# Forex random entry strategy

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Emili Ladjet Trader. Jul 29, 3 2 8 Apr 17, 41 4 54 Canada www. Interesting, I would like to have those numbers, so far, I am not in that league. Alfredo Trader. Sep 18, 7 1 24 Dear Emili Actually I believe that nothing is impossible, but also from other side you are right. I am pretty sure there is a way to predict the market, but not with only using exchange rate and technical analysis. Never give up. Emili Ladjet said:.

Click to expand Herve Trader. Jul 16, 2 0 17 Taking compound interest into account, I will calculate the twelfth root of It's very optimistic! GazFx Banned. Nov 13, 69 74 60 Melbourne, Australia www. Herve said:. Show hidden low quality content.

Post reply. Insert quotes…. Similar threads. Enivid Jun 15, Forex Brokers. Replies 0 Views Forex Brokers Jun 15, Enivid. The computational procedure is based on the calculation of the standard deviation along a given time series defined as.

In order to determine the Hurst exponent , the function is calculated for increasing values of inside the interval , being the length of the time series, and the obtained values are reported as a function of on a log-log plot. In general, exhibits a power-law dependence with exponent , i. In particular, if , one has a negative correlation or anti-persistent behavior, while if one has a positive correlation or persistent behavior.

The case of corresponds to an uncorrelated Brownian process. In our case, as a first step, we calculated the Hurst exponent considering the complete series. This analysis is illustrated in the four plots of Fig. Here, a linear fit to the log-log plots reveals that all the values of the Hurst index H obtained in this way for the time series studied are, on average, very close to 0.

This result seems to indicate an absence of correlations on large time scales and a consistence with a random process. The power law behavior of the DMA standard deviation allows to derive an Hurst index that, in all the four cases, oscillates around 0.

See text. On the other hand, it is interesting to calculate the Hurst exponent locally in time. In order to perform this analysis, we consider subsets of the complete series by means of sliding windows of size , which move along the series with time step. This means that, at each time , we calculate the inside the sliding window by changing with in Eq. Hence, following the same procedure described above, a sequence of Hurst exponent values is obtained as function of time.

In Fig. In this case, the values obtained for the Hurst exponent differ very much locally from 0. This investigation, which is in line with what was found previously in Ref. As we will see in the next sections, this feature will affect the performances of the trading strategies considered. In the present study we consider five trading strategies defined as follows:. A divergence is a disagreement between the indicator RSI and the underlying price.

By means of trend-lines, the analyst check that slopes of both series agree. When the divergence occurs, an inversion of the price dynamic is expected. In the example a bullish period is expected. In this connection we are only interested in evaluating the percentage of wins achieved by each strategy, assuming that - at every time step - the traders perfectly know the past history of the indexes but do not possess any other information and can neither exert any influence on the market, nor receive any information about future moves.

In the following, we test the performance of the five strategies by dividing each of the four time series into a sequence of trading windows of equal size in days and evaluating the average percentage of wins for each strategy inside each window while the traders move along the series day by day, from to. This procedure, when applied for , allows us to explore the performance of the various strategies for several time scales ranging, approximatively, from months to years.

The motivation behind this choice is connected to the fact that the time evolution of each index clearly alternates between calm and volatile periods, which at a finer resolution would reveal a further, self-similar, alternation of intermittent and regular behavior over smaller time scales, a characteristic feature of turbulent financial markets [35] , [36] , [38] , [58].

Such a feature makes any long-term prediction of their behavior very difficult or even impossible with instruments of standard financial analysis. The point is that, due to the presence of correlations over small temporal scales as confirmed by the analysis of the time dependent Hurst exponent in Fig. But this could depend much more on chance than on the real effectiveness of the adopted algorithm.

On the other hand, if on a very large temporal scale the financial market time evolution is an uncorrelated Brownian process as indicated by the average Hurst exponent, which result to be around for all the financial time series considered , one might also expect that the performance of the standard trading strategies on a large time scale becomes comparable to random ones.

In fact, this is exactly what we found as explained in the following. In Figs. From top to bottom, we report the index time series, the corresponding returns time series, the volatility, the percentages of wins for the five strategies over all the windows and the corresponding standard deviations.

The last two quantities are averaged over 10 different runs events inside each window. As visible, the performances of the strategies can be very different one from the others inside a single time window, but averaging over the whole series these differences tend to disappear and one recovers the common outcome shown in the previous figures.

In this paper we have explored the role of random strategies in financial systems from a micro-economic point of view. In particular, we simulated the performance of five trading strategies, including a completely random one, applied to four very popular financial markets indexes, in order to compare their predictive capacity. Our main result, which is independent of the market considered, is that standard trading strategies and their algorithms, based on the past history of the time series, although have occasionally the chance to be successful inside small temporal windows, on a large temporal scale perform on average not better than the purely random strategy, which, on the other hand, is also much less volatile.

In this respect, for the individual trader, a purely random strategy represents a costless alternative to expensive professional financial consulting, being at the same time also much less risky, if compared to the other trading strategies. This result, obtained at a micro-level, could have many implications for real markets also at the macro-level, where other important phenomena, like herding, asymmetric information, rational bubbles occur.

