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Tools.py
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# 撈股票
import requests
import json
import pandas as pd
import matplotlib.pyplot as plt
from datetime import date
import talib
import mpl_finance as mpf
import seaborn as sns
import numpy as np
import datetime
from dateutil.relativedelta import relativedelta
import index_generate
import csv
import time
import os
# plt.rcParams['font.family']='SimHei' # 顯示中文('SimHei' for MacOS)
from sklearn import preprocessing
global df1
def checkCodeInDir(stockcode): # 查詢是否存在，存在回傳1 不存在回傳0 並建立
PATH_TO_STOCKDATA = './stock_data'
raw_target = stockcode + '.json'
PATH_input_json = './stock_data/'+stockcode+'.json'
PATH_TO_STOCKDATA_INDEX = './stock_data_index'
index_target = stockcode + '_index.json'
rawInFile = 0
indexInFile = 0
df_raw = pd.DataFrame()
df_index = pd.DataFrame()
for root, dirs, files in os.walk(PATH_TO_STOCKDATA):
if raw_target in files:
print('found data ['+ raw_target + ']at : '+root)
rawInFile = 1
df_raw=pd.read_json(PATH_input_json)
if(rawInFile==0):
print("["+ raw_target + "] is not be found ,crawling data from ''https://www.twse.com.tw/exchangeReport/STOCK_DAY?'',\nplease wait ")
df_raw=get_stockdata(stockcode)
for root, dirs, files in os.walk(PATH_TO_STOCKDATA_INDEX):
if index_target in files:
print('found data ['+ index_target + ']at : '+root)
indexInFile = 1
if (indexInFile == 0):
print(" [" + index_target + "] is not be found ,stock_index_generator is running,\nplease wait ")
df_index=index_generate.stock_index_generator(df_raw,stockcode)
else:
PATH_TO_STOCKDATA_INDEX = './stock_data_index/'
index_target = stockcode+'_index.json'
df_index = pd.read_json(PATH_TO_STOCKDATA_INDEX+index_target)
return df_raw,df_index
def get_stockdata(stockcode): # 接從網路抓股票代碼 並儲存在./stock_data/XXXXX.json 裡面
date = "2010/01/01"
inday = datetime.datetime.strptime(date, "%Y/%m/%d")
ago = [(inday + relativedelta(months=+i)).strftime('%Y%m%d')
for i in range(131)]
month = ago[0]
data = {
'response': 'json',
'date': month,
'stockNo': stockcode,
}
dres = requests.get(
'https://www.twse.com.tw/exchangeReport/STOCK_DAY?', params=data)
result = json.loads(dres.text)
df1 = pd.DataFrame(result['data'])
df1.columns = result['fields']
print(month)
time.sleep(6)
for month in ago[1:119]:
data = {
'response': 'json',
'date': month,
'stockNo': stockcode,
}
dres = requests.get(
'https://www.twse.com.tw/exchangeReport/STOCK_DAY?', params=data)
result = json.loads(dres.text)
tmp = pd.DataFrame(result['data'])
tmp.columns = result['fields']
df1 = df1.append(tmp).reset_index(drop=True)
print(month)
time.sleep(3)
df1['開盤價'] = df1['開盤價'].str.replace(
',', '').astype(dtype=float, errors='ignore')
df1['最高價'] = df1['最高價'].str.replace(
',', '').astype(dtype=float, errors='ignore')
df1['最低價'] = df1['最低價'].str.replace(
',', '').astype(dtype=float, errors='ignore')
df1['收盤價'] = df1['收盤價'].str.replace(
',', '').astype(dtype=float, errors='ignore')
df1['成交股數'] = df1['成交股數'].str.replace(
',', '').astype(dtype=float, errors='ignore')
df1['成交金額'] = df1['成交金額'].str.replace(
',', '').astype(dtype=float, errors='ignore')
df1['成交筆數'] = df1['成交筆數'].str.replace(
',', '').astype(dtype=float, errors='ignore')
df1['漲跌價差'] = df1['漲跌價差'].str.replace('+', '')
df1['漲跌價差'] = df1['漲跌價差'].str.replace(
'X', '').astype(dtype=float, errors='ignore')
pd.set_option('display.max_rows', 1000)
pd.set_option('display.max_columns', 1000)
indexNames = df1[df1['成交金額'] == 0].index
df1 = df1.drop(indexNames)
df1 = df1.reset_index(drop=True)
df1.to_json('./stock_data/' + stockcode + '.json')
return (df1)
def image_generate(DATE): # 輸入時間、已存在json的股票代碼 輸出加上今日往前推算19日的指標圖片 日期須>=20100226
pass
def get_list_ans(stockcode):
PATH_input_json = './stock_data/'+stockcode+'.json'
df1 = pd.read_json(PATH_input_json)
for index, row in df1.iteritems():
