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An RNN (Recurrent Neural Network) model to predict stock price.

  • Predicting Stock Price of a company is one of the difficult task in Machine Learning/Artificial Intelligence. This is difficult due to its non-linear and complex patterns. There are many factors such as historic prices, news and market sentiments effect stock price. Major effect is due to historic prices, previous one or many days stock price effects today’s price, hence this is a time series problem. We have chosen Deep Neural Network (RNN) approach to solve this time series (forecasting) problem as it can handle huge volume of data while training the model (when compared with normal Machine Learning models) and the model can be made as complies as possible (thanks to neural networks).
  • We have taken IBM stock data from https://www.kaggle.com/borismarjanovic/price-volume-data-for-all-us-stocks-etfs
  • Recurrent Neural Networks, tries to understand n – sequential time steps and tries to generate a value close to target value.
  • We will implement below RNN architecture we used to solve the problem.

  • The Input features are “Date”, “Open”, “High”, “Low” and “Volume”.
  • The output/Targe feature is “Close” price.
  • We will use 60 historic Input Data records(call them as Time Steps) and predict 61st day’s “Close” price. The same is depected in above diagram.


import pandas as pd
import numpy as np
import tensorflow as tf
import keras
import matplotlib.pyplot as plt
import seaborn as sns



ibm_stock_data = pd.read_csv('../data/ibm_stock_prices.csv')


(14059, 7)



Dropped “OpenInt” as it is not needed

ibm_stock_data.drop('OpenInt', axis=1, inplace=True)

Convert date to number


ibm_stock_data['Date'] = ibm_stock_data['Date'].astype('datetime64')
min_date = ibm_stock_data['Date'].min()
print("min_date : ", min_date)
max_date = ibm_stock_data['Date'].max()
print("max_date : ", max_date)
def calculate_days_since_min_date(row_date):
    return row_date - min_date
ibm_stock_data['numeric_date'] = ibm_stock_data['Date'].apply(lambda x:calculate_days_since_min_date(x))



min_date :  1962-01-02 00:00:00
max_date :  2017-11-10 00:00:00



Convert “timedelta64[ns]” to float64

ibm_stock_data['numeric_date'] = ibm_stock_data['numeric_date'].astype('timedelta64[D]') 

ibm_stock_data.drop('Date', axis=1, inplace=True)


Visualize “Closing Price” pattern

plt.plot(ibm_stock_data["numeric_date"], ibm_stock_data["Close"])
plt.title('IBM stock Closeing Price history')
plt.ylabel('Closing Price')

Perform TRAIN/TEST split, we should be doing a sequential split as we are trying to recognize sequential data patterns. We have taken first 13020 records as TRAIN set and last 900 records as TEST (unseen dataset).


stock_train_set = ibm_stock_data[ibm_stock_data.numeric_date <= 19000.0]
stock_test_set = ibm_stock_data[ibm_stock_data.numeric_date > 19000.0]
stock_train_set = stock_train_set.iloc[0:13020]
stock_test_set = stock_test_set.iloc[0:900]
X_train_orig = stock_train_set[['Open', 'High', 'Low', 'Volume', 'numeric_date']].values
y_train = stock_train_set['Close'].values
X_test_orig = stock_test_set[['Open', 'High', 'Low', 'Volume', 'numeric_date']].values
y_test = stock_test_set['Close'].values

Scaling all features to felicitate faster convergence of Neural Networks model (Gradient Descent).


from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler()
X_train = sc.fit_transform(X_train_orig)
X_test = sc.transform(X_test_orig)


Taking 60 time steps to TRAIN and PREDICT. The model needs a 2-Dimensional input and a 1-Dimensional output matrix. Below diagram is a pictorial representation of the concept. build_time_series_data method will we format data in desired format.

def build_time_series_data(x, y):
    row_dim = TIME_STEPS
    col_dim = x.shape[1]
    third_dim = x.shape[0] - TIME_STEPS
    print(third_dim, row_dim, col_dim)
    x_time_series_data = np.zeros((third_dim, row_dim, col_dim))
    y_time_series_data = np.zeros((third_dim))
    num_date_corresponding_to_y = np.zeros((third_dim))
    for i in range(third_dim):
        x_time_series_data[i] = x[i:TIME_STEPS+i]
        y_time_series_data[i] = y[TIME_STEPS+i]
        num_date_corresponding_to_y[i] = sc.inverse_transform(x)[TIME_STEPS+i][4]  # as 5th columns in X is the "numeric_date"
    return x_time_series_data, y_time_series_data, num_date_corresponding_to_y
x_train_time_series_data, y_train_time_series_data, train_num_date_corresponding_to_y = build_time_series_data(X_train, y_train)
x_test_time_series_data, y_test_time_series_data, test_num_date_corresponding_to_y = build_time_series_data(X_test, y_test)



from keras import Sequential
from keras.layers import GRU
from keras.layers import LSTM
from keras.layers import Dense
from keras.layers.core import Dropout
from keras.layers.core import Flatten
from keras.layers import GRU
from keras import optimizers
from keras import regularizers



t_row_dim = TIME_STEPS
t_col_dim = X_train.shape[1]
t_third_dim = X_train.shape[0] - TIME_STEPS
rnn_model = Sequential()
rnn_model.add(GRU(3, return_sequences=True, input_shape=(t_row_dim, t_col_dim) ))
rnn_model.add(GRU(3, return_sequences=False))
optimizer = optimizers.Adam(lr=0.7)
rnn_model.compile(optimizer = 'adam', loss = 'mean_squared_error')



train_predict= rnn_model.predict(x_train_time_series_data)
test_predict= rnn_model.predict(x_test_time_series_data)
total_data_predicted = np.vstack([train_predict, test_predict ])
num_date = np.hstack([train_num_date_corresponding_to_y, test_num_date_corresponding_to_y])
y_close_price_data = np.hstack([y_train_time_series_data, y_test_time_series_data])

The model is performing very well. In below visualization (Yello – actual close price, Blue – predicted close price), from beginning till 19000 it is TRAIN set, beyond that it is TEST (Unseen) dataset, the model is working well on unseen data as well.


fig = plt.figure(figsize=(20,20))
ax = fig.add_subplot(111)
ax.set_title('IBM - TRAIN Set - Stock Closing Price')
plt.plot(num_date, y_close_price_data, color='y', label='Acrual Closing Price')
plt.plot(num_date, total_data_predicted.ravel(),color='b', label='Predicted Closing Price')
#plt.plot(data[:,0], m*data[:,0] + b,color='red',label='Our Fitting Line')
ax.set_ylabel('Closing Price')


— By
L Venkata Rama Raju
CEO & Founder
DataJango Technologies Pvt. Ltd.,


2 thoughts on “Stock Price Prediction with RNN (Recurrent Neural Network – GRU cells)”

  1. Twistedfate says:

    I am getting the error “TIME_STEPS not defined”

  2. Rama Raju says:

    we have taken 60 time steps, TIME_STEPS = 60.

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