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| Title: | 基於市場相對價格型態之股價預測模型
Stock price prediction based on relative price patterns |
| Authors: | 曾祐展
Tseng, Yu-Chan |
| Contributors: | 彭彥璁
Peng, Yan-Tsung
曾祐展
Tseng, Yu-Chan |
| Keywords: | 深度學習
多尺度分析
卷積神經網路
視覺變壓器
時間序列
Deep Learning
Multi-scale Analysis
Convolutional Neural Networks
Vision Transformer
Time Series |
| Date: | 2025 |
| Issue Date: | 2025-09-01 16:18:22 (UTC+8) |
| Abstract: | 隨著金融市場資訊的爆炸性增長與波動迅速加劇，投資者必須在極短的時間內分析大量且複雜的市場資料，以制定有效且即時的交易策略。傳統的技術分析方法雖能透過價格模式與技術指標判斷市場趨勢，但在面臨多尺度資料整合及即時決策需求時，常顯得不足以應付。本研究提出一套創新的智慧交易系統，透過深度學習方法融合多尺度K線圖，以提升市場趨勢預測的準確性與即時性。
研究首先將多尺度價格序列映射為圖像特徵，分別以傳統線性模型羅吉斯回歸（Logistic Regression），以及深度學習模型卷積神經網路（Convolutional Neural Networks, CNN）與基於Transformer架構的視覺Transformer（Vision Transformer, ViT）進行特徵萃取與方向分類；其次，為驗證影像化方法之優勢，另以不經圖像轉換的數值 OHLCV 序列輸入長短期記憶網路（LSTM）進行對照；並納入雙均線交叉策略作為傳統基線。實驗採用兩段獨立之一年期間資料進行樣本外檢驗，以評估模型穩健性與時序遷移效應。實證結果顯示，本研究所開發之多尺度智慧交易系統，在不同模型結構下均能穩定表現，顯著提高預測準確度與交易績效，成功整合多維市場資訊，為投資人提供具實務價值且可靠的決策支援工具。
With the explosive growth of financial-market information and increasingly rapid price volatility, investors must analyse massive, complex data within extremely short time frames to formulate effective and timely trading strategies. Although traditional technical analysis can infer market trends via price patterns and technical indicators, it often falls short when confronted with the need for multi-scale data integration and real-time decision-making. This study proposes an innovative intelligent trading system that leverages deep learning to fuse multi-scale candlestick (K-line) charts, thereby enhancing both the accuracy and timeliness of market-trend prediction.
The research first maps price sequences at multiple time resolutions into image representations and employs three classification frameworks - traditional linear Logistic Regression (LR), a Convolutional Neural Network (CNN), and a Transformer-based Vision Transformer (ViT) - to extract features and predict market direction. To verify the advantage of image-based approaches, raw OHLCV sequences are also fed directly into a Long Short-Term Memory (LSTM) network for comparison, while a dual moving-average crossover strategy serves as a traditional baseline. Two non-overlapping one-year periods are used for out-of-sample evaluation, enabling assessment of model robustness and temporal transferability. Empirical results show that the proposed multi-scale intelligent trading system delivers stable performance across all model architectures, significantly improving predictive accuracy and trading profitability. By successfully integrating multidimensional market information, the system provides investors with a practical and reliable decision-support tool. |
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| Description: | 碩士
國立政治大學
資訊科學系碩士在職專班
111971004 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111971004 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系碩士在職專班] 學位論文
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