How to predict stock prices in Python using TensorFlow?
How to Predict Stock Prices in Python using TensorFlow 2 and Keras 1 Preparing the Dataset. Params: ticker (str/pd.DataFrame): the ticker you want to load, examples include AAPL, TESL, etc. ... 2 Training the Model. TEST_SIZE: The testing set rate. ... 3 Testing the Model. ...
Can we use keras to predict house prices?
In this post, we will be exploring how to use a package called Keras to build our first neural network to predict if house prices are above or below median value. In particular, we will go through the full Deep Learning pipeline, from:
Is it possible to predict a stock’s future price?
The successful prediction of a stock’s future price could yield a significant profit. In this application, we used the LSTM network to predict the closing stock price using the past 60-day stock price.
Can a neural network predict stock market prices?
The purpose of this tutorial is to build a neural network in TensorFlow 2 and Keras that predicts stock market prices. More specifically, we will build a Recurrent Neural Network with LSTM cells as it is the current state-of-the-art in time series forecasting. Alright, let's get started.
Can stock price be prediction using machine learning?
Stock Price Prediction using machine learning helps you discover the future value of company stock and other financial assets traded on an exchange. The entire idea of predicting stock prices is to gain significant profits.
Is it possible to predict stock prices?
The stock market is known for being volatile, dynamic, and nonlinear. Accurate stock price prediction is extremely challenging because of multiple (macro and micro) factors, such as politics, global economic conditions, unexpected events, a company's financial performance, and so on.
How do you predict future stock prices?
Major Indicators that Predict Stock Price MovementIncrease/Decrease in Mutual Fund Holding. ... Influence of FPI & FII on Stock Price Movement. ... Delivery Percentage in Stock Trading Volume. ... Increase/Decrease in Promoter Holding. ... Change in Business model/Promoters/Venturing into New Business.More items...•
Which algorithm is used for stock price prediction?
In summary, Machine Learning Algorithms are widely utilized by many organizations in Stock market prediction. This article will walk through a simple implementation of analyzing and forecasting the stock prices of a Popular Worldwide Online Retail Store in Python using various Machine Learning Algorithms.
Which neural network is best for stock prediction?
Recurrent Neural Networks may provide better predictions than the neural networks used in this study, e.g., LSTM (Long Short-Term Memory). Since statements and opinions of renowned personalities are known to affect stock prices, some Sentiment Analysis can help in getting an extra edge in stock price prediction.
Can neural networks predict stock prices?
In the case of stock prices, one has to take into account events that are external to the market. Probably, it would not be possible to predict such events using a neural network. The fact that more traders went bankrupt than became billionaire tells us that a human is not often able to tell the future.
What is the best tool to predict stock market?
The MACD is the best way to predict the movement of a stock. Fibonacci retracement: Fibonacci retracement is based on the assumption that markets retrace by certain predictable percentages, the most common among them being 38.2 per cent, 50 per cent and 61.8 per cent.
What is the algorithm for stock prices?
The algorithm of stock price is coded in its demand and supply. A share transaction takes place between a buyer and a seller at a price. The price at which the transaction is executed sets the stock price.
Machine Learning to Predict Stock Prices
As financial institutions begin to embrace artificial intelligence, machine learning is increasingly utilized to help make trading decisions.
LSTM: A Brief Explanation
L S TMs are an improved version of recurrent neural networks (RNNs). RNNs are analogous to human learning. When humans think, we don’t start our thinking from scratch each second.
Data Normalization
Normalization is changing the values of numeric columns in the dataset to a common scale, which helps the performance of our model. To scale the training dataset we use Scikit-Learn’s MinMaxScaler with numbers between zero and one.
Incorporating Timesteps Into Data
We should input our data in the form of a 3D array to the LSTM model. First, we create data in 60 timesteps before using numpy to convert it into an array. Finally, we convert the data into a 3D array with X_train samples, 60 timestamps, and one feature at each step.
