joeng03

import pandas as pd
df = pd.read_csv('GS.csv')
df['Date'] = pd.to_datetime(df.Date)
df.index = df['Date']
plt.figure(figsize=(16,8))
plt.plot(df['Close'])

joeng03

from sklearn.preprocessing import MinMaxScaler
data = df.sort_index(ascending=True, axis=0)
new_data = pd.DataFrame(index=range(0,len(df)),columns=['Date', 'Close'])
for i in range(0,len(data)):
    new_data['Date'][i] = data['Date'][i]
    new_data['Close'][i] = data['Close'][i]
new_data.index = new_data.Date
new_data.drop('Date', axis=1, inplace=True)
dataset = new_data.values
scaler = MinMaxScaler(feature_range=(0, 1))

joeng03

from keras.models import Sequential
from keras.layers import Dense, Dropout,LSTM
from keras.optimizers import Adam
model = Sequential()
model.add(LSTM(units=100,input_shape=(x_train.shape[1],1),return_sequences=True))
model.add(LSTM(units=100))
model.add(Dropout(0.4))
model.add(Dense(1))
ADAM = Adam(0.0005, beta_1=0.9, beta_2=0.999, amsgrad=False)
model.compile(loss='mean_squared_error', optimizer=ADAM)

joeng03

import pandas as pd
import numpy as np
import pandas_datareader as pdr
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
TI= TechnicalIndicators()
close_data=TI.close_data[['4. close']]
macd_data=TI.macd_data
rsi_data=TI.rsi_data
bbands_data=TI.bbands_data

joeng03

import pandas as pd
import numpy as np
import pandas_datareader as pdr
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
TI= TechnicalIndicators()
close_data=TI.close_data[['4. close']]
macd_data=TI.macd_data
rsi_data=TI.rsi_data
bbands_data=TI.bbands_data

joeng03

for i in range(65,y_train.shape[0]):
    a_train.append(X_train[i-65:i-5,0])
    b_train.append(y_train[i-5:i,0])
for x in range(65,y_test.shape[0]):
    c_test.append(X_test[x-65:x-5,0])
    d_test.append(y_test[x-5:x,0])

joeng03

function move_by_vector(element,direction,duration=1000) {
   var elStyle = window.getComputedStyle(element);
   var x_isNegated =(direction[0]<0)?true:false;
   var y_isNegated =(direction[1]<0)?true:false;
   var x_coord= elStyle.getPropertyValue('left').replace("px", "");
   var y_coord= elStyle.getPropertyValue('top').replace("px", "");
   var x_destination = Number(x_coord) + direction[0];
   var y_destination = Number(y_coord) + direction[1];
   var x_frameDistance = direction[0]/ (duration / 10);
   var y_frameDistance = direction[1] / (duration / 10);

joeng03

function move(element,direction,duration=1000){
   var elStyle = window.getComputedStyle(element);
   var x_coord= elStyle.getPropertyValue('left').replace("px", "");
   var y_coord= elStyle.getPropertyValue('top').replace("px", "");
   var x_frameDistance = direction[0]/ (duration / 10);
   var y_frameDistance = direction[1] / (duration / 10);
   function moveAFrame() {
      elStyle = window.getComputedStyle(element);
      x_coord = elStyle.getPropertyValue('left').replace("px",""); 
      var x_newLocation = Number(x_coord) + x_frameDistance;

joeng03

#import the required libraries

import requests
from datetime import timedelta,date 
import numpy as np
from scipy.signal import argrelextrema
import matplotlib.pyplot as plt
import time

joeng03

data=requests.get('https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&outputsize=full&symbol=FB&apikey=YOURAPIKEY')
data=data.json()
prices_low,prices_high,prices_close=[],[],[]
for i in range(200,1,-1):
  d=date.today()-timedelta(i)
  d=d.strftime("%Y-%m-%d")
  try:
    prices_high.append(float(data["Time Series (Daily)"][d]["2. high"]))
    prices_low.append(float(data["Time Series (Daily)"][d]["3. low"]))
    prices_close.append(float(data["Time Series (Daily)"][d]["4. close"]))