股票技術分析報告

動機

• 隨著金融市場的發展，技術分析被廣泛應用於股票投資決策中。本研究將透過 R 語言，分析 台積電 (2330.TW)的歷史價格，並計算SMA、EMA、布林通道 (BollingerBands)、MACD、RSI 等技術指標。

方法

• 本研究的技術指標計算流程如下：

1. 下載股票歷史數據
2. 計算移動平均線（SMA & EMA）
3. 計算布林通道（Bollinger Bands）
4. 計算 MACD 指標
5. 計算 RSI 指標
6. 使用 dygraphs 建立互動式圖表

程式說明

• 載入必要套件和抓取股票資料

# 載入套件
library(quantmod)
library(TTR)
library(dygraphs)

# 設定股票代碼
ticker <- "2330.TW"

# 設定資料下載的起始和結束日期
start_date <- as.Date("2024-01-01")
end_date <- Sys.Date()

# 下載股票資料
  getSymbols(ticker, from = start_date, to = end_date, auto.assign = TRUE)

## [1] "2330.TW"

  # 取得股票數據
  stock_data <- get(ticker)

  # ========== 1. 修正變數名稱 ==========
  colnames(stock_data) <- c("Open", "High", "Low", "Close", "Volume", "Adjusted")
  
  # 取 OHLC 數據
  ohlc_xts <- stock_data[, c("Open", "High", "Low", "Close")]
  # ========== 2. 計算技術指標 ==========
  # 20 日簡單移動平均線 (SMA)
  sma_20 <- SMA(ohlc_xts$Close, n = 20)
  sma_20 <- xts(sma_20, order.by = index(stock_data))
  colnames(sma_20) <- "SMA_20"
  
  # 20 日指數移動平均線 (EMA)
  ema_20 <- EMA(ohlc_xts$Close, n = 20)
  ema_20 <- xts(ema_20, order.by = index(stock_data))
  colnames(ema_20) <- "EMA_20"
  
  # 計算布林通道 (Bollinger Bands)
  bb_xts <- BBands(HLC(stock_data), n = 20, sd = 2)
  bb_up <- xts(bb_xts$up, order.by = index(stock_data))
  bb_dn <- xts(bb_xts$dn, order.by = index(stock_data))
  colnames(bb_up) <- "BB_Upper"
  colnames(bb_dn) <- "BB_Lower"
  
  # 計算 MACD
  macd_xts <- MACD(ohlc_xts$Close, nFast = 12, nSlow = 26, nSig = 9, maType = "EMA", percent = FALSE)
  macd_xts <- xts(macd_xts, order.by = index(stock_data))
  colnames(macd_xts) <- c("DIF", "MACD_Signal")
  
  # 計算 RSI (14)
  rsi_xts <- RSI(ohlc_xts$Close, n = 14)
  rsi_xts <- xts(rsi_xts, order.by = index(stock_data))
  colnames(rsi_xts) <- "RSI_14"
  # ========== 3. 合併數據 ==========
  combined1_xts <- merge(ohlc_xts, sma_20, ema_20, bb_up, bb_dn, all = FALSE)
  combined2_xts <- merge(ohlc_xts, macd_xts, all = FALSE)
  combined3_xts <- merge(ohlc_xts, rsi_xts, all = FALSE)
  # ========== 4. 繪製 Viewer 1: K 線圖 + EMA + SMA + BBands ==========
  dygraph(combined1_xts, main = paste(ticker, "K Line + SMA & EMA & BBands")) %>%
    dyCandlestick() %>%  # 讓 dygraph 變成 K 線圖
    dySeries("SMA_20", label = "SMA (20)", color = "green", strokeWidth = 2) %>%
    dySeries("EMA_20", label = "EMA (20)", color = "purple", strokeWidth = 2) %>%
    dySeries("BB_Upper", label = "BB Upper", color = "red", strokeWidth = 1.5, strokePattern = "dashed") %>%
    dySeries("BB_Lower", label = "BB Lower", color = "red", strokeWidth = 1.5, strokePattern = "dashed") %>%
    dyAxis("y", label = "Stock Price") %>%
    dyOptions(drawGrid = TRUE, gridLineColor = "lightgray") %>%
    #dyLegend(show = "always", width = 250, labelsSeparateLines = TRUE) %>%
    dyRangeSelector()

  # ========== 5. 繪製 Viewer 2: K 線圖 + MACD ==========
  dygraph(combined2_xts, main = paste(ticker, "K Line + MACD")) %>%
    dyCandlestick() %>%  # 讓 dygraph 變成 K 線圖
    dySeries("DIF", label = "DIF", color = "blue", axis = "y2") %>%
    dySeries("MACD_Signal", label = "MACD Signal", color = "black", axis = "y2") %>%
    dyAxis("y", label = "Stock Price") %>%
    dyAxis("y2", label = "MACD", independentTicks = TRUE) %>%
    dyOptions(drawGrid = TRUE, gridLineColor = "lightgray") %>%
    #dyLegend(show = "always", width = 250, labelsSeparateLines = TRUE) %>%
    dyRangeSelector()

  # ========== 6. 繪製 Viewer 3: K 線圖 + RSI ==========
  dygraph(combined3_xts, main = paste(ticker, "K Line + RSI")) %>%
    dyCandlestick() %>%  # 讓 dygraph 變成 K 線圖
    dySeries("RSI_14", label = "RSI (14)", color = "orange", axis = "y2") %>%
    dyAxis("y", label = "Stock Price") %>%
    dyAxis("y2", label = "RSI", independentTicks = TRUE) %>%
    dyOptions(drawGrid = TRUE, gridLineColor = "lightgray") %>%
    #dyLegend(show = "always", width = 250, labelsSeparateLines = TRUE) %>%
    dyRangeSelector()