, Relative Strength Index, Commodity Channel Index,

ZigZag indicator and so on. The training for these technical indicators is

widely available on the internet. Hence, we will not cover how to use them in

this article. For this tutorial, we also provide the data for Standard & Poor

500 index and EURUSD. You can always use some free data source like

Yahoo Finance or Google Finance to get price data. If you want to perform

your own analysis, then copy and paste those data in the order of Date, Open,

High, Low, Close, and Volume. Make sure that heading row is placed on the

top. Latest data should be placed on the bottom.

Next, we will move on to how to use the algorithm for the time series

forecasting. The graphical user interface for the time series forecasting looks

like below the screenshot. We provide the specific forecasting method called

Exponential-smoothing methods. Exponential smoothing methods are the

main workhorse in the forecasting industry. In the forecasting industry as in

the demand forecasting and business forecasting, exponential smoothing is

popular tools like the Moving average indicator and Relative strength index

in the technical analysis. In addition, ARIMA (Autoregressive Integrated

Moving Average) and GARCH (Generalized Autoregressive Conditional

Heteroscedasticity) models are other popular tools widely used in practice.

However, their theory tends to be more complex than the exponential

smoothing methods. Neural Networks are often considered as the alternative

forecasting method against these common forecasting methods.

Figure 14-16: Graphical user interface for time series forecasting

In the model selection stage, you can choose nine different exponential

smoothing forecasting models.

Model 1: No Trend, No Seasonality

Model 2: Additive Trend, No Seasonality

Model 3: Damped Additive Trend, No Seasonality

Model 4: No Trend, Additive Seasonality

Model 5: Additive Trend, Additive Seasonality

Model 6: Damped Additive Trend, Additive Seasonality

Model 7: No Trend, Multiplicative Seasonality

Model 8: Additive Trend, Multiplicative Seasonality

Model 9: Damped Additive Trend, Multiplicative Seasonality

The price patterns of these nine forecasting models are shown in Figure 14-

17. Probably the price pattern table will provide you better understanding on

these models. However, both price pattern table and the nine forecasting

model names fit together logically.

Figure 14-17: The original Gardner’s table to visualize the characteristics of

different time series data (Gardner, 1987, p175)

Let us try to make forecasting for EURUSD close data. We will be using

Damped Additive Model with no seasonality. The steps for time series

forecasting is like below:

Step 1: choose forecasting model

Step 2: Select Data to forecast

Step 3: Finally, click on “Calculate” button

Figure 14-18: Choose right forecasting model for your data

After you have clicked on Data Range button, you need to select your data.

Only include one colum since forecasting algorithm only requires one time

series data. Never include more than one column. It will throw error. In fact,

you can choose to forecast any data as long as they are time series data,

recorded in the fixed time interval. With time series forecasting, you can even

forecast Open price or Volume or Relative Strength Indicator, and so on if

you wish. In this tutorial, we have chosen to forecase the Close price of

EURUSD for simplicity.

Figure 14-19: Select one column to forecast

Then tick on the box “Heading included in the range” because we included

heading row. When you do not include heading row, then untick on the box

“Heading included in the range”. If you do it the other way around, then it

will throw error. Finally, click the “Calculate” button.

Figure 14-20: Click on Calculate button

If you have done everything correctly, then you will get the chart with

forecasting with prediction interval. Please note that sometimes, you might

have to change maximum and minimum scales of the price axis manually to

fit the lines in the chart.

Figure 14-21: Forecasting with Damped Additive Trend Exponential

smoothing model

In the next example, we will try to forecast the airline passenger data. Airline

passenger data is the textbook example with seasonality and trend. Here is the

screenshot for your information.

Figure 14-22: Steps to forecast with airline passenger data

If you have done everything correctly, then you will have a forecasting chart

like this. This forecasting chart shows how the airline passenger data will

look like in next 10 month. As you see, the forecasting model adapted the

seasonal patterns in the data.

Figure 14-23: Forecasting with damped additive trend additive seasonal

exponential smoothing model

Here are some tasks you should perform to complete this tutorial. Of course,

measure the time taken to complete the computation for each task. For your

information, Task 6 and 7 can take some time to complete the computation.

Do not disturb the running macro until they are completed.

Task 5: Forecast EURUSD close data with 200 data sets using damped Trend

model.

Task 6: Forecast EURUSD close data with 800 data sets using damped Trend

model.

Task 7: Forecast EURUSD close data with 800 data sets using damped Trend

model + Box-Cox transformation.

Compare the computation time for all seven tasks. You can answer the

following questions. Question 5 may be skipped if you prefer.

Question 1: Which task provides the slowest computation time?

Question 2: Which task provides the fastest computation time?

Question 3: Why Task 4 is much faster than Task 6?

Question 4: Why do you think Task 6 and Task 7 have marginally different

computation time?

Question 5: How slow or fast Artificial intelligence model, for example Deep

Machine Learning, could be in comparison to Task 7 when the same amounts

of data set are used?

