def fetch(twid='2330', csv="", start_time=None, end_time=datetime.now()):
if start_time == None:
start_time = datetime.strptime(start, "2010-1-1")
if csv == "":
csv = twid + ".csv"
stock = Stock(twid)
stock.fetch_from(int(start_time.strftime("%Y")),
int(start_time.strftime("%m")))
stock_to_googleCSV(stock, csv)
def get_data(ticker):
stock = Stock(ticker)
index = Stock('3056')
data = stock.fetch_from(n_yrs_ago.year, n_yrs_ago.month)
data_index = index.fetch_from(n_yrs_ago.year, n_yrs_ago.month)
s_close = []
tpe_index = []
for i in range(len(data)):
s_close.append(data[i][6])
tpe_index.append(data_index[i][6] * 500)
df_close = pd.DataFrame(s_close)
df_index = pd.DataFrame(tpe_index)
df_close_index = pd.DataFrame(list(zip(s_close, tpe_index)))
conv_to_csv(df_close, df_index, df_close_index, ticker)
frame = [df_close, df_index]
return frame
def complate_data(self, sid):
ret = False
last_date = self.date[-1]
stock = Stock(sid, initial_fetch=False)
fetch_from_date = mdates.num2date(last_date) + timedelta(-10)
stock.fetch_from(fetch_from_date.year, fetch_from_date.month)
col_num = len(self.raw[0]) # csv 檔的 column 數
append_str = ""
for index in range(0, col_num - 5):
append_str = append_str + ",0"
for index in range(-len(stock.date), 0):
d = stock.date[index]
o = stock.open[index]
h = stock.high[index]
l = stock.low[index]
c = stock.close[index]
if (mdates.date2num(d) > last_date):
str = "{0},{1},{2},{3},{4}".format(d.strftime('%Y/%m/%d'), o,
h, l, c)
print("資料回補：" + str)
f = open(self.filename, 'a+')
f.write(str + append_str + "\n")
f.close()
ret = True
#return ret # no yahoo
#yahoo 資料確認
# d,o,h,l,c = self.complete_data_yahoo(sid)
# #print(d,o,h,l,c)
# try:
# if(mdates.date2num(d) > mdates.date2num(stock.date[-1]) and mdates.date2num(d) > last_date ):
# str = "{0},{1},{2},{3},{4}".format(d.strftime('%Y/%m/%d'),o,h,l,c )
# print("資料回補 yahoo：" + str)
# f = open(self.filename, 'a+')
# f.write(str+append_str+"\n")
# f.close()
# ret = True
# except:
# print("Error 資料回補 yahoo：" + str)
# pass
# 有更新資料時，回傳True
return ret
def dump_updated_sid(dir_path, codes):
"""
stock = Stock('2330')
ma_p = stock.moving_average(stock.price, 5) # 計算五日均價
ma_c = stock.moving_average(stock.capacity, 5) # 計算五日均量
ma_p_cont = stock.continuous(ma_p) # 計算五日均價持續天數
ma_br = stock.ma_bias_ratio(5, 10) # 計算五日、十日乖離值
"""
for sid in codes:
print('code {}'.format(sid))
stock = Stock(sid)
stock.fetch_from(2018, 8)
# Data(date=datetime.datetime(2017, 5, 2, 0, 0), capacity=45851963, turnover=9053856108, open=198.5, high=199.0, low=195.5, close=196.5, change=2.0, transaction=15718)
sid2date2data = {}
for ins in stock.data:
sys.stdout.write('.')
date = ins.date.strftime('%Y%m%d')
open_p = ins.open
close_p = ins.close
high = ins.high
low = ins.low
change = ins.change
capacity = ins.capacity
transaction = ins.transaction
turnover = ins.turnover
if sid not in sid2date2data:
sid2date2data[sid] = {}
data = (high, low, open_p, close_p, change, capacity, transaction,
turnover)
sid2date2data[sid][date] = data
for sid in sid2date2data:
file_path = os.path.join(dir_path, '{}.csv'.format(sid))
with open(file_path, 'w') as w:
for date in sorted(sid2date2data[sid].keys()):
high, low, open_p, close_p, change, capacity, transaction, turnover = sid2date2data[
sid][date]
w.write(','.join(
map(str, [
sid, date, open_p, close_p, high, low, change,
capacity, transaction, turnover
])) + '\n')
print('')
import DateAgoTest
import pandas as pd
import matplotlib.pyplot as plt
from talib import abstract
from twstock import Stock
stockID = '2330'
stock = Stock(stockID)
total = []
DList = DateAgoTest('2019-07', 7)
stockData = stock.fetch_from(2019, 7)
head = ["date", "capacity", "turnover", "open",
"high", "low", "close", "change", "transaction"]
for value in stockData:
day = str(value[0]).split()
if day[0] in DList:
total.append([day[0], value[1], value[2], value[3],
value[4], value[5], value[6], value[7], value[8])
df = pd.DataFrame(total, columns = head)
df.set_index('date', inplace = True)
df['close'].plot(figsize=(16, 8))
else:
yrs = localtime.tm_year
mon = localtime.tm_mon
print(yrs, mon)
stock_num = input("Enter Stock: ")
for i in range(1, 6):
time.sleep(1)
print("Wait.... ", 5 - i, "second")
stock = Stock(stock_num)
print("First Stage complete ")
data = pd.DataFrame(stock.fetch_from(yrs, mon))
data.set_index('date', inplace=True)
data.index = data.index.format(formatter=lambda x: x.strftime('%Y-%m-%d'))
print("Fetching complete")
fig = plt.figure(figsize=(24, 16))
#用add_axes創建副圖框
ax = fig.add_axes([0.05, 0.25, 0.95, 0.75])
ax2 = fig.add_axes([0.05, 0.05, 0.95, 0.25])
ax.set_title(stock_num + "KChart", fontsize=12)
ax2.set_xticks(range(0, len(data.index), 10))
ax2.set_xticklabels(data.index[::10], rotation=0)
#使用mpl_finance套件
mpf.candlestick2_ochl(ax,
data['open'],
data['close'],
#!/usr/bin/env python3
import pandas as pd
import matplotlib.pyplot as plt
from talib import abstract
from twstock import Stock
stock = Stock('2330')
df = pd.DataFrame(stock.fetch_from(2019, 7))
df.set_index('date', inplace=True)
df['close'].plot(figsize=(16, 8))
plt.show()
import pandas as pd
from datetime import datetime
import twstock
from twstock import Stock
import time
from tqdm import tqdm
stock = Stock("2330")
time.sleep(2)
# 延遲兩秒
stock.fetch_from(2020, 5)
date = stock.date #日期
high_price = stock.high #盤中最高價
low_price = stock.low # 盤中最低價
close_price = stock.close #收盤價
open_price = stock.open # 開盤價
capacity = stock.capacity # 總成總成交股數
turnover = stock.turnover # 總成交金額
change = stock.change # 漲跌價差
transaction = stock.transaction # 成交筆數
day_number = len(date)
# 建立 和天數相符的 [0] list
day_list = []
day_list = [0] * day_number
#把取出的time 轉乘int 再存入day_list
for i in tqdm(range(day_number)):