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for i in range(0,len(local_max)-3): | |
(m,c),r,_,_,_= np.polyfit(local_max_idx[i:i+3],local_max[i:i+3],1,full=True) | |
if(m<=3 and m>=-3 and (r[0]<20 and r[0]>-20)): | |
start=local_max_idx[i+2] | |
for k in range(start,start+7): | |
if(k<len(prices_close) and prices_close[k]>(k*m+c)): | |
plt.figure(figsize=(10,5)) | |
plt.plot(local_max_idx,m*local_max_idx+c,'m') | |
plt.plot(prices_close) | |
plt.plot(k,prices_close[k],'bo') |
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local_min_idx=argrelextrema(prices_low,np.less)[0] | |
local_max_idx=argrelextrema(prices_high,np.greater)[0] | |
local_min_idx=np.array(local_min_idx) | |
local_max_idx=np.array(local_max_idx) | |
local_min=[] | |
local_max=[] | |
for loc in local_min_idx: | |
local_min.append(prices_low[loc]) | |
for loc in local_max_idx: |
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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"])) |
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#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 |
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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; |
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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); |
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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]) |
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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 |
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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 |
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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) |
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