def query_history_k_data(start_date, end_date):
"""
获取历史A股K线数据：query_history_k_data()
方法说明：获取A股历史交易数据（包括均线数据），
可以通过参数设置获取日k线、周k线、月k线，以及5分钟、15分钟、30分钟和60分钟k线数据，
适合搭配均线数据进行选股和分析。
返回类型：pandas的DataFrame类型。
可查询不复权、前复权、后复权数据。
"""
all_stock = pd.read_csv(path + "all_stock.csv")
all_stock.drop_duplicates(subset=['code'], keep='first', inplace=True)
#### 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
#print('login respond error_code:'+lg.error_code)
#print('login respond error_msg:'+lg.error_msg)
#### 获取沪深A股历史K线数据 ####
# 详细指标参数，参见“历史行情指标参数”章节
result_all = pd.DataFrame([])
for code in all_stock.code:
rs = bs.query_history_k_data(
code,
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST",
start_date=start_date,
end_date=end_date,
frequency="d",
adjustflag="2")
#print('query_history_k_data respond error_code:'+rs.error_code)
#print('query_history_k_data respond error_msg:'+rs.error_msg)
#### 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
result_all = result_all.append(result)
#### 结果集输出到csv文件 ####
try:
history_A_stock_k_data = pd.read_csv(path +
"history_A_stock_k_data.csv")
except:
history_A_stock_k_data = pd.DataFrame([])
result_all = result_all.append(history_A_stock_k_data)
result_all.drop_duplicates(subset=['code', 'date'],
keep='first',
inplace=True)
result_all.to_csv(path + "history_A_stock_k_data.csv", index=False)
#print(result)
#### 登出系统 ####
bs.logout()
return
def computeKDJ(code, startdate, enddate):
# 获取股票日K线数据
rs = bs.query_history_k_data(code,
"date,code,high,close,low,tradeStatus",
start_date=startdate,
end_date=enddate,
frequency="d",
adjustflag="3")
# 打印结果集
result_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
result_list.append(rs.get_row_data())
df_init = pd.DataFrame(result_list, columns=rs.fields)
# 剔除停盘数据
df_status = df_init[df_init['tradeStatus'] == '1']
low = df_status['low'].astype(float)
del df_status['low']
df_status.insert(0, 'low', low)
high = df_status['high'].astype(float)
del df_status['high']
df_status.insert(0, 'high', high)
close = df_status['close'].astype(float)
del df_status['close']
df_status.insert(0, 'close', close)
# 计算KDJ指标,前9个数据为空
low_list = df_status['low'].rolling(window=9).min()
high_list = df_status['high'].rolling(window=9).max()
rsv = (df_status['close'] - low_list) / (high_list - low_list) * 100
df_data = pd.DataFrame()
df_data['K'] = rsv.ewm(com=2).mean()
df_data['D'] = df_data['K'].ewm(com=2).mean()
df_data['J'] = 3 * df_data['K'] - 2 * df_data['D']
df_data['date'] = df_status['date'].values
df_data.index = df_status['date'].values
df_data.index.name = 'date'
# 删除空数据
df_data = df_data.dropna()
# 计算KDJ指标金叉、死叉情况
df_data['x'] = ''
kdj_position = df_data['K'] > df_data['D']
df_data.loc[kdj_position[(kdj_position == True)
& (kdj_position.shift() == False)].index,
'x'] = 'j'
df_data.loc[kdj_position[(kdj_position == False)
& (kdj_position.shift() == True)].index,
'x'] = 's'
return (df_data)
def return_constraintdict(stockcodelist):
login_result = bs.login(user_id='anonymous', password='******')
print('login respond error_msg:' + login_result.error_msg)
startdate = '2018-08-21' # 开始日期，从2018年1月1日开始
today = datetime.datetime.now() # 获取今天的时间 年 月 日 小时 分钟 秒 毫秒
delta = datetime.timedelta(days=1) # 两个时间的差值为1天
# 获取截至上一个交易日的历史行情
predate = today - delta # 今天的时间 减去 两时间差值，定义为 predate 上一日这个时候的时间值
strpredate = datetime.datetime.strftime(
predate, '%Y-%m-%d') # strpredate 是用 strftime 格式化后的 上一日时间表示
print(strpredate) # 打印上一天的日期，格式化过的：年-月-日
for stockcode in stockcodelist:
rs = bs.query_history_k_data(
"%s" % stockcode,
"date,code,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM",
start_date=startdate,
