Python Machine Learning on Amazon stock prices

This Python code reads Amazon's historical stock prices from 2014 to 2019. I downloaded the CSV file from Yahoo Finance. The chart below shows how well this algorithm predicts stocks prices when compared to actual stock prices. The code was cobbled together from snippets at Analytics Vidhya and Medium.

# importing libraries

import pandas as pd

import numpy as np

from datetime import date, datetime

import calendar

#importing required libraries

from sklearn.preprocessing import MinMaxScaler

from keras.models import Sequential

from keras.layers import Dense, Dropout, LSTM

# reading the data
df = pd.read_csv('amzn2.csv')


# looking at the first five rows of the data

print('\n Original data:')

print(df.head())

print('\n Shape of original data:')

print(df.shape)


# setting the index as date

df['Date'] = pd.to_datetime(df.Date,format='%Y-%m-%d')

df.index = df['Date']


#creating dataframe

data = df.sort_index(ascending=True, axis=0)

new_data = pd.DataFrame(index=range(0,len(df)),columns=['Date', 'Close'])

#populate new data frame

for i in range(0,len(data)):

    new_data['Date'][i] = data['Date'][i]

    new_data['Close'][i] = data['Close'][i]


#setting index

new_data.index = new_data.Date

new_data.drop('Date', axis=1, inplace=True)


#creating train and test sets

dataset = new_data.values

#the csv file has 1260 records

train = dataset[0:630,:]

valid = dataset[630:,:]


#converting dataset into x_train and y_train

scaler = MinMaxScaler(feature_range=(0, 1))

scaled_data = scaler.fit_transform(dataset)


x_train, y_train = [], []

for i in range(60,len(train)):

    x_train.append(scaled_data[i-60:i,0])

    y_train.append(scaled_data[i,0])

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


x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))


# create and fit the LSTM network

model = Sequential()

model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1],1)))

model.add(LSTM(units=50))

model.add(Dense(1))


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

model.fit(x_train, y_train, epochs=1, batch_size=1, verbose=2)


#predicting 246 values, using past 60 from the train data

inputs = new_data[len(new_data) - len(valid) - 60:].values

inputs = inputs.reshape(-1,1)

inputs  = scaler.transform(inputs)


X_test = []

for i in range(60,inputs.shape[0]):

    X_test.append(inputs[i-60:i,0])

X_test = np.array(X_test)


X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))

closing_price = model.predict(X_test)

closing_price = scaler.inverse_transform(closing_price)

rms=np.sqrt(np.mean(np.power((valid-closing_price),2)))

print('\n Root Mean Square Deviation:')

print(rms)

#for plotting

#plot

import matplotlib.pyplot as plt

train = new_data[:630]

valid = new_data[630:]

valid['Predictions'] = closing_price

plt.plot(train['Close'])

plt.plot(valid[['Close','Predictions']])

plt.show()