def getLastOneAndADV(self, code):
import DBStock.dbQueryPools as dbpool
import tools.timeTool as tT
#print(tT.getDateStrNow(), tT.getDateStrBefore())
ts = dbpool.queryMySQL_plot_stock_market(code, tT.getDateStrBefore(), tT.getDateStrNow())
#print(ts)
ts['Date'] = pd.to_datetime(ts['Date'])
ts.set_index('Date', inplace=True)
ts = ts[(True^ts['Close'].isin([0]))]#条件删除去除值为0的行
import FeatureBase.FeatureUtils as FU
ts = FU.ADV(ts)
#打印不换行
#pd.set_option('display.height',1000)
#pd.set_option('display.max_rows',500)
#pd.set_option('display.max_columns',500)
pd.set_option('display.width',1000)
#print(ts.tail(1))
#self.dataAndADV = ts
return ts.tail(1)
'''#FeatureUtils.py
start = datetime.datetime(2012, 1, 1)
end = datetime.datetime(2013, 1, 1)
NSEI = web.DataReader("AREX", "yahoo", start, end)
data = pd.DataFrame(NSEI)
#print(data)
# Compute the Bollinger Bands for NIFTY using the 50-day Moving average
n = 12
NIFTY_BBANDS = BBANDS(data, n)
print(NIFTY_BBANDS)'''
startdate = '2017-12-09'
enddate = '2018-12-13'
code = '600016'
import DBStock.dbQueryPools as dbpool
data = dbpool.queryMySQL_plot_stock_market(code, startdate, enddate)
#print(data)
'''n = 12
NIFTY_BBANDS = BBANDS(data, n)
print(NIFTY_BBANDS)'''
'''n = 20
NIFTY_CCI = CCI(data, n)
CCI = NIFTY_CCI['CCI']
print(NIFTY_CCI)
# Plotting the Price Series chart and the Commodity Channel index below
fig = plt.figure(figsize=(7,5))
ax = fig.add_subplot(2, 1, 1)
ax.set_xticklabels([])
plt.plot(data['Close'],lw=1)
plt.title('NSE Price Chart')
def create_lagged_series(symbol, startdate, enddate, lags=5):
# Obtain stock information from Yahoo Finance
'''ts = DataReader(
symbol, "yahoo",
startdate-datetime.timedelta(days=365),
enddate
)'''
import DBStock.dbQueryPools as dbpool
ts = dbpool.queryMySQL_plot_stock_market(symbol, startdate, enddate)
ts['Date'] = pd.to_datetime(ts['Date'])
ts.set_index('Date', inplace=True)
ts = ts[(True^ts['Close'].isin([0]))]#条件删除去除值为0的行
#打印不换行
#pd.set_option('display.height',1000)
#pd.set_option('display.max_rows',500)
#pd.set_option('display.max_columns',500)
pd.set_option('display.width',1000)
print(ts)
# Create the new lagged DataFrame
tslag = pd.DataFrame(index=ts.index)
tslag["Today"] = ts["Close"]
tslag["Volume"] = ts["Volume"]
# Create the shifted lag series of prior trading period close values
for i in range(0, lags):
tslag["Lag%s" % str(i+1)] = ts["Close"].shift(i+1)
#print(tslag)
# Create the returns DataFrame
tsret = pd.DataFrame(index=tslag.index)
#tsret["Volume"] = tslag["Volume"]
tsret["Today"] = tslag["Today"].pct_change()*100.0
#tsret["Close"] = ts["Close"]
tsret = tsret.join(ts)
#print('tsret')
#print(tsret)
# If any of the values of percentage returns equal zero, set them to
# a small number (stops issues with QDA model in scikit-learn)
for i,x in enumerate(tsret["Today"]):
if (abs(x) < 0.0001):
tsret["Today"][i] = 0.0001
#print(tsret)
# Create the lagged percentage returns columns
for i in range(0, lags):
tsret["Lag%s" % str(i+1)] = \
tsret["Today"].shift(i+1)
#tslag["Lag%s" % str(i+1)].pct_change()*100.0
#if (abs(tsret["Lag%s" % str(i+1)]) < 0.0001):
# tsret["Lag%s" % str(i+1)] = 0.0001
#print(tsret)
# Create the "Direction" column (+1 or -1) indicating an up/down day
tsret["TargetValue"] = tsret["Close"].shift(-1)
tsret = tsret[tsret.index >= startdate]
return tsret