def buildHistory(codes=None, force_update=False):
import pandas as pd
if codes is None:
codes = genStockList(force_update=force_update)
stock = twstock.Stock(codes[0])
vols = pd.DataFrame({codes[0]: stock.capacity}, index=stock.date)
for code in codes[1:]:
stock = twstock.Stock(code)
vols[code] = stock.capacity
dump(vols, 'vol.pkl')
def get_best(stockid): # 檢查是否符合四大買賣點
stock = twstock.Stock(stockid)
bp = twstock.BestFourPoint(stock).best_four_point()
if (bp):
return ('買進' if bp[0] else '賣出', bp[1]) #←傳回買進或賣出的建議
else:
return (False, False) #←都不符合
def run(argv):
for sid in argv:
s = twstock.Stock(sid)
print('-------------- %s ---------------- ' % sid)
print('high : {:>5} {:>5} {:>5} {:>5} {:>5}'.format(*s.high[-5:]))
print('low : {:>5} {:>5} {:>5} {:>5} {:>5}'.format(*s.low[-5:]))
print('price: {:>5} {:>5} {:>5} {:>5} {:>5}'.format(*s.price[-5:]))
def getstockdata(code,year, month):
import twstock
stock = twstock.Stock(code)
stock.fetch(year, month) # 獲取 2000 年 10 月至今日之股票資料
return stock
def get_best(stockid):
stock = twstock.Stock(str(stockid))
bp = twstock.BestFourPoint(stock).best_four_point()
if (bp):
return (str(stockid), '買進' if bp[0] else '賣出', bp[1])
else:
return (False, False)
def GetOneStock(self,StockCode):
stock = twstock.Stock(StockCode)
high = stock.high
low = stock.low
price = stock.price
capacity = stock.capacity
try:
avg5_Price= stock.moving_average(stock.price,5)
avg10_Price= stock.moving_average(stock.price,10)
avg5_Cap= stock.moving_average(stock.capacity,5)
avg10_Cap= stock.moving_average(stock.capacity,10)
except:
avg5_Price = None
avg10_Price = None
avg5_Cap = None
avg10_Cap = None
self.AllStockData[StockCode] = {
'high' : high,
'low' : low,
'price':price,
'capacity': capacity,
'avg5_Price' : avg5_Price,
'avg10_Price': avg10_Price,
'avg5_Cap' : avg5_Cap,
'avg10_Cap' : avg10_Cap
}
time.sleep(random.uniform(10,15))
def run(argv, initial_fetch: bool=True, proxies_list: list=[]):
for sid in argv:
s = twstock.Stock(sid, initial_fetch=initial_fetch, proxies_list=proxies_list)
print('-------------- %s ---------------- ' % sid)
print('high : {:>5} {:>5} {:>5} {:>5} {:>5}'.format(*s.high[-5:]))
print('low : {:>5} {:>5} {:>5} {:>5} {:>5}'.format(*s.low[-5:]))
print('price: {:>5} {:>5} {:>5} {:>5} {:>5}'.format(*s.price[-5:]))
def on_enter_show_price(self, event):
global stock_number
stock = twstock.Stock(stock_number)
reply_token = event.reply_token
if weekdays == 5 or weekdays == 6:
send_text_message(
reply_token, '今日為週末，未開盤 \n近5個開盤日股價為： \n' +
str(stock.price[-5:]) + '\n輸入「早安」回到主選單')
else:
if hour_time < 900:
send_text_message(
reply_token, '今日尚未開盤\n近5個開盤日股價為： \n' +
str(stock.price[-5:]) + '\n輸入「早安」回到主選單')
elif hour_time >= 900 and hour_time < 1330:
send_text_message(
reply_token,
'目前開盤中，查詢時間為：' + str(local_dt) + '\n 近4個開盤日股價及現在股價為： \n' +
str(stock.price[-5:]) + '\n輸入「早安」回到主選單')
elif hour_time >= 1330:
send_text_message(
reply_token, '今日已收盤\n近4個開盤日股價及今日收盤股價為： \n' +
str(stock.price[-5:]) + '\n輸入「早安」回到主選單')