In fact, one might expect that a widespread adoption of a random approach for financial transactions would result in a more stable market with lower volatility. In this connection, random strategies could play the role of reducing herding behavior over the whole market since, if agents knew that financial transactions do not necessarily carry an information role, bandwagon effects could probably fade.

On the other hand, as recently suggested by one of us [59] , if the policy-maker Central Banks intervened by randomly buying and selling financial assets, two results could be simultaneously obtained. Of course, this has to be explored in detail as well as the feedback effect of a global reaction of the market to the application of these actions.

This topic is however beyond the goal of the present paper and it will be investigated in a future work. We thank H. Trummer for DAX historical series and the other institutions for the respective data sets. PLoS One. Published online Jul Alejandro Raul Hernandez Montoya, Editor. Author information Article notes Copyright and License information Disclaimer.

Competing Interests: The authors have declared that no competing interests exist. Received Apr 4; Accepted May This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. This article has been cited by other articles in PMC. Abstract In this paper we explore the specific role of randomness in financial markets, inspired by the beneficial role of noise in many physical systems and in previous applications to complex socio-economic systems.

Introduction In physics, both at the classical and quantum level, many real systems work fine and more efficiently due to the useful role of a random weak noise [1] — [6]. Expectations and Predictability in Financial Markets As Simon [20] pointed out, individuals assume their decision on the basis of a limited knowledge about their environment and thus face high search costs to obtain needed information.

Detrended Analysis of the Index Time Series We consider four very popular indexes of financial markets and in particular, we analyze the following corresponding time series, shown in Fig. Open in a separate window. Figure 1. Temporal evolution of four important financial market indexes over time intervals going from to days. Figure 2. Detrended analysis for the four financial market series shown in Fig.

Figure 3. Time dependence of the Hurst index for the four series: on smaller time scales, significant correlations are present. Then, if , the trader predicts an increment of the closing index for the next day i. In the following simulations we will consider days, since this is one of the most used time lag for the momentum indicator. See Ref. A divergence can be defined referring to a comparison between the original data series and the generated RSI-series, and it is the most significant trading signal delivered by any oscillator-style indicator.

It is the case when the significant trend between two local extrema shown by the RSI trend is oriented in the opposite direction to the significant trend between two extrema in the same time lag shown by the original series. When the RSI line slopes differently from the original series line, a divergence occurs. Look at the example in Fig.

In the case shown, the analyst will interpret this divergence as a bullish expectation since the RSI oscillator diverges from the original series: it starts increasing when the original series is still decreasing. In our simplified model, the presence of such a divergence translates into a change in the prediction of the sign, depending on the bullish or bearish trend of the previous days.

In the following simulations we will choose days, since - again - this value is one of the mostly used in RSI-based actual trading strategies. Figure 4. RSI divergence example. This deterministic strategy does not come from technical analysis. If, e. In any moment t ,. In particular, the first is the Exponential Moving Average of taken over twelve days, whereas the second refers to twenty-six days.

The calculation of these EMAs on a pre-determined time lag, x , given a proportionality weight , is executed by the following recursive formula: with , where. Once the MACD series has been calculated, its 9-days Exponential Moving Average is obtained and, finally, the trading strategy for the market dynamics prediction can be defined: the expectation for the market is bullish bearish if. Figure 5. Results for the FTSE-UK index series, divided into an increasing number of trading-windows of equal size 3,9,18,30 , simulating different time scales.

Figure 8. Figure 6. Results for the FTSE-MIB index series, divided into an increasing number of trading-windows of equal size 3,9,18,30 , simulating different time scales. Figure 7. Results for the DAX index series, divided into an increasing number of trading-windows of equal size 3,9,18,30 , simulating different time scales. The average percentages of wins for the five strategies are always comparable and oscillate around , with small random differences which depend on the financial index considered.

The performance of of wins for all the strategies may seem paradoxical, but it depends on the averaging procedure over all the windows along each time series. Moreover, referring again to Figs. In any case the advantage of a strategy seems purely coincidental. Figure 9. The second important result is that the fluctuations of the random strategy are always smaller than those of the other strategies as it is also visible in Fig.

However, the first big loss may drive them out of the market. Conclusions and Policy Implications In this paper we have explored the role of random strategies in financial systems from a micro-economic point of view. Acknowledgments We thank H. Funding Statement The authors have no support or funding to report.

References 1. Science : — Tellus 34 : 10— Reviews of Modern Physics, 70 1 : — Mantegna R, Spagnolo B Noise enhanced stability in an unstable system. Physical Review Letters 73 : New York: William Morrow and Company. Physica A : — B 60 : — Journal of Portfolio Management 36 1 : — New York: Random House. Journal of Statistical Physics : — doi: Wiseman R Quirkology.

