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Technical analysis of Forex by MACD Indicator
Abstract
In these days, trading automation is one of the major topics in the field of financial research. Buy and sell are the key rule to an automated trading system which is possible to generate by various technical indicators in Forex. Benefits and disadvantages of each indicators, has its own. Regarding to our first research result which was based on P-sar indicator and published in the IIAFC conference[1]. In this paper, we will focus on the MACD indicator for four currencies namely EURUSD, GBPUSD, USDCHF and USDJPY individually to identify effectiveness of the indicator regarding to the amount of profit generated, using hourly data of market stretch from January 2001 to December 2010. Virtual Historical Trading Software (VHTS) is developed for the purpose of computing the indicator based on its original formulas and interpretations; for applying the assumptions; for trading based on buy and sell signals generated by the MACD indicator.
... It should be noted that there are several articles in the literature that do not agree with such assumption [9], [10], [11]. These technical indicators [12], [13], [14], [15] are, as previously mentioned, based on historical share prices values and it hence seems reasonable to use these historical prices as an input for the neural networks. ...
p> Aiming at the trading problem of gold and bitcoin in the financial market, this paper establishes a trading strategy based on KDJ and MACD indicators, and establishes an effective frontier curve model based on the change of mean variance to determine the investment ratio, and uses Lagrange multiplier method to maximize the trader's return rate. </p
This article describes an automated trading strategy for financial markets called Stop and Go Fibo (SGF). The proposed strategy uses the Fibonacci regression technique combined with a method of monitoring the upward and downward trends in asset prices to carry out buy(sell) transactions followed by resale (repurchase), whenever there is a significant fluctuation in the asset negotiated. The basis of the SGF strategy is to define the prices of a hierarchical sequence of support and resistance lines controlled by the retraction levels of the sequence of the Fibonacci ratios.These levels are used as entry and exit prices for negotiations depending on the satisfaction of a set of parameterized quantitative rules. To assess the performance of the robot that automates the method, we introduce a set of highly relevant metrics in the context of the application, not the theoretical ones used frequently. The study was carried out with daily series of the adjusted closing price of very volatile shares(ASRT, IHT, SCX, CMT, MGI, CPS, DKS, ARKR, and CPSS)from January 2011 to April 2021, downloaded from the Yahoo finance repository. Fifty percent of the series was used for training the method and 50% for testing. The method was trained, calculating the optimized value of its 5 parameters, using two performance metrics: the gross profit (difference between gross gain and gross loss) and the profit factor (ratio of gross gain to gross loss). The high operational performance observed in volatile assets like suggests that the strategy can be implemented for automated trading in the financial market.
Reviews previous research on exchange rate forecasting, identifies some problems in building a predictive model and examines the profitability of using various technical rules (as used by traders) in the USD/DM and USD/BP foreign exchange markets. Takes 1989-1996 data, divided into two sub-periods with different macroeconomic features; and compares the results from the technical rules in detail and with a buy and hold strategy. Finds that no rules produced statistically significant profits for the whole period (although they did for the first sub-period) and some evidence that buy and hold is superior, especially if risk is taken into account. Considers the implications of the findings and the underlying reasons for them.
Mechanical trading rules seem to have more potential than previous tests found. Fama and Blume (1966), looking at the Dow 30 of the late 1950s, found no profits for the best (½-percent) rule after adjusting for transactions costs. Fifteen of these stocks looked profitable in their sample, however; for the same rule, the surviving fourteen show statistically significant profits for 1970–1982 for transactions costs obtainable by floor traders. The test used here assumes constant risk premia, or more generally, that risk premia are on average approximately the same on days “in” as for the total period.
We use genetic programming techniques to identify optimal technical trading rules. We find strong evidence of economically significant out-of-sample excess returns to the rules for each of six exchange rates ($/DM, $/Yen, $/SF, $/£, DM/Yen, SF/£), over the period 1981–95. Some of the rules have a structure similar to those used by technical analysts. Betas calculated for the returns according to various benchmark portfolios provide no evidence that the returns to these rules are compensation for bearing systematic risk. ‘Bootstrapping’ results for the $/DM indicate that the trading rules are detecting patterns in the data that are not captured by standard statistical models.