self.go_back()
def fetch_data(year, month):
data = twstock.Stock('0056')
with open('0056.txt', 'a+') as fh:
content = json.dumps(data.fetch(year, month), cls=RoundTripEncoder)
#logger.info(f"Fetch {year} {month}")
fh.write(content)
fh.write('\n')
def get_all_this_month():
year = int(input('year:'))
month = int(input('month:'))
for root, dirs, files in os.walk(os.getcwd() + '\\StockList'):
for f in files:
if f[-5:] == '.xlsx':
try:
number = int(f[:4])
stock = twstock.Stock(str(number))
# if twstock.codes[str(number)].market == '上市':
# time.sleep(Global.wait_time) # small delay for website block
# Global.wait_time -= 1
# if Global.wait_time < 2:
# Global.wait_time = 6
print(root, f)
file = openpyxl.load_workbook(root + '\\' + f)
sheet = file.get_sheet_by_name('Sheet1')
handle_data(stock, year, month, root + '\\raw%d%02d' % (year, month), sheet)
if Global.skip:
error_file = open(root + '\\raw%d%02d.fail' % (year, month), 'w')
error_file.close()
else:
if os.path.isfile(root + '\\' + '\\raw%d%02d.fail'):
os.remove(root + '\\' + '\\raw%d%02d.fail')
except:
print('get_history: unknown connect error3 ...')
time.sleep(60)
file.save(root + '\\' + f)
file.close()
def real_time_query_worker(realtime_list, bfp_list, idx, stock_id):
realtime_list[idx] = float(
twstock.realtime.get(stock_id)['realtime']['latest_trade_price'])
bfp = twstock.BestFourPoint(twstock.Stock(stock_id)).best_four_point()
if bfp:
bfp_list[idx] = 'B' if bfp[0] else 'S'
else:
bfp_list[idx] = ""
def fix_history():
for root, dirs, files in os.walk(os.getcwd() + '\\StockList'):
files.reverse()
for f in files:
if f[-5:] == '.fail':
file = openpyxl.load_workbook(root + '\\' + files[-1])
sheet = file.get_sheet_by_name('Sheet1')
print(root, files[-1], f)
number = int(files[-1][:4])
fail_year = int(f[3:7])
fail_month = int(f[7:9])
print('Stock:%d Y:%d M:%02d' % (number, fail_year, fail_month))
start_date = twstock.codes[str(number)][4].split('/')
start_date = datetime.date(int(start_date[0]), int(start_date[1]), int(start_date[2]))
target_date = datetime.date(2002, 11, 1)
if start_date > target_date:
target_date = start_date
year = fail_year
month = fail_month
stock = twstock.Stock(str(number))
i = 0
try:
while True:
# handle data
print(' %d %d parsing...' % (year, month))
if not os.path.isfile(root +'\\raw%d%02d'%(year,month)):
handle_history_data(stock, year, month, root + '\\raw%d%02d' % (year, month), sheet)
if Global.skip:
error_file = open(root +'\\raw%d%02d.fail'%(year,month), 'w')
error_file.close()
if year == datetime.date.today().year and month == datetime.date.today().month:
break
else:
if os.path.isfile(root + '\\' + f):
os.remove(root + '\\' + f)
if target_date.year == year and target_date.month == month:
print(target_date.year, target_date.month, ' end')
break
i += 1
month -= 1
if month == 0:
year -= 1
month = 12
file.save(root + '\\' + files[-1])
file.close()
except:
file.save(root + '\\' + files[-1])
file.close()
print('fix_history: unknown connect error ...')
