def post():
K.clear_session()
namasaham = request.form['namasaham']
model = joblib.load('model' + namasaham)
#split tanggal,bulan,tahun
date = request.form['tanggal']
date = datetime.datetime.strptime(date, '%Y-%m-%d')
tanggal = date.strftime('%d')
if tanggal[0] == str(0):
tanggal1 = int(tanggal[1])
else:
tanggal1 = int(tanggal)
print(tanggal1)
bulan = date.strftime('%m')
if bulan[0] == str(0):
bulan1 = int(bulan[1])
else:
bulan1 = int(bulan)
tahun = date.strftime('%Y')
tahun1 = int(tahun)
tanggal_predict = str(tahun + '-' + bulan + '-' + tanggal)
#import data stock price
real_saham = Fetcher(namasaham + ".JK", [2018, 1, 1],
[tahun1, bulan1, tanggal1],
interval="1d")
real_saham = real_saham.getHistorical()
real_saham = real_saham.iloc[:, 0:2]
real_saham = real_saham.dropna()
#set index and drop data in predict_date
real_saham = real_saham.set_index("Date")
if tanggal_predict in real_saham.index.values:
real_saham = real_saham.drop(tanggal_predict, axis=0)
real_saham = real_saham.tail(10)
#transform
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler()
saham = sc.fit_transform(real_saham)
window = 3
predictX = dataset_predict.createDataset(saham, window)
predictX = predictX.reshape(len(saham) - window + 1, window, 1)
predictY = model.predict(predictX)
#denormalisasi
predictY = sc.inverse_transform(predictY)
predict_next_day = "Rp. {}".format(int(predictY[-1][0]))
price_previous_day = "Rp. {}".format(int(real_saham['Open'][-1]))
previous_date = real_saham.index[-1]
return render_template('predict.html',
predict=predict_next_day,
price_previous=price_previous_day,
tanggal_predict=tanggal_predict,
previous_date=previous_date,
namasaham=namasaham)
def get_historical(stock_ticker):
data = Fetcher(stock_ticker, [1990, 1, 1]).getHistorical()
data['Date'] = pd.to_datetime(data['Date'])
data = data.drop(columns=['Open', 'High', 'Low', 'Adj Close', 'Volume'])
data['Date'] = pd.to_datetime(data['Date']).values.astype(
np.int64) // 10**6
data = data.rename(columns={"Date": "x", "Close": "y"})
data = data.dropna()
data = data.to_dict('records')
return data
