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KDJCalculation.py
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KDJCalculation.py
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import tushare as ts
import pandas as pd
import numpy as np
import csv
import re, time
import string
##queryDate = '20190102'
queryDate = '20200408'
downloadStartDate = '20140101'
downloadEndDate = '20200430'
downloadStartDate = '20140101'
downloadEndDate = '20200720'
##
##startDate = '20150101'
##endDate = '20151231'
##
##startDate = '20160101'
##endDate = '20161231'
##
##startDate = '20170101'
##endDate = '20171231'
##
##
startDate = '20180101'
endDate = '20181231'
##
startDate = '20160101'
endDate = '20201231'
##startDate = '20200101'
##endDate = '20201231'
STOCK_FILE = '601688.csv' #华泰证券，
EXCHANGE_SHARE = 400
GRID_SET = 0.03 #网格差价
INITIAL_SHARE = 17001 #起始的股份，1000股，底仓
#****************************************************************************
######dwnld_stock_num = '512000'
######dwnld_stock_num = '159915'
######dwnldStartDate = '2018-01-01'
######dwnldEndDate = '2020-04-06'
######def get_hist_data():
###### print('开始请求数据-start')
###### df = ts.get_hist_data(dwnld_stock_num, start=dwnldStartDate, end=dwnldEndDate)
###### df = df.sort_index(ascending=True) #升序排列
###### print(df)
###### print('开始请求数据-2')
###### df.to_csv('C:/python/csv/'+dwnld_stock_num+'.csv', columns=['open', 'high','low','close','volume','ma20'])
###### print('开始请求数据-end')
download_stock_num='002746.SZ'
download_stock_num='601166.SH'
def get_hist_data_qfq(_freq):
print('开始请求数据-start')
#adj='qfq'前复权
if _freq == 'D':
dir_path == 'C:/python/csv/'
elif _freq == 'W':
dir_path = 'C:/python/csv_wk/'
elif _freq == 'M':
dir_path = 'C:/python/csv_mon/'
print (dir_path)
df = ts.pro_bar(ts_code=download_stock_num, adj='qfq', freq = _freq, start_date=downloadStartDate, end_date=downloadEndDate)
del df['ts_code']
df = df.sort_values(by='trade_date' , ascending=True)
print(df)
df.to_csv(dir_path+download_stock_num[0:6]+'.csv', columns=['trade_date','open', 'high','low','close'])
def get_k_data():
each_code = '300333'
#?????????????????
df_x = ts.get_k_data(code=each_code, start='2017-03-01')
print (df_x)
ma5 = df_x['close'][-5:].mean()
ma10 = df_x['close'][-10:].mean()
print (ma5)
print (ma10)
#读取某天交易的所有股票信息
def saveOneDayAllCsv():
print('开始保存所有股票信息......')
pro = ts.pro_api()
df = pro.query('daily_basic', ts_code='', trade_date=queryDate,fields='ts_code,pe,pb,total_share,float_share')
df.to_csv('C:/python/csv/oneDayAllStock.csv')
print('完成保存所有股票信息')
#指定某个日期查询PB
def getPB(_date, _stock_num):
pro = ts.pro_api()
df = pro.query('daily_basic', ts_code=_stock_num, trade_date=_date,fields='ts_code,pe,pb,total_share,float_share')
pb = df['pb'][0]
return pb
######def getPB():
######## print('查询PB')
###### pro = ts.pro_api()
###### df = pro.query('daily_basic', ts_code='601688.SH', trade_date=queryDate,fields='ts_code,pe,pb,total_share,float_share')
######## time.sleep(1) #您每分钟最多访问该接口200次. 周线和月线1秒只能下载2次
###### pb = df['pb'][0]
###### return pb
######## print (pb)
######## pb = df.pb
######## print (pb)
######## df.to_csv('C:/python/csv/oneDayAllStock.csv')
######## print('完成查询PB')
#从保存的所有股票文件中，把总股本小于5亿的过滤出来，
#并且把每支股票某个时间段内 startDate--endDate 的交易信息各自保存到自己的文件里。
def saveTotalShareBelow5ToEachFile(_freq):
print('开始保存所有股票的前复权数据......')
