Microsoft Stocks Price Prediction using LSTM
What is the Stock Market?
The stock market refers to the collection of markets and exchanges where regular activities of buying, selling, and issuance of shares of publicly-held companies take place. Such financial activities are conducted through institutionalized formal exchanges or over-the-counter (OTC) marketplaces which operate under a defined set of regulations. There can be multiple stock trading venues in a country or a region which allow transactions in stocks and other forms of securities.
Understanding the Problem Statement
We’ll dive into the implementation part of this article soon, but first it’s important to establish what we’re aiming to solve. Broadly, stock market analysis is divided into two parts — Fundamental Analysis and Technical Analysis.
- Fundamental Analysis involves analyzing the company’s future profitability on the basis of its current business environment and financial performance.
- Technical Analysis, on the other hand, includes reading the charts and using statistical figures to identify the trends in the stock market.
As you might have guessed, our focus will be on the technical analysis part. We’ll be using the dataset of Microsoft stock prices from April 2015 to April 2021 to build a model capable of estimating the stock prices.
Importing the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 20,10
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range=(0, 1))
from math import floor,ceil,sqrt
import sys
import warnings
import datetime as dt
if not sys.warnoptions:
warnings.simplefilter("ignore")
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTMReading the dataset
df = pd.read_csv('/kaggle/input/microsoft-stock-time-series-analysis/Microsoft_Stock.csv')Major Points of Understanding
There are multiple variables in the dataset — Date, Open, High, Low, Close and volume.
The columns Open and Close represent the starting and final price at which the stock is traded on a particular day.
High and Low represent the maximum and minimum price of the share for the day.
Volume is the number of shares bought or sold in the day
Another important thing to note is that the market is closed on weekends and public holidays. Some date values are missing — 4/3/2015, 4/4/2015 and 4/5/2015. Of these dates, 3rd April 2015 was a public holiday due to the occasion of Good Friday, while 4th and 5th April were weekends
The profit or loss calculation is usually determined by the closing price of a stock for the day, hence we will consider the closing price as the target variable. Let’s plot the target variable to understand how it’s shaping up in our data.
df['Date'] = pd.to_datetime(df.Date,format='%m/%d/%Y %H:%M:%S')
df.index = df['Date']
plt.figure(figsize=(16,8))
plt.plot(df['Close'], label='Close Price history',color='g')
plt.xlabel('Date',size=20)
plt.ylabel('Stock Price',size=20)
plt.title('Stock Price of Microsoft over the Years',size=25)
Long Short Term Memory (LSTM)
Introduction
LSTMs are widely used for sequence prediction problems and have proven to be extremely effective. The reason they work so well is because LSTM is able to store past information that is important, and forget the information that is not. LSTM has three gates:
The input gate: The input gate adds information to the cell state The forget gate: It removes the information that is no longer required by the model The output gate: Output Gate at LSTM selects the information to be shown as output.
For a more detailed understanding of LSTM and its architecture, you can go through the below article: https://colah.github.io/posts/2015-08-Understanding-LSTMs/
For now, let us implement LSTM as a black box and check it’s performance on our particular data.
def lstm_prediction(df):
shape=df.shape[0]
df_new=df[['Close']]
df_new.head()
dataset = df_new.values
train=df_new[:ceil(shape*0.75)]
valid=df_new[ceil(shape*0.75):]
print('-----------------------------------------------------------------------------')
print('-----------STOCK PRICE PREDICTION BY LONG SHORT TERM MEMORY (LSTM)-----------')
print('-----------------------------------------------------------------------------')
print('Shape of Training Set',train.shape)
print('Shape of Validation Set',valid.shape)
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(dataset)
x_train, y_train = [], []
for i in range(40,len(train)):
x_train.append(scaled_data[i-40: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))
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)
inputs = df_new[len(df_new) - len(valid) - 40:].values
inputs = inputs.reshape(-1,1)
inputs = scaler.transform(inputs)
X_test = []
for i in range(40,inputs.shape[0]):
X_test.append(inputs[i-40: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('RMSE value on validation set:',rms)
print('-----------------------------------------------------------')
print('-----------------------------------------------------------')
valid['Predictions'] = closing_price
plt.plot(train['Close'])
plt.plot(valid[['Close','Predictions']])
plt.xlabel('Date',size=20)
plt.ylabel('Stock Price',size=20)
plt.title('Stock Price Prediction by Long Short Term Memory (LSTM)',size=20)
plt.legend(['Model Training Data','Actual Data','Predicted Data'])lstm_prediction(df)-----------------------------------------------------------------------------
-----------STOCK PRICE PREDICTION BY LONG SHORT TERM MEMORY (LSTM)-----------
-----------------------------------------------------------------------------
Shape of Training Set (1134, 1)
Shape of Validation Set (377, 1)
1094/1094 - 18s - loss: 6.5706e-04
RMSE value on validation set: Close 8.884761
dtype: float64
Observation
The LSTM model can be tuned for various parameters such as changing the number of LSTM layers, adding dropout value or increasing the number of epochs. But are the predictions from LSTM enough to identify whether the stock price will increase or decrease? Certainly not!
Stock price is affected by the news about the company and other factors like demonetization or merger/demerger of the companies. There are certain intangible factors as well which can often be impossible to predict beforehand.