end_date=strpredate,
frequency="d",
adjustflag="2")
# rs 为从bs中查询到的 该股票的(按从2018年1月1日起每一日的)k线数据信息，一共有14列
print('query_history_k_data respond error_code:' + rs.error_code)
print('query_history_k_data respond error_msg:' + rs.error_msg)
# print (rs) <baostock.data.resultset.ResultData object at 0x00000000144FDC50> (是一个数据集)
# 打印结果集
result_list = [] # 结果列表 下面用 append 逐步添加数据
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
result_list.append(
rs.get_row_data()) # 将 rs row_data/行数据 添加到result_list 中去
print(result_list) # 打印 result_list 结果集里的数据
result = pd.DataFrame(
result_list, columns=rs.fields
) # 定义一个 result 变量， 用pd.DataFrame的2个参数 result_list和 columns=rs.fields处理
print(result) # result 是一个类，被 pandas.core.frame.DataFrame 定义
#print (type(result))
closelist = list(result['close'])
closelist = [float(price) for price in closelist]
# 调用 TA-Lib 中的 MA 函数， 计算20日均线值
malist = ta.MA(np.array(closelist), timeperiod=20)
if len(malist) > 20 and closelist[-20] > 0:
ma20value = malist[-1]
summit20day = max(closelist[-10:])
# 以突破10日高点且在20日均线以上作为买入条件
resistancelinedict[stockcode] = max(ma20value, summit20day)
else:
resistancelinedict[stockcode] = float(closelist[-1])
bs.logout()
return resistancelinedict
def computeMACD(code, startdate, enddate):
login_result = bs.login(user_id='anonymous', password='******')
print(login_result)
# 获取股票日 K 线数据
rs = bs.query_history_k_data(code,
"date,code,close,tradeStatus",
start_date=startdate,
end_date=enddate,
frequency="d", adjustflag="3")
# 打印结果集
result_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
result_list.append(rs.get_row_data())
df = pd.DataFrame(result_list, columns=rs.fields)
# 剔除停盘数据
df2 = df[df['tradeStatus'] == '1']
# 获取 dif,dea,hist，它们的数据类似是 tuple，且跟 df2 的 date 日期一一对应
# 记住了 dif,dea,hist 前 33 个为 Nan，所以推荐用于计算的数据量一般为你所求日期之间数据量的3倍
# 这里计算的 hist 就是 dif-dea,而很多证券商计算的 MACD=hist*2=(dif-dea)*2
dif, dea, hist = ta.MACD(df2['close'].astype(
float).values, fastperiod=12, slowperiod=26, signalperiod=9)
df3 = pd.DataFrame({'dif': dif[33:], 'dea': dea[33:], 'hist':
hist[33:]},
index = df2['date'][33:], columns = ['dif', 'dea',
'hist'])
df3.plot(title='MACD')
# 寻找 MACD 金叉和死叉
datenumber = int(df3.shape[0])
for i in range(datenumber - 1):
if((df3.iloc[i, 0] <= df3.iloc[i, 1]) & (df3.iloc[i + 1, 0] >= df3.iloc[i + 1, 1])):
print("MACD 金叉的日期:" + df3.index[i + 1])
#TODO 加入微信触发提醒
if ((df3.iloc[i, 0] >= df3.iloc[i, 1]) & (df3.iloc[i + 1, 0] <=
df3.iloc[i + 1, 1])):
print("MACD 死叉的日期:" + df3.index[i + 1])
# TODO 加入微信触发提醒
plt.show()
df.to_csv("/Users/zou/PycharmProjects/weiyl_919/macd.csv", encoding='gbk')
bs.logout()
return (dif, dea, hist)
def start_crawl(self):
bs.login(user_id="anonymous", password="******")
code_list = self.dm.get_code_list_02()
for item in code_list:
ticker = item["ticker"]
max_try = 8
for tries in range(max_try):
rs = bs.query_history_k_data(ticker, "date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg",
start_date='2016-01-01', end_date='2017-12-31', frequency="d", adjustflag="2")
if rs.error_code == '0':
self.parse_pager(rs, ticker)
break
elif tries < (max_try - 1):
sleep(2)
continue
bs.logout()
def computeRSI(code, startdate, enddate):
"""计算证券在起止时间内的 RSI 指标。
:param code:证券代码
:param startdate:起始日期
:param enddate:截止日期
:return:
"""
login_result = bs.login(user_id='anonymous', password='******')
print(login_result.error_msg)
# 获取股票日 K 线数据,adjustflag 复权状态(1:后复权， 2:前复权，3: 不复权)
rs = bs.query_history_k_data(code,
"date,code,close,tradeStatus",
start_date=startdate,
end_date=enddate,
frequency="d",
adjustflag="3")
# 打印结果集
result_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
