## Fibonacci EMA Trading System For Short and Medium Term Trading

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Technical analysis is based on the assumption that the future price of a stock can be predicted from its history. Several technical trading systems exist for generating buy and sell signals in stock prices. Simple moving average crossovers are popular tools for trading. In this study, simple moving average crossovers with different periods are analyzed **nifty moving average trading systems** on historical daily data of NIFTY 50 index.

The profit and loss distribution in these trades are studied to identify profitable and stable crossover periods. The choppy price action known as whipsaws incur nifty moving average trading systems number of small losses in the crossover based trading system. The phenomenon of rare trending price movements and its impact on the trading system are demonstrated. Forecasting of stock prices is a challenging task involving mathematics, science and business analysis [1].

There are two approaches for predicting future prices for the purpose of investment and trading. The fundamental methods requires the market participants to analyze the prospects of the company through the application of business, financial and macroeconomic principles [2].

The much debated efficient markets theory states that stock prices cannot be reliably predicted using historical data [3]. The fundamental analyst ignores historical price movements and looks at the company trying to identify its potential.

The other approach is the technical methods based on the idea that future prices can be nifty moving average trading systems using past prices [4]. The technical analyst or the chartist takes a mathematical approach using a variety of technical indicators that show the momentum, volume and direction of price movement.

The fundamental methods rely heavily on the skill and foresight of the analyst, while the technical analyst rely on the long term expected returns and robustness of the trading system. From a theoretical standpoint, technical analysis does not seem to be sound and safe to be applied.

However the success of many technical analysts have led to its widespread adoption in the stock markets. Among the several technical indicators in use, the moving averages present the most simple and straightforward tool. Similar results are reported by Conrad and Kaul [6]. Cutler, Poterba and Summers reported positive autocorrelation in monthly returns but negative autocorrelation in years horizon [7]. The statistical analysis of moving average based trading system on Dow Jones Index can be found in the work of William Brock et al.

It was reported that buying based on moving average crossovers and breakouts were more profitable than selling. The asymmetry and nonlinearities in returns suggest that linear characteristic mean estimators fail to capture the price dynamics. The security returns of moving average rules were analyzed by Ramazan [9].

It was found that linear conditional mean specifications with past trading signals as predictors were more profitable than nifty moving average trading systems models of past returns. Gunasekarage and Power analyzed moving average rules in South Asian indices and reported better performance than buy and hold strategy [11]. Gradojevic and Gencay applied fuzzy filtering rules to enhance moving technical trading [13].

They reported a substantial reduction in erroneous trading recommendations using fuzzy approach. The idea of combining technical trading rules using particle swarm optimization was explored by Wang and Philip [14].

Their system outperformed all the component rules. In this study, trading systems based on simple moving average crossovers will be examined in the daily NIFTY 50 index nifty moving average trading systems [15] from till August The goal of the study is to identify best time periods for high profits as well as consistent profitability. The psychological aspects nifty moving average trading systems the trading system is also examined with special emphasis on the rare profitable price movements that are crucial in technical trading.

The n-day simple moving average SMA [16] of a security price denoted by S at time t is defined as. The prices are the daily closing prices of the security which is NIFTY 50 index in the case of this study. The trading system based on n-day SMA nifty moving average trading systems be described as. The trade is always on meaning either the trader is in a long position or a short position. When the buy condition is generated any short nifty moving average trading systems is covered and when the sell condition is generated the long position is covered.

This form of the trading system is simple to back test and to understand the statistical nature of returns. The exit signal is generated in day marked 2 with returns of over points in a matter of 2 months. The success of SMA crossovers is in not missing strong trends such as these. However the weakness is apparent in Fig. Several entry and exit signals are generated in Fig. If the market remains without clear direction for a longer period, algorithmic trading based on moving averages can be devastating to the investment.

Although hundreds of indicators are used, the reliable early detection of false signals is a topic of intense research. The strategy in table 1 is simulated on a closing price basis. The trader is assumed to make the trading decision just before the close of every day.

The SMA periods of 2 to are considered in the simulation. The returns are assumed to be equal to the change in the underlying spot price. The futures premium and commissions are not taken into consideration. The net returns are displayed in Fig. The number of trade opportunities or crossovers for each period is displayed in Fig. The total net returns for the SMA period of 2 is the highest.

Also the number of crossovers is also the highest for the period of 2. This indicates that at buying at every positive closing day and selling at every negative closing day has yielded the greatest net returns for the simulated period. The Profit to loss ratio for the SMA period of 2 is 1. In every ten trades about six trades will yield positive results. This is due to the high sensitivity of the indicator. The slowest indicators around a period of 97 yields a ratio of 2, i.

For a good tradeoff between overall net returns and good Profit to loss ratio the period of 27 can be considered. The maximum drawdown of the account is an important factor in considering the trading strategy. It is displayed in Fig. The weakness of fast moving indicators with low value of SMA periods is visible from the Fig.

The period of 2 which delivers the highest net returns can bring a downfall of The drawdown occurs usually at the beginning of the simulation. This can be attributed to the large number of small losses incurred due to false crossovers of the highly sensitive periods. The drawdown has both a psychological effect on the trader as well as financial impact.

This phenomenon of high sensitivity with the highest returns can be explained by the black swan theory proposed by Taleb [18]. The large number of false triggers of the strategy result in a number of small losses and few small profits.

When the markets are trading within a range, the losses accrue much more than the profits. But during a few rare trending movements where the index moves consistently in the same direction for several days, the fast indicator captures most of the movement.

These few profits usually around four to five per year are sufficient to restore any deficit in the account as well as leave with a good surplus. The success of the trader is in applying the strategy day after day with persistence. If due to some reasons, the large movements are missed, then the entire trading system will yield negative results. This phenomenon is clearly illustrated in Fig.

The strength of technical indicators is in allowing most of these large black swan movements to be captured. The effect of few strong moves is further illustrated in Fig. It shows the evolution of account balance in index points for the cases of missing the top few best trades in the SMA 2 system.

The trading system would result in loss if a total of 25 best trades out nifty moving average trading systems are missed in a period of 5 years. This demonstrates the fat tail distribution effect of trading returns. The longest losing streaks in each period are shown in Fig. A marked increase nifty moving average trading systems losing streaks from these values would give an early warning that the markets nifty moving average trading systems entered a period of different dynamics than the one in this simulation.

The highest of eight losing trades in a row has **nifty moving average trading systems** for the fast indicators SMA 7. The results clearly indicate the sensitive nature of the indicators under this study. The sensitiveness is common to all technical analysis. This has led to many to completely denounce technical analysis as an aid to investing. However once the nature of the technical trading system is understood, it can be a valuable tool.

The importance of not missing the best trades means the trading system must be always in a position. The success then depends on proper risk and money management to handle the string of small losses. The payoffs of a simple trading system based on crossovers of prices above and below the simple moving average of different periods are analyzed. This simple system is a proxy for understanding most of the technical trading system and gain valuable nifty moving average trading systems into successful nifty moving average trading systems.

It was learnt that most of the profits came from few select trades that occur very rarely over the course of the trading period.

A large number of small losses occur frequently and the efficient management of these small losses is crucial to the success of the trading system. Technical trading is most successful when used for short to medium term forecast and can be a valuable investing tool when used with proper risk and money management.

As a prediction tool, the success rates are very low compared to random behavior. However due to the effect of black swan movements, the net result can be high after a sufficiently long period. In the future advanced technical indicators can be studied. The impact of hedging needs to be explored in detail. Poterba and Lawrence H.