In [2]:
# Description: this program uses an artificial recurrent neural network called Long Short Term Memory (LSTM)
# to predict the closing stock price of a corporation (IBM)
# pip install pandas_datareader
# pip install tensorflow
import math
import pandas_datareader as web
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import Dense, LSTM
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
In [11]:
# get stock data from yahoo
df = web.DataReader('IBM', data_source='yahoo', start='2012-01-01', end='2021-04-25')
df
Out[11]:
| High | Low | Open | Close | Volume | Adj Close | |
|---|---|---|---|---|---|---|
| Date | ||||||
| 2012-01-03 | 188.710007 | 186.000000 | 186.729996 | 186.300003 | 5646000.0 | 134.881241 |
| 2012-01-04 | 186.330002 | 184.940002 | 185.570007 | 185.539993 | 4346700.0 | 134.330948 |
| 2012-01-05 | 185.029999 | 183.100006 | 184.809998 | 184.660004 | 4463100.0 | 133.693863 |
| 2012-01-06 | 184.479996 | 182.309998 | 184.389999 | 182.539993 | 4897100.0 | 132.158981 |
| 2012-01-09 | 182.270004 | 180.270004 | 182.199997 | 181.589996 | 5201200.0 | 131.471176 |
| … | … | … | … | … | … | … |
| 2021-04-19 | 133.820007 | 132.580002 | 133.600006 | 133.119995 | 8198600.0 | 133.119995 |
| 2021-04-20 | 139.770004 | 136.699997 | 137.070007 | 138.160004 | 15480600.0 | 138.160004 |
| 2021-04-21 | 143.729996 | 137.710007 | 138.059998 | 143.550003 | 11909000.0 | 143.550003 |
| 2021-04-22 | 144.740005 | 141.000000 | 143.699997 | 141.279999 | 7101400.0 | 141.279999 |
| 2021-04-23 | 143.610001 | 140.949997 | 141.309998 | 142.429993 | 4556000.0 | 142.429993 |
2342 rows × 6 columns
In [12]:
df.shape
Out[12]:
(2342, 6)
In [13]:
# Visualize the closing price history
plt.figure(figsize=(16,8))
plt.title('Close Price History')
plt.plot(df['Close'])
plt.xlabel('Date', fontsize=18)
plt.ylabel('Close Price USD$', fontsize=18)
plt.show()
In [15]:
# create a new dataframe with only the Close colunm
data = df.filter(['Close'])
dataset = data.values
training_data_len = math.ceil(len(dataset) * 0.8)
training_data_len
Out[15]:
1874
In [16]:
# Scale the data
scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(dataset)
scaled_data
Out[16]:
array([[0.75625879],
[0.74997928],
[0.74270844],
...,
[0.4030406 ],
[0.38428489],
[0.39378661]])
In [19]:
# create the training data set
# create the scaled training data set
train_data = scaled_data[0:training_data_len, :]
x_train = []
y_train = []
for i in range(60, len(train_data)):
x_train.append(train_data[i-60:i, 0])
y_train.append(train_data[i, 0])
if i<=60:
print(x_train)
print(y_train)
print
[array([0.75625879, 0.74997928, 0.74270844, 0.72519203, 0.71734277,
0.7150293 , 0.72337442, 0.70874991, 0.69726516, 0.70420556,
0.7130464 , 0.70850205, 0.77460136, 0.78666441, 0.8027761 ,
0.80112364, 0.79492683, 0.79063046, 0.80748573, 0.80831202,
0.80847718, 0.79947118, 0.81690488, 0.81012977, 0.81450883,
0.81120379, 0.81269109, 0.80682472, 0.80847718, 0.80517227,
0.80542013, 0.81178222, 0.81508714, 0.81483928, 0.8188052 ,
0.84970667, 0.85094598, 0.84904566, 0.85276372, 0.84243571,
0.84904566, 0.85962154, 0.87490706, 0.84681478, 0.85102869,
0.86788396, 0.87457649, 0.87771625, 0.90068575, 0.90845244,
0.91902832, 0.9191109 , 0.91671485, 0.90456909, 0.90820458,
0.91481453, 0.91473183, 0.93365283, 0.92877791, 0.92968678])]
[0.9377840338711563]
In [20]:
# convert the x_train and y_train to numpy arrays
x_train, y_train = np.array(x_train), np.array(y_train)
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
x_train.shape
Out[20]:
(1814, 60, 1)
In [21]:
# build the LSTM model
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(LSTM(50, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))
In [23]:
# compile the model
model.compile(optimizer='adam', loss='mean_squared_error')
In [24]:
# train the model
model.fit(x_train, y_train, batch_size=1, epochs=1)
1814/1814 [==============================] - 72s 38ms/step - loss: 0.0057
Out[24]:
<tensorflow.python.keras.callbacks.History at 0x7f0eb01d3ad0>
In [25]:
# create the testing data set
# create a new array containing scaled values from index 1814 to 2342
test_data = scaled_data[training_data_len - 60: , :]
x_test = []
y_test = dataset[training_data_len: , :]
for i in range(60, len(test_data)):
x_test.append(test_data[i-60:i, 0])
In [26]:
x_test = np.array(x_test)
In [27]:
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
In [28]:
#get teh models predicted price values
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)
In [29]:
# get the root mean squared error (RMSE)
rmse = np.sqrt(np.mean(predictions - y_test)**2)
rmse
Out[29]:
1.0317235441289396
In [30]:
# plot the data
train = data[:training_data_len]
valid = data[training_data_len:]
valid['Predictions'] = predictions
plt.figure(figsize=(16,8))
plt.title('Model')
plt.xlabel('Date')
plt.ylabel('Close Price USD$', fontsize=18)
plt.plot(train['Close'])
plt.plot(valid[['Close', 'Predictions']])
plt.legend(['Train', 'Val', 'Predictions'], loc='lower right')
plt.show()
/home/ubuntu/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy after removing the cwd from sys.path.
In [31]:
valid
Out[31]:
| Close | Predictions | |
|---|---|---|
| Date | ||
| 2019-06-17 | 134.949997 | 133.716370 |
| 2019-06-18 | 136.380005 | 133.921265 |
| 2019-06-19 | 137.080002 | 134.221985 |
| 2019-06-20 | 138.850006 | 134.618698 |
| 2019-06-21 | 139.199997 | 135.268433 |
| … | … | … |
| 2021-04-19 | 133.119995 | 132.004868 |
| 2021-04-20 | 138.160004 | 131.903015 |
| 2021-04-21 | 143.550003 | 132.635849 |
| 2021-04-22 | 141.279999 | 134.464783 |
| 2021-04-23 | 142.429993 | 136.136017 |
468 rows × 2 columns
In [35]:
# get the quote
apple_quote = web.DataReader('IBM', data_source='yahoo', start='2012-01-01', end='2021-04-25')
new_df = apple_quote.filter(['Close'])
last_60_days = new_df[-60:].values
#scale the data to be values between 0 and 1
last_60_days_scaled = scaler.transform(last_60_days)
X_test = []
X_test.append(last_60_days_scaled)
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
pred_price = model.predict(X_test)
pred_price = scaler.inverse_transform(pred_price)
print(pred_price)
[[137.67413]]
In [37]:
# get the quote
apple_quote2 = web.DataReader('IBM', data_source='yahoo', start='2021-04-27', end='2021-04-27')
print(apple_quote2['Close'])
Date 2021-04-27 142.009995 Name: Close, dtype: float64
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