result_list.append(rs.get_row_data())
df_init = pd.DataFrame(result_list, columns=rs.fields)
# 剔除停盘数据
df_status = df_init[df_init['tradeStatus'] == '1']
df_status['close'] = df_status['close'].astype(float)
rsi_12days = ta.RSI(df_status['close'], timeperiod=12)
rsi_6days = ta.RSI(df_status['close'], timeperiod=6)
rsi_24days = ta.RSI(df_status['close'], timeperiod=24)
df_status['rsi_6days'] = rsi_6days
df_status['rsi_12days'] = rsi_12days
df_status['rsi_24days'] = rsi_24days
# RSI 超卖和超买
rsi_buy_position = df_status['rsi_6days'] > 80
rsi_sell_position = df_status['rsi_6days'] < 20
df_status.loc[rsi_buy_position[(rsi_buy_position == True) &
(rsi_buy_position.shift() == False)].index,
'超买'] = '超买'
# TODO 加入微信触发提醒
df_status.loc[rsi_sell_position[(rsi_sell_position == True) & (
rsi_sell_position.shift() == False)].index, '超卖'] = '超卖'
# TODO 加入微信触发提醒
return df_status
def download_price(start, end, ind_or_sto):
# print(start, end)
# login system
lg = bs.login()
# Display login infomration
logger.info("login error code: {}, logging error messge: {}".format(lg.error_code, lg.error_msg))
# Login error messgae, a detialed explanation of the error.
df_stock = pd.read_excel(r'Tickers_download.xlsx') # , sheet_name='Sheet 1'
new_stock_list = []
for i in df_stock.index:
# print(df_stock['Ticker'][i])
new_stock_list.append(df_stock['Ticker'][i])
# print(new_stock_list)
downloaded = os.listdir('downloads_price/')
had = set()
for f in downloaded:
parts = f.split("_")
had.add(int(parts[0]))
t = start
total = end - start
count = 0
for i in new_stock_list[start:end]:
count += 1
if (t in had):
logger.info("====omit {}====".format(t))
t += 1
continue
else:
logger.info("====process {}_{}, {} out of {}====".format(i, t, count, total))
result_list = []
if ind_or_sto == "index":
rs = bs.query_history_k_data_plus(i, "date,code,open,high,low,close,volume,amount", frequency="d")
elif ind_or_sto == "stock":
rs = bs.query_history_k_data(i, "date,code,open,high,low,close,volume,amount,adjustflag,turn,peTTM,pbMRQ,psTTM,pcfNcfTTM,pctChg,isST", frequency="d", adjustflag="2")
logger.info("error code: {}, error message: {}".format(rs.error_code, rs.error_msg))
while (rs.error_code == '0') & rs.next():
result_list.append(rs.get_row_data())
result = pd.DataFrame(result_list, columns=rs.fields)
output = "downloads_price/{}_{}.csv".format(t, i)
t += 1
result.to_csv(output, encoding="gbk", index=False)
def getStocksDetail(stocks, startDay, endDay, frequency, adjustflag,
lastTradeDay):
stock_data_list = []
stock_fields = []
for index, stock in stocks.iterrows():
if stock['tradeStatus'] == '1' and 'sh.600' in stock['code']:
rs = bs.query_history_k_data(
stock['code'],
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST",
startDay, endDay, frequency, adjustflag)
print('请求历史数据返回信息:' + rs.error_msg)
stock_fields = rs.fields
while (rs.error_code == '0') & rs.next():
rowData = rs.get_row_data()
stock_data_list.append(rowData)
# todo 添加名称、添加昨日涨跌幅
return pd.DataFrame(stock_data_list, columns=stock_fields)
def bs_k_data_stock(code_val='sz.000651',
start_val='2009-01-01',
end_val='2019-06-01',
freq_val='d',
adjust_val='3'):
# 登陆系统
lg = bs.login()
# 获取历史行情数据
fields = "date,open,high,low,close,volume"
df_bs = bs.query_history_k_data(
code_val,
fields,
start_date=start_val,
end_date=end_val,
frequency=freq_val,
adjustflag=adjust_val) # <class 'baostock.data.resultset.ResultData'>
# frequency="d"取日k线，adjustflag="3"默认不复权，1：后复权；2：前复权
data_list = []
while (df_bs.error_code == '0') & df_bs.next():
# 获取一条记录，将记录合并在一起
data_list.append(df_bs.get_row_data())
result = pd.DataFrame(data_list, columns=df_bs.fields)
