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)
#import os
#os.chdir('C:\\Users\\hadit\\OneDrive - Prophix Software, Inc\\AutoTrading')
import pandas as pd
import numpy as np
from datetime import datetime
import matplotlib.pyplot as plt
from yahoo_historical import Fetcher
from quant_predict import quant_pred
ticker = 'BTC-USD'
dstart = [2018, 12, 1]
alg = 'macd'
df = Fetcher(ticker, dstart).getHistorical()
#df = df[['Date','Close']]
df.Date = pd.to_datetime(df.Date)
# df = df[:-1]
df.tail()
start = '2019-1-1'
end = '2020-8-1'
signals, gl_alg, gl_ref = quant_pred(df, alg, start, end)
# end='2019-12-31'
# signals, gl_alg_19, gl_ref_19 = quant_pred(df, alg, start, end)
# start = '2020-1-1'
# end = '2020-7-10'
# signals, gl_alg_20, gl_ref_20 = quant_pred(df, alg, start, end)
