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FlyDailyInfo.py
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34 lines (30 loc) · 1.42 KB
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#!/usr/bin/python
import tushare as ts
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
# 统计非一字板的个数 一字板判定（涨停并且开盘涨幅大于8%）
# 根据一字板涨停条件抽取出一个df[date] 生成series 然后进行count，在将series根据date count 添加到原df
def get_daily_limit_along_factor(df):
limit_df = df[np.logical_and((100 - (df['open'] / df['close']) * (100 + df['p_change'])) >= 8,
df['p_change'] > 9.8)]
rest_df = df.drop(limit_df)['date']
date_series = Series(rest_df)
# 这个series包含了每个date中的非一字板个数
date_num_series = date_series.value_counts()
date_num_df = DataFrame({'date': date_num_series.index,
'daily_limit_along_factor': date_num_series.values})
# print(date_num_df)
# 默认根据date作为两个表的key值进行连接合并
return pd.merge(df, date_num_df)
if __name__ == '__main__':
print("hello")
# df = ts.get_hist_data('603283').reset_index()
# df2 = ts.get_hist_data('002907').reset_index()
# df3 = ts.get_hist_data('300664').reset_index()
# df4 = ts.get_hist_data('603655').reset_index()
# df5 = ts.get_hist_data('300433').reset_index()
# df6 = ts.get_hist_data('600903').reset_index()
# dfa = pd.concat([df, df2, df3, df4, df5, df6])
# print(df5)
# f = get_daily_limit_along_factor(dfa)