def baoStockSlope(stock_index, start_date, stat_days, pred_days, if_print=1):
stat_days = int(stat_days)
pred_days = int(pred_days)
df_dir = tmp_data_dict.get("baostock")
df_dir = os.path.join(data_path, "history_data", "baostock", "2020-12-17")
k1 = klineDate(start_date, stat_days, pred_days)
stat_end_date, pred_start_date, pred_end_date = k1.make_date()
try:
df1 = pd.read_csv(os.path.join(df_dir, stock_index + ".csv"))
df1 = df1.drop_duplicates()
df2 = df1[(df1["date"] >= pred_start_date)
& (df1["date"] <= pred_end_date)]
rows = df2.shape[0]
slope = LinearReg.single_linear_reg(df2, "close")[0]
if if_print == 1:
print("{},{},{},{},{},{},{}".format(stock_index, start_date,
pred_start_date, pred_end_date,
pred_days, rows, slope))
except:
slope = -999
rows = 0
if if_print == 1:
print("{},{},{},{},{},{},{}".format(stock_index, start_date,
pred_start_date, pred_end_date,
pred_days, 0, -999))
return_data = dict(stock_index=stock_index,
start_date=start_date,
pred_start_date=pred_start_date,
pred_end_date=pred_end_date,
rows=rows,
slope=slope)
return return_data
def stockSlope(stock_index, start_date, stat_days, pred_days):
try:
df1 = pd.read_csv(os.path.join(df_dir, stock_index + ".csv"))
k1 = klineDate(start_date, stat_days, pred_days)
stat_end_date, pred_start_date, pred_end_date = k1.make_date()
df2 = df1[(df1["date"] >= pred_start_date)
& (df1["date"] <= pred_end_date)]
slope = LinearReg.single_linear_reg(df2, "close")[0]
print("{},{},{},{},{}".format(stock_index, pred_start_date,
pred_end_date, pred_days, slope))
except:
print("{},{},{},{},{}".format(stock_index, "aa", "bb", pred_days,
-999))
def stockSlope(stock_index, start_date, stat_days, pred_days):
try:
data_dir = data_dict.get("baostock")
df1 = pd.read_csv(
os.path.join(data_dir, "history_data", stock_index) + '.csv')
k1 = klineDate(start_date, stat_days, pred_days)
stat_end_date, pred_start_date, pred_end_date = k1.make_date()
df2 = df1[(df1["date"] >= pred_start_date)
& (df1["date"] <= pred_end_date)]
slope = LinearReg.single_linear_reg(df2, "close")[0]
#print("{},{},{},{},{}".format(stock_index,pred_start_date,pred_end_date,pred_days,slope))
except:
#print("{},{},{},{},{}".format(stock_index,"aa","bb",pred_days,-999))
slope = -999
pred_start_date = -999
pred_end_date = -999
return slope, pred_start_date, pred_end_date
def getSlope(DF, start_date, stat_days, pred_days, col):
df1 = cleanData.cleanColName(DF)
df1 = cleanData.setDt(df1)
df_columns = df1.columns
if col not in df_columns:
logging.error("no input slope columns: " + col)
sys.exit(1)
try:
k1 = klineDate(start_date, stat_days, pred_days)
stat_end_date, pred_start_date, pred_end_date = k1.make_date()
df2 = df1[(df1["dt"] >= pred_start_date)
& (df1["dt"] <= pred_end_date)]
slope = LinearReg.single_linear_reg(df2, col)[0]
except:
slope = -999
pred_start_date = -999
pred_end_date = -999
return slope, pred_start_date, pred_end_date
df_diaoyan = getDiaoYanDF(df,start_date,end_date)
df_diaoyan_head_n = df_diaoyan.head(head_n)
stk_diaoyan_list = df_diaoyan_head_n['stock_index'].tolist()
stk_diaoyan_tup = tuple(stk_diaoyan_list)
return stk_diaoyan_tup,df_diaoyan_head_n
if __name__ == "__main__":
from functions.LinearReg import LinearReg
from functions.day_history import kLines
from functions.day_history.getDataFromSpark import *
head_n = 100
dy_start_date = '2020-08-03'
stat_days = 20
pred_days = 1
stat_end_date,pred_start_date,pred_end_date = kLines.klineDate(dy_start_date,stat_days,pred_days).make_date()
df = loadData()
df1 = getDiaoYanDF(df,dy_start_date,stat_end_date)
#stat_end_date,pred_start_date,pred_end_date = kLines.klineDate(dy_start_date,stat_days,pred_days).make_date()
#stat_dates_list = '_'.join([dy_start_date,stat_end_date,pred_start_date,pred_end_date])
stk_diaoyan_tup,df_diaoyan_head_n = headDiaoYanStockList(df,head_n,dy_start_date,stat_end_date)
print(df_diaoyan_head_n.head(10))
'''
para = {
'stock_tuple': stk_diaoyan_tup,
'start_date': pred_start_date,
'end_date': pred_end_date
}
getSparkData = getDataFromSpark(para)
getSparkData.getDataFromSpark()
jijin_count = jijin_count(df2)
df2['chigu_money'] = df2['chigu_money'].astype(float)
df3 = df2.groupby('stock_index')['chigu_money'].sum().reset_index().sort_values('chigu_money',ascending=False)
from functions.day_history.kLines import klineDate
start_date = "2020-06-01"
stat_days = 20
pred_days = 20
stat_end_date,pred_start_date,pred_end_date = klineDate(start_date,stat_days,pred_days).make_date()
df_p1 = df2[(df2["stock_date"]>="2020-06-01")&(df2["stock_date"]<=stat_end_date)]
df_p2 = df2[(df2["stock_date"]>=pred_start_date)&(df2["stock_date"]<=pred_end_date)]
pd.merge(df_p1[["stock_index","chigu_money"]],df_p2[["stock_index","chigu_money"]],how="inner",on=["stock_index"])
df2 = df1[df1['stock_date']=='2019-06-30']
df2 = df1[(df1['stock_date']=='2019-06-30')&(df1['stock_index']==428)]
df2 = df1[(df1['stock_date']=='2019-12-30')&(df1['stock_index']==917)]
df2['jijin_name']
