stock_price_prediction.py

# -*- coding: utf-8 -*-
"""Stock Price Prediction.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/19o9fQBCGLKnKkGmSmZrND-f80W7DSIw6
"""

import math
import pandas_datareader as web
import numpy as np
import pandas as pd
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 quota
df = web.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2020-11-21')
# Show the data
df

# Get the number of rows and columns in the data set
df.shape

# Visualize the closing price history
plt.figure(figsize=(16,8))
plt.title('Close Price History')
plt.plot(df['Close'])
plt.xlabel('Data', fontsize=18)
plt.ylabel('Close Price USD ($)', fontsize=18)
plt.show()

# Create a new adataframe with only the 'Close column
data = df.filter(['Close'])
# Convert the dataframe to a numpy array
dataset = data.values
# Get the number of rows to train the model on
training_data_len = math.ceil( len(dataset) * .8 )

training_data_len

# Scale the data
scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(dataset)

scaled_data

# Create the training data set
# Create the scaled training data set
train_data = scaled_data[0:training_data_len , :]
# SPlit the data into x_train and y_train data sets
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])
  if i <= 61:
    print(x_train)
    print(y_train)
    print()

# Convert the x_train and y_train to numpy arrays
x_train, y_train = np.array(x_train), np.array(y_train)

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

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

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

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

# Create the testing data set
# Create a new array containing scaled from index 1543 to 2003
test_data = scaled_data[training_data_len - 60: , :]
# Create the data set x_test and 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 the data to a numpy array
x_test = np.array(x_test)

# Reshape the data
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))

# Get the models predicted price values
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)

# Get the root mean squared error (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('Close Price USD ($)', fontsize=18)
plt.plot(train['Close'])
plt.plot(valid[['Close', 'Predictions']])
plt.legend(['Train', 'Val', 'Predictions'], loc='lower right')
plt.show()

# Show the valid and predicted price
valid

# Get the quote
apple_quote = web.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2020-11-21')
# Create a new dataframe
new_df = apple_quote.filter(['Close'])
# Get the list 60 day closing price values and convert the dataframe to an array
last_60_days = new_df[-60:].values
# Scale the data to be values between 0 and 1
last_60_days_scaled = scaler.transform(last_60_days)
# Create an empty list
X_test = []
# Append the past 60 days
X_test.append(last_60_days_scaled)
# Convert the X_test data set to a numpy array
X_test = np.array(X_test)
# Reshape the data
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
# Get the predicted scaled price
pred_price = model.predict(X_test)
# Undo the scaling
pred_price = scaler.inverse_transform(pred_price)
print(pred_price)

# Get the quote
apple_quote2 = web.DataReader('AAPL', data_source='yahoo', start='2020-11-20', end='2020-11-20')
print(apple_quote2['Close'])