Creating the LSTM Model
Before we can develop the LSTM, we have to make a few imports from Keras: Sequential for initializing the neural network, LSTM to add the LSTM layer, Dropout for preventing overfitting with dropout layers, and Dense to add a densely connected neural network layer.
Making Predictions on the Test Set
Before predicting future stock prices, we have to modify the test set (notice similarities to the edits we made to the training set): merge the training set and the test set on the 0 axis, set 60 as the time step again, use MinMaxScaler, and reshape data. Then, inverse_transform puts the stock prices in a normal readable format.
Plotting the Results
After all these steps, we can use matplotlib to visualize the result of our predicted stock price and the actual stock price.
Preparing the Dataset
As a first step, we need to write a function that downloads the dataset from the Internet and preprocess it:
Model Creation
Now that we have a proper function to load and prepare the dataset, we need another core function to build our model:
Training the Model
Now that we have all the core functions ready, let's train our model, but before we do that, let's initialize all our parameters (so you can edit them later on your needs):
Testing the Model
Now that we've trained our model, let's evaluate it and see how it's doing on the testing set, the below function takes a pandas Dataframe and plots the true and predicted prices in the same plot using matplotlib, we'll use it later:
Conclusion
Alright, that's it for this tutorial, you can tweak the parameters and see how you can improve the model performance, try to train on more epochs, say 700 or even more, increase or decrease the BATCH_SIZE and see if does change to the better, or play around with N_STEPS and LOOKUP_STEPS and see which combination works best.
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Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices
Editor’s note: This tutorial illustrates how to get started forecasting time series with LSTM models. Stock market data is a great choice for this because it’s quite regular and widely available to everyone. Please don’t take this as financial advice or use it to make any trades of your own.
Introduction
LSTMs are very powerful in sequence prediction problems because they’re able to store past information. This is important in our case because the previous price of a stock is crucial in predicting its future price.
Loading the Dataset
The next step is to load in our training dataset and select the Open and High columns that we’ll use in our modeling.
Feature Scaling
From previous experience with deep learning models, we know that we have to scale our data for optimal performance. In our case, we’ll use Scikit- Learn’s MinMaxScaler and scale our dataset to numbers between zero and one.
Creating Data with Timesteps
LSTMs expect our data to be in a specific format, usually a 3D array. We start by creating data in 60 timesteps and converting it into an array using NumPy. Next, we convert the data into a 3D dimension array with X_train samples, 60 timestamps, and one feature at each step.
Building the LSTM
In order to build the LSTM, we need to import a couple of modules from Keras:
Predicting Future Stock using the Test Set
First we need to import the test set that we’ll use to make our predictions on.
Data Preparation
I created a new data frame containing only the target variable - Closing stock price and converted it into an array to train the model. For this project, traning dataset had 75% of values. With time series data, the sequence of values is important.
Building the model and making Predictions
A Long Short-Term Model was built using Keras which had 50 units, 4 hidden layers and a dense layer (output) to predict the normalized closing stock price. The model was compile using the mean squared error as loss function and to reduce the loss or to optimize the algorithm, I used the Adam optimizer.
2. Loading and Preparation of Data
For the project we will be using the all_stocks_5yrs csv file which includes stock data for 5 years and has seven columns which are listed below.
3. Understanding the Data
From the whole dataset, we will first extract all the unique stock ticks name with the help of unique function. In the dataset, we have 444 different stock names.
4. Creating a new Dataframe and Training data
To make our study easier we will only consider the closing market price and predict the closing market price using Python. The whole train data preparation is shown in the steps below. Comments are added for your reference.
5. Building LSTM Model
The LSTM model will have two LSTM layers with 50 neurons and two Dense layers, one with 25 neurons and the other with one neuron.
6. Compiling the Model
The LSTM model is compiled using the mean squared error (MSE) loss function and the adam optimizer.
7. Testing the model on testing data
The code below will get all the rows above the training_data_len from the column of the closing price. Then convert the x_test data set into the NumPy arrays so that they can be used to train the LSTM model.
8. Error Calculation
RMSE is the root mean squared error, which helps to measure the accuracy of the model.