Stock Market Prediction using Univariate Recurrent Neural Networks (RNN) with Python

Predicting the price of financial assets has fascinated researchers and analysts for many decades. While the traditional prediction methods of technical analysis and fundamental analysis are still widely used, interest is increasingly turning to machine-generated predictions based on deep learning. Deep-learning libraries such as Keras or Scikit-Learn provide easy access to powerful prediction algorithms. With these libraries, anyone with a few programming skills can develop a neural network today. This article shows how that works by creating Python’s univariate model for stock market forecasting. Our model will be a Keras neural network with LSTM layers that produces single-step forecasts for the S&P500 stock market index.

The rest of this article proceeds in two parts: We briefly introduce univariate modeling and neural networks. Then we start with the coding part and go through all the steps to train a neural network, including data ingestion, data preprocessing, and the design, training, testing, and usage of a predictive neural network model.

Stock Market Prediction using Univariate Recurrent Neural Networks: The Bulls and the Bears
The Bulls and the Bears – a never-ending struggle

Single-Step Univariate Stock Market Prediction

The prediction approach described in this article is known as single-step single-variate time series forecasting. This approach is similar to technical chart analysis in that it assumes that predicting the price of an asset is fundamentally a time series problem. The goal is to identify patterns in a time series that indicate how the series will develop in the future.

This tutorial predicts the value for a single time step (1 day). In other words, we consider a single time series of data (single-variate). However, predicting multiple steps or increasing the time-step length would also be possible. In both cases, the predictions will range further into the future. I have covered this topic in a separate post on time series forecasting.

We will develop a univariate prediction model that predicts a single feature on historical prices for a specific period. More complex multivariate models use additional features such as moving averages, momentum indicators, or market sentiment. I have covered multivariate stock market prediction in a separate tutorial.

Basics of Neural Networks

Recurrent Neural Networks (RNN) are mighty for analyzing time series. An RRN is a specific form of a neural network. In contrast to a feed-forward neural network, where all the information flows left to right, RNNs use Long-short-term memory (LSTM)-layers that recirculate output results back and forth through the network. This is particularly useful in the field of time series analysis. It enables an RNN to learn patterns that occur over different periods, e.g., days and months, and potentially overlap, thus often resulting in more accurate predictions.

Exemplary model of a four-layered Neural Network
An exemplary model of a four-layered Neural Network

This article develops a univariate model that uses an RRN architecture with LSTM layers to predict the closing price of the S&P500 index. We need Python programming, the Anaconda environment, and Python packages for data manipulation and analytics to build such a neural network.

Understanding Neural Networks in all depth is not a prerequisite for this tutorial. But if you want to learn more about their architecture and functioning, I can recommend this YouTube video.

Implementation of a Univariate Regression Model using Keras Recurrent Neural Networks

In the following, we develop a single-variate neural network model that forecasts the S&P500 stock market index. The code is available on the GitHub repository.


Before starting the coding part, make sure that you have set up your Python 3 environment and required packages. If you don’t have an environment yet, you can follow the steps in this article to set up the Anaconda environment.

Also, make sure you install all required packages. In this tutorial, we will be working with the following standard packages: 

In addition, we will be using Keras (2.0 or higher) with Tensorflow backend and the machine learning library scikit-learn.

You can install packages using console commands:

  • pip install <package name>
  • conda install <package name> (if you are using the anaconda packet manager)

Step #1 Load the Data

Let’s start by setting up the imports and loading the price data from via an API. To extract the data, we’ll use the pandas DataReader package – a popular library that provides a function to extract data from various Internet sources into pandas DataFrames. Note that if pandas DataReader does not work, you can use the yfinance package.

The following code extracts the price data for the S&P500 index from yahoo finance. If you wonder what “^GSPC” means, this is the symbol for the S&P500, a stock market index of the 500 most extensive stocks listed in the US stock market. You can use the symbols of other assets, e.g., BTC-USD for Bitcoin. The data is limited to the timeframe between 2010-01-01 and the current date. So when you execute the code, the results will show a more significant period, as in this tutorial.