cnt = 0
if _freq == 'D':
dir_path = 'C:/python/csv/'
elif _freq == 'W':
dir_path = 'C:/python/csv_wk/'
elif _freq == 'M':
dir_path = 'C:/python/csv_mon/'
with open('C:/python/csv/oneDayAllStock.csv', 'r', encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
df = ts.pro_bar(ts_code=row['ts_code'], adj='qfq', freq = _freq, start_date=downloadStartDate, end_date=downloadEndDate)
df = df.sort_values(by='trade_date' , ascending=True)
df.to_csv(dir_path +row['ts_code'][0:6]+'.csv', columns=['trade_date','open', 'high','low','close'])
cnt =cnt +1
print('完成保存所有股票的前复权数据 '+dir_path, row['ts_code'][0:6], cnt)
time.sleep(1) #周线和月线1秒只能下载2次
## if (float(row['total_share']) < 500000000000): #and re.match('300', row['ts_code']):
## try:
## df = ts.get_hist_data(row['ts_code'][:6], start=dwnldStartDate, end=dwnldEndDate)
## df = df.sort_index(ascending=True)
## df.to_csv('C:/python/csv/'+ row['ts_code'][:6] + '.csv', columns=['open','high','low','close','volume','p_change','ma5','ma20'])
## except Exception as e:
## print(row['ts_code'],end=':')
## print(e)
print('完成保存所有股票的前复权数据 '+dir_path)
def string_to_float(str):
return float(str)
def roundf2(data):
return round(float(data), 2)
def extract_csv_data(row, _date,_o,_h,_l,_c,_v):
if row[1] == 'trade_date':
return
if row[1] < startDate or row[1] > endDate:
return
_date.append(row[1])
_o.append(float(row[2]))
_h.append(float(row[3]))
_l.append(float(row[4]))
_c.append(float(row[5]))
########STOCK_FILE = '600030.csv' #中信证券
######STOCK_FILE = '601318.csv' #中信证券
########STOCK_FILE = '512000.csv' #券商etf
##DATA_PATH = 'C:/python/csv/'
##
##DATA_PATH = 'C:/python/csv_wk/'
##DATA_PATH_NAME = DATA_PATH + STOCK_FILE
def read_csv_file(_freq, _date,_open,_high,_low,_close,_volume):
if _freq == 'D':
dir_path = 'C:/python/csv/'
elif _freq == 'W':
dir_path = 'C:/python/csv_wk/'
elif _freq == 'M':
dir_path = 'C:/python/csv_mon/'
DATA_PATH_NAME = dir_path + STOCK_FILE
print (DATA_PATH_NAME)
with open(DATA_PATH_NAME,"r",encoding="utf-8") as csvfile:
#读取csv文件，返回的是迭代类型
reader = csv.reader(csvfile)
for row in reader :
extract_csv_data(row, _date,_open,_high,_low,_close,_volume)
kdj_stock_num = '601688.SH'
def kdj_cal(_freq): #均匀KDJ
## print('说明1：因为当天最高最低不知道哪个先发生，所以一天只能买或者卖，优先买\
## 说明2：如果计算出的次日卖出价，比次日开盘价低，以开盘价计算成交价，并补偿修正利润')
print (STOCK_FILE)
N = 9
K = 50
D = 50
buy_flag = False
total_profit = 1
_date=[];_open=[];_high=[];_low=[];_close=[];_volume=[]; #define data
read_csv_file(_freq,_date,_open,_high,_low,_close,_volume)
length = len(_close)
if length== 0:
return
for i in range(0, length):
if i-N>0:
low = min(_low[i-N+1:i+1])
high = max(_high[i-N+1:i+1])
RSV = round((_close[i]-low)*100/(high - low),2)
K = round((2*K/3+RSV/3),2)
D = round((2*D/3+K/3),2)
J = round(3*K-2*D,2)
## if K<20 and buy_flag == False:
## buy_flag = True
## buy_price = _close[i]
## print (_date[i],'buy',buy_flag, ' K_value=',K,' Price=',_close[i])
if K<20 and buy_flag == False:
pb = getPB(_date[i],kdj_stock_num)
## print (pb,_date[i],K)
if pb <1.5:
buy_flag = True
buy_price = _close[i]
print (_date[i],'buy',buy_flag, ' K_value=',K,' Price=',_close[i],' PB=',pb)
elif K>75 and buy_flag == True:
buy_flag = False
profit = (_close[i] - buy_price)/buy_price
total_profit = total_profit*(1+profit)
print (_date[i],'sell',buy_flag, ' K_value=',K, ' Price=',_close[i], ' Profit=',round(profit,3), ' TotalProfit=',round(total_profit,3))
def main():
_freq = 'D' #下载日线
## _freq = 'W' #下载周线
## _freq = 'M' #下载月线
######## get_k_data() #debuf
## get_hist_data() #下载下载etf，基金等品种，如：券商etf 512000
## get_hist_data_qfq(_freq) #前复权下载某个股票的历史数据，参数为日线，周线，月线
## saveOneDayAllCsv() #只调用一次即可
## saveTotalShareBelow5ToEachFile(_freq) #只调用一次即可，参数为日线，周线，月线
kdj_cal(_freq)
if __name__ == '__main__':
print('开始请求数据')
main()