time.sleep(60)
break # only scan once
def genLastDay(twStockList):
allStock = []
for key in sorted(twStockList.keys()):
stockInfo = twStockList[key]
# 先只撈股票
if stockInfo.type == '\u80a1\u7968' or stockInfo.type == '股票':
sleep(randint(8, 10))
eachStackData = {}
eachStackData["type"] = stockInfo.type
eachStackData["code"] = stockInfo.code
eachStackData["name"] = stockInfo.name
eachStackData["ISIN"] = stockInfo.ISIN
eachStackData["start"] = stockInfo.start
eachStackData["market"] = stockInfo.market
eachStackData["group"] = stockInfo.group
eachStackData["CFI"] = stockInfo.CFI
eachStackData["id"] = key
print("讀取中: " + stockInfo.name + "(" + stockInfo.code + ")")
stock = twstock.Stock(str(stockInfo.code))
# 取得各股一個月內有開盤的資料
dataNear31 = stock.fetch_31()
print(dataNear31)
eachStackData["datas"] = []
# 一個月內只取最新三天
for i in dataNear31[-3:]:
dayInfoTemp = {}
dayInfoTemp["date"] = str(i.date)
# capacity 總成交股數 (單位: 股)
dayInfoTemp["capacity"] = i.capacity
# turnover 總成交金額 (單位: 新台幣/元)
dayInfoTemp["turnover"] = i.turnover
# open 開盤價
dayInfoTemp["open"] = i.open
# high 盤中最高價
dayInfoTemp["high"] = i.high
# low 盤中最低價
dayInfoTemp["low"] = i.low
# close 收盤價
dayInfoTemp["close"] = i.close
# change 漲跌價差
dayInfoTemp["change"] = i.change
# transaction 成交筆數
dayInfoTemp["transaction"] = i.transaction
eachStackData["datas"].append(dayInfoTemp)
print(eachStackData)
allStock.append(eachStackData)
# 寫檔案
stockDataFile = open("stock.json", "w")
stockDataFile.write(
json.dumps(allStock, sort_keys=True, indent=4, separators=(',', ':')))
stockDataFile.close()
# genLast3Day(twStockList)
def __init__(self, twstock_id, start):
self.stock = twstock.Stock(twstock_id)
self.stock.fetch_from(start.year, start.month - 3)
self.ID = twstock_id
self.Name = twstock.codes[twstock_id].name
self.RawData = None
self.DataFrame = None
self.PNGFilename = '['+ datetime.date.today().strftime("%Y.%m.%d") + '][' + twstock_id + '] Result.png'
self.Result = None
def get_best(stockid):
stock = twstock.Stock(stockid)
bp = twstock.BestFourPoint(stock)
if bp != None:
if bp.best_four_point()[0] == True:
return ('買進', bp.best_four_point()[1])
else:
return ('賣出', bp.best_four_point()[1])
else:
return (False, False)
def on_enter_show_technical(self, event):
global stock_number
stock = twstock.Stock(stock_number)
bfp = twstock.BestFourPoint(stock)
comment = bfp.best_four_point_to_buy()
reply_token = event.reply_token
send_text_message(
reply_token,
'請至以下網址查詢： \n' + 'https://tw.stock.yahoo.com/q/ta?s=' +
str(stock_number) + '\n簡評：' + str(comment) + '\n\n輸入「早安」回到主選單')
self.go_back()
def get_best(stock_id): #參數:股票代碼 回傳:建議(買/賣)、建議資訊
stock = twstock.Stock(stock_id) #以股票代碼建立Stock物件
bp = twstock.BestFourPoint(stock).best_four_point()
'''回傳檢測四大買賣點資訊給bp
bp為tuple型態,若適合買進bp[0]為True,否則為False
bp[1]則是建議買/賣的原因(字串)'''
if (bp):
return ('買進' if bp[0] else '賣出', bp[1])
else:
return (False, False)
def monthP(Snum): #month Price
stockFig = "%s.png" % (Snum)
stock = twstock.Stock(Snum)
stockData = {"close": stock.close, "date": stock.date, "open": stock.open}
df1 = pd.DataFrame.from_dict(stockData)
df1.plot(x="date", y="close")
#heroku底下中文字型會出現KeyError，待解決
#plt.xlabel("日期", fontproperties = fontprop)
#plt.ylabel("股價", fontproperties = fontprop)
plt.title("[%s]" % (stock.sid))