result.close = result.close.astype('float64')
result.open = result.open.astype('float64')
result.low = result.low.astype('float64')
result.high = result.high.astype('float64')
result.volume = result.volume.astype('float64')
result.volume = result.volume / 100 # 单位转换：股-手
result.date = pd.DatetimeIndex(result.date)
result.set_index("date", drop=True, inplace=True)
result.index = result.index.set_names('Date')
recon_data = {'High': result.high, 'Low': result.low, 'Open': result.open, 'Close': result.close,\
'Volume': result.volume}
df_recon = pd.DataFrame(recon_data)
# 登出系统
bs.logout()
return df_recon
"""
def __parse_baostock_data(self, code, start, end, freq):
'''
虽然价格都进行了复权，但是成交量和换手率都没有前复权(tushare和baostock都没做)，同花顺也是， 东方财富做了。
前复权的价格和同方财富以及同花顺有出入。
'''
'''
能获取2011-01-01至当前时间的数据；
date 交易所行情日期 格式：YYYY-MM-DD
code 证券代码 格式：sh.600000。sh：上海，sz：深圳
open 今开盘价格 精度：小数点后4位；单位：人民币元
high 最高价 精度：小数点后4位；单位：人民币元
low 最低价 精度：小数点后4位；单位：人民币元
close 今收盘价 精度：小数点后4位；单位：人民币元
preclose 昨日收盘价 精度：小数点后4位；单位：人民币元
volume 成交数量 单位：股
amount 成交金额 精度：小数点后4位；单位：人民币元
adjustflag 复权状态 不复权、前复权、后复权
turn 换手率 精度：小数点后6位；单位：%
tradestatus 交易状态 1：正常交易 0：停牌
pctChg 涨跌幅 精度：小数点后6位
peTTM 动态市盈率 精度：小数点后4位
psTTM 市销率 精度：小数点后4位
pcfNcfTTM 市现率 精度：小数点后4位
pbMRQ 市净率 精度：小数点后4位
isST 是否ST 1是，0否'''
try:
rs = bs.query_history_k_data(
code,
"date,code,open,high,low,close,volume,turn",
start_date=start,
end_date=end,
frequency=freq,
adjustflag="2")
except BaseException as e:
logger.exception('query_history_k_data error')
raise e
# print(rs.data);
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
return result
def start_crawl(self):
bs.login()
code_list = self.dm.get_code_list_02()
for item in code_list:
ticker = item["ticker"]
max_try = 8
for tries in range(max_try):
rs = bs.query_history_k_data(
ticker,
"date,code,close,peTTM,pbMRQ,psTTM,pcfNcfTTM",
start_date='2016-01-01',
end_date='2017-12-31',
frequency="d",
adjustflag="2")
if rs.error_code == '0':
self.parse_pager(rs, ticker)
break
elif tries < (max_try - 1):
sleep(2)
continue
bs.logout()
def _get(code: str, date_start: str, date_end: str, period='d'):
"""
baostock模块查询历史
:param code: 'sz.002007'
:param date_start: '2020-04-24'
:param date_end: '2020-04-28'
:param period: "d"
:return:
"""
rs = bs.query_history_k_data(
code,
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST",
start_date=date_start,
end_date=date_end,
frequency=period,
adjustflag="2") # frequency="d"取日k线，adjustflag="3"默认不复权
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
return data_list
def get_data(code):
rs = bs.query_history_k_data(
code,
"date,code,open,close,high,low,volume,amount,turn",
start_date='2010-01-01',
end_date='2019-04-01',
frequency="d",
adjustflag="3")
print('query_history_k_data respond error_code:' + rs.error_code)
print('query_history_k_data respond error_msg:' + rs.error_msg)
data_list = []
while (rs.error_code == '0') & rs.next():
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
#result.drop(['adjustflag'],axis=1,inplace=True)
result.to_csv(
str("data/datas/" + code + "_data.csv"),
index=False,
)
# print(result)
return None
def _get_stock_kdata(self, code: string,
update_time: datetime.datetime) -> list:
'''
1. 查询数据库现有数据，获取离当日最近日期的数据（判断当日是否是最后一个工作日，是的话，就去下一个工作日作为startDate）, endDate作为截止日期
2. 根据上一步的startDate和endDate，获取历史数据
3. 保存获取到的数据到数据库里
'''
print("%s, %s, %s" %
(__name__, "update_time", update_time.strftime("%Y-%m-%d")))
print("%s, %s, %s" %
(__name__, "now_time", timezone.now().strftime("%Y-%m-%d")))
if update_time.strftime("%Y-%m-%d") < timezone.now().strftime(
"%Y-%m-%d"):
start_date = update_time.strftime("%Y-%m-%d")
end_date = timezone.now().strftime("%Y-%m-%d")