One thing you should notice in this tutorial is that the algorithm for time

series forecasting uses optimization. This is why it takes longer to complete

the task. Solver Add-In does the optimization to estimate parameters of

forecasting model. In the forecasting algorithm, we use the nonlinear GRG

method, which is known for the “Generalized Reduced Gradient”. This is one

of the convex optimizations, we have discussed. It is also possible to use

genetic algorithm or simulated annealing instead of the nonlinear GRG

method. However, the efficiency of optimization could be different

marginally per each optimization method and per each mathematical model.

Hence, you have to choose the right method if you want to save the

computation time. Comparing to artificial intelligence model, the time series

forecasting is light and fast. Hence, if you are going to use time series

forecasting for financial market, the recommended practice is to make new

forecasting model whenever new data is included. In artificial intelligence,

they often retain the same model for reuse for some period. In time series

forecasting, never retain old forecasting model for reuse. We will keep

creating the latest forecasting with latest data available. This is the one of the

benefit of time series forecasting methods due to its light and fast algorithm.

In fact, time series forecasting is a comprehensive topic in the Applied

Statistics. There are many practitioners of the time series forecasting across

many different industries. For the choice of forecasting models, you might

have to use some other forecasting textbook to cover the basics.

15. References

Carney, S.M. (1998) Harmonic Trading. Profiting from the Natural Order of

the Financial Markets (Vol. 1). Pearson Education, Inc.

Elliott, R. N., Douglas, D. C., Sherwood, M. W., Laidlaw, D. & Sweet, P.

(1948) The wave principle (Note that first formal publication date of The

Wave Principle was August 31 1938).

Elliott, R. N. (1982) Nature's Law: The Secret of the Universe, Institute for

Economic & Financial Research.

Frost, A. J. & Prechter, R. R. (2005) Elliott wave principle: key to market

behavior, Elliott Wave International.

Gann, W. D. (1996) Truth of the stock tape and Wall Street stock selector,

Health Research Books.

Gartley, H. M. (1935) Profits in the stock market, Health Research Books.

Pesavento, L. & Shapiro, S. (1997) Fibonacci Ratios with Pattern

Recognition.

Pesavento, L. & Jouflas, L. (2010) Trade what you see: how to profit from

pattern recognition, John Wiley & Sons.

Schabacker, R. (1932) Technical Analysis and Stock Market Profits,

Harriman House Limited.

DeMiguel, V., Garlappi, L. & Uppal, R. (2009) Optimal Versus Naive

Diversification: How Inefficient is the 1/N Portfolio Strategy?, Review of

Financial Studies, 22(5), 1915-1953.

Diebold, F., Rudebusch, G. & Sichel, D. (1993) Business Cycles, Indicators,

and Forecasting, null.

Elton, E. J. & Gruber, M. J. (1997) Modern portfolio theory, 1950 to date,

Journal of Banking & Finance, 21(11–12), 1743-1759.

Fabozzi, F. J. & Francis, J. C. (1979) Mutual Fund Systematic Risk for Bull

and Bear Markets: An Empirical Examination, The Journal of Finance, 34(5),

1243-1250.

Frost, A. J. & Prechter, R. R. (2005) Elliott wave principle: key to market

behavior, Elliott Wave International.

Gordon, S. & St-Amour, P. (1999) “A Preference Regime Model of Bull and

Bear Markets,” null.

Hendershott, T., Jones, C. M. & Menkveld, A. J. (2011) Does Algorithmic

Trading Improve Liquidity?, The Journal of Finance, 66(1), 1-33.

Levich, R. M. & Thomas Iii, L. R. (1993) The significance of technical

trading-rule profits in the foreign exchange market: a bootstrap approach,

Journal of International Money and Finance, 12(5), 451-474.

Maheu, J. M. & McCurdy, T. H. (2000) Identifying Bull and Bear Markets in

Stock Returns, Journal of Business & Economic Statistics, 18(1), 100-112.

Mao, J. C. T. (1970) ESSENTIALS OF PORTFOLIO DIVERSIFICATION

STRATEGY, The Journal of Finance, 25(5), 1109-1121.

Neely, C. J. & Weller, P. A. (2003) Intraday technical trading in the foreign

exchange market, Journal of International Money and Finance, 22(2), 223-

237.

Olszweski, F. & Zhou, G. (2013) Strategy diversification: Combining

momentum and carry strategies within a foreign exchange portfolio, J Deriv

Hedge Funds, 19(4), 311-320.

Talebi, H., Hoang, W. & Gavrilova, M. L. (2014) Multi-scale Foreign

Exchange Rates Ensemble for Classification of Trends in Forex Market,

Procedia Computer Science, 29(0), 2065-2075.

Taylor, M. P. & Allen, H. (1992) The use of technical analysis in the foreign

exchange market, Journal of International Money and Finance, 11(3), 304-

314.