plt.savefig(stockFig)
plt.close()
respon = uploadFig(stockFig)
return respon
def run(argv):
print('四大買賣點判斷 Best Four Point')
print('------------------------------')
for sid in argv:
bfp = twstock.BestFourPoint(twstock.Stock(sid))
bfp = bfp.best_four_point()
print('%s: ' % (sid), end='')
if bfp:
if bfp[0]:
print('Buy ', bfp[1])
else:
print('Sell ', bfp[1])
else:
print("Don't touch")
def download_today_prices():
gupiao_path = os.path.join(settings.BASE_DIR, 'static', 'goodadmin',
'gupiao.csv')
stock_cache = {}
#slist = load_stocks.read_stocklist('C:\\Study\\HTC\\Django\\tango_with_django_project\\static\\goodadmin\\gupiao.csv')
print(gupiao_path)
slist = load_stocks.read_stocklist(gupiao_path)
tday = datetime.datetime.utcnow().replace(tzinfo=pytz.UTC)
tday_tw = Talk2twStocks.UTC_To_Taipei(tday)
print(tday_tw)
stock_price_path = os.path.join(settings.BASE_DIR, 'static', 'goodadmin',
'stocks_' + str(tday_tw.date()) + '.csv')
stock_file = open(stock_price_path, 'w', newline='')
stock_writer = csv.writer(stock_file)
stock_writer.writerow(['code', 'price'])
failcount = 0
for scode in slist:
print('{0} processing...'.format(scode))
try:
rt_stock = twstock.Stock(scode, initial_fetch=False)
rt_stock.fetch_from(tday.year, tday.month)
time.sleep(1)
if rt_stock.close is not None and len(rt_stock.close) != 0:
p = rt_stock.close[-1]
stock_writer.writerow([scode, str(p)])
stock_cache[scode] = p
print(str(p))
else:
print('fails')
failcount = failcount + 1
except Exception:
print('something wrong happend with {0}'.format(scode))
continue
print('fail num {0}'.format(failcount))
stock_file.close()
return stock_cache, tday_tw
def buttonClicked(self):
sid = self.ui.comboBox.currentText().split("-")[0].strip()
days = int(self.ui.lineEdit.text().strip())
stockdata = twstock.realtime.get(sid)
self.ui.label_3.setText(stockdata["info"]["name"] + "(" + sid + ")" + ":" + " 收盤價:" + \
stockdata["realtime"]["latest_trade_price"] + " 開盤價:" + stockdata["realtime"]["open"] + \
" 最高價:" + stockdata["realtime"]["high"] + " 最低價:" + stockdata["realtime"]["low"] + \
" 時間:" + stockdata["info"]["time"])
stock = twstock.Stock(sid)
mean5 = stock.moving_average(stock.price, 5)
mean20 = stock.moving_average(stock.price, 20)
capacity = stock.capacity
for j in range(len(capacity)):
capacity[j] = int(capacity[j] / 1000)
self.ui.label_4.setText("近" + str(days) + "日股價:" + str(stock.price[-days:]).strip("[]"))
self.ui.label_5.setText("近" + str(days) + "日的五日均線" + str(mean5[-days:]).strip("[]"))
self.ui.label_6.setText("近" + str(days) + "日的二十日均線" + str(mean20[-days:]).strip("[]"))
self.ui.label_7.setText("近" + str(days) + "日的成交量(張)" + str(capacity[-days:]).strip("[]"))
self.ui.label_8.setText("倒數第二天之五日均價:" + str(mean5[-2]).strip("[]") + "倒數第二天之二十日均價:" + str(mean20[-2]).strip("[]") + \
"最後一天之五日均價:" + str(mean5[-1]).strip("[]") + "最後一天之二十日均價:" + str(mean20[-1]).strip("[]"))
if mean5[-2] < mean20[-2]:
if mean5[-1]>mean20[-1]:
self.ui.label_9.setText("五日均線突破二十日均線")
else:
self.ui.label_9.setText("五日均線仍然低於二十日均線")
else:
if mean5[-1] > mean20[-1]:
self.ui.label_9.setText("五日均線仍大於二十日均線")
else:
self.ui.label_9.setText("五日均線跌破二十日均線")
plt.cla()
plt.clf()
self.ui.figure.clf()
ax = self.ui.figure.add_axes([0.1, 0.1, 0.8, 0.8])
ax.set_title(sid)