else:
start_date, end_date = self._update_date_range(code)
# 根据start date 和end date 获取历史数据
rs = baostock.query_history_k_data(
code,
"code,close,peTTM,pbMRQ,psTTM,pcfNcfTTM,date",
start_date=start_date,
end_date=end_date)
result = list()
while (rs.error_code == '0') & rs.next():
kdata = rs.get_row_data()
# 因为取的是数据库中时间离当前最近的一条数据作为start time，所以这条数据不用重新保存
if self.is_exist(kdata, start_date):
continue
result.append(
StockKData(
code=kdata[0],
close=Decimal(kdata[1]) if kdata[1] else Decimal(0),
peTTM=Decimal(kdata[2]) if kdata[2] else Decimal(0),
pbMRQ=Decimal(kdata[3]) if kdata[3] else Decimal(0),
psTTM=Decimal(kdata[4]) if kdata[4] else Decimal(0),
pcfNcfTTM=Decimal(kdata[5]) if kdata[5] else Decimal(0),
date=timezone.make_aware(
datetime.datetime.strptime(kdata[6], '%Y-%m-%d'))))
return result
def load_his_k_data(self, stack_code, start_date, end_date):
#### 获取历史K线数据 ####
data_list = []
res = bs.query_history_k_data(
stack_code,
index_pro if self._freq == 'd' else index_mini,
start_date=start_date,
end_date=end_date,
frequency=self._freq,
adjustflag="3") #frequency="d"取日k线，adjustflag="3"默认不复权
show_error(sys._getframe().f_code.co_name, res)
while (res.error_code == '0') & res.next():
# 获取一条记录，将记录合并在一起
data_list.append(res.get_row_data())
df_data = pd.DataFrame(data_list, columns=res.fields)
df_data.set_index(["date"], inplace=True)
df_data.index = pd.to_datetime(df_data.index)
df_data[['low', 'high', 'open', 'close', 'volume'
]] = df_data[['low', 'high', 'open', 'close',
'volume']].astype(float)
self._k_data = df_data
self._k_data_plus = df_data
def normalize_one_stock(bs, stock_code):
if stock_code[:2] == '60':
stock_code = 'sh.' + stock_code
else:
stock_code = 'sz.' + stock_code
rs = bs.query_history_k_data(stock_code,
"date, amount",
start_date='2014-01-01',
end_date='2018-11-30',
frequency="d",
adjustflag="2")
print('Now dealing stock {}'.format(stock_code))
print(rs.error_msg)
print(rs.error_code)
# 获取具体的信息
result_list = []
while (rs.error_code == '0') & rs.next():
result_list.append(rs.get_row_data())
result_list = [float(date[1]) for date in result_list]
range = max(result_list) - min(result_list)
minval = min(result_list)
normalize_list = [(i - minval) / range for i in result_list]
return normalize_list
def start_crawl():
lg = bs.login()
print('login response error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
code_list = dm.get_code_list()
for item in code_list:
ticker = item["ticker"]
max_try = 8
for tries in code_list:
rs = bs.query_history_k_data(
ticker,
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatue,pctChg",
start_date='2018-01-01',
end_date='2018-07-28',
frequency="d",
adjustflag="2")
if rs.error_code == '0':
parse_pager(rs, ticker)
break
elif tries < (max_try - 1):
sleep(2)
continue
bs.logout()
def return_constraintdict(stock_code_list, startdate):
login_result = bs.login()
print ('login respond error_msg:' + login_result.error_msg)
today = datetime.datetime.now()
delta = datetime.timedelta(days = 1)
#get the history market data till last exchange date
predate = today - delta
strpredate = datetime.datetime.strftime(predate, '%Y-%m-%d')
for stock_code in stock_code_list:
rs = bs.query_history_k_data("%s" % stock_code, "date, code, open, close, preclose, volume, amount, adjustflag, turn, tradestatus, pctChg, peTTM, pbMRQ, psTTM, pcfNcfTTM",
start_date = startdate, end_date=strpredate, frequency="d", adjustflag="2")
print ('query_history_k_data respond error_code:' + rs.error_code)
print ('query_history_k_data respond error_msg:' + rs.error_msg)
result_list = []
while(rs.error_code == '0') & rs.next():