# A tutorial for this file is available at
# Tested with Python 3.88

import math 
import numpy as np # Fundamental package for scientific computing with Python
import pandas as pd # For analysing and manipulating data
from datetime import date, timedelta # Date Functions
import matplotlib.pyplot as plt # For visualization
import matplotlib.dates as mdates # Formatting dates
from sklearn.metrics import mean_absolute_error, mean_squared_error # For measuring model performance / errors
from sklearn.preprocessing import MinMaxScaler #to normalize the price data 
from tensorflow.keras.models import Sequential # Deep learning library, used for neural networks
from tensorflow.keras.layers import LSTM, Dense # Deep learning classes for recurrent and regular densely-connected layers
import tensorflow as tf
import seaborn as sns
sns.set_style('white', { 'axes.spines.right': False, '': False})

# check the tensorflow version and the number of available GPUs
print('Tensorflow Version: ' + tf.__version__)
physical_devices = tf.config.list_physical_devices('GPU')
print("Num GPUs:", len(physical_devices))

# Setting the timeframe for the data extraction
today =
end_date = today.strftime("%Y-%m-%d")
start_date = '2010-01-01'

# Getting S&P500 quotes
stockname = 'S&P500'
symbol = '^GSPC'

# You can either use webreader or yfinance to load the data from yahoo finance
# import pandas_datareader as webreader
# df = webreader.DataReader(symbol, start=start_date, end=end_date, data_source="yahoo")

import yfinance as yf #Alternative package if webreader does not work: pip install yfinance
df =, start=start_date, end=end_date)

# Taking a look at the shape of the dataset

(2596, 6)

The data upon which our stock market prediction model will be trained and validated

Step #2 Explore the Data

When you load a new data set into your project, it is often a good idea to familiarize yourself with it before taking further steps. When working with time-series data, visually viewing the data in a line plot is the primary way. Use the following code to create the line plot for the S&P500 data.

# Creating a Lineplot
years = mdates.YearLocator() 
fig, ax1 = plt.subplots(figsize=(16, 6))
ax1.legend([stockname], fontsize=12)
plt.title(stockname + ' from '+ start_date + ' to ' + end_date)
sns.lineplot(data=df['Close'], label=stockname, linewidth=1.0)
plt.ylabel('S&P500 Points')
Historical data on the price of S&P500, Univariate neural networks for time series prediction

If you follow the course of the stock markets a little, the chart above might look familiar to you.

Step #3 Scaling the Data

It’s best to scale the data before training a neural network. We will use the MinMaxScaler to normalize the price values in our data to a range between 0 and 1.

# Feature Selection - Only Close Data
train_df = df.filter(['Close'])
data_unscaled = train_df.values

# Get the number of rows to train the model on 80% of the data 
train_data_length = math.ceil(len(data_unscaled) * 0.8)

# Transform features by scaling each feature to a range between 0 and 1
mmscaler = MinMaxScaler(feature_range=(0, 1))
np_data = mmscaler.fit_transform(data_unscaled)
scaled data

Step #4 Creating the Input Shape

Before we can begin with the training of the NN, we need to split the data into separate test sets for training and validation and ensure that it is in the right shape. We will train the NN on a decade of market price data. Then we predict the price of the next day based on the last 50 days of market prices. As illustrated below, we will use 80% of the data as training data and keep 20% as test data to later evaluate the performance of our univariate model.

Building a machine learning model typically involves splitting data into train and test
Splitting data into train and test

Our neural network will have two layers, an input layer, and an output layer. The input data shape must correspond with the number of neurons in the neural network’s input layer. Therefore, we must also decide on the neural network architecture before bringing our data in the right shape.

4.1 Designing the Input Shape

Next, we create the training data based on which we will train our neural network. We make multiple slices of the training data (x_train), so-called mini-batches. The neural network processes the mini-batch one by one during the training process and creates a separate forecast for each mini-batch. The illustration below shows the shape of the data:

Sample dataset for time series forecasting divided into several train batches
The sample dataset for time series forecasting is split into several train batches.

Neural networks learn in an iterative process. The algorithm reduces the prediction errors by adjusting the connection strength between the neurons (weights) in this process. The model needs a second list (y_train) to evaluate the forecast quality, containing the valid price values from our ground truth. The model compares the predictions with the ground truth during training and calculates the training error to minimize it over time.

4.2 Data Preprocessing

The code block below will carry out the steps to prepare the data. It is a standard procedure that will split the data into several mini-batches. Each minibatch contains an input sequence and a corresponding output sequence, the target.

