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Short-term trend prediction in financial time series data

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Abstract

This paper presents a method to predict short-term trends in financial time series data found in the foreign exchange market. Trends in the Forex market appear with similar chart patterns. We approach the chart patterns in the financial markets from a discovery of motifs in a time series perspective. Our method uses a modified Zigzag technical indicator to segment the data and discover motifs, expectation maximization to cluster the motifs and support vector machines to classify the motifs and predict accurate trading parameters for the identified motifs. The available input data are adapted to each trading time frame with a sliding window. The accuracy of the prediction models is tested across several different currency pairs, spanning 5 years of historical data from 2010 to 2015. The experimental results suggest that using the Zigzag technical indicator to discover motifs that identify short-term trends in financial data results in a high prediction accuracy and trade profits.

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Notes

  1. Please refer to the “Appendix A.1” for the definitions of Forex terms take profit and stop loss.

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Authors and Affiliations

  1. Central Bank of Turkey, Ankara, Turkey

    Mustafa Onur Özorhan

  2. Department of Computer Engineering, METU, Ankara, Turkey

    İsmail Hakkı Toroslu & Onur Tolga Şehitoğlu

Authors
  1. Mustafa Onur Özorhan
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  2. İsmail Hakkı Toroslu
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  3. Onur Tolga Şehitoğlu
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Correspondence to Mustafa Onur Özorhan.

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A Appendix

A Appendix

1.1 A.1. Common forex terms

Definition A.1

(Take profit)—Take profit is a point in the time series where the trading system locks the profits and closes an open position. It is defined as a number of points from the initial price point.

For example, if a long transaction is expected to reach to a level 1.2500 at the end of the motif event horizon, that transaction can be terminated earlier if a level 1.3000 has been reached before arriving to the end of the event horizon, which gives even a better profit than the expected one. Notice that waiting for the end of the event horizon may produce better or worse result. The user may set the take profit level in terms of the expected value.

Definition A.2

(Stop loss)—Stop loss is a point in the time series where the trading system cuts the losses and closes an open position. Similar to the take profit, it is defined as a number of points from the initial price point.

This is the reverse of the take-profit value. Considering the same transaction, it can be terminated at level 1.2000 with a stop loss, accepting some loss before arriving to the event horizon. This decision can be made to prevent potentially higher losses. Notice that waiting for the event horizon may produce better or worse result. Also, the user sets the level in terms of the expected value.

Definition A.3

(Signal point)—Signal point is a point in the time series where the trading system opens a new position. The position can be a sell or buy position, depending on the underlying signal.

In our system, signal points are the last and completing points of detected motifs, which are all definitive Zigzag points. However, due to Zigzag’s backtracking mechanism, the last Zigzag point might be updated as long as it is not a signal point. The last Zigzag point in a motif becomes a signal point once the motif ends up in a cluster that has enough directional bias to be used for trading.

Consider a W-shaped motif where the 3rd peak is expected to be followed by an upward move. In real time, patterns are discovered in backward searches. So, at each time point, the data points must be searched, if this backward-searched sequence is similar to any of the motifs. For instance, if such a W-shaped motif has been found to be similar with the real data such that the current point corresponds to the last top point of W, this generates a signal for a long transaction to start (assuming 3rd peak is followed by and upward move).

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Özorhan, M.O., Toroslu, İ.H. & Şehitoğlu, O.T. Short-term trend prediction in financial time series data. Knowl Inf Syst 61, 397–429 (2019). https://doi.org/10.1007/s10115-018-1303-x

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