def single_data_cal_index(iSymbol):
df = ts.pro_bar(api=pro,
ts_code=iSymbol,
adj='qfq',
start_date=start_fetch_date)
df = df.sort_values('trade_date')
df = ic.macd_index_cal(df)
df = ic.kdj_index_cal(df)
df = ic.ema_index_cal(df)
df = ic.roc_index_cal(df)
df = df.fillna(0)
startDate = start_analysis_date
df1 = df[df['trade_date'] >= startDate]
df1.to_pickle('symbol_data/%s.pkl' % df1['ts_code'][0])
def single_data_cal_index(iSymbol, method='yahoo'):
try:
if method == 'yahoo':
df = YFetchData(iSymbol, START_ANALYSIS_DATE).data
elif method == 'tushare':
df = TFetchData(iSymbol, START_ANALYSIS_DATE).data
else:
pass
df = ic.macd_index_cal(df)
df = ic.kdj_index_cal(df)
df = ic.ema_index_cal(df)
df = ic.roc_index_cal(df)
df = df.fillna(0)
df.to_pickle('symbol_data/%s.pkl' % df['ts_code'][0][:6])
except:
pass
def download_data(iSymbol):
try:
df = yf.download(iSymbol, start="2018-01-01", auto_adjust=True)
df.columns = ['open', 'high', 'low', 'close', 'volume']
df.loc[:, 'ts_code'] = iSymbol
df.loc[:, 'trade_date'] = df.index.strftime('%Y%m%d').values
df = df[[
'ts_code', 'trade_date', 'open', 'high', 'low', 'close', 'volume'
]]
df = ic.macd_index_cal(df)
df = ic.kdj_index_cal(df)
df = ic.ema_index_cal(df)
df = ic.roc_index_cal(df)
df = df.fillna(0)
df.to_pickle('symbol_data_yahoo/%s.pkl' % iSymbol)
#print(df.head())
except:
pass
def _open_convert_ts_files(self):
"""
Opens the tushare from www.tushare.com , converting
them into pandas DataFrames within a symbol dictionary.
For this handler it will be assumed that the data is
taken from Yahoo. Thus its format will be respected.
"""
comb_index = None
for s in self.symbol_list:
# Load the tushare with no header information, indexed on date
start1 = self.start_date.strftime('%Y%m%d')
ts.set_token(
'bf3b4e51fcc67507e8694e9a3f2bd591be93bea276f9d86f564fe28f')
pro = ts.pro_api()
df = ts.pro_bar(api=pro, ts_code=s, adj='qfq', start_date=start1)
df.index = pd.DatetimeIndex(df.trade_date)
df = df.sort_index()
df = ic.ema_index_cal(df)
df = ic.macd_index_cal(df)
df = ic.kdj_index_cal(df)
df = ic.roc_index_cal(df)
df = df.fillna(0)
self.symbol_data[s] = df
# Combine the index to pad forward values
if comb_index is None:
comb_index = self.symbol_data[s].index
else:
comb_index.union(self.symbol_data[s].index)
# Set the latest symbol_data to None
self.latest_symbol_data[s] = []
# Reindex the dataframes
for s in self.symbol_list:
self.symbol_data[s] = self.symbol_data[s].\
reindex(index=comb_index, method='pad').iterrows()
ax2 = plt.axes([0.1, 0.1, 0.8, 0.2], sharex=ax1)
ax2.plot(df['iNum'], df[['incomeSum']])
# make these tick labels invisible
ax2.set_ylabel('incomeSum')
ax2.grid(True)
plt.setp(ax2.get_xticklabels(), visible=True)
return fig
if __name__ == '__main__':
import tushare as ts
import indexCal as ic
ts.set_token('bf3b4e51fcc67507e8694e9a3f2bd591be93bea276f9d86f564fe28f')
pro = ts.pro_api()
df = ts.pro_bar(api=pro,
ts_code='000002.SZ',
adj='qfq',
start_date='20181201')
#df = ts.pro_bar(ts_code='399005.SH', asset='I', start_date='20180101', end_date='20181212')
#df=df.sort_values('trade_date')
df = df.sort_values('trade_date')
df['iNum'] = np.arange(len(df))
df = ic.macd_index_cal(df)
df = ic.kdj_index_cal(df)
df = ic.roc_index_cal(df)
df = ic.vol_index_cal(df)
df['iNum'] = np.arange(len(df))
date_tickers = df['trade_date']
kplot(df, 'MACD')