def getClickButtonData(bot, update):
data = intraday.meta(apiToken=api_token, symbolId=num, output='raw')
if update.callback_query.data == 'info':
if 'industryZhTw' in data:
text = ('產業別：' + data['industryZhTw'] + '\n' + '交易幣別：' +
data['currency'] + '\n' + '股票中文簡稱：' + data['nameZhTw'] +
'\n' + '開盤參考價：' + str(data['priceReference']) + '\n' +
'漲停價：' + str(data['priceHighLimit']) + '\n' + '跌停價：' +
str(data["priceLowLimit"]) + '\n' + '股票類別：' +
data['typeZhTw'])
else:
text = ('交易幣別：' + data['currency'] + '\n' + '股票中文簡稱：' +
data['nameZhTw'] + '\n' + '開盤參考價：' +
str(data['priceReference']) + '\n' + '漲停價：' +
str(data['priceHighLimit']) + '\n' + '跌停價：' +
str(data["priceLowLimit"]) + '\n' + '股票類別：' +
data['typeZhTw'])
update.callback_query.message.reply_text(text)
if update.callback_query.data == 'trade':
df2 = intraday.quote(apiToken=api_token, symbolId=num, output='raw')
df3 = df2['trade']
text = ('• ' + data['nameZhTw'] + '(' + num + ')' + '最新一筆交易：' + '\n' +
'成交價：' + str(df3['price']) + '\n' + '成交張數：' +
str(df3['unit']) + '\n' + '成交量：' + str(df3['volume']) + '\n' +
'成交序號：' + str(df3['serial']))
update.callback_query.message.reply_text(text)
def target_change_strategy(self, symbol_id, rise_target_change,
drop_target_change):
message = intraday.quote(apiToken=self.api_token,
symbolId=symbol_id,
output='raw')
current_price = message['trade']['price']
symbol_info = intraday.meta(symbolId=symbol_id,
apiToken=self.api_token,
output='raw')
adjust_open = symbol_info['priceReference']
if (current_price - adjust_open) / adjust_open > rise_target_change:
self.lineNotifyMessage('快訊！' + symbol_id + '漲幅已經高過' +
str(rise_target_change * 100) + '%\n' +
'https://www.fugle.tw/trade?symbol_id=' +
symbol_id + '&openExternalBrowser=1')
elif (current_price - adjust_open) / adjust_open < -drop_target_change:
self.lineNotifyMessage('快訊！' + symbol_id + '跌幅已經低過' +
str(drop_target_change * 100) + '%\n' +
'https://www.fugle.tw/trade?symbol_id=' +
symbol_id + '&openExternalBrowser=1')
else:
pass
return current_price, adjust_open
def serv_buy_sell(self, keyword, msg_received):
"""
To get information for buy sell in a day.
"""
print('【serv_buy_sell】')
# Check whether has share number in memory
if self.temp_share_no:
share_data = intraday.meta(apiToken=FUGLE_API_TOKEN,
symbolId='{}'.format(
self.temp_share_no),
output='raw')
if share_data['canDayBuySell'] == True and share_data[
'canDaySellBuy'] == True:
text = "菜雞調查後 😃，建議【{}】{}可進行當冲交易，但最終決定在您手中！😉".format(
self.share_db[self.temp_share_no], self.temp_share_no)
else:
text = "菜雞調查後 😃，建議【{}】{}可不進行當冲交易，但最終決定在您手中！😉".format(
self.share_db[self.temp_share_no], self.temp_share_no)
else:
text = "抱歉，菜雞不懂您 😥，無法得知您想瞭解的股票！請您重新輸入！"
self.prev_action = "serv_buy_sell"
self.out_msg = self.temp_msg = text
success = self.send_message()
return success
def getStockInfo(self, id):
ret = dict()
output_meta = intraday.meta(apiToken=self.apiToken,
output="dataframe",
symbolId=id)
output_chart = intraday.chart(apiToken=self.apiToken,
output="dataframe",
symbolId=id)
ref_price = output_meta.iloc[
0, output_meta.columns.get_loc("priceReference")]
current_price = output_chart.iloc[
-1, output_chart.columns.get_loc("close")]
if ref_price > current_price:
graph = output_chart.plot(x="at", y='close', color='green')
elif ref_price == current_price:
graph = output_chart.plot(x="at", y='close', color='gray')
else:
graph = output_chart.plot(x="at", y='close', color='red')
graph.axhline(ref_price,
linestyle='dashed',
color='xkcd:dark grey',
alpha=0.6,
label='參考價',
marker='')
graph.legend().set_visible(False)
fig = graph.get_figure()
buf = io.BytesIO()
fig.savefig(buf, format='png')
ret['Name'] = output_meta.iloc[0,
output_meta.columns.get_loc("nameZhTw")]
