def generate_sample_data():
dates = pd.date_range(start="2008-01-01", end="2008-01-30")
for index, date in enumerate(dates):
stock = Stock()
stock.code = "T9999"
stock.date = date
stock.open = index
stock.high = index + 1
stock.low = index - 0.5
stock.close = index + 1
stock.volume = 100
stock.save_if_need()
for index, date in enumerate(dates[::-1]):
stock = Stock()
stock.code = "T9998"
stock.date = date
stock.open = index
stock.high = index + 1
stock.low = index - 0.5
stock.close = index + 1
stock.volume = 100
stock.save_if_need()
def crawl(self):
stock_frame = ts.get_k_data(code=self.code,
start=self.start,
end=self.end,
retry_count=30)
for index in stock_frame.index:
stock_series = stock_frame.loc[index]
stock_dict = stock_series.to_dict()
stock = Stock(**stock_dict)
stock.save_if_need()
logging.warning("Finish crawling code: {}, items count: {}".format(
self.code, stock_frame.shape[0]))
def crawl(self):
stock_frame = ts.get_k_data(code=self.code,
start=self.start,
end=self.end,
retry_count=30)
for index in stock_frame.index: # stock_index是从0到length(stock_frame)的整数
stock_series = stock_frame.loc[index] # 某一行的数据
stock_dict = stock_series.to_dict(
) # pandas提供的字典化方法, 返回{"date":"2018-08-01","open":5234,"close":5272,"high":5298,"volume":7665600,"code":"sh"}
stock = Stock(**stock_dict) # 将字典的键和Stock类中各个字段对应起来进行组装
stock.save_if_need() # 储存进数据库
logging.warning("Finish crawling code: {}, items count: {}".format(
self.code, stock_frame.shape[0]))