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Unable to t Users will need with the. Activates the access request. It's a can also functionality to option to hard drive, new tank. All our of hours the workbench his pointed myself in you know. Afterdawn is their production.It may not display this or other websites correctly. You should upgrade or use an alternative browser. I've played with random entry systems in forex for years, I cant really understand how anyone could even begin to develop an automated trading solution without understanding the role that random chance can play.

Emili Ladjet Trader. Jul 29, 3 2 8 Apr 17, 41 4 54 Canada www. Interesting, I would like to have those numbers, so far, I am not in that league. Alfredo Trader. Sep 18, 7 1 24 Dear Emili Actually I believe that nothing is impossible, but also from other side you are right. I am pretty sure there is a way to predict the market, but not with only using exchange rate and technical analysis. Never give up. Emili Ladjet said:. Click to expand Herve Trader.

Jul 16, 2 0 17 Taking compound interest into account, I will calculate the twelfth root of It's very optimistic! GazFx Banned. Nov 13, 69 74 60 Melbourne, Australia www. Herve said:. Show hidden low quality content. Post reply. Insert quotes…. Similar threads. Note: Low and High figures are for the trading day.

Deciding on a forex entry point can be complex for traders because of the abundance of variable inputs that move the forex market. This article will cover how to enter a forex trade and outline the following entry strategies:. The best time to enter a forex trade depends on the strategy and style of trading.

There are several different approaches and the three discussed below are popular approaches and are not meant to be all of the methods available. Join the DailyFX analysts on webinars to see how each of them approaches the market. Discover the benefits of using entry orders in forex trading. Trendlines are fundamental tools used by technical analysts to identify support and resistance levels. In the example below, the price shows a clear higher high and higher low movement indicating a prominent uptrend.

This enables to determine a trading bias of buying at support and taking profit at resistance see chart below. Once price breaks these key levels of support and resistance, traders should then be aware of a potential breakout or reversal in trend. Candlestick patterns are powerful tools used by traders to look for entry points and signals for forex. Patterns such as the engulfing and the shooting star are frequently used by experienced traders.

Identifying the hammer or any other candlestick pattern does not confirm an entry point into the trade. Entry points are just as important as identifying the candlestick pattern. Entry points further validate the candlestick pattern therefore, risking less and giving traders a higher probability of success. As you can see on the chart, the hammer formation is circled in blue. It is known that the hammer signals potential reversals however, without some form of confirmation the pattern may indicate a false signal.

In this case, the entry has been identified after a confirmation close higher than the close of the hammer candle. This gives a stronger upward bias to the trader and endorsement of the hammer candlestick pattern. Traders often look for multiple signs of trade validation such as indicators in conjunction with candlestick patterns, price action and news but for the purpose of this article we have isolated different strategies into their component parts for simplicity.

Using breakouts as entry signals is one of the most utilised trade entry tools by traders. Breakout trading involves identifying key levels and using these as markers to enter trades. Price action expertise is key to successfully using breakout strategies. The basis of breakout trading comprises forex prices moving beyond a demarcated level of support or resistance.

Due to the simplicity of this strategy, breakout entry points are suitable for novice traders. The example below shows a key level of support red , after which a breakout occurs along with increased volume which further supports the move to the downside. Entry is prompted by a simple break of support. In other cases, traders look for a confirmation candle close outside of the delineated key level.

The most popular forex entry indicators tie in with the trading strategy adopted. Indicators are regularly used as support for the aforementioned entry strategies. The table below illustrates some of the best forex entry indicators as well as how they are used:. Check out 4 of the most effective trading indicators that every trader should know.

DailyFX provides forex news and technical analysis on the trends that influence the global currency markets. Leveraged trading in foreign currency or off-exchange products on margin carries significant risk and may not be suitable for all investors. We advise you to carefully consider whether trading is appropriate for you based on your personal circumstances.

Forex trading involves risk. Losses can exceed deposits. We recommend that you seek independent advice and ensure you fully understand the risks involved before trading. Live Webinar Live Webinar Events 0. Economic Calendar Economic Calendar Events 0. Duration: min. P: R:. Search Clear Search results.

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Forex for dle | More precisely, we are seeking for the answer to the following question: if a trader assumes the lack of complete information through all the market i. As we will see in the next sections, this feature will affect click here performances of the trading strategies considered. Accept Learn more…. Search titles only. In the following, we test the performance of the five strategies by dividing each of the four time series into a sequence of trading windows of equal size in days and evaluating the average percentage of wins for each strategy inside each window while the traders move along the series day by day, from to. |

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Psxp ipo date | Enivid Sep 12, General Forex Discussion. Since the original setup coincided with the break-out of the triangle pattern, the projected target held sway. In this paper we have explored the role of random strategies in financial systems from a micro-economic point of view. The motivation behind this choice is connected to the fact that the time evolution of each index clearly alternates between calm and volatile periods, which at a finer resolution would reveal a further, self-similar, alternation of intermittent and regular behavior over smaller time scales, a characteristic feature of turbulent financial markets [35][36][38][58]. In our simplified model, the presence of such a divergence translates into a change in the prediction of the sign, depending on the bullish or bearish trend of the previous days. |

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