本篇旨在測試 pandas-ta 套件的 OBV 技術指標函式 ta.obv() 或 df.ta.obv().
本系列全部文章索引參考 :
8. 計算 OBV (能量潮) 指標 :
OBV 是 Joe Granville 在 1963 年提出的一個量價關係指標, 結合收盤價與成交量來衡量市場買賣力道和趨勢動能, 依據收盤價漲跌或持平累積計算成交量而得到 OBV 值, 計算原理如下 :
- 收盤價上漲 (今日收盤 > 昨日收盤) : 今日成交量加到 OBV
- 收盤價下跌 (今日收盤 < 昨日收盤) : 今日成交量從 OBV 扣除
- 收盤價持平 : OBV 不變
OBV 指標的核心概念為 "量先價行", 認為成交量的變化通常會領先於價格的變化, OBV 透過累積計算成交量來研判市場資金的流向和聚集趨勢, 從而推測股價未來可能的走向, 關鍵在於觀察其走勢方向與價格走勢是配合或背離, 可判斷市場趨勢的潛在反轉訊號.
pandas-ta 套件提供 ta.obv() 函式計算 OBV 指標, 以下利用 yfinance 取得台股 0050 價量資料來計算 OBV 指標, 並於 K 線圖的副圖中繪製 OBV 曲線.
首先匯入 yfinance 與 pandas-ta 套件 :
>>> import yfinance as yf
>>> import pandas_ta as ta
下載 yfinance 股票價量資料 :
>>> df=yf.download('0050.TW', start='2024-11-06', end='2025-01-09', auto_adjust=False)
[*********************100%***********************] 1 of 1 completed
為了取值與繪製 K 線圖, 須將 DataFrame 改為傳統的單層欄位 :
>>> df.columns=df.columns.map(lambda x: x[0])
顯示前 10 筆資料 :
>>> df.head()
Adj Close Close High Low Open Volume
Date
2024-11-06 47.797909 48.799999 49.512501 48.375000 48.512501 66825504
2024-11-07 48.348858 49.362499 49.512501 48.625000 48.724998 53994208
2024-11-08 48.728401 49.750000 50.000000 49.674999 49.849998 51800604
2024-11-11 48.752892 49.775002 49.775002 49.200001 49.712502 49456052
2024-11-12 47.504074 48.500000 48.962502 48.500000 48.799999 105088348
在計算指標之前, 先用 help(ta.obv) 檢視 OBV 指標的參數結構 :
>>> help(ta.obv)
Help on function obv in module pandas_ta.volume.obv:
obv(close, volume, talib=None, offset=None, **kwargs)
On Balance Volume (OBV)
On Balance Volume is a cumulative indicator to measure buying and selling
pressure.
Sources:
https://www.tradingview.com/wiki/On_Balance_Volume_(OBV)
https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/on-balance-volume-obv/
https://www.motivewave.com/studies/on_balance_volume.htm
Calculation:
signed_volume = signed_series(close, initial=1) * volume
obv = signed_volume.cumsum()
Args:
close (pd.Series): Series of 'close's
volume (pd.Series): Series of 'volume's
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
offset (int): How many periods to offset the result. Default: 0
Kwargs:
fillna (value, optional): pd.DataFrame.fillna(value)
fill_method (value, optional): Type of fill method
Returns:
pd.Series: New feature generated.
