Stock price analysis is an important research area and one of the major applications of machine learning. This tutorial shows you how to perform stock price prediction using machine learning and deep learning techniques. Here, we use an LSTM network to train a model on Google stock data.
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What is the stock market?
A stock market is a public market where stocks of listed companies can be bought and sold. Stocks, also called stocks, represent ownership in a company. A stock exchange is an intermediary that allows stocks to be bought and sold.
Importance of stock market
- The stock market helps companies raise capital.
- It helps in creating personal wealth.
- The stock market acts as an indicator of the state of the economy.
- It is widely used as a source of capital for people to invest money in companies with high growth potential.
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stock price prediction
Stock price prediction using machine learning algorithms helps discover the future value of company stocks and other financial assets traded on exchanges. The whole idea of predicting stock prices is to make big profits. Predicting how the stock market will perform is a difficult task. Other factors are also involved in prediction, such as physical and psychological factors, rational and irrational behavior. All these factors combine to make stock prices dynamic and volatile. This makes it extremely difficult to predict stock prices with high accuracy.
Understanding the long short-term memory network
We'll use Long Short Term Memory Network (LSTM) to build a model to predict Google's stock price.
LTSM is a type of recurrent neural network for learning long-term dependencies. It is often used for processing and forecasting time series data.
From the image above, you can see that LSTM has a chain-like structure. A typical RNN has a single neural network layer. On the other hand, LSTM has four interaction layers with anomalous communication.
LSTM works in a three-step process.
- The first step in LSTM is to decide what information to omit from the cell at that particular time step. It is determined with the help of the sigmoid function. Compute the function by examining the previous state (ht-1) and the current input xt.
- The second layer has two functions. The first is the sigmoid function and the second is the Tanh function. The sigmoid function determines which values (0 or 1) are passed through. The Tanh function assigns a weight to the passed value and determines its level of importance from -1 to 1.
- The third step is to determine the final output. First, we need to run a sigmoid layer that determines which part of the cell state is output. Next, we need to input the state of the cell into the tanh function to push the value into the range -1 to 1 and multiply it by the output of the sigmoid gate.
Once you understand the basics of LSTM, you can move on to the hands-on demonstration portion of this tutorial on stock price prediction using machine learning.
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Google stock price prediction using LSTM
1. Import the library.
2. Load the training dataset.
Google training data includes information from January 3, 2012 to December 30, 2016. There are 5 columns. The Open Price column shows the price at which a stock started trading when the market opened on a particular day. The closing price column refers to the price of an individual stock when the stock exchange closes the market for the day. The High Price column shows the highest price the stock traded during the period. The Low column shows the lowest price for the period. Volume is the total amount of trading activity within a given period of time.
3. Train the model using the open stock price column.
4. Normalization of the dataset.
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5. Create the X_train and y_train data structures.
6. Reshape the data.
7. Build your model by importing the Crucial library and adding various layers to your LSTM.
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8. Model fitting.
9. Extract the actual stock price for January 2017.
10. Prepare the model inputs.
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11. Predict the stock price value for January 2017.
12. Plot the actual price and predicted price of Google stock.
As you can see above, this model is able to predict the actual stock price trend very closely. The accuracy of the model can be increased by training it with more data and increasing the number of LSTM layers.
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conclusion
Stock market plays a prominent role in our daily life. It is an important factor for a country's GDP growth. In this tutorial, you learned the basics of the stock market and how to perform stock predictions using machine learning.
Do you have any questions about this tutorial on stock price prediction using machine learning? If so, please write them in the comments section. Our team of experts will help answer your questions.
If you want to learn more about machine learning, including various ML applications across industries, be sure to explore Simplilearn's AI and Machine Learning graduate program in partnership with Purdue University and IBM. This comprehensive 12-month program covers everything from statistics, machine learning, deep learning, and reinforcement learning to natural language programming and more. Learn from world-class experts and at the end of the program you'll have great support from industry and academic leaders and the most in-demand frying pans in organizations around the world today.
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