Code2.py

# -*- coding: utf-8 -*-
"""Untitled4.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1x-za71znk_O7JhGvIwLKMmifqh2oRxSi

# STOCK PREDICTION USING NUERAL NETWORKS
### Using Artificial Reccurent Neural Network called Long Short Term Memory (LSTM) for predicting the closing stock price of a corporation.
"""

!pip install sklearn

#Import libraries
import math
import pandas as pd
import numpy as np
import pandas_datareader as web
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')

#get the stock data
df = web.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2020-04-30')
#display data
df

#Can change end date to present date using time module

#Visualize Closing History
plt.figure(figsize=(16,8))
plt.title("Closing Prize Chart")
plt.plot(df['Close'])
plt.xlabel('Date', fontsize=18)
plt.ylabel('Close Price USD ($)',fontsize=18)
plt.show

#New dataframe with only close coloumn
data = df.filter(['Close'])
#Convert dataframe to a numpy array
dataset =data.values

#Get 80% of the data st=et length used for training the LSTM
training_data_len = math.ceil(len(dataset)*.8)

training_data_len

#Preprocessing of Data
Scaler = MinMaxScaler(feature_range=(0,1))
scaled_data=Scaler.fit_transform(dataset)

scaled_data

#Create the training dataset

#Creating Scaled Training Dataset

train_data = scaled_data[0:training_data_len,:]

#Split data into x_train and y_train
x_train = []
y_train = []

for i in range (60,len(train_data)):
  x_train.append(train_data[i-60:i,0])
  y_train.append(train_data[i, 0])

# Convert x_train and y_train to numpy arrays

x_train, y_train = np.array(x_train), np.array(y_train)

#Reshape the x_train dataset
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
x_train.shape

#Build the LSTM Model
model = Sequential()
#Add  NN Layers
model.add(LSTM (50, return_sequences=True, input_shape=(x_train.shape[1],1))) #l1
model.add(LSTM (50, return_sequences=False)) #l2
model.add(Dense(25))
model.add(Dense(1))

#Compile the model

model.compile(optimizer='adam', loss='mean_squared_error')

model.fit(x_train, y_train, batch_size=1, epochs=1)

#Create test dataset
test_data = scaled_data[training_data_len-60:,:]

#Create x_test, y_test
x_test=[]
y_test = dataset[training_data_len:,:]

for i in range(60, len(test_data)):
  x_test.append(test_data[i-60:i,0])

#Convert to numpy_array
x_test = np.array(x_test)

#Reshape the test data from 2D to 3D
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))

#Get models predicted values
predictions = model.predict(x_test)
predictions = Scaler.inverse_transform(predictions)

#Get RMSE

rmse  = np.sqrt(np.mean((predictions - y_test)**2))
rmse

#Plot the data
train = data[:training_data_len]
valid = data[training_data_len:]
valid['Predictions'] = predictions

#Visualize the data
plt.figure(figsize=(16,8))
plt.title('Model')
plt.xlabel('Date', fontsize=18)
plt.ylabel('Closing Price in USD($)', fontsize=18)
plt.plot(train['Close'])
plt.plot(valid[['Close','Predictions']])
plt.legend(['Train','Validation','Prediction'], loc='lower right')
plt.show()

#Show Actual and Predicted Prices
valid

#Getting Quote for 31/04/2020

apple_quote = web.DataReader('AAPL', data_source='yahoo', start='')