可見 ta.obv() 有兩個必要參數 : close (收盤價) 與 volume (成交量), 呼叫 df.ta.obv() 並指定 close='Close' 指定未還原權值收盤價進行計算, 這是因為 df 有 Adj Close 與 Close 兩個欄位, 為避免 pandas-ta 自動抓欄位時抓到 Adj Close, 故明確指定, volume 則不用特別指定 :
>>> df.ta.obv(close='Close', append=True)
Date
2024-11-06 66825504.0
2024-11-07 120819712.0
2024-11-08 172620316.0
2024-11-11 222076368.0
2024-11-12 116988020.0
2024-11-13 80209660.0
2024-11-14 31623200.0
2024-11-15 61165524.0
2024-11-18 7587460.0
2024-11-19 38060744.0
2024-11-20 37748744.0
2024-11-21 -25757724.0
2024-11-22 6104624.0
2024-11-25 -25202028.0
2024-11-26 -70765316.0
2024-11-27 -127056060.0
2024-11-28 -179492516.0
2024-11-29 -146979636.0
2024-12-02 -104803324.0
2024-12-03 -64990900.0
2024-12-04 -39169904.0
2024-12-05 -9385092.0
2024-12-06 -36623116.0
2024-12-09 -18063660.0
2024-12-10 -35972888.0
2024-12-11 -62831644.0
2024-12-12 -42722680.0
2024-12-13 -24640972.0
2024-12-16 4733396.0
2024-12-17 41910312.0
2024-12-18 41910312.0
2024-12-19 -10269876.0
2024-12-20 -54763452.0
2024-12-23 -10059188.0
2024-12-24 22724420.0
2024-12-25 46348912.0
2024-12-26 27134668.0
2024-12-27 55084336.0
2024-12-30 20375272.0
2024-12-31 -10970864.0
2025-01-02 -62161672.0
2025-01-03 -33708188.0
2025-01-06 64802984.0
2025-01-07 128578360.0
2025-01-08 81155492.0
Name: OBV, dtype: float64
因為計算結果為單欄資料, 故 df.ta.obv() 傳回值為 Series 物件, 此傳回值因 append=True 會被自動加入 df 中, 欄位名稱為 OBV :
>>> df.columns
Index(['Adj Close', 'Close', 'High', 'Low', 'Open', 'Volume', 'OBV'], dtype='object')
>>> df
Adj Close Close High ... Open Volume OBV
Date ...
2024-11-06 47.797909 48.799999 49.512501 ... 48.512501 66825504 66825504.0
2024-11-07 48.348858 49.362499 49.512501 ... 48.724998 53994208 120819712.0
2024-11-08 48.728401 49.750000 50.000000 ... 49.849998 51800604 172620316.0
2024-11-11 48.752892 49.775002 49.775002 ... 49.712502 49456052 222076368.0
2024-11-12 47.504074 48.500000 48.962502 ... 48.799999 105088348 116988020.0
2024-11-13 47.197987 48.187500 48.500000 ... 48.462502 36778360 80209660.0
2024-11-14 46.769474 47.750000 48.125000 ... 48.075001 48586460 31623200.0
2024-11-15 47.100044 48.087502 48.212502 ... 48.012501 29542324 61165524.0
2024-11-18 46.438900 47.412498 47.924999 ... 47.862499 53578064 7587460.0
2024-11-19 47.075554 48.062500 48.237499 ... 47.674999 30473284 38060744.0
2024-11-20 46.879662 47.862499 47.862499 ... 47.862499 312000 37748744.0
2024-11-21 46.365444 47.337502 47.537498 ... 47.500000 63506468 -25757724.0
2024-11-22 47.271446 48.262501 48.299999 ... 47.775002 31862348 6104624.0
2024-11-25 47.100044 48.087502 48.687500 ... 48.674999 31306652 -25202028.0
2024-11-26 46.487877 47.462502 47.575001 ... 47.525002 45563288 -70765316.0
2024-11-27 45.814495 46.775002 47.362499 ... 47.287498 56290744 -127056060.0
2024-11-28 45.741032 46.700001 46.775002 ... 46.750000 52436456 -179492516.0
2024-11-29 45.851223 46.812500 47.000000 ... 46.237499 32512880 -146979636.0
