Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/130541
|
| Title: | 卷積神經網路於黃金期貨技術指標投資之應用
Application of Convolutional Neural Network on Gold Future Technical Index |
| Authors: | 蔡宛伶
Tsai, Wan-Ling |
| Contributors: | 林士貴
蔡銘峰
Lin, Shih-Kuei
Tsai, Ming-Feng
蔡宛伶
Tsai, Wan-Ling |
| Keywords: | 卷積神經網路
深度學習
技術分析
技術指標
黃金期貨
Convolutional Neural Network
Deep Learning
Technical Analysis
Technical Index
Gold Future |
| Date: | 2020 |
| Issue Date: | 2020-07-01 13:41:00 (UTC+8) |
| Abstract: | 本文探討卷積神經網路與技術指標結合之黃金期貨交易投資策略，以黃金期貨的技術線圖作為模型訓練資料，篩選出報酬率夠高的交易訊號，達成精準投資之目的。
在資本市場當中，許多人憑藉著技術分析資訊找出股價波動的規律，但除了傳統的數值資料之外，技術分析當中還有許多技術線圖，提供我們具象化的資訊，這些圖像資訊遂成為非常重要的投資決策依據。
深度學習在近十年中有非常顯著的成長，其中的卷積神經網路尤其在圖像辨識領域有長足的突破，如今卷積神經網路已成為主流圖像辨識所使用的方法，因此本文應用卷積神經網路，透過技術分析中大量的技術線圖，旨在分類出具有獲利潛力的交易訊號。
This article is mainly about applying convolutional neural networks to gold futures technical indicators trading strategy. The technical indicator graph of gold futures is used as model training data to screen out trading signals with a high return rate, aiming to increase average return.
In the capital market, many people rely on technical analysis to find out the pattern of stock price. In addition to traditional numerical data, there are many technical indicator graphs could provide specific information. The image information is then become a very important basis for investment decisions.
Deep learning has grown significantly in the past decade. Among all kinds of deep learning models, the convolutional neural network has achieved a great performance on image recognition. This article applied convolutional neural networks to technical analysis by using technical indicator graphs, aiming to classify trading signals with potential high-profit. |
| Reference: | 一、中文文獻
[1] 李杰穎. (2019). 卷積神經網路結合技術指標交易策略在台灣加權指數期貨之應用.
[2] 劉昭雨，2017，卷積神經網路在金融技術指標之應用
二、英文文獻
[3] Malkiel, B. G., & Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.
[4] Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of finance, 47(5), 1731-1764.
[5] Pruitt, S. W., & White, R. E. (1988). The CRISMA Trading System: Who Says Technical Analysis Can`. Journal of Portfolio Management, 14(3), 55.
[6] Tsai, C. F., & Wang, S. P. (2009, March). Stock price forecasting by hybrid machine learning techniques. In Proceedings of the international multiconference of engineers and computer scientists (Vol. 1, No. 755, p. 60).
[7] Di Persio, L., & Honchar, O. (2016). Artificial neural networks architectures for stock price prediction: Comparisons and applications. International journal of circuits, systems and signal processing, 10(2016), 403-413.
[8] Tsantekidis, A., Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M., & Iosifidis, A. (2017, July). Forecasting stock prices from the limit order book using convolutional neural networks. In 2017 IEEE 19th Conference on Business Informatics (CBI) (Vol. 1, pp. 7-12). IEEE.
[9] Pyo, S., Lee, J., Cha, M., & Jang, H. (2017). Predictability of machine learning techniques to forecast the trends of market index prices: Hypothesis testing for the Korean stock markets. PloS one, 12(11).
[10] Alexander, S. S. (1961). Price movements in speculative markets: Trends or random walks. Industrial Management Review (pre-1986), 2(2), 7. |
| Description: | 碩士
國立政治大學
金融學系
107352006 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107352006 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202000508 |
| Appears in Collections: | [金融學系] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 200601.pdf | | 2815Kb | Adobe PDF2 | 0 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