# Set the sequence length - this is the timeframe used to make a single prediction
sequence_length = 50

# Prediction Index
index_Close = train_df.columns.get_loc("Close")
# Split the training data into train and train data sets
# As a first step, we get the number of rows to train the model on 80% of the data 
train_data_len = math.ceil(np_data.shape[0] * 0.8)

# Create the training and test data
train_data = np_data[0:train_data_len, :]
test_data = np_data[train_data_len - sequence_length:, :]

# The RNN needs data with the format of [samples, time steps, features]
# Here, we create N samples, sequence_length time steps per sample, and 6 features
def partition_dataset(sequence_length, train_df):
    x, y = [], []
    data_len = train_df.shape[0]
    for i in range(sequence_length, data_len):
        x.append(train_df[i-sequence_length:i,:]) #contains sequence_length values 0-sequence_length * columsn
        y.append(train_df[i, index_Close]) #contains the prediction values for validation (3rd column = Close),  for single-step prediction
    # Convert the x and y to numpy arrays
    x = np.array(x)
    y = np.array(y)
    return x, y

# Generate training data and test data
x_train, y_train = partition_dataset(sequence_length, train_data)
x_test, y_test = partition_dataset(sequence_length, test_data)

# Print the shapes: the result is: (rows, training_sequence, features) (prediction value, )
print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)

# Validate that the prediction value and the input match up
# The last close price of the second input sample should equal the first prediction value

(1966, 50, 1)

x_train contains 1966 mini-batches. Each contains a series of quotes for 50 dates. In y_train, we have 1966 validation values – one for each mini-batch. Be aware that numbers depend on the timeframe and vary when executing the code.

Step #5 Designing the Model Architecture

Before we can train the model, we first need to decide on the model’s architecture. Above all, the architecture comprises the type and number of layers and the number of neurons in each layer.

5.1 Choosing Layers

How can we determine the number of layers? Selecting the correct number of layers from the start is difficult or even impossible. A common approach is trying different architectures and finding out what works best by trial and error. Then the architecture and the performance of the univariate model are tested and refined in multiple iterations.

We will use a fully connected network structure with four layers. Use the Dense and LSTM classes from the Keras deep-learning library to define this structure. The architecture combines two layers of the LSTM class with two layers of the Dense class. I have chosen this architecture because it is comparably simple and an excellent start to tackling time series problems.

Basic architecture of our recurrent neural network
The architecture of our recurrent Neural Network

5.2 Choosing the Number of Neurons

And how do we determine the number of neurons? The number of neurons in the first layers must cover the input data’s size. Our input comprises values for 50 dates. Thus, the input shape needs at least 50 neurons for each value. In the last layer, we will have only one neuron, which means that our prediction will contain a single price point for a single time step.

# Configure the neural network model
model = Sequential()

neurons = sequence_length

# Model with sequence_length Neurons 
# inputshape = sequence_length Timestamps
model.add(LSTM(neurons, return_sequences=True, input_shape=(x_train.shape[1], 1))) 
model.add(LSTM(neurons, return_sequences=False))
model.add(Dense(25, activation='relu'))

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

Step #6 Train the Univariate Model

Now it’s time to fit the model to the data. The training time may vary between seconds and minutes, depending on your system’s computing power. For instance, the training time is usually a couple of minutes on my local notebook processor (Intel Core i7).

# Training the model, y_train, batch_size=16, epochs=25)

We have fitted our model to the training data.

Step #7 Making Test Predictions

So how does our stock market prediction model perform? We need to feed the model with the test data to evaluate the model’s performance. For this purpose, we provide the test data (x_test) that we have generated in a previous step to the model to get some predictions. We must remember that we initially scaled the input data to 0 and 1. Therefore, before interpreting the results, we must inverse the MinMaxScaling from the predictions.

# Get the predicted values
y_pred_scaled = model.predict(x_test)
y_pred = mmscaler.inverse_transform(y_pred_scaled)
y_test_unscaled = mmscaler.inverse_transform(y_test.reshape(-1, 1))

Step #8 Evaluate Model Performance

Different indicators can help us to evaluate the performance of our model. We calculate the forecast error by subtracting valid test data (y_test) from predictions.

# Mean Absolute Error (MAE)
MAE = mean_absolute_error(y_test_unscaled, y_pred)
print(f'Median Absolute Error (MAE): {np.round(MAE, 2)}')

# Mean Absolute Percentage Error (MAPE)
MAPE = np.mean((np.abs(np.subtract(y_test_unscaled, y_pred)/ y_test_unscaled))) * 100
print(f'Mean Absolute Percentage Error (MAPE): {np.round(MAPE, 2)} %')

# Median Absolute Percentage Error (MDAPE)
MDAPE = np.median((np.abs(np.subtract(y_test_unscaled, y_pred)/ y_test_unscaled)) ) * 100
print(f'Median Absolute Percentage Error (MDAPE): {np.round(MDAPE, 2)} %')

MAE: 32.0
RMSE: 17.6

The MAE can be negative or positive. If it is positive, our predictions lie below the valid values. For our model, the calculated MAE is (32.0). From the MAE, we can tell that our model generally tends to predict a bit too pessimistic.

