import os
import datetime
import pandas as pd
import matplotlib.pyplot as plt
import util.sheep as sheep
import util.basic as basic
tool = basic.basic()
labels = ['low_ma5', 'low', 'ma1', 'ma5']
# labels = ['low_ma5']
path = 'D:\\workgit\\stock\\util\\stockdata\\'
today = datetime.datetime.today().date()
while (not os.path.isfile(path + str(today) + '\data.csv')) or (
not os.path.isfile(path + str(today) + '\daily-basic.csv')) or (
not os.path.isfile(path + str(today) + '\stock-label.csv')):
today = today - datetime.timedelta(1)
print(today)
stock_label = pd.read_csv(path + str(today) + '\stock-label.csv',
index_col=0,
dtype={'trade_date': object})
data = pd.read_csv(path + str(today) + '\data.csv',
index_col=0,
dtype={'trade_date': object})
print(data.shape[0], data.shape[0] / 3600)
stock_baisc = pd.read_csv(
path + str(today) + '\daily-basic.csv',
import util.basic as basic
t=basic.basic()
all=t.trade_daily(cal=240)
pre_n=t.pre_date(all[["trade_date"]])
all=all.merge(pre_n,on="trade_date")
df=all[["ts_code","trade_date","low"]].rename({"trade_date":"p1","low":"pre_low"})
# all["up_avg"] = all.apply(lambda x: 1 if x["avg"] - x["pre_avg"] > 0 else 0, axis=1)
# all["up_ma"] = all.apply(lambda x: 1 if x["ma"] - x["pre_ma"] > 0 else 0, axis=1)
all["low>pre"] = all.apply(lambda x: 1 if x["low"] > x["pre_low"] else 0, axis=1)
