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Algorithmic Trading: Consistent Profit Strategies For Brazilian Traders

Algorithmic Trading: Consistent Profit Strategies For Brazilian Traders

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By Amer Bakhach Amer Bakhach Scilit Google Scholar 1, * , Venkata L. Raju Chinthalapati Venkata L. Raju Chinthalapati Scilit Google Scholar 2, Edward P. K. Tsang Edward P. K. Tsang Scilit Google Scholar 1 and Abdul Rahman El Sayed Abdul Rahman El Sayed Scilit Google Scholar 3

Received: 17 September 2018 / Revised: 22 October 2018 / Accepted: 26 October 2018 / Published: 28 October 2018

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The directional change (DC) framework is an approach for summarizing price movements in financial time series. Some studies have attempted to develop trading strategies based on the DC framework. Dynamic Backlash Agent (DBA) is a trading strategy developed based on the DC framework. Despite DBA’s promising results, DBA did not use either order size management or risk management components. In this paper, we present an improved version of DBA called Intelligent DBA (IDBA). IDBA overcomes the weaknesses of DBA by incorporating native order size management and risk management modules. We examine the effect of IDBA on the forex market. The results suggest that IDBA can provide significantly higher returns than DBA. The results also show that IDBA outperforms other DC-based trading strategies and can generate annualized returns of around 30% after subtracting bid-ask spreads (but not transaction costs).

Directional change (DC) is an approach to summarizing market price movements [1]. Under the DC framework, the market is thrown into alternating uptrends, which we call an uptrend, and downtrends, which we call a downtrend [2]. A trend is identified as a change in market price that is greater than or equal to a given threshold. This threshold, so-called

Set by the observer and usually expressed as a percentage. The trend ends whenever the price changes at the same threshold,

Algorithmic Trading: Consistent Profit Strategies For Brazilian Traders

, is observed in the opposite direction. For example, a market downtrend ends when we see a large increase in price

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; in this case we say that the market is changing direction towards an uptrend. Similarly, a market uptrend ends when we see a big drop in price

Before proceeding, we cite other studies that show that the DC framework has helped in the analysis of financial markets. For example, Glattfelder et al. [3] discovered twelve scaling laws that reveal new characteristics in the Forex (FX) market. These scaling laws are based on the concept of direct current. Their goal was to establish mathematical relationships between price movements, duration and frequency. In addition, Bisig et al. [4] presented the so-called Market Quake Scale (SMQ) based on the DC concept. SMQ aims to quantify foreign exchange market activity during significant economic and political events.

Furthermore, Masry [5] reported a study that deciphers foreign exchange market activity based on the DC concept. She presented an approach that lays “the foundation for understanding how foreign exchange market activity changes as price action progresses” and explains how minor differences in market activity can change the price trend, under certain conditions, during an overshoot event (OS) [5] . Bakhach et al. [6] presented a model for predicting market trend direction according to the DC framework. They tried to predict whether the DC trend would continue to a certain threshold before the market trend would reverse. AlKhamees and Fasli [7] proposed a DC-based approach with a dynamic threshold definition method to analyze financial market reaction to published news (e.g., political, economic). They claimed that their approach could be used by investors to detect market movements so that they could potentially react and take advantage of them.

Tsang et al. [8] introduced an approach to profiling companies and financial markets. Their methodology includes a set of innovative indicators based on the DC framework. These indicators aim to analyze and classify financial markets. They concluded that the information provided by DC-based and time-series analysis complement each other. Tsang and Chen [9] proposed an approach to profile a particular market through a moving time window and track changes in market positions over time. They used some of the DC indicators previously presented in [8] observed under different price events. They applied their approach to identify regime change during the Brexit period (specifically between May and July 2016).

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In addition, some studies have attempted to develop trading strategies based on the DC framework (e.g. [10, 11, 12, 13]). In this paper, we are particularly interested in a DC-based trading strategy called the Dynamic Backlash Agent (DBA) by Bakhach et al. [14]. Preliminary results suggest that DBA can generate positive returns in many cases. However, DBA has two critical weaknesses: (a) it has no order size management; and (b) does not have any risk management module. In this paper, we present an improved version of DBA called Intelligent DBA (IDBA). IDBA is designed to overcome these two weaknesses of DBA.

This paper proceeds as follows: Section 2 describes the concept of direction changes. Section 3 provides a brief overview of existing trading strategies based on the DC framework. Section 4 provides a summary of the trading strategy called DBA. We introduce the components of IDBA and explain how it works in Section 5. We discuss the selection and preparation of datasets in Section 6. Details of the experiments conducted to evaluate the performance of IDBA are given in Section 7. Section 8 reports and discusses the results of these attempts. Finally, we summarize the main findings of this paper in Section 9.

In this section, we explain how market prices are summarized based on the DC concept. Under the DC framework, the market is represented as alternating uptrends and downtrends. The basic idea is that the size of the price change during an uptrend or downtrend must be at least equal to a certain threshold

Algorithmic Trading: Consistent Profit Strategies For Brazilian Traders

Is the percentage considered significant by the observer (usually expressed as a percentage). For example, Figure 1 shows the drop in price between points A and A

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. This price drop is equal to the chosen, hypothetical, threshold of 0.1%. In this case, we say that we have a DC downtrend that starts at point A. Any price change less than the identified threshold,

, will not be considered a trend when summarizing price movements [1, 2]. According to the DC framework, every uptrend is followed by a downtrend and vice versa. Discovering a new uptrend or downtrend is a key task. Discovering a new downtrend or uptrend is a two-step algorithmic approach:

Figure 2 illustrates the identification of extremes and DCC points for a given financial time series. In Figure 2, points A, B, C, D, E, F and G are “extreme points”, while points A

Are “DCC points”. An extreme point can be seen as a local minimum (eg point D in Figure 2) or a local maximum (eg point C in Figure 2). The extreme point is recognized only backwards – exactly at the DCC point (ie, when the inequality in equation (1) becomes true). For example, in Figure 2, at point A

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) the price from which the trend starts. Finally, when equation (1) is valid, i.e. when a new DC trend (either ascending or descending) is recognized,

Under the DC framework, the trend is decomposed into a DC event and an overshoot event (OS). The DC event starts with the extreme point and ends with the DCC point. We call a particular DC event its starting point, i.e. extreme point, and its DCC point. For example, in Figure 2, a DC event that starts at point A and ends at point A

The DC summary of a particular market is the identification of DC and OS events, managed by a threshold

Algorithmic Trading: Consistent Profit Strategies For Brazilian Traders

0.1%. Note that we can produce multiple DC summaries for

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