def create_bond_dataframe(periods_range=10):
data = pd.read_csv('./data/kezhuanzhai.csv', index_col=0)
result_data = data.copy()
calender = pd.read_csv('./data/calender.csv', converters={'cal_date': str})
forecast_strategy.end_date = '20201231'
for index, item in data.iterrows():
_, ndate = forecast_strategy.find_buy_day(item.CBSTOCKCODE,
item.CBIPOANNCDATE, 0,
calender)
df_ndate = db.get_k_data(item.CBSTOCKCODE, ndate.replace('-', '', 2),
ndate.replace('-', '', 2))
if len(df_ndate) > 0:
result_data.loc[index, 'ndate_open'] = df_ndate.open.values[0]
result_data.loc[index, 'ndate_close'] = df_ndate.close.values[0]
result_data.loc[index,
'ndate_pct_chg'] = df_ndate.pct_chg.values[0]
result_data.loc[index,
'ndate_date'] = df_ndate.trade_date.values[0]
for i in range(1, periods_range + 1):
is_exist, trade_date = forecast_strategy.find_buy_day(
item.CBSTOCKCODE, item.CBIPOANNCDATE, i, calender)
if is_exist:
dataframe = db.get_k_data(item.CBSTOCKCODE,
trade_date.replace('-', '', 2),
trade_date.replace('-', '', 2))
if len(df_ndate) > 0:
result_data.loc[
index, f'date{str(i)}_open'] = dataframe.open.values[0]
result_data.loc[
index,
f'date{str(i)}_close'] = dataframe.close.values[0]
result_data.loc[
index,
f'date{str(i)}_pct_chg'] = dataframe.pct_chg.values[0]
result_data.loc[
index,
f'date{str(i)}_date'] = dataframe.trade_date.values[0]
else:
if trade_date is not None:
result_data.loc[index,
f'date{str(i)}_date'] = trade_date.replace(
'-', '', 2)
if pd.isnull(item.CBLISTDATE):
continue
try:
dflist = db.get_k_data(item.CBSTOCKCODE,
item.CBLISTDATE.replace('-', '', 2),
item.CBLISTDATE.replace('-', '', 2))
except:
continue
if len(dflist) == 0:
continue
result_data.loc[index, 'list_open'] = dflist.open.values[0]
result_data.loc[index, 'list_close'] = dflist.close.values[0]
result_data.loc[index, 'list_pct_chg'] = dflist.pct_chg.values[0]
result_data.loc[index, 'list_date'] = dflist.trade_date.values[0]
result_data.to_csv('./data/convertible-bond.csv')
return result_data
def get_dt_data(code, start, end):
global dt_data
global ts_count
start = tran_dateformat(start)
end = tran_dateformat(end)
dt = dt_data[(dt_data['ts_code'] == code)
& (dt_data['start_to_end'] == (start + end))]
if len(dt) == 0:
try:
dt = get_k_data(code, start.replace('-', '', 3),
end.replace('-', '', 3))
except:
time.sleep(40)
dt = get_dt_data(code, start, end)
pass
if dt is None:
return dt
if len(dt) == 0:
return dt
dt['start_to_end'] = start + end
ts_count += 1
dt_data = dt_data.append(dt)
return dt
def getDatas(ts_code, begindate, enddate, *args, **kwargs):
begindate = begindate.strftime('%Y%m%d')
enddate = enddate.strftime('%Y%m%d')
ds_df = db2df.get_k_data(ts_code, begindate, enddate)
ds_df.index = pd.to_datetime(ds_df.trade_date)
ds_df = ds_df[['open', 'high', 'low', 'close', 'vol']]
return ds_df
def get_dt_data(code, start, end):
dt = get_k_data(code, start.replace('-', '', 3), end.replace('-', '', 3))
if dt is None:
return dt
if len(dt) == 0:
return dt
return dt
def get_dt_data(code, start, end):
global dt_data
start = tran_dateformat(start.replace('-', '', 3))
end = tran_dateformat(end.replace('-', '', 3))
dt = dt_data[(dt_data['ts_code'] == code) & (dt_data['start_to_end'] == (start + end))]
if len(dt) == 0:
dt = get_k_data(code, start.replace('-', '', 3), end.replace('-', '', 3))
if dt is None:
return dt
if len(dt) == 0:
return dt
dt['start_to_end'] = start + end
dt_data = dt_data.append(dt)
return dt
def get_basic_info(code, start, end):
global basic_info
try:
start = tran_dateformat(start).replace('-', '', 3)
except:
print('aaaa', code, start, end)
pass
end = tran_dateformat(end).replace('-', '', 3)
df = basic_info[(basic_info['ts_code'] == code)
& (basic_info['start_to_end'] == pd.to_numeric(start + end)
)].drop_duplicates('trade_date')
if df is None or len(df) == 0:
# df = pro.daily_basic(ts_code=code, start_date=start, end_date=end,
# fields='ts_code,close,trade_date,turnover_rate_f,volume_ratio,pe_ttm,circ_mv')
df = get_k_data(code, start, end)
if df is None or len(df) == 0:
return None
df['start_to_end'] = start + end
basic_info = basic_info.append(df)
df = df.reset_index().drop(columns='index')
return df
dt = get_k_data(code, start.replace('-', '', 3), end.replace('-', '', 3))
if dt is None:
return dt
if len(dt) == 0:
return dt
return dt
st_path = './data/st_stock.csv'
stock_info_path = './data/stock_basic_info.csv'
loan_path = './data/rongquanall.csv'
dt_data = pd.read_csv('./data/dt_data.csv')
check_trade_condition = CheckTradeCondition(st_path, stock_info_path, loan_path, buy='open', sell='close',
long_short='long')
high_limit_open = get_k_data('002684.SZ', '20201103', '20201103') # st股票，一字涨停 5%
high_limit_close = get_k_data('300670.SZ', '20201103', '20201103') # 收盘涨停
low_limit_open = get_k_data('002496.SZ', '20201103', '20201103') # st 股票，一字跌停 -5%
low_limit_close = get_k_data('600225.SH', '20201103', '20201103') # st 股票，收盘跌停 -5%
engine = create_engine('mysql+pymysql://root:myh123@localhost:3306/quant?charset=utf8',pool_recycle=1)
def get_k_data(ts_code, start, end) -> pd.DataFrame:
global engine
sql = "SELECT * FROM quant.stock_daily where ts_code ='" + ts_code + "' and trade_date between '" + start + "' and '" + end + "'"
return pd.read_sql(sql, engine)
class TestCheckTradeCondition(TestCase):