2024-12-02 46.940880 47.924999 47.924999 ... 47.125000 42176312 -104803324.0
2024-12-03 47.577534 48.575001 48.712502 ... 48.575001 39812424 -64990900.0
2024-12-04 47.859127 48.862499 48.862499 ... 48.674999 25820996 -39169904.0
2024-12-05 48.116238 49.125000 49.150002 ... 48.937500 29784812 -9385092.0
2024-12-06 47.932587 48.937500 49.174999 ... 49.125000 27238024 -36623116.0
2024-12-09 47.993805 49.000000 49.062500 ... 49.037498 18559456 -18063660.0
2024-12-10 47.650993 48.650002 49.112499 ... 48.937500 17909228 -35972888.0
2024-12-11 47.234715 48.224998 48.562500 ... 48.250000 26858756 -62831644.0
2024-12-12 47.785667 48.787498 48.937500 ... 48.724998 20108964 -42722680.0
2024-12-13 47.822399 48.825001 48.900002 ... 48.662498 18081708 -24640972.0
2024-12-16 48.018291 49.025002 49.412498 ... 49.250000 29374368 4733396.0
2024-12-17 48.263161 49.275002 49.512501 ... 49.275002 37176916 41910312.0
2024-12-18 48.263161 49.275002 49.462502 ... 49.200001 26215508 41910312.0
2024-12-19 47.602016 48.599998 48.700001 ... 48.587502 52180188 -10269876.0
2024-12-20 47.161259 48.150002 48.437500 ... 48.437500 44493576 -54763452.0
2024-12-23 48.299889 49.312500 49.325001 ... 48.674999 44704264 -10059188.0
2024-12-24 48.434566 49.450001 49.775002 ... 49.625000 32783608 22724420.0
2024-12-25 48.581482 49.599998 49.625000 ... 49.625000 23624492 46348912.0
2024-12-26 48.544750 49.562500 49.625000 ... 49.599998 19214244 27134668.0
2024-12-27 48.703915 49.724998 49.762501 ... 49.400002 27949668 55084336.0
2024-12-30 48.434566 49.450001 49.662498 ... 49.537498 34709064 20375272.0
2024-12-31 47.932587 48.937500 49.224998 ... 49.174999 31346136 -10970864.0
2025-01-02 47.516315 48.512501 48.912498 ... 48.912498 51190808 -62161672.0
2025-01-03 47.993805 49.000000 49.237499 ... 49.000000 28453484 -33708188.0
2025-01-06 49.499729 50.537498 50.575001 ... 49.662498 98511172 64802984.0
2025-01-07 50.050682 51.099998 51.500000 ... 51.237499 63775376 128578360.0
2025-01-08 49.303841 50.337502 50.837502 ... 50.662498 47422868 81155492.0
[45 rows x 7 columns]
用 kbar 模組繪製 K 線圖, 並且在 pannel=2 副圖繪製 OBV 曲線 :
>>> from kbar import KBar
>>> kb=KBar(df)
設定字型為: Microsoft JhengHei
呼叫 KBar 物件的 addplot() 方法新增副圖繪製 OBV 曲線 (副圖是從 panel 2 起算) :
>>> kb.addplot(
df['OBV'],
panel=2,
color='red', # OBV 線顏色
width=1.2,
ylabel='OBV' # 設定此副圖的 Y 軸標籤
)
呼叫 plot() 繪製 K 線圖 :
>>> kb.plot(
volume=True, # 顯示成交量 (預設占用 Panel=1)
mav=(5, 10), # 主圖加均線
title='K 線圖與 OBV 指標'
)
使用指定字型: Microsoft JhengHei
字型候選清單: ['Microsoft JhengHei', 'DejaVu Sans', 'Arial']
結果如下 :
OBV 是累積值, 觀察 OBV 主要看趨勢而不是每一天的變化大小 :
- OBV 是否與股價同向 (趨勢確認)
- OBV 是否早於股價突破 (量先價行)
OBV 曲線的判讀核心在於觀察它與股價趨勢的同步性或背離性 :
| OBV 走勢 | 股價走勢 | 市場意義 | 訊號解讀 |
|---|---|---|---|
| 持續上升 | 同步上漲 | 資金持續流入，買盤積極且強勁 | 上漲趨勢確認 (趨勢穩固) |
| 持續下降 | 同步下跌 | 資金持續流出，賣壓沉重且強勁 | 下跌趨勢確認 (趨勢穩固) |
| 盤整整理 | 橫盤整理 | 買賣力量相對均衡。 | 觀望或等待突破 |
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