ret['RealPrice'] = current_price
ret['ID'] = id
ret['photo'] = buf
return ret
def _get_meta(self, symbol_id):
meta = intraday.meta(apiToken=self.api_token, symbolId=symbol_id)
reply = '此為【' + meta['nameZhTw'][0] + '】的基本資訊\n'\
'◆ 股票代碼：' + symbol_id + '\n'\
'◆ 交易幣別代號：' + meta['currency'][0] + '\n'\
'◆ 股票類型：' + meta['typeZhTw'][0] + '\n'\
'◆ 是否為指數：' + self._change(meta['isIndex'][0]) + '\n'\
'◆ 開盤參考價：' + str(meta['priceReference'][0]) + '\n'\
'◆ 漲停價：' + str(meta['priceHighLimit'][0]) + '\n'\
'◆ 跌停價：' + str(meta['priceLowLimit'][0])
return reply
def priceReference(bot, update):
q_data = intraday.meta(apiToken=api_token,
symbolId=str(aa[0]),
output="raw")['priceReference']
qq_data = intraday.meta(apiToken=api_token,
symbolId=str(aa[0]),
output="raw")['priceHighLimit']
qqq_data = intraday.meta(apiToken=api_token,
symbolId=str(aa[0]),
output="raw")['priceLowLimit']
qq = 'priceReference' + str(q_data)
qq1 = 'priceHighLimit' + str(qq_data)
qq2 = 'priceLowLimit' + str(qqq_data)
# print(qq)
update.message.reply_text(qq)
update.message.reply_text(qq1)
update.message.reply_text(qq2)
print(qq)
def get_stock_info(self, num):
data = intraday.meta(apiToken=self.config['FUGLE']['fugle_TOKEN'] , symbolId=num , output='raw')
# data = intraday.meta(apiToken=Bot.config['FUGLE']['fugle_TOKEN'] , symbolId='0050', output='raw')
df1 = intraday.chart(apiToken=self.config['FUGLE']['fugle_TOKEN'] , symbolId=num)
# df1 = intraday.chart(apiToken=Bot.config['FUGLE']['fugle_TOKEN'] , symbolId='0050')
if 'error' in df1.columns:
return '請輸入正確的股票代碼：'
elif df1.shape[0] == 0:
if 'nameZhTw' not in data:
text = 'api 無回應'
elif 'industryZhTw' in data:
text = "【{}({})】 \n⦁ 產業: {} \n⦁ 開盤股價: {} \n⦁ 昨日漲停價: {} \n⦁ 昨日跌停價: {} ".format(
data['nameZhTw'],
num,
data['industryZhTw'],
data['priceReference'],
data['priceHighLimit'],
data['priceLowLimit']
)
else:
text = "【{}({})】 \n⦁ 開盤股價: {} \n⦁ 昨日漲停價: {} \n⦁ 昨日跌停價: {} ".format(
data['nameZhTw'],
num,
data['priceReference'],
data['priceHighLimit'],
data['priceLowLimit']
)
return text
else:
df1 = df1.iloc[-1]
if 'industryZhTw' in data:
text = "【{}({})】 \n⦁ 產業: {} \n⦁ 當前股價: {} \n⦁ 今日漲跌: {}({}%) ".format(
data['nameZhTw'],
num,
data['industryZhTw'],
df1['close'],
round(df1['close']-data['priceReference'], 2),
round(((df1['close']-data['priceReference'])/data['priceReference'])*100, 2)
)
else:
text = "【{}({})】 \n⦁ 當前股價: {} \n⦁ 今日漲跌: {}({}%) ".format(
data['nameZhTw'],
num,
df1['close'],
round(df1['close']-data['priceReference'], 2),
round(((df1['close']-data['priceReference'])/data['priceReference'])*100, 2)
)
return text
def info(bot, update):
global num
num = update.message.text
data = intraday.meta(apiToken=api_token, symbolId=num, output='raw')
'''對話框按鈕'''
global reply_markup
reply_markup = InlineKeyboardMarkup([[
InlineKeyboardButton('基本資訊', callback_data='info'),
InlineKeyboardButton('最新一筆交易', callback_data='trade'),
]])
'''輸入id，且判斷id是否存在'''
if 'id' in num:
if int(num[2:]) not in userdata().df['userid']:
update.message.reply_text('ID%s 不存在' % (num[2:]))
else:
update.message.reply_text('登入成功 ID：%s，請輸入欲查詢的股票代碼：' % (num[2:]))
userdata().write(update.message.chat.id, num[2:])
elif 'error' in intraday.chart(apiToken=api_token,
symbolId='2330').columns:
'''直接輸入股票代碼'''
update.message.reply_text('請輸入正確的股票代碼：')
else:
df1 = intraday.chart(apiToken=api_token, symbolId=num)
df1 = df1.iloc[-1]
text = ('•' + data['nameZhTw'] + '(' + num + ')\t\t' + '股價：' +
str(df1['close']) + '\t\t' + '漲跌：' +
str(data['priceReference'] - df1['close']) + '\t(' + str(
round(((data['priceReference'] - df1['close']) /
data['priceReference']) * 100, 2)) + '%)')
bot.send_message(
update.message.chat.id,
text + '\n\n' +
'{} 您還可以查詢：'.format(update.message.from_user.first_name),
