两个计算股票技术性指标的包| Python 主题月

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安装命令如下:

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

可以在mbalib 网站上查询各个指标的含义。例如: wiki.mbalib.com/wiki/三重指数平滑…

缩写 描述
K KDJ中的K值
D KDJ中的D值
J KDJ中的J值
MACD 异同移动平均线
MOM 动量线
BIAS 乖离率
CMO 钱德动量摆动指标
TRIX 三重指数平滑平均线
OBV 能量潮
ROC 变动率指标
AMA 移动平均平行线差指标
VR 成交量变异率
PSY 心理线指标
Force Index 强力指数指标
DPO 区间震荡线
VHF 十字过滤线指标
RVI 相对活力指数

实现

先导入几个包,除了talib、numpy和pandas以外还有stockstats、pandas_talib

import pandas as pd
import numpy as np
import talib
import stockstats
import pandas_talib
'''
这里虽然没有定义df这个变量,但这很明显就是dateframe格式的某只股票基础数据
包括开盘价、收盘价、最高价、最低价和成交量
建议用tushare来获取数据(当然仅限日数据)
'''
stockStat = stockstats.StockDataFrame.retype(df)
close = df.close
highPrice = df.high
lowPrice = df.low
volume = df.volume

然后把一些人家库已经实现好的指标放出来

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']

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

AMA

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

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

DPO

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

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)
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

参考博客

pypi.org/project/sto…

anaconda.org/conda-forge…

www.geek-share.com/detail/2749…