ax.plot(range(1, 32), stock.price)
ax.set_xlabel("日期")
ax.set_ylabel("日股價")
self.ui.canvas.draw()
def get_four_best(stockid):
try:
print("等個15秒鐘~泡杯咖啡")
time.sleep(15)
stock = twstock.Stock(stockid)
best = twstock.BestFourPoint(stock).best_four_point()
if best:
if best[0]:
info = "買進: "
else:
info = "賣出: "
return (info, best[1])
else:
return (False, False)
except Error:
send_ifttt(stockid, "0.0", "程式出現錯誤")
print(Error)
def handle_message(event):
print(event)
text=event.message.text
if (text=="Hi"):
reply_text = "Hello John"
#Your user ID
elif(text=="你好"):
reply_text = "哈囉"
elif(text=="機器人"):
reply_text = "叫我嗎"
elif(text.startswith('#')):
text=text[1:]
content=''
stock_rt=twstock.realtime.get(text)
#my_datetime = datetime.fromtimestamp(stock_rt['timestamp']+8*60*60)
#my_time = my_datetime.strftime('%H:%M:%S')
#my_datetime = dt.fromtimestamp(stock_rt['timestamp']+8*60*60)
#my_time = my_datetime.strftime('%H:%M:%S')
content += '%s (%s) \n' %(
stock_rt['info']['name'],
stock_rt['info']['code'])
# my_time)
content += '現價: %s / 開盤: %s\n'%(
stock_rt['realtime']['latest_trade_price'],
stock_rt['realtime']['open'])
content += '最高: %s / 最低: %s\n' %(
stock_rt['realtime']['high'],
stock_rt['realtime']['low'])
content += '量: %s\n' %(stock_rt['realtime']['accumulate_trade_volume'])
stock = twstock.Stock(text)#twstock.Stock('2330')
content+='===='
content+='最近五日價格: \n'
price5 = stock.price[-5:][::-1]
date5 = stock.date[-5:][::-1]
for i in range(len(price5)):
#content += '[%s] %s\n' %(date5[i].strftime("%Y-%m-%d %H:%M:%S"), price5[i])
content += '[%s] %s\n' %(date5[i].strftime("%Y-%m-%d"), price5[i])
line_bot_api.reply_message(
event.reply_token,
TextSendMessage(text=content)
)
else:
reply_text = text
#message=TextSendMessage(text=event.message.text)
message=TextSendMessage(reply_text)
line_bot_api.reply_message(event.reply_token,message)
def long_strategy(stockid):
stock = twstock.Stock(stockid)
price20 = stock.price[-20:] #月
price100 = stock.price[-100:] #年
ma_p20 = np.mean(price20)
ma_p100 = np.mean(price100)
if ma_p20 >= ma_p100:
#print("可觀察，建議靠近月均價買進較佳，月線價格："+str(ma_p20))
str1 = "符合20週均線大於20日均線之策略，(建議價格靠近20週均價買進：" + str("%.2f" % ma_p20) + ")"
return str1
else:
#print("不符合上漲趨勢，不建議買進！")
str1 = "不符合20週均線大於20日均線之策略，不建議買進"
return str1
def main():
if len(sys.argv) != 2:
print("Please run the script as:\n$python3 adam.py stock_num")
exit(1)
stock_num = sys.argv[1]
stock = twstock.Stock(str(stock_num))
# Convert to ohlc data
quotes = []
for i in range(len(stock.date)):
quotes.append((mdates.date2num(stock.date[i]), stock.open[i],
stock.high[i], stock.low[i], stock.price[i]))
# Print candlestick
fig, ax = plt.subplots()
plt.title(str(stock_num))
weekday_candlestick(quotes, ax, width=0.5, colorup='r', colordown='k')
def GetDataByTry(strST_Number, intY, intM):
lstST_Data = []
stock = "No connection"
for i in range(100):
#print("Count: "+str(i))
j = 10*i+160
try:
stock = twstock.Stock(strST_Number)
lstPrice = stock.fetch(intY, intM)
print (lstPrice)
if lstPrice!= []:
#print(str(j) + " seconds: Get data")
return lstPrice, stock
except:
strTxt = (str(j) + " seconds: No data")
print (strTxt)
time.sleep(j)
return lstST_Data, stock
def getStockInfo(stock_code):
LOGGER.info(f"Processing {stock_code} ...")
