股票常用指标

指标

先把要用指标放上来

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

import pandas as pd
import numpy as np
import talib
import stockstats
import pandas_talib
import tushare as ts

df=ts.get_hist_data('601857')

df1=df.copy()
'''
  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




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.DataFrame()

sig['K']=list(sig_k)
sig['D']=list(sig_d)
sig['J']=list(sig_j)



#sig = pd.DataFrame([list(sig_k), list(sig_d), list(sig_j)],  columns=['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(np.array(close), timeperiod=10)
sig['TRIX'] = talib.TRIX(np.array(close), timeperiod=14)
sig['OBV'] = talib.OBV(np.array(close), volume)
sig['ROC'] = talib.ROC(np.array(close), timeperiod=10)
sig['VR'] = list(stockStat['vr'])
sig['Force_Index'] = list(pandas_talib.FORCE(df, 12)['Force_12'])

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

bias=BIAS(close, timeperiod=20)

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

ama=AMA(stockStat)

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

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)

vhf=VHF(close)

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

rvi=RVI(df1)