兩個計算股票技術性指標的包｜ Python 主題月

安裝命令以下：

pip install stockstats
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conda install -c conda-forge ta-lib

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能夠在mbalib 網站上查詢各個指標的含義。例如： wiki.mbalib.com/wiki/三重指數平滑…

縮寫	描述
K	KDJ中的K值
D	KDJ中的D值
J	KDJ中的J值
MACD	異同移動平均線
MOM	動量線
BIAS	乖離率
CMO	錢德動量擺動指標
TRIX	三重指數平滑平均線
OBV	能量潮
ROC	變更率指標
AMA	移動平均平行線差指標
VR	成交量變異率
PSY	心理線指標
Force Index	強力指數指標
DPO	區間震盪線
VHF	十字過濾線指標
RVI	相對活力指數

實現

先導入幾個包，除了talib、numpy和pandas之外還有stockstats、pandas_talib

import pandas as pd
import numpy as np
import talib
import stockstats
import pandas_talib
''' 這裏雖然沒有定義df這個變量，但這很明顯就是dateframe格式的某隻股票基礎數據 包括開盤價、收盤價、最高價、最低價和成交量 建議用tushare來獲取數據(固然僅限日數據) '''
stockStat = stockstats.StockDataFrame.retype(df)
close = df.close
highPrice = df.high
lowPrice = df.low
volume = df.volume

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而後把一些人家庫已經實現好的指標放出來

df.rename(columns={'close': 'Close', 'volume': 'Volume'}, inplace=True)

sig_k , sig_d  = talib.STOCH(np.array(highPrice), np.array(lowPrice),
np.array(close), fastk_period=9,slowk_period=3,
slowk_matype=0, slowd_period=3, slowd_matype=0)
sig_j = sig_k * 3 - sig_d  * 2
sig = pd.concat([sig_k, sig_d, sig_j], axis=1, keys=['K', 'D', 'J'])
sig['MACD'], MACDsignal, MACDhist = talib.MACD(np.array(close), fastperiod=6,
slowperiod=12, signalperiod=9)
sig['MOM'] = talib.MOM(np.array(close), timeperiod=5)
sig['CMO'] = talib.CMO(close, timeperiod=10)
sig['TRIX'] = talib.TRIX(close, timeperiod=14)
sig['OBV'] = talib.OBV(close, volume)
sig['ROC'] = talib.ROC(close, timeperiod=10)
sig['VR'] = stockStat['vr']
sig['Force_Index'] = pandas_talib.FORCE(df, 12)['Force_12']
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BIAS

def BIAS(close, timeperiod=20):
    if isinstance(close,np.ndarray):
        pass
    else:
        close = np.array(close)
        MA = talib.MA(close,timeperiod=timeperiod)
        return (close-MA)/MA

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AMA

def AMA(stockStat):
    return talib.MA(stockStat['dma'],  timeperiod=10)
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PSY

def PSY(priceData, period):
    difference = priceData[1:] - priceData[:-1]
    difference = np.append(0, difference)
    difference_dir = np.where(difference > 0, 1, 0)
    psy = np.zeros((len(priceData),))
    psy[:period] *= np.nan
    for i in range(period, len(priceData)):
    psy[i] = (difference_dir[i-period+1:i+1].sum()) / period
    return psy*100
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DPO

def DPO(close):  
    p = talib.MA(close, timeperiod=11)  
    p.shift()  
    return close-p
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VHF

def VHF(close):
    LCP = talib.MIN(close, timeperiod=28)
    HCP = talib.MAX(close, timeperiod=28)
    NUM = HCP - LCP
    pre = close.copy()
    pre = pre.shift()
    DEN = abs(close-close.shift())
    DEN = talib.MA(DEN, timeperiod=28)*28
    return NUM.div(DEN)
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def RVI(df):
    close = df.close
    open = df.open
    high = df.high
    low = df.low
    X = close-open+2*(close.shift()-open.shift())+
    2*(close.shift(periods=2)-open.shift(periods=2))*(close.shift(periods=3)-
    open.shift(periods=3))/6
    Y = high-low+2*(high.shift()-low.shift())+
    2*(high.shift(periods=2)-low.shift(periods=2))*(high.shift(periods=3)-
    low.shift(periods=3))/6
    Z = talib.MA(X, timeperiod=10)*10
    D = talib.MA(Y, timeperiod=10)*10
    return Z/D
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參考博客

pypi.org/project/sto…

anaconda.org/conda-forge…

www.geek-share.com/detail/2749…