result_list.append(rs.get_row_data())
result = pd.DataFrame(result_list, columns=rs.fields)
print (result)
closelist = list(result['close'])
closelist = [float(price) for price in closelist]
def get_data(self, id, start='2018-01-01', end='2018-07-17'):
#### 获取沪深A股历史K线数据 ####
# 详细指标参数，参见“历史行情指标参数”章节
rs = bs.query_history_k_data(
"sh." + str(id),
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,isST",
start_date=start,
end_date=end,
frequency="d",
adjustflag="3")
print('query_history_k_data respond error_code:' + rs.error_code)
print('query_history_k_data respond error_msg:' + rs.error_msg)
#### 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
#### 结果集输出到csv文件 ####
# result.to_csv("D:\\history_A_stock_k_data.csv", index=False)
# print(result['turn'])
return result
def computeKDJ(self, code, startdate, enddate, is_show, is_refresh,
period):
login_result = bs.login(user_id='anonymous', password='******')
print(login_result.error_msg)
# 获取股票日K线数据
rs = bs.query_history_k_data(code,
"date,code,high,close,low,tradeStatus",
start_date=startdate,
end_date=enddate,
frequency="d",
adjustflag="3")
# 打印结果集
result_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
result_list.append(rs.get_row_data())
df_init = pd.DataFrame(result_list, columns=rs.fields)
# 剔除停盘数据
df_status = df_init[df_init['tradeStatus'] == '1']
low = df_status['low'].astype(float)
del df_status['low']
df_status.insert(0, 'low', low)
high = df_status['high'].astype(float)
del df_status['high']
df_status.insert(0, 'high', high)
close = df_status['close'].astype(float)
del df_status['close']
df_status.insert(0, 'close', close)
# 计算KDJ指标,前9个数据为空
low_list = df_status['low'].rolling(window=9).min()
high_list = df_status['high'].rolling(window=9).max()
rsv = (df_status['close'] - low_list) / (high_list - low_list) * 100
df_data = pd.DataFrame()
df_data['K'] = rsv.ewm(com=2).mean()
df_data['D'] = df_data['K'].ewm(com=2).mean()
df_data['J'] = 3 * df_data['K'] - 2 * df_data['D']
df_data.index = df_status['date'].values
df_data.index.name = 'date'
# 删除空数据
df_data = df_data.dropna()
# 记录index切片list
loc_list = range(len(df_data))[::period]
df_data = df_data.iloc[loc_list, :]
df_data.plot(title=f'KDJ_{period}day')
#计算KDJ指标金叉、死叉情况
df_data['KDJ_金叉死叉'] = ''
kdj_position = df_data['K'] > df_data['D']
df_data.loc[kdj_position[(kdj_position == True)
& (kdj_position.shift() == False)].index,
'KDJ_金叉死叉'] = '金叉'
df_data.loc[kdj_position[(kdj_position == False)
& (kdj_position.shift() == True)].index,
'KDJ_金叉死叉'] = '死叉'
if is_show:
plt.show()
if is_refresh:
df_data.to_csv(os.path.join(root, "KDJ.csv"), encoding='gbk')
bs.logout()
return (df_data)
@author shilong
'''
import baostock as bs
import pandas as pd
import matplotlib.pyplot as plt
lg=bs.login()
#print('login respond error_code:'+lg.error_code)
#print('login respond error_msg:'+lg.error_msg)
fields= "date,code,open,high,low,close"
rs = bs.query_history_k_data("sz.000831", fields,
start_date='2019-01-01', end_date='2019-02-21',
frequency="d", adjustflag="2")
#frequency="d"取日k线，adjustflag="3"默认不复权，
#1：后复权；2：前复权
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
result.index=pd.to_datetime(result.date)
#### 结果集输出到csv文件 ####
result.to_csv("gif/history_k_data.csv",
encoding="gbk", index=False)
print(result)
result.head()
import baostock as bs
import pandas as pd
#### 登陆系统 ####
lg = bs.login(user_id="anonymous", password="******")
#### 获取沪深A股估值指标(日频)数据 ####
# peTTM 动态市盈率
# psTTM 市销率
# pcfNcfTTM 市现率
# pbMRQ 市净率
rs = bs.query_history_k_data("sh.600000",
"date,code,close,peTTM,pbMRQ,psTTM,pcfNcfTTM",
start_date='2015-01-01',
