Price Transform conversion price (TA-lib)

　　Price transfer function TA-Lib provided four modules, mainly for calculating the mean between the opening price, closing price, high, low, particularly in the following table.

AVGPRICE: average Price, average price function: ta.AVGPRICE (open, high, low, close)

MEDPRICE: Median Price, Median Price: ta.MEDPRICE (high, low)

TYPPRICE: Typical Price, Representative Price: ta.TYPPRICE (high, low, close)

WCLPRICE：Weighted Close Price，加权收盘价：ta.WCLPRICE(high,low,close)

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import talib as ta
import tushare as ts

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

def get_data(code, start='2015-01-01'):
    df = ts.get_k_data(code, start)
    df.index = pd.to_datetime(df.date)
    df =df.sort_index ()
     return DF 

DF = get_Data ( ' SH ' ) [[ ' Open ' , ' Close ' , ' High ' , ' Low ' ]] 

# open, high, low, close, mean 
DF [ ' Average ' ] = ta.AVGPRICE (df.open, df.high, df.low, df.close)
 # high, low median 
DF [ ' median ' ] = ta.MEDPRICE (df.high, DF .low)
 # high, low, closing price mean 
DF [ ' Typical' ] = Ta.TYPPRICE (df.high, df.low, df.close)
 # high, low, closing price weighted 
DF [ ' weight ' ] = ta.WCLPRICE (df.high, df.low, DF .close) 

df.head () 

df.loc [ ' 2019-01-01 ' :, [ ' Close ' , ' Average ' , ' Median ' , ' Typical ' , ' weight ' ] 
] .plot (figsize = (12 is ,. 6 )) 
AX = plt.gca () 
ax.spines [ 'right'] .set_color ( ' none ' ) 
ax.spines [ ' Top ' ] .set_color ( ' none ' ) 
plt.title ( ' Shanghai index and conversion price ' , fontSize = 15 ) 
plt.xlabel ( '' ) 
plt.show ()

Price Transform conversion price (TA-lib)

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