indexNames = df1[df1[index] == '--'].index
df1 = df1.drop(indexNames)
df1.sort_index(inplace=True)
df1 = df1.fillna(0)
df1 = df1.drop(range(14))
df1 = df1.reset_index(drop=True)
List_Low = np.array(df1['最低價'])
List_Hig = np.array(df1['最高價'])
List_price = np.array(df1['收盤價'])
List_ans = np.zeros(19,dtype=int)
List_ans_V3=np.zeros(19,dtype=int)
for i in range(19, List_price.size-1):
LL = float(List_Low[i-1])
LH = float(List_Hig[i-1])
CL = float(List_Low[i])
CH = float(List_Hig[i])
RL = float(List_Low[i+1])
RH = float(List_Hig[i+1])
LP = float(List_price[i-1])
CP = float(List_price[i])
RP = float(List_price[i + 1])
cnt = 2
while(CP == LP):
LP = float(List_price[i-cnt])
cnt = cnt+1
cnt = 2
while(CP == RP):
RP = float(List_price[i+cnt])
cnt = cnt + 1
if LP > CP and RP>CP:
List_ans = np.append(List_ans, int(1))
elif CP > LP and CP>RP:
List_ans = np.append(List_ans, int(-1))
else:
List_ans = np.append(List_ans, int(0))
if RP > CP:
List_ans_V3 = np.append(List_ans_V3,int(1))
else:
List_ans_V3 = np.append(List_ans_V3,int(-1))
'''
if LL>CH and RL>CH :
List_ans=np.append(List_ans,1)
elif CL>=LH and CL>RH:
List_ans=np.append(List_ans,-1)
else:
List_ans=np.append(List_ans,0)
'''
List_ans = np.append(List_ans, 0)
List_ans_V3=np.append(List_ans_V3, 0)
import sys
np.set_printoptions(threshold = sys.maxsize)
return List_ans, List_price, List_ans_V3
def get_list_ans(stockcode):
PATH_input_json = './stock_data/'+stockcode+'.json'
df1 = pd.read_json(PATH_input_json)
for index, row in df1.iteritems():
indexNames = df1[df1[index] == '--'].index
df1 = df1.drop(indexNames)
df1.sort_index(inplace=True)
df1 = df1.fillna(0)
df1 = df1.drop(range(14))
df1 = df1.reset_index(drop=True)
List_Low = np.array(df1['最低價'])
List_Hig = np.array(df1['最高價'])
List_price = np.array(df1['收盤價'])
List_ans = np.zeros(19,dtype=int)
List_ans_V3=np.zeros(19,dtype=int)
for i in range(19, List_price.size-1):
LL = float(List_Low[i-1])
LH = float(List_Hig[i-1])
CL = float(List_Low[i])
CH = float(List_Hig[i])
RL = float(List_Low[i+1])
RH = float(List_Hig[i+1])
LP = float(List_price[i-1])
CP = float(List_price[i])
RP = float(List_price[i + 1])
cnt = 2
while(CP == LP):
LP = float(List_price[i-cnt])
cnt = cnt+1
cnt = 2
while(CP == RP):
RP = float(List_price[i+cnt])
cnt = cnt + 1
if LP > CP and RP>CP:
List_ans = np.append(List_ans, int(1))
elif CP > LP and CP>RP:
List_ans = np.append(List_ans, int(-1))
else:
List_ans = np.append(List_ans, int(0))
if RP > CP:
List_ans_V3 = np.append(List_ans_V3,int(1))
else:
List_ans_V3 = np.append(List_ans_V3,int(-1))
'''
if LL>CH and RL>CH :
List_ans=np.append(List_ans,1)
elif CL>=LH and CL>RH:
List_ans=np.append(List_ans,-1)
else:
List_ans=np.append(List_ans,0)
'''
List_ans = np.append(List_ans, 0)
List_ans_V3=np.append(List_ans_V3, 0)
import sys
np.set_printoptions(threshold = sys.maxsize)
return List_ans, List_price, List_ans_V3
def get_list_ans_V4(stockcode):
PATH_input_json = './stock_data/'+stockcode+'.json'
df1 = pd.read_json(PATH_input_json)
for index, row in df1.iteritems():
indexNames = df1[df1[index] == '--'].index
df1 = df1.drop(indexNames)
df1.sort_index(inplace=True)
df1 = df1.fillna(0)
df1 = df1.drop(range(14))
df1 = df1.reset_index(drop=True)
List_price = np.array(df1['收盤價']).astype(np.float64)
List_ans = np.zeros(19,dtype=int)
for i in range(19, List_price.size - 1):
Begin=i-19
End=i-1
Mid=i-10
Max=i-19
Min=i-19
maxv=List_price[Begin]
minv=List_price[Begin]
for j in range(Begin,End):
num=List_price[j]
if(num>maxv):
maxv=num
Max=j
elif(num<minv):
minv=num
Min=j
if Max==Mid:
List_ans = np.append(List_ans, -1)
elif Min==Mid:
List_ans = np.append(List_ans, 1)
else:
List_ans = np.append(List_ans, 0)
List_ans = np.append(List_ans, 0)
import sys
np.set_printoptions(threshold = sys.maxsize)
return List_ans, List_price
if __name__ == "__main__":
checkCodeInDir("2303")