end_date='2017-12-31',
frequency="d",
adjustflag="3")
#### 打印结果集 ####
result_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
result_list.append(rs.get_row_data())
result = pd.DataFrame(result_list, columns=rs.fields)
#### 结果集输出到csv文件 ####
result.to_csv("peTTM_sh.600000_data.csv", encoding="gbk", index=False)
print(result)
#### 登出系统 ####
bs.logout()
'''
证券宝API 获取 stock data
'''
import baostock as bs
import matplotlib.pyplot as plt
import pandas as pd
#### 登陆系统 ####
lg = bs.login()
#### 获取历史K线数据 ####
# query_history_k_data()
fields = "date,code,open,high,low,close"
rs = bs.query_history_k_data("sh.000001",
fields,
start_date='2000-01-01',
end_date='2018-09-07',
frequency="d",
adjustflag="2")
#frequency="d"取日k线，adjustflag="3"默认不复权，
#1：后复权；2：前复权
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
result.index = pd.to_datetime(result.date)
#### 结果集输出到csv文件 ####
#result.to_csv("c:/zjy/history_k_data.csv",
# encoding="gbk", index=False)
result.head()
import tushare as ts
#### 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:'+lg.error_code)
print('login respond error_msg:'+lg.error_msg)
#### 获取沪深A股历史K线数据 ####
# 详细指标参数，参见“历史行情指标参数”章节
rs = bs.query_history_k_data("sz.002597",
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,isST,peTTM,pbMRQ",
start_date='2014-01-01', end_date='2018-06-01',
frequency="d", adjustflag="2")
print(type(rs))
print('query_history_k_data respond error_code:'+rs.error_code)
print('query_history_k_data respond error_msg:'+rs.error_msg)
#### 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
import baostock as bs
import pandas as pd
#### 登陆系统 ####
lg = bs.login()
# 显示登陆返回信息
print('login respond error_code:'+lg.error_code)
print('login respond error_msg:'+lg.error_msg)
#### 获取历史K线数据 ####
# 详细指标参数，参见“历史行情指标参数”章节
rs = bs.query_history_k_data("sh.600000",
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,peTTM,pbMRQ,psTTM,pcfNcfTTM,isST",
start_date='2017-06-01', end_date='2017-12-31',
frequency="d", adjustflag="3") #frequency="d"取日k线，adjustflag="3"默认不复权
print('query_history_k_data respond error_code:'+rs.error_code)
print('query_history_k_data respond error_msg:'+rs.error_msg)
#### 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
#### 结果集输出到csv文件 ####
result.to_csv("D:/history_k_data.csv", encoding="gbk", index=False)
print(result)
#### 登出系统 ####
bs.logout()
import fileinput
i = 1
# 登陆系统
lg = bs.login(user_id="anonymous", password="******")
with fileinput.input(files=('D:\code.EBK')) as f:
for line in f:
print(i)
# print(line)
line = line.replace("\n", "")
# 详细指标参数，参见“历史行情指标参数”章节
rs = bs.query_history_k_data(
line,
"date,code,open,high,low,close,volume,amount,adjustflag",
start_date='2021-05-13',
end_date='2021-05-17',
frequency="15",
adjustflag="3")
#print(rs.error_code)
#print(rs.error_msg!='')
# 获取具体的信息
result_list = []
while (rs.error_code == '0') & rs.next():
# 分页查询，将每页信息合并在一起
result_list.append(rs.get_row_data())
index = 0
for el in result_list:
import baostock as bs
import pandas as pd
if __name__ == "__main__":
# 登陆系统
lg = bs.login(user_id="anonymous", password="******")
# 显示登陆返回信息
print('login respond error_code:' + lg.error_code)
print('login respond error_msg:' + lg.error_msg)
# 获取沪深A股历史K线数据
# 详细指标参数，参见“历史行情指标参数”章节
rs = bs.query_history_k_data(
"sh.600000",
"date,code,open,high,low,close,preclose,volume,amount,adjustflag,turn,tradestatus,pctChg,isST",
start_date='2017-07-01',
end_date='2017-12-31',
frequency="d",
adjustflag="3")
print('query_history_k_data respond error_code:' + rs.error_code)
print('query_history_k_data respond error_msg:' + rs.error_msg)
# 打印结果集
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
