Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2025 352291 Algorithmic Trading 1 3.0 3 ★ Wu, Mu-en GIFM 27 4 科研823

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2025	352291	Algorithmic Trading	1	3.0	3	★	Wu, Mu-en	GIFM	27	4	科研823

Course Plan：

Instructor Wu, Mu-en　Office Hours

Email mnasia1@gmail.com

Last Updated 2025-08-14 01:31:49

Course Objective 本課程介紹如何將金融操作的方式，以明確的演算法去定義、描述與執行。課程內容伴隨使用R語言進行，並探討如何運用時下最流行的機器學習技術於金融交易? 課程後半段邀請業師教學程式交易軟體使用，及期末論文與成果報告 This course introduces how to define, describe, and execute financial trading in a clear algorithm. The course content is accompanied by the use of the R language and explores how to use the most popular machine learning techniques for financial trading. In the second half of the course, the lecturers are invited to teach the program trading software. Students have to present the final projects and reports.

Course Schedule 1 課程介紹 Introduction 2 開始使用R語言 Using R Language 3 金融資料與金融交易 Financial data and financial trading 3 資料下載與處理 Data download and processing 4 技術指標視覺化 technical indicator and visualization 5 交易策略建構 trading strategy 6 交易策略建構 trading strategy 7 策略績效 performance of trading strategy 8 加減碼與停損停利 position sizing and stop-loss and profit 9 加減碼與停損停利 position sizing and stop-loss and profit 10 程式交易軟體 programming trading software 11 程式交易軟體 programming trading software 12 XQ軟體 XQ software 13 XQ軟體 XQ software 14 論文報告 paper presentation 15 論文報告 paper presentation 16 論文報告 paper presentation 17 期末成果發表 Final project 18 期末成果發表 Final project

Evaluation and grading policy 評分方式：出席 10%、課堂練習 40%、論文報告25%、期末報告25% 論文報告：代訂期末報告：採用台股歷史資料(股票 or 期貨待定)，使用R語言進行交易策略開發。 Evaluations: Attendance 10%, practice in class: 40%, paper presentation 25%, final project 25%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:No
老師上課提供投影片與相關網站

The access to curricular consultation 建立臉書社團進行上課練習與回家作業、與Line群組進行討論

Expanding teaching and resources

The course corresponds to the SDGs SDG1：消除貧窮（No Poverty）
SDG10：減少不平等（Reduced Inequalities）

Does the course incorporate AI ● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process）
● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction）
● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）

Note
　

Instructor	Wu, Mu-en　Office Hours
Email	mnasia1@gmail.com
Last Updated	2025-08-14 01:31:49
Course Objective	本課程介紹如何將金融操作的方式，以明確的演算法去定義、描述與執行。課程內容伴隨使用R語言進行，並探討如何運用時下最流行的機器學習技術於金融交易? 課程後半段邀請業師教學程式交易軟體使用，及期末論文與成果報告 This course introduces how to define, describe, and execute financial trading in a clear algorithm. The course content is accompanied by the use of the R language and explores how to use the most popular machine learning techniques for financial trading. In the second half of the course, the lecturers are invited to teach the program trading software. Students have to present the final projects and reports.
Course Schedule	1 課程介紹 Introduction 2 開始使用R語言 Using R Language 3 金融資料與金融交易 Financial data and financial trading 3 資料下載與處理 Data download and processing 4 技術指標視覺化 technical indicator and visualization 5 交易策略建構 trading strategy 6 交易策略建構 trading strategy 7 策略績效 performance of trading strategy 8 加減碼與停損停利 position sizing and stop-loss and profit 9 加減碼與停損停利 position sizing and stop-loss and profit 10 程式交易軟體 programming trading software 11 程式交易軟體 programming trading software 12 XQ軟體 XQ software 13 XQ軟體 XQ software 14 論文報告 paper presentation 15 論文報告 paper presentation 16 論文報告 paper presentation 17 期末成果發表 Final project 18 期末成果發表 Final project
Evaluation and grading policy	評分方式：出席 10%、課堂練習 40%、論文報告25%、期末報告25% 論文報告：代訂期末報告：採用台股歷史資料(股票 or 期貨待定)，使用R語言進行交易策略開發。 Evaluations: Attendance 10%, practice in class: 40%, paper presentation 25%, final project 25%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No 老師上課提供投影片與相關網站
The access to curricular consultation	建立臉書社團進行上課練習與回家作業、與Line群組進行討論
Expanding teaching and resources
The course corresponds to the SDGs	SDG1：消除貧窮（No Poverty） SDG10：減少不平等（Reduced Inequalities）
Does the course incorporate AI	● 使用生成式AI作為教學過程的輔助工具（Use generative AI tools as an aid to the teaching process） ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 使用生成式AI進行課堂即時互動（Use generative AI for real-time classroom interaction） ● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI）
Note