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)