The mean squared error (RMSE) is always positive. More significant errors tend to impact the RMSE as they are squared substantially. In our case, the RMSE is 17.6, indicating that the prediction error is relatively constant. In other words, the predictions are mostly not entirely wrong.

Visualizing test predictions helps in the process of evaluating the model. Therefore we will plot predicted and valid values.

# The date from which on the date is displayed
display_start_date = "2018-01-01" 

# Add the difference between the valid and predicted prices
train = pd.DataFrame(train_df[:train_data_length + 1]).rename(columns={'Close': 'x_train'})
valid = pd.DataFrame(train_df[train_data_length:]).rename(columns={'Close': 'y_test'})
valid.insert(1, "y_pred", y_pred, True)
valid.insert(1, "residuals", valid["y_pred"] - valid["y_test"], True)
df_union = pd.concat([train, valid])

# Zoom in to a closer timeframe
df_union_zoom = df_union[df_union.index > display_start_date]

# Create the lineplot
fig, ax = plt.subplots(figsize=(16, 8), sharex=True)
plt.title("Predictions vs Ground Truth")
plt.ylabel(stockname, fontsize=18)
sns.lineplot(data=df_union_zoom, linewidth=1.0, palette='CMRmap')

We can see that the orange zone contains the test predictions. The grey area marks the difference between test predictions and ground truth. As indicated by the different performance measures, we can see that the predictions are typically near the ground truth.

Stock Market Prediction model: Prediction vs Ground Truth

We have also added the absolute errors on the bottom. Where the difference is negative, the predicted value was too optimistic. Where the difference is positive, the predictive value was too pessimistic.

Step #9 Stock Market Prediction – Predicting a Single Day Ahead

Now that we have tested our model, we can use it to make a prediction. We use a new data set as the input for our prediction model. The model returns a forecast for a single time the next day.

# Get fresh data
df_new = df.filter(['Close'])

# Get the last N day closing price values and scale the data to be values between 0 and 1
last_days_scaled = mmscaler.transform(df_new[-sequence_length:].values)

# Create an empty list and Append past n days
X_test = []

# Convert the X_test data set to a numpy array and reshape the data
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))

# Get the predicted scaled price, undo the scaling and output the predictions
pred_price = model.predict(X_test)
pred_price_unscaled = mmscaler.inverse_transform(pred_price)

# Print last price and predicted price for the next day
price_today = round(df_new['Close'][-1], 2)
predicted_price = round(pred_price_unscaled.ravel()[0], 2)
percent = round(100 - (predicted_price * 100)/price_today, 2)

prefix = '+' if percent > 0 else ''
print(f'The close price for {stockname} at {today} was {price_today}')
print(f'The predicted close price is {predicted_price} ({prefix}{percent}%)')

The price for S&P500 on 2020-04-04 was: 2578.0
The predicted S&P500 price on the date 2020-04-05 is: 2600.0

So, the model predicts a value of 2600.0 for the S&P500 on 2020-04-07.


In this tutorial, you have learned to create, train and test a four-layered recurrent neural network for stock market prediction using Python and Keras. Finally, we have used this model to predict the S&P500 stock market index. You can easily create models for other assets by replacing the stock symbol with another stock code. A list of common symbols for stocks or stock indexes is available on Don’t forget to retrain the model with a fresh copy of the price data.

The model created in this post makes predictions for a single time step. If you want to learn how to make time-series predictions that range further, you might want to check out the part II of this tutorial series: Creating a Multistep Forecast in Python.

I hope you enjoyed this article. I am always trying to improve and learn from my audience. So, please let me know in the comments if you have questions or remarks!

Sources and Further Reading

  1. Charu C. Aggarwal (2018) Neural Networks and Deep Learning
  2. Jansen (2020) Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python
  3. Aurélien Géron (2019) Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
  4. David Forsyth (2019) Applied Machine Learning Springer
  5. Andriy Burkov (2020) Machine Learning Engineering

The links above to Amazon are affiliate links. By buying through these links, you support the blog and help to cover the hosting costs. Using the links does not affect the price.


  • Florian Follonier

    Hi, I am Florian, a Zurich-based consultant for AI and Data. Since the completion of my Ph.D. in 2017, I have been working on the design and implementation of ML use cases in the Swiss financial sector. I started this blog in 2020 with the goal in mind to share my experiences and create a place where you can find key concepts of machine learning and materials that will allow you to kick-start your own Python projects.

3 thoughts on “Stock Market Prediction using Univariate Recurrent Neural Networks (RNN) with Python”

  1. This was a great article! The writing was clear and the code was easy to follow and use in my own project. This has helped me extend my forecasting functionality greatly!

    Thanks a ton Florian!


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