reply_to_message_id=update.message.message_id,
reply_markup=reply_markup)
def ButtonCallback_handler(bot, update):
order, sid = update.callback_query.data.split(' ')
user_name = update.callback_query.from_user.first_name + update.callback_query.from_user.last_name # update.message.from_user.first_name
user_id = update.callback_query.from_user.id
if order == 'stock':
text = self.get_stock_info(num=str(sid))
url = 'https://www.fugle.tw/ai/{}?'.format(sid)
button = InlineKeyboardMarkup(
[[
InlineKeyboardButton(
'更多基本資訊', callback_data='info {}'.format(sid)),
InlineKeyboardButton(
'最新一筆交易', callback_data='trade {}'.format(sid))
],
[
InlineKeyboardButton(
'推薦相關股票',
callback_data='recommend {}'.format(sid)),
InlineKeyboardButton('前往網站', url=url)
]])
bot.send_message(
user_id,
text,
# reply_to_message_id = update.message.message_id,
reply_markup=button)
if order == 'recommend':
try:
result = self.call_m3(stock_id=int(sid))
text = self.get_stock_info(num=str(result))
url = 'https://www.fugle.tw/ai/{}?'.format(result)
button = InlineKeyboardMarkup(
[[
InlineKeyboardButton(
'更多基本資訊',
callback_data='info {}'.format(result)),
InlineKeyboardButton(
'最新一筆交易',
callback_data='trade {}'.format(result))
],
[
InlineKeyboardButton(
'推薦相關股票',
callback_data='recommend {}'.format(result)),
InlineKeyboardButton('前往網站', url=url)
]])
bot.send_message(user_id,
text,
reply_to_message_id=update.callback_query.
message.message_id,
reply_markup=button)
self.logger.info("user_name{} | respond_text:{}".format(
user_name, text))
except:
update.message.reply_text('請輸入存在的股票id。 \nex:6456、4552')
self.logger.info('m3 - uid error')
if order == 'info':
data = intraday.meta(
apiToken=self.config['FUGLE']['fugle_TOKEN'],
symbolId=sid,
output='raw')
# data = intraday.meta(apiToken=Bot.config['FUGLE']['fugle_TOKEN'] , symbolId='0050' , output='raw')
try:
if 'industryZhTw' in data:
text = ('【' + data['nameZhTw'] + '(' + sid + ')' +
' 基本資訊】 \n ⦁ 產業別：' + data['industryZhTw'] +
'\n ⦁ 交易幣別：' + data['currency'] +
'\n ⦁ 股票中文簡稱：' + data['nameZhTw'] +
'\n ⦁ 開盤參考價：' + str(data['priceReference']) +
'\n ⦁ 漲停價：' + str(data['priceHighLimit']) +
'\n ⦁ 跌停價：' + str(data["priceLowLimit"]) +
'\n ⦁ 股票類別：' + data['typeZhTw'])
else:
text = ('【' + data['nameZhTw'] + '(' + sid + ')' +
' 基本資訊】 \n ⦁ 交易幣別：' + data['currency'] +
'\n ⦁ 股票中文簡稱：' + data['nameZhTw'] +
'\n ⦁ 開盤參考價：' + str(data['priceReference']) +
'\n ⦁ 漲停價：' + str(data['priceHighLimit']) +
'\n ⦁ 跌停價：' + str(data["priceLowLimit"]) +
'\n ⦁ 股票類別：' + data['typeZhTw'])
except:
text = '查無此代碼'
update.callback_query.message.reply_text(text)
if order == 'trade':
data = intraday.meta(
apiToken=self.config['FUGLE']['fugle_TOKEN'],
symbolId=sid,
output='raw')
dt = intraday.quote(
apiToken=self.config['FUGLE']['fugle_TOKEN'],
symbolId=sid,
output='raw')
# intraday.quote(apiToken=Bot.config['FUGLE']['fugle_TOKEN'], symbolId=num, output='raw')
if 'trade' in dt.keys():
trade = dt['trade']
text = ('【' + data['nameZhTw'] + '(' + sid + ')' +
' 最新一筆交易】 \n' + ' ⦁ 成交價：' + str(trade['price']) +
'\n ⦁ 成交張數：' + str(trade['unit']) + '\n ⦁ 成交量：' +
str(trade['volume']) + '\n ⦁ 成交序號：' +
str(trade['serial']))
else:
text = '查無最新資訊'
update.callback_query.message.reply_text(text)
def test_intraday_meta_output_raw():
meta = intraday.meta(output="raw")
assert type(meta) is dict
def test_intraday_meta_output_dataframe():
meta = intraday.meta(output="dataframe")
assert type(meta) is DataFrame
def test_intraday_meta_output():
with raises(ValueError) as excinfo:
intraday.meta(output="")
assert excinfo.type is ValueError
assert str(excinfo.value) == 'output must be one of ["dataframe", "raw"]'
def test_intraday_meta():
meta = intraday.meta()
assert type(meta) is DataFrame
