import tushare as ts import pandas as pd from pandas import DataFrame import numpy as np import datetime import time
from datetime import timedelta import matplotlib.pyplot as plt from mpl_finance import candlestick_ochl from matplotlib.pylab import date2num import matplotlib as mpl import seaborn as sns from matplotlib import ticker
result_list = [] #获取数据接口 pro = ts.pro_api('9b1829f5b0a8901e73f4297d03e12db5ef6550d8b99b8d0417b8eb6f') #获取上市日期大于一年的股票列表 d = timedelta(days=-365) curr_time = datetime.datetime.now() curr_time = curr_time+d curr_time_str = curr_time.strftime('%Y%m%d') df_stock_list = pro.query('stock_basic', exchange='', list_status='L', fields='ts_code,symbol,name,area,industry,list_date') df_stock_list_1 = df_stock_list[df_stock_list['list_date'] < curr_time_str] print(df_stock_list_1.head())
ts_code_list = df_stock_list_1.head(1000)
df_scanned_list = pd.read_excel('scanned_list.xlsx')
def stock_judging(ts_code,start_date): df = pro.daily(ts_code= ts_code, start_date=curr_time_str) df = df.sort_values(by='trade_date', ascending=True) df['trade_date2'] = df['trade_date'].copy() df['trade_date'] = pd.to_datetime(df['trade_date']).map(date2num) df['dates'] = np.arange(0, len(df))
df['30'] = df.close.rolling(30).mean()
df['72'] = df.close.rolling(72).mean()
df['144'] = df.close.rolling(144).mean()
df['250'] = df.close.rolling(250).mean()
df_1 = df[-1:]
df_2 = df[-2:]
if len(df_1) > 0:
print(df_1.ix[0, ['ts_code']])
if bool(df_1.ix[0, ['close']].values > df_2.ix[0, ['close']].values):
if df_1.ix[0, ['30']].values > df_1.ix[0, ['72']].values:
if df_1.ix[0, ['72']].values > df_1.ix[0, ['144']].values:
if df_1.ix[0, ['144']].values > df_1.ix[0, ['250']].values:
return df_1.iloc[0, 0].values
for ts_code in ts_code_list['ts_code']: result = stock_judging(ts_code, curr_time_str) if result: result_list.append(result) #DataFrame(ts_code).to_excel('scanned_list.xlsx', sheet_name='sheet1', index=False, header=True)
print(result_list)
time.sleep(30)
print(result_list)
'''
df = pro.index_daily(ts_code='399300.SZ', start_date='20190101')
df = df.sort_values(by='trade_date', ascending=True) df['trade_date2'] = df['trade_date'].copy() df['trade_date'] = pd.to_datetime(df['trade_date']).map(date2num) df['dates'] = np.arange(0, len(df))
df['30'] = df.close.rolling(30).mean() df['72'] = df.close.rolling(72).mean() df['144'] = df.close.rolling(144).mean() df['250'] = df.close.rolling(250).mean()
df_1 = df[-1:] df_2 = df[-2:] print(df_1) print(df_1.iloc[0,3])
if df_1.iloc[0,3]> df_2.iloc[0,2]: if df_1['30'] > df_1['72']: if df_1['72'] > df_1['144']: if df_1['144'] > df_1['250']: print (df_1.iloc[0,0])
print(stock_judging('399300.SZ','20190101'))
'''
''' sns.set() mpl.rcParams['font.family'] = 'sans-serif' mpl.rcParams['font.sans-serif'] = 'SimHei'
def format_date(x,pos): if x<0 or x>len(date_tickers)-1: return '' return date_tickers[int(x)] date_tickers = df.trade_date2.values fig, ax = plt.subplots(figsize=(10, 5)) ax.xaxis.set_major_formatter(ticker.FuncFormatter(format_date)) #调用方法，绘制K线图 candlestick_ochl(ax = ax, quotes=df[['dates', 'open', 'close', 'high', 'low']].values, width=0.7, colorup='red', colordown='green', alpha=0.7) #df['close'].rolling(window=3).mean().plot(color='red', label='3') #df['close'].rolling(window=5).mean().plot(color='blue', label='5') #df['close'].rolling(window=10).mean().plot(color='green', label='10') for ma in ['30', '72', '144', '250']: plt.plot(df['dates'], df[ma]) plt.legend() #绘制图例 #设置X轴的标签 plt.xticks(range(len(df.index.values)), df.index.values, rotation=30 )
plt.title('399300') plt.show() '''