Stock Price Prediction Using LSTM

Implementation 

Importing Modules

We can download the dataset from the given link. The dataset contains 1259 entries with seven different columns. The close column is the target feature. The data is from 2015 to 2020. After downloading the dataset, import all the required modules.

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from sklearn import model_selection
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
df=pd.read_csv("TSLA.csv")
df.head(10)

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Output

Train-Test Split

training = df.iloc[:800, 1:2].values
testing = df.iloc[800:, 1:2].values

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Scaling the Data

LSTM is very sensitive to the scale of the data, Here the scale of the Close value is in a kind of scale, we should always try to transform the value.

Here we will use min-max scalar to transform the values from 0 to 1.We should reshape so that we can use fit transform.

sc = MinMaxScaler(feature_range = (0, 1))
training_scaled = sc.fit_transform(training_set)

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Data Preprocessing

x_train = []
y_train = []
for iter in range(60, 800):
    x_train.append(training_scaled[iter-60:iter, 0])
    y_train.append(training_scaled[iter, 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))

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LSTM Model

Now, it's time to build the model. We will make the LSTM with 50 neurons and four hidden layers. Finally, we will use one neuron in the output layer to predict the normalized stock price. We will use Mean Square Error(MSE) loss function and the Adam stochastic gradient descent optimizer.

algo = Sequential()
algo.add(LSTM(units = 50, return_sequences = True, input_shape = (x_train.shape[1], 1)))
algo.add(Dropout(0.2))
algo.add(LSTM(units = 50, return_sequences = True))
algo.add(Dropout(0.2))
algo.add(LSTM(units = 50, return_sequences = True))
algo.add(Dropout(0.2))
algo.add(LSTM(units = 50))
algo.add(Dropout(0.2))
algo.add(Dense(units = 1))
algo.compile(optimizer = 'adam', loss = 'mean_squared_error')
algo.fit(x_train, y_train, epochs = 100, batch_size = 32)

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Prediction

data_train = df.iloc[:800, 1:2]
data_test = df.iloc[800:, 1:2]
data_total = pd.concat((data_train, data_test), axis = 0)
toal_input = data_total[len(data_total) - len(data_test) - 60:].values
total_input = total_input.reshape(-1,1)
total_input = sc.transform(total_input)
x_test = []
for iter in range(60, 519):
    x_test.append(total_input[iter-60:iter, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
print(x_test.shape)

predicted_price = algo.predict(x_test)
predicted_price = sc.inverse_transform(predicted_price)

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Plotting

plt.plot(df.loc[800:, 'Date'],data_test.values, color = 'red', label = 'Real TESLA Stock Price')
plt.plot(df.loc[800:, 'Date'],predicted_price, color = 'blue', label = 'Predicted TESLA Stock Price')
plt.xticks(np.arange(0,459,50))
plt.title('TESLA Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel('TESLA Stock Price')
plt.legend()
plt.show()

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Our model performed very well. It can accurately follow most of the unexcepted jumps and drops.

Frequently Asked Questions

1. Is it possible to predict stock prices?

Ans. There are chances that we can predict or instead forecast some market trends to get a higher chance of success in the market, as this is essentially what market analysts do. Still, these forecasts are closer to educated guesses than 99% accurate, precise predictions.

2. What is the use of stock price prediction?

Ans. Stock market prediction is the act of trying to determine the future value of company stock or other financial instruments traded on an exchange. The successful prediction of a stock's future price could yield significant profit.

3. Which ML model is best for stock prediction?

Ans. Introduction to LSTMs for the time-series data. Now, let's move on to the LSTM model. LSTM, short for Long Short-term Memory, is a robust algorithm for time series. It can capture historical trend patterns and predict future values with high accuracy.

Key Takeaways

Let us brief the article.

Firstly, we briefly introduced the stock price prediction and why different cant performs well with time-series data. Moving on, we saw the idea behind the working of LSTM. Lastly, we implemented the coding part using the LSTM model. 

Stock prices are influenced by company news and other factors such as demonetization or company mergers and demergers. In addition, many intangible variables are difficult to predict in advance.

I hope you all like this article. 

Happy Learning Ninjas!