Based on the SPA test (test for superior predictive ability), Sortino and reversed Sortino ratios, we examined the profitability of a universe of 8061 technical trading rules in ten futures markets including five financial and five commodity underlying assets. We tested whether the best performing rule really beats its buy-and-hold benchmark strategy in bullish and bearish markets, respectively, during the in-sample testing period. The best rules' performance relative to the benchmark is also tested during the one-year out-of-sample period for all ten sets of data. A novel set of multi-indicator rules, MFI–RSI, and four popular categories of single-indicator rules, filter rules, moving averages, on-balance volume averages and momentum strategy in volume, were employed to form our universe of trading rules. The results on the SPA test suggest market efficiency in nine of the ten futures markets, while the results on the Sortino and reversed Sortino ratios reveal persistent outperformance of the best ‘downside’ and ‘upside’ rules relative to the buy-and-hold benchmark across time in four and three futures markets, respectively.
Recent studies in finance domain suggest that technical analysis may have merit to predictability of stock. Technical rules are widely used for market assessment and timing. For example, moving average rules are used to make "buy" or "sell" decisions at each day. In this paper, to explore the potential prediction power of technical analysis, we present a genetic programming based system FGP (Financial Genetic Programming), which specialises in taking some well known technical rules and adapting them to prediction problems. FGP uses the power of genetic programming to generate decision trees through efficient combination of technical rules with self-adjusted thresholds. The generated rules are more suitable for the prediction problem at hand. FGP was tested extensively on historical S&P 500 data through a specific prediction problem. Preliminary results show that it outperforms commonly used, non-adaptive, individual technical rules with respect to prediction accuracy and annualised rate of return over two different out-of-sample test periods (three and a half year in each period).
This paper examines the profitability of momentum strategies implemented on international stock market indices. Our results indicate statiscally significant evidence of momentum profits. The momentum profits arise mainly from time-series predictability in stock market indices very little profit comes from predictability in the currency markets. We also find higher profits for momentum portfolios implemented on markets with higher volume in the previous period, indicating that return continuation is stronger following an increase in trading volume. This result confirms the informational role of volume and its applicability in technical analysis.
“Support” and “resistance” levels—points at which an exchange rate trend may be interrupted and reversed—are widely used for short-term exchange rate forecasting. Nevertheless, the levels’ ability to predict intraday trend interruptions has never been rigorously evaluated. This article undertakes such an analysis, using support and resistance levels provided to customers by six firms active in the foreign exchange market. The author offers strong evidence that the levels help to predict intraday trend interruptions. However, the levels’ predictive power is found to vary across the exchange rates and firms examined.
This paper examines the impact of electronic trading systems on the bid-ask spreads in the foreign exchange market. The paper finds: first, the EBS reduces spreads significantly; second, the EBS is more influential than the Reuters system for the currency pair DEM/USD; third, dealers with information advantage tend to quote relatively wider spreads with the new systems; fourth, geographical differences in market liquidity are reduced through the new systems, and finally, the effects occur immediately and persist in the long-term. Thus, both proposed positive and negative impacts of the electronic systems are found to be true in this paper, but our findings also suggest that positive effects dominate and the electronic systems overall increase FX market liquidity.
Momentum strategies based on continuations in stock prices have attracted a wide following among money managers and investors. We evaluated the profitability of price momentum strategies based on past return and earnings momentum strategies based on standardized unexpected earnings and revisions of consensus forecasts. The strategies proved to be profitable for intermediate horizons. Chasing momentum can generate high turnover, however; hence, implementation of momentum, strategies requires a focus on managing trading costs. Comparing the strategies yielded evidence that they reflect distinct phenomena and provided information about the sources of profits. The results indicate that the market is slow to incorporate the full impact of information in its valuations.
This paper examines the impact of electronic trading systems on the bid-ask spreads in the foreign exchange market. The paper finds: first, the EBS reduces spreads significantly; second, the EBS is more influential than the Reuters system for the currency pair DEM/USD; third, dealers with information advantage tend to quote relatively wider spreads with the new systems; fourth, geographical differences in market liquidity are reduced through the new systems, and finally, the effects occur immediately and persist in the long-term. Thus, both proposed positive and negative impacts of the electronic systems are found to be true in this paper, but our findings also suggest that positive effects dominate and the electronic systems overall increase FX market liquidity.