print(data_list)
result = pd.DataFrame(data_list, columns=rs.fields)
# print(result)
import pandas as pd
import fileinput
i = 1
with fileinput.input(files=('c:\code.EBK')) as f:
for line in f:
print(i)
print(line)
line = line.replace("\n", "")
# 登陆系统
lg = bs.login(user_id="anonymous", password="******")
# 详细指标参数，参见“历史行情指标参数”章节
rs = bs.query_history_k_data(
line,
"date,code,open,high,low,close,volume,amount,adjustflag",
start_date='2019-07-03',
end_date='2019-07-08',
frequency="d",
adjustflag="3")
#print(rs.error_code)
#print(rs.error_msg!='')
# 获取具体的信息
result_list = []
while (rs.error_code == '0') & rs.next():
# 分页查询，将每页信息合并在一起
result_list.append(rs.get_row_data())
for el in result_list:
pricedata = ";INSERT INTO dbo.lishijiage (code,riqi,kai,shou,di,gao,chengjiaoliang) VALUES ('%s', '%s','%s',N'%s',N'%s',N'%s',N'%s')" % (
el[1], el[0], el[2], el[5], el[4], el[3], el[7])
with open('D:\\' + str(i) + '.sql', 'a+') as f2:
#### 获取指数(综合指数、规模指数、一级行业指数、二级行业指数、策略指数、成长指数、价值指数、主题指数)K线数据 ####
##综合指数，例如：sh.000001 上证指数，sz.399106 深证综指 等；
##规模指数，例如：sh.000016 上证50，sh.000300 沪深300，sh.000905 中证500，sz.399001 深证成指等；
##一级行业指数，例如：sh.000037 上证医药，sz.399433 国证交运 等；
##二级行业指数，例如：sh.000952 300地产，sz.399951 300银行 等；
##策略指数，例如：sh.000050 50等权，sh.000982 500等权 等；
##成长指数，例如：sz.399376 小盘成长 等；
##价值指数，例如：sh.000029 180价值 等；
##主题指数，例如：sh.000015 红利指数，sh.000063 上证周期 等；
# 详细指标参数，参见“历史行情指标参数”章节
rs = bs.query_history_k_data(
"sh.600000",
"date,code,open,high,low,close,preclose,volume,amount,pctChg",
start_date='2017-01-01',
end_date='2017-06-30',
frequency="d",
adjustflag="3")
print('query_history_k_data respond error_code:' + rs.error_code)
print('query_history_k_data respond error_msg:' + rs.error_msg)
#### 打印结果集 ####
data_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
data_list.append(rs.get_row_data())
result = pd.DataFrame(data_list, columns=rs.fields)
#### 结果集输出到csv文件 ####
result.to_csv("D:\\history_Index_k_data.csv", index=False)
print(result)
def computeMACD(self, code, startdate, enddate, is_show, is_refresh,
period):
login_result = bs.login(user_id='anonymous', password='******')
print(login_result)
# 获取股票日 K 线数据
rs = bs.query_history_k_data(code,
"date,code,close,tradeStatus",
start_date=startdate,
end_date=enddate,
frequency="d",
adjustflag="3")
# 打印结果集
result_list = []
while (rs.error_code == '0') & rs.next():
# 获取一条记录，将记录合并在一起
result_list.append(rs.get_row_data())
df = pd.DataFrame(result_list, columns=rs.fields)
# 剔除停盘数据
df2 = df[df['tradeStatus'] == '1']
# 获取 dif,dea,hist，它们的数据类似是 tuple，且跟 df2 的 date 日期一一对应
# 记住了 dif,dea,hist 前 33 个为 Nan，所以推荐用于计算的数据量一般为你所求日期之间数据量的3倍
# 这里计算的 hist 就是 dif-dea,而很多证券商计算的 MACD=hist*2=(dif-dea)*2
dif, dea, hist = ta.MACD(df2['close'].astype(float).values,
fastperiod=12,
slowperiod=26,
signalperiod=9)
df3 = pd.DataFrame(
{
'dif': dif[33:],
'dea': dea[33:],
'hist': hist[33:]
},
index=df2['date'][33:],
columns=['dif', 'dea', 'hist'])
loc_list = range(len(df3))[::period]
df3 = df3.iloc[loc_list, :]
df3.plot(title=f'MACD_{period}day')
# 寻找 MACD 金叉和死叉
datenumber = int(df3.shape[0])
for i in range(datenumber - 1):
if ((df3.iloc[i, 0] <= df3.iloc[i, 1]) &
(df3.iloc[i + 1, 0] >= df3.iloc[i + 1, 1])):
print("MACD 金叉的日期:" + df3.index[i + 1])
# df_data.loc[kdj_position[(kdj_position == True) &
# (kdj_position.shift() == False)].index, 'KDJ_金叉死叉'] = '金叉'
if ((df3.iloc[i, 0] >= df3.iloc[i, 1]) &
(df3.iloc[i + 1, 0] <= df3.iloc[i + 1, 1])):
print("MACD 死叉的日期:" + df3.index[i + 1])
# 记录index切片list
if is_show:
plt.show()
if is_refresh:
df.to_csv(os.path.join(root, "macd.csv"), encoding='gbk')
bs.logout()
return (dif, dea, hist)