Predicting Stock Prices Using Long Short-Term Memory (LSTM) and Python

Predicting stock prices is a challenging problem for many investors. There are a variety of stock market prediction techniques, but one of the most promising is the use of Long Short-Term Memory (LSTM) and Python. In this article, we’ll explore how to use LSTM and Python to predict stock prices and provide code examples for you to follow. If you want to further learn about LTSM and other machine learning algorithms check out these guides.

What is LSTM?

LSTM is a type of Recurrent Neural Network (RNN) that is used for processing sequential data. RNNs are a type of machine learning algorithm that can learn from previous data and use it to make predictions. This makes them well suited for tasks like predicting stock prices, as stock prices are largely dependent on previous price movements.

By using LSTM, investors can make better predictions about the future of a stock’s price by learning from the past.

How to Use LSTM and Python to Predict Stock Prices

Using LSTM and Python to predict stock prices is relatively straightforward. First, you need to collect historical stock market data for the stocks you are interested in. You can use a variety of sources for this data, including Yahoo Finance, Google Finance, and Quandl.

Once you have the data, you need to preprocess it and transform it into a format that can be used by the LSTM model. This includes normalizing the data, splitting it into training and testing sets, and creating a sliding window of data points.

Next, you need to create the LSTM model. This involves setting up the model architecture, compiling the model, and fitting it to the training data.

Finally, you can use the model to make predictions on the test data.

Example Code

For those who are interested in seeing example code for using LSTM and Python to predict stock prices, here is an example taken from the TensorFlow website:

from tensorflow.keras import layers

model = tf.keras.Sequential()
# Add an LSTM layer with 64 internal units.
model.add(layers.LSTM(64, input_shape=(None, float_data.shape[-1])))
# Add a Dense layer with 1 unit.
model.add(layers.Dense(1))

model.compile(optimizer=tf.keras.optimizers.RMSprop(), loss='mae')
history = model.fit(train_gen,
                    steps_per_epoch=500,
                    epochs=20,
                    validation_data=val_gen,
                    validation_steps=val_steps)

To make the LSTM model actually work for predicting stock prices, you would need to complete the preprocessing steps, such as normalizing the data, splitting it into training and testing sets, and creating a sliding window of data points. You would also need to provide the code for loading the data into the model and making predictions. Here is an example of how you could complete these steps:

# Import necessary libraries
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM

# Load historical stock market data
df = pd.read_csv('stock_data.csv')

# Preprocess the data
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(df['Close'].values.reshape(-1, 1))

# Create training and testing data sets
train_data = scaled_data[:int(len(scaled_data)*0.8)]
test_data = scaled_data[int(len(scaled_data)*0.8):]

# Create sliding window of data points
def create_dataset(dataset, time_steps=1):
    dataX, dataY = [], []
    for i in range(len(dataset)-time_steps-1):
        a = dataset[i:(i+time_steps), 0]
        dataX.append(a)
        dataY.append(dataset[i + time_steps, 0])
    return np.array(dataX), np.array(dataY)

time_steps = 60
X_train, y_train = create_dataset(train_data, time_steps)
X_test, y_test = create_dataset(test_data, time_steps)

# Create the LSTM model
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(units=1))

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

# Fit the model to the training data
model.fit(X_train, y_train, epochs=50, batch_size=32)

# Make predictions on the test data
predictions = model.predict(X_test)
predictions = scaler.inverse_transform(predictions)

# Plot the predictions against the actual stock prices
import matplotlib.pyplot as plt
plt.plot(y_test)
plt.plot(predictions)
plt.show()

Note: This code is only an example and may not work as expected for every use case. It is important to thoroughly understand the code and make any necessary modifications before using it for your own purposes.

Conclusion

Using LSTM and Python to predict stock prices is a powerful tool for investors. By using this technique, investors can learn from past data and make better predictions about the future of a stock’s price. We’ve provided an example of how to use LSTM and Python to predict stock prices and included sample code for you to follow.