retry = 0
stock = None
while retry <= StockInfoSetting.RETRY_MAX_TIME and stock is None:
try:
stock = twstock.Stock(stock_code)
break
except ConnectionError:
LOGGER.warning(
f'Grab {stock_code} Fail, Retry ({retry}/{StockInfoSetting.RETRY_MAX_TIME})'
)
except Exception:
LOGGER.warning(
f'Grab {stock_code} Fail, Retry ({retry}/{StockInfoSetting.RETRY_MAX_TIME})',
exc_info=True)
retry += 1
sleep(1)
return stock
def stockRT(Snum): #Stock Number
respon = ""
stock_rt = twstock.realtime.get(Snum)
cur_datetime = datetime.fromtimestamp(stock_rt["timestamp"] + 8 * 60 * 60)
cur_time = cur_datetime.strftime("%H:%M:%S")
respon += "%s (%s) %s\n" % (stock_rt["info"]["name"],
stock_rt["info"]["code"], cur_time)
respon += "現價: %s / 開盤: %s\n" % (
stock_rt["realtime"]["latest_trade_price"],
stock_rt["realtime"]["open"])
respon += "最高: %s / 最低: %s\n" % (stock_rt["realtime"]["high"],
stock_rt["realtime"]["low"])
respon += "量: %s\n" % (stock_rt["realtime"]["accumulate_trade_volume"])
#stock = twstock.Stock("2330")
stock = twstock.Stock(Snum)
respon += "-----\n"
respon += "最近五日價格: \n"
price5 = stock.price[-5:][::-1]
date5 = stock.date[-5:][::-1]
for i in range(len(price5)):
respon += "[%s] %s\n" % (date5[i].strftime("%Y-%m-%d"), price5[i])
return respon
def get_stock(cls, year, month):
path = get_h5_name(year, month)
if not os.path.exists(path):
stock_code_dtype = pd.CategoricalDtype(cls.get_twse().index)
todo = set(cls.get_twse().index)
if os.path.exists(f'{path}.done.pkl'):
df_s, done = pickle.load(open(f'{path}.done.pkl', 'rb'))
else:
df_s = []
done = set()
pickle.dump((df_s, done), open(f'{path}.done.pkl', 'wb'))
todo = todo.difference(done)
for i, stock_code in enumerate(todo):
try:
stock = ts.Stock(stock_code,
initial_fetch=False).fetch(year, month)
df_s.append(pd.DataFrame(stock))
df_s[-1]['code'] = pd.Series(stock_code,
index=df_s[-1].index,
dtype=stock_code_dtype)
done.add(stock_code)
print(
f'{stock_code} -- {100*len(done)/len(cls.get_twse().index):g}%'
)
pickle.dump((df_s, done), open(f'{path}.done.pkl', 'wb'))
except ConnectionError as e:
print(e)
cls._change_vpn()
except Exception as e:
print(e)
cls._change_vpn()
df = pd.concat(df_s)
df.set_index(['code', 'date'])
df.to_hdf(path, get_h5_key_name(year, month))
else:
df = pd.read_hdf(path, get_h5_key_name(year, month))
return df
def stock_pic(search_no):
stock_real = twstock.realtime.get(search_no)
stock_name = stock_real['info']['name']
stock_no = twstock.Stock(search_no)
close_price, dates = [], []
for stock_duration in stock_no.fetch_from(2019, 6):
close_price.append(stock_duration.close)
dates.append(stock_duration.date)
fig = plt.figure(dpi=80, figsize=(12, 8))
plt.plot(dates, close_price)
fig.autofmt_xdate(rotation=60)
plt.title(
stock_name,
fontproperties="simHei",
fontsize=24,
)
plt.xlabel("date", fontsize=14)
plt.ylabel("close_price", fontsize=14)
plt.tick_params(axis='both', labelsize=12, color='red')
plt.show()