This article reports the results of a questionnaire survey conducted in February 1995 on the use by foreign exchange dealers in Hong Kong of fundamental and technical analyses to form their forecasts of exchange rate movements. Our findings reveal that>85% of respondents rely on both fundamental and technical analyses for predicting future rate movements at different time horizons. At shorter horizons, there exists a skew towards reliance on technical analysis as opposed to fundamental analysis, but the skew becomes steadily reversed as the length of horizon considered is extended. Technical analysis is considered slightly more useful in forecasting trends than fundamental analysis, but significantly more useful in predicting turning points. Interest rate-related news is found to be a relatively important fundamental factor in exchange rate forecasting, while moving average and/or other trend-following systems are the most useful technical technique.
This paper examines the effect that heterogeneous customer orders flows have on exchange rates by using a new, and the largest, proprietary dataset of weekly net order flow segmented by customer type across nine of the most liquid currency pairs. We make several contributions. Firstly, we investigate the extent to which customer order flow can help to explain exchange rate movements over and above the influence of macro-economic variables. Secondly, we address the issue of whether order flows contain (private) information which explain exchange rates changes. Thirdly, we look at the usefulness of order flow in forecasting exchange rate movements at longer horizons than those generally considered in the micro-structure literature. Finally we address the question of whether the out-of-sample exchange rate forecasts generated by order flows can be employed profitably in the foreign exchange markets.
The foreign exchange market (FOREX) is the largest financial market in the world, with a volume of over $2 trillion daily. Decision making about buying and selling the existing products in this market depends on several effective factors which cause the high risk in it and make it a sensitive job. So in this paper a new method which is extracted from the multiple decision making methods named eigenvector–DEA–TOPSIS methodology is presented to evaluate the risk of the number of related portfolios to this market. The eigenvector technique is used to determine the weights of criteria and some linguistic terms are applied for assessing portfolio risks under each criterion, then in order to determine the value of linguistic terms we use the data envelopment analysis (DEA) method. Finally we use TOPSIS method for aggregating portfolio risks under different criteria into an overall risk score for each portfolio and ranking the portfolios according to their risks. The integrated eigenvector–DEA–TOPSIS methodology is applicable to any number of decision alternatives and is illustrated with a numerical example.
The primary aim of the paper is to place current methodological discussions in macroeconometric modeling contrasting the ‘theory first’ versus the ‘data first’ perspectives in the context of a broader methodological framework with a view to constructively appraise them. In particular, the paper focuses on Colander’s argument in his paper “Economists, Incentives, Judgement, and the European CVAR Approach to Macroeconometrics” contrasting two different perspectives in Europe and the US that are currently dominating empirical macroeconometric modeling and delves deeper into their methodological/philosophical underpinnings. It is argued that the key to establishing a constructive dialogue between them is provided by a better understanding of the role of data in modern statistical inference, and how that relates to the centuries old issue of the realisticness of economic theories.
The authors investigate the informational role of volume and its applicability for technical analysis. They develop a new equilibrium model in which aggregate supply is fixed and traders receive signals with differing quality. The authors show that volume provides information on information quality that cannot be deduced from the price statistic. They show how volume, information precision, and price movements relate, and demonstrate how sequences of volume and prices can be informative. The authors also show that traders who use information contained in market statistics do better than traders who do not. Technical analysis, thus, arises as a natural component of the agents' learning process. Copyright 1994 by American Finance Association.
Technical analysis, also known as 'charting,' has been a part of financial practice for many decades, but this discipline has not received the same level of academic scrutiny and acceptance as more traditional approaches such as fundamental analysis. One of the main obstacles is the highly subjective nature of technical analysis-the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and we apply this method to a large number of U.S. stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution-conditioned on specific technical indicators such as head-and-shoulders or double bottoms-we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value. Copyright The American Finance Association 2000.
