A trend tracking strategy for gold future: An artificial neutral network analysis

Chaoteng Jordan Chen; Ying Huang; Kin Keung Lai

doi:10.1109/BIFE.2013.8

A trend tracking strategy for gold future: An artificial neutral network analysis

Chaoteng Jordan Chen, Ying Huang, Kin Keung Lai

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

In this paper, we construct a simple data-driven trend tracking strategy for gold future in a view of contrarians. The artificial neutral network (ANN) is adopted to determine the price trend signal, by which the degree of tightness could be adjusted based on observed data. We attempt to capture the small profits when the price is deviated from the Bollinger band in the gold future market by intraday trading. High frequency data of gold future is used to train and test the strategy. Despite of the trading cost, the back-tests show that our strategy has delivered positive returns and is adaptive to different price trends. Finally, we evaluate the profitability with the consideration of trading cost, revealing that the strategy is applicable in practice.

Original language	English
Title of host publication	Proceedings - 2013 6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013
Publisher	IEEE
Pages	31-35
ISBN (Print)	9781479947775
DOIs	https://doi.org/10.1109/BIFE.2013.8
Publication status	Published - 18 Nov 2014
Event	6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013 - Hangzhou, Zhejiang, China Duration: 14 Nov 2013 → 16 Nov 2013

Conference

Conference	6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013
Place	China
City	Hangzhou, Zhejiang
Period	14/11/13 → 16/11/13

Research Keywords

algorithmic trading
gold future
neural network
technical analysis
trend tracking

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@inproceedings{e9da2a57410847a2bc4283d7c81ce22d,

title = "A trend tracking strategy for gold future: An artificial neutral network analysis",

abstract = "In this paper, we construct a simple data-driven trend tracking strategy for gold future in a view of contrarians. The artificial neutral network (ANN) is adopted to determine the price trend signal, by which the degree of tightness could be adjusted based on observed data. We attempt to capture the small profits when the price is deviated from the Bollinger band in the gold future market by intraday trading. High frequency data of gold future is used to train and test the strategy. Despite of the trading cost, the back-tests show that our strategy has delivered positive returns and is adaptive to different price trends. Finally, we evaluate the profitability with the consideration of trading cost, revealing that the strategy is applicable in practice.",

keywords = "algorithmic trading, gold future, neural network, technical analysis, trend tracking",

author = "Chen, {Chaoteng Jordan} and Ying Huang and Lai, {Kin Keung}",

year = "2014",

month = nov,

day = "18",

doi = "10.1109/BIFE.2013.8",

language = "English",

isbn = "9781479947775",

pages = "31--35",

booktitle = "Proceedings - 2013 6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013",

publisher = "IEEE",

address = "United States",

note = "6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013 ; Conference date: 14-11-2013 Through 16-11-2013",

}

Chen, CJ, Huang, Y & Lai, KK 2014, A trend tracking strategy for gold future: An artificial neutral network analysis. in Proceedings - 2013 6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013., 6961085, IEEE, pp. 31-35, 6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013, Hangzhou, Zhejiang, China, 14/11/13. https://doi.org/10.1109/BIFE.2013.8

A trend tracking strategy for gold future: An artificial neutral network analysis. / Chen, Chaoteng Jordan; Huang, Ying; Lai, Kin Keung.
Proceedings - 2013 6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013. IEEE, 2014. p. 31-35 6961085.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A trend tracking strategy for gold future

T2 - 6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013

AU - Chen, Chaoteng Jordan

AU - Huang, Ying

AU - Lai, Kin Keung

PY - 2014/11/18

Y1 - 2014/11/18

N2 - In this paper, we construct a simple data-driven trend tracking strategy for gold future in a view of contrarians. The artificial neutral network (ANN) is adopted to determine the price trend signal, by which the degree of tightness could be adjusted based on observed data. We attempt to capture the small profits when the price is deviated from the Bollinger band in the gold future market by intraday trading. High frequency data of gold future is used to train and test the strategy. Despite of the trading cost, the back-tests show that our strategy has delivered positive returns and is adaptive to different price trends. Finally, we evaluate the profitability with the consideration of trading cost, revealing that the strategy is applicable in practice.

AB - In this paper, we construct a simple data-driven trend tracking strategy for gold future in a view of contrarians. The artificial neutral network (ANN) is adopted to determine the price trend signal, by which the degree of tightness could be adjusted based on observed data. We attempt to capture the small profits when the price is deviated from the Bollinger band in the gold future market by intraday trading. High frequency data of gold future is used to train and test the strategy. Despite of the trading cost, the back-tests show that our strategy has delivered positive returns and is adaptive to different price trends. Finally, we evaluate the profitability with the consideration of trading cost, revealing that the strategy is applicable in practice.

KW - algorithmic trading

KW - gold future

KW - neural network

KW - technical analysis

KW - trend tracking

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DO - 10.1109/BIFE.2013.8

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781479947775

SP - 31

EP - 35

BT - Proceedings - 2013 6th International Conference on Business Intelligence and Financial Engineering, BIFE 2013

PB - IEEE

Y2 - 14 November 2013 through 16 November 2013

ER -

A trend tracking strategy for gold future: An artificial neutral network analysis

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