def histdata():
a = datetime.datetime.now()
a = a.strftime('%Y-%m-%d')
namasaham = request.form['namasaham']
tgl1 = request.form['tanggal1']
tgl2 = request.form['tanggal2']
def convert_date(date):
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)
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_lengkap = str(tahun + '-' + bulan + '-' + tanggal)
return tanggal1, bulan1, tahun1, tanggal_lengkap
tgl1_1, bulan1_1, tahun1_1, tgl_lengkap1 = convert_date(tgl1)
tgl2_1, bulan2_1, tahun2_1, tgl_lengkap2 = convert_date(tgl2)
#import data stock price
real_saham = Fetcher(namasaham + ".JK", [tahun1_1, bulan1_1, tgl1_1],
[tahun2_1, bulan2_1, tgl2_1],
interval="1d")
real_saham = real_saham.getHistorical()
real_saham = real_saham.dropna()
#plot data
plt.plot(real_saham['Date'], real_saham['Open'], 'blue')
plt.title('Plot Stock Price of {}'.format(namasaham))
plt.xlabel('Date')
plt.ylabel('Price (Rp)')
plt.xticks(real_saham['Date'], rotation=90)
plt.tight_layout()
#filename
b = datetime.datetime.now()
namafile = (str(b).split('.'))[-1]
addressplot = './storage/{}.png'.format(namafile)
urlplot = '/fileupload/{}.png'.format(namafile)
plt.savefig(addressplot)
plot = urlplot
plt.close()
return render_template('histdata.html',
tables=real_saham.to_html(),
a=a,
tgl1=tgl_lengkap1,
tgl2=tgl_lengkap2,
namasaham=namasaham,
plot=plot)
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
# load model with joblib
import numpy as np
import pandas as pd
namasaham="bbri"
namasaham=namasaham.upper()
import joblib
model=joblib.load('model{}'.format(namasaham))
#dataset from yahoo finance
from yahoo_historical import Fetcher
real_saham= Fetcher(namasaham+".JK", [2019,6,18], [2019,7,18], interval="1d")
real_saham=real_saham.getHistorical()
date_and_open=real_saham.iloc[:,0:2]
real_saham=real_saham.iloc[:,1:2]
real_saham=real_saham.dropna()
print(real_saham)
from sklearn.preprocessing import MinMaxScaler
sc=MinMaxScaler()
saham=sc.fit_transform(real_saham)
print(saham)
def createDataset(data, window):
dataX= []
for i in range(len(data)-window):
temp = []
for j in range(i, i+window):
temp.append(data[j,0])
dataX.append(temp)
return np.array(dataX)
##Dataset from yahoo finance
from yahoo_historical import Fetcher
namasaham="bmri"
namasaham=namasaham.upper()
saham= Fetcher(namasaham+".JK", [2012,7,12], [2019,7,12], interval="1d")
saham=saham.getHistorical()
#Open Price
saham=saham.iloc[:,1:2]
# print(saham)
##Data Preprocessing
#delete na values
saham=saham.dropna()
#split dataset into training and testing
percent_train=0.75
trainingset=saham.iloc[:int(len(saham)*percent_train),:]
testset=saham.iloc[int(len(saham)*percent_train):,:]
# Scale the features
from sklearn.preprocessing import MinMaxScaler
sc=MinMaxScaler()
trainingset=sc.fit_transform(trainingset)
testset=sc.fit_transform(testset)
# Build trainX and trainY
def createDataset(data, window):
dataX, dataY = [], []
def dataset(saham,tanggal1,tanggal2):
real_saham=Fetcher("{}.JK".format(saham), tanggal1, tanggal2, interval="1d")
real_saham=real_saham.getHistorical()
real_saham=real_saham.dropna()
return real_saham