Tolls are frequently discussed policies to reduce traffic in cities. However, road pricing measures are seldom implemented due to high investments and unpopularity. Transportation planning tools can support planning authorities by solving those problems if they take into account the following aspects: - Demographic attributes like income and time constraints - Time reactions to the policy - Schedule changes of population’s individuals during the whole day Our approach uses multi-agent simulations to model and simulate full daily plans. Each of our agents has a utility function that appraises the performance of a typical, microscopically simulated day. The sum of all utility changes to a policy change can be interpreted as the change in the system’s welfare thus the economic evaluation of a measure straightforward. The approach is tested with travel behavior of the Zurich metropolitan region in Switzerland. Several tolling schemes are investigated. It is shown that the simulation can be used to model travelers' reactions to time-dependent tolls in a way most existing transportation planning tools are not able to do. It is demonstrated that route adjustment only, as is done in many traditional transport planning packages, results in no economic gains from the tolls. As time-dependent tolls are a much-debated subject in transportation politics, the ability to fully model such tolls and the reactions of travelers may help to find better toll schemes. In a world where individuals have more and more freedom to schedule their daily plans, agent-based simulations offer an intuitive way to research complex topics with lots of interdependencies.
This paper tests two of the simplest and most popular trading rules--moving average and trading range break--by utilizing the Dow Jones Index from 1897 to 1986. Standard statistical analysis is extended through the use of bootstrap techniques. Overall, their results provide strong support for the technical strategies. The returns obtained from these strategies are not consistent with four popular null models: the random walk, the AR(1), the GARCH-M, and the Exponential GARCH. Buy signals consistently generate higher returns than sell signals, and further, the returns following buy signals are less volatile than returns following sell signals. Moreover, returns following sell signals are negative, which is not easily explained by any of the currently existing equilibrium models. Copyright 1992 by American Finance Association.
Filter rule profits found in foreign exchange markets in the early days of the current managed float persist in later periods, as shown by statistical tests developed and implemented here. The test is consistent with, but independent of, a wide variety of asset pricing models. The profits found cannot be explained by risk if risk premia are constant over time. Inclusion of the home‐foreign interest rate differential in computing profits has little effect on the comparison of filter returns to those of buy‐and‐hold.
Numerous studies in the finance literature have investigated technical analysis to determine its validity as an investment tool. Several of these studies conclude that technical analysis does have merit, however, it is noted that the effects of data-snooping are not fully accounted for. In this paper we utilize White's Reality Check bootstrap methodology (White (1997)) to evaluate simple technical trading rules while quantifying the data-snooping bias and fully adjusting for its effect in the context of the full universe from which the trading rules were drawn. Hence, for the first time, the paper presents a means of calculating a comprehensive test of performance across all trading rules. In particular, we consider the study of Brock, Lakonishok, and LeBaron (1992), expand their universe of 26 trading rules, apply the rules to 100 years of daily data on the Dow Jones Industrial Average, and determine the effects of data-snooping. During the sample period inspected by Brock, Lakonishok and LeBaron, we find that the best technical trading rule is capable of generating superior performance even after accounting for data- snooping. However, we also find that the best technical trading rule does not provide superior performance when used to trade in the subsequent 10-year post-sample period.
Technical analysis of FOREX by P-SAP Indicator in International Islamic Accounting and Finance Conference (IIAFC)
- S H M Yazdi
- Z H Lashkary
YAZDI, S.H.M. and Z.H. LASHKARY, Technical analysis of FOREX
by P-SAP Indicator in International Islamic Accounting and Finance
Conference (IIAFC). 2012: Malaysia.
Understanding MACD (Moving Average Convergence Divergence)
- Gerald Appel
Gerald Appel, E.D., Understanding MACD (Moving Average
Convergence Divergence). 2008: Traders Press, Inc. (2008).
Technical analysis of FOREX by P-SAP Indicator in International Islamic Accounting and Finance Conference (IIAFC) 2012: Malaysia
- Z H Lashkary
YAZDI, S.H.M. and Z.H. LASHKARY, Technical analysis of FOREX
by P-SAP Indicator in International Islamic Accounting and Finance
Conference (IIAFC). 2012: Malaysia.