Intro to FinTech (金融科技導論)

Roger Jang (張智星), Jimmy Chen (陳君明)

2024 weekly recording

Schedule of Fall 2025

[Week01, 2025/09/03] [Week02, 2025/09/10] [Week03, 2025/09/17] [Week04, 2025/09/24]

Note that the schedule will be updated as we move on.

1. 2025/09/03
   
   • Agenda
     • Intro. to the course by Roger using course website
     • Intro. to math/method part by Roger:
     • Intro. to blockchain part by Jimmy
   • Assignment
     • Please go over the course website thoroughly to understand all the regulations.
       • You need to view "Pre-class reading" before coming to the class. Please gather your questions and we can discuss them in the class.
       • For "Optional" part, it's optional and not in the final exam. But I highly suggest your should read this part if you are not familiar with the corresponding topics.
     • Get familar with Python. You are going to use it in our assignment and final project.
       • Python 入門教學課程(Video)
       • 30天學python
2. 2025/09/10
   
   • Pre-class reading
     • 基本金融知識
       1. Compounding:
       2. IRR:
       3. 柴鼠兄弟：銀行定存怎麼算利息錢？一次搞懂利率&殖利率 (8 min, 2017)
       4. 柴鼠兄弟：股票怎麼買？600秒學完股票交易基礎 (10 min, 2018)
       5. 柴鼠兄弟：股票國語言-基本/籌碼/技術/消息 (8 min, 2018)
       6. 市場先生：ROI是什麼意思？股票的投資報酬率怎麼算？
     • 數學基礎
       1. Matrix formulas:
       2. Lagrange multipliers
          1. Lagrange Multipliers | Geometric Meaning & Full Example (12 min, 2019)
          2. Exercises for Lagrange multipliers
       3. Variance of differences of random variables | Probability and Statistics | Khan Academy (11 min, 2010)
   • Optional:
     • 數學基礎：Lagrange multipliers
       1. Lagrange乘數 (14 min, 2013)
       2. Lagrange Multipliers Practice Problems (17 min, 2017)
       3. IRR 可能有兩個合理解答都滿足數學條件
     • 機器學習簡介
       1. Intro. to machine learning:
       2. Commonly used datasets for ML:
       3. K-nearest-neightbor:
     • Python
       1. Python 用於機器學習的練習題：Python: Machine learning - Scikit-learn Exercises, Practice, Solution
   • In-class activities
     • Reminder: Modification of the slides of "Compounding". (Course material errata (教材勘誤))
     • Intro to HW00 by TA
     • In-class survey: What do you want to learn from this class?
3. 2025/09/17
   
   • Pre-class reading
     • 技術指標
       1. K 線圖 (Candlestick chart):
       2. 股票技術分析 (Technical analysis):
       3. MA 技術指標:
       4. RSI 技術指標:
     • 基本金融知識
       1. CMoney: 為什麼 EPS(每股盈餘)不是越高越好？一分鐘帶你搞懂 股價、EPS、本益比的關係（EPS & 本益比）
       2. CMoney: 股票股利、現金股利是什麼？配股、配息怎麼計算？（從股息「配股配息」看出經營者心態）
       3. CMoney: 什麼是除權除息？投資台股必須了解的事
       4. 市場先生：除權除息是什麼？除權息如何計算？一次看懂除權息
       5. 市場先生：什麼是夏普率(Sharpe Ratio)？要如何計算？
       6. Sharpe ratio:
     • 數學基礎
       1. Jensen's inequality (which leads to AM-GM inequality)
          • Jensen's inequality：
          • Exercises on mean inequality (Some with solutions!)
       2. Cauchy-Scharz inequality
          • Quick Cauchy Schwarz Proof: (5 min, 2018)
          • Another simple way to prove it: $(ax-p)^2+(bx-q)^2+(cx-r)^2=0$ has a single root or no root at all. ==> $\Delta \leq 0$ ==> $(a^2+b^2+c^2)(p^2+q^2+r^2)\geq(ap+bq+cr)^2$, Q.E.D.
          • Exercises of Cauchy-Schwarz Inequality (Some with solutions!)
   • Optional:
     • 基本金融知識
       1. What is Sharpe Ratio? (Conversion of SR)
     • 技術指標（有點像是在算命...）
       1. 柴鼠兄弟：技術面：徹底搞懂技術線圖七大元素∼先從各種K線開始 (股票技術面) (10 min, 2018)
       2. 柴鼠兄弟：技術面：判斷股價強弱的關鍵~先學會看均線 (股票技術面) (9 min, 2018)
       3. 柴鼠兄弟：技術面：為什麼股市老手愛看量？從買賣雙方冷熱認識成交量原理 (11 min, 2019)
       4. 柴鼠兄弟：技術面：拆解分析KD技術指標 二條曲線背後如工藝品般的三道工法 (12 min, 2020)
       5. 柴鼠兄弟：技術面：台積電RSI怎麼算才能跟電腦一樣？看股價相對強弱 快慢RSI技術指標順便學起來 (10 min, 2021)
     • 數學基礎
       • Problem-Solving Trick No One Taught You: RMS-AM-GM-HM Inequality: (8 min, 2018, great animation!)
4. 2025/09/24
   
   • Pre-class reading
     • 基本金融知識
       1. 柴鼠兄弟：基本面：股票怎麼選？基本面先問這三題 (股本/EPS) (8 min, 2018)
       2. 柴鼠兄弟：基本面：0050也藏有ROE不死鳥啊！新手學基本面二大指標 評估企業賺錢能力 (7 min, 2018)
       3. 柴鼠兄弟：基本面：買貴還是買便宜？股票估值的基本面三大指標∼(本益比/股價淨值比/殖利率) (8 min, 2018)
       4. 柴鼠兄弟：基礎篇-什麼是ETF? (9 min, 2018) （若你有興趣鑽研 ETF，可參考 ETF 全系列。）
       5. Stocksardine: 股價淨值比 – 簡單但常被誤用的指標
     • Dynamic programming (DP)
       1. Intro to Dynamic Programming:
       2. Back to the future: The optimal strategy when the stock price is fully know in advance:
     • LLM & RAG
       • 生成式AI輕度簡介： (29 min, 2024)（李宏毅）
       • 相關性03：文本匹配（TF-IDF、BM25、詞距）： (19 min, 2024)（王數森老師）
       • Word Embedding and Word2Vec, Clearly Explained!!!: (16 min, 2023)（by Stat Quest with Josh Starmer）
       • RAG introduction:
   • Optional
     • Other applications of DP
       1. DP for longest common subsequence (LCS):
       2. DP for matrix chain product (MCP):
     • LLM & RAG
       • 以生成式AI為基礎的知識檢索 (69 min, 2024/07/03)（楊德倫的口試）
       • 相關性01：搜索相關性的定義與分檔 (21 min, 2024)（王數森老師）
       • 文字探勘之前處理與TF-IDF介紹
       • Unsupervised Learning - Word Embedding (41 min, 2024)（李宏毅老師）
       • Approximate Nearest Neighbours in FAISS: Cell Probe 101: (7 min, 2023)（by Mark）