Trend Analysis
Pada sesi ini kita akan bahas tensorflow dalam lingkup Trend Analysis. Kita batasi sampai cara menjalankan kodenya saja. Detil dari algoritmanya akan dibahas tersendiri di bagian lain.
Stock Price Prediction Using Python & Machine Learning (LSTM).
In this video you will learn how to create an artificial neural network called Long Short Term Memory to predict the future price of stock.
Disclaimer: The material in this video is purely educational and should not be taken as professional investment advice. Invest at your own discretion.
NOTE: In the video to calculate the RMSE I put the following statement:
rmse=np.sqrt(np.mean((predictions- y_test)**2))
When in fact I meant to put :
rmse=np.sqrt(np.mean(((predictions- y_test)**2)))
You can use the following statements to calculate RMSE:
1. rmse =np.sqrt(np.mean(((predictions- y_test)**2)))
2. rmse = np.sqrt(np.mean(np.power((np.array(y_test)-np.array(predictions)),2)))
3. rmse = np.sqrt(((predictions - y_test) ** 2).mean())
conda env remove -n ENV_NAME seperti berikut:
$ conda info --envs
# conda environments:
#
base * C:\Users\Chetabahana\AppData\Local\Packages\anaconda3
tf250 C:\Users\Chetabahana\AppData\Local\Packages\anaconda3\envs\tf250
$ conda env remove -n tf250
Remove all packages in environment C:\Users\Chetabahana\AppData\Local\Packages\anaconda3\envs\tf250:
$ exit
Bisa kita lihat bahwa modul tensorflow ini lumayan mengambil ruang / space. Maka penempatan envs di USB dapat menjadi solusi karena harganya jauh lebih murah dibanding Hardisk.
# Description: This program uses an artificial neural network called Long Short Term Memory (LSTM)
# to predict the closing stock price of a corporation (Apple Inc.) using the past 60 days stock price.
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')
# Get the stock quote
df = web.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2019-12-17')
# Show the data
print(df)
# Visualize the closing price history
plt.plot(figsize=(16,8))
plt.title('Close Price History')
plt.plot(df['Close'])
plt.xlabel('Data', fontsize=18)
plt.ylabel('Close Price USD($)', fontsize=18)
plt.show() 
- Yahoo returning: pandas_datareader._utils.RemoteDataError:
- HTTPSConnectionPool(host='finance.yahoo.com', port=443): Read timed out
Jika Anda jalankan kode ini ada ada kemungkinan kita akan menjumpai pesan koneksi internet ke finance.yahoo.com mengalami timeout seperti tampak pada tangkapan layar berikut:
Ini terjadi karena koneksi internet yang kita gunakan lambat sehingga terjadi time out. Solusinya adalah gunakan koneksi yang lebih cepat maka data yang diminta akan muncul.
#Create a new dataframe with only the 'Close column'
data = df.filter(['Close'])
#Convert the dataframe to a numpy array
dataset = data.values
#Get the number of rows to train the model on
training_data_len = math.ceil ( len(dataset) * .8 )
print(training_data_len)
#Scale the data
scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(dataset)
print(scaled_data)
#Create the training dataset
#Create the scaled training data set
train_data = scaled_data[0:training_data_len , :]
#Split the data into x_train and y_traim data sets
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()
#Convert the x_train and y_train to numpy arrays
x_train, y_train = np.array(x_train), np.array(y_train)
#Reshape the data
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
print(x_train.shape)
#Convert the x_train and y_train to numpy arrays
x_train, y_train = np.array(x_train), np.array(y_train)
#Reshape the data
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
print(x_train.shape)
#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))
#Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')
#Train the model
model.fit(x_train, y_train, batch_size=1, epochs=1)
#Create the testong data set
#Create a neq array containing scaled values from indez 1543 to 2003
test_data = scaled_data[training_data_len - 60: , :]
#Create the data sets x_test and y_test
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])
#Convert the data to an numpy array
x_test = np.array(x_test)
#Reshape the data
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1 ))
#Get the models predicted price values
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)
#Get the root mean squared error (RMSE)
rmse = np.sqrt( np.mean( predictions - y_test )** 2 )
print(rmse)
#Plot the data
train = data[:training_data_len]
valid = data[training_data_len:]
valid['Predictions'] = predictions
#Visualize the data
plt.plot(figsize=(16,8))
plt.title('Model')
plt.xlabel('Date', fontsize=18)
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()
Disini muncul pesan seoerti berikut ini:
C:\Users\Chetabahana\assets\_feeds\eksekusi\tensorflow\tensorflow\python\pycharm\main.py:101: 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
valid['Predictions'] = predictionsKarena grafiknya valid maka kita bisa abaikan pesan ini. Berikut akan ditunjukan detil datanya.
#Show the valid and predicted price
print(valid)
#Get the quote
apple_quote = web.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2019-12-17')
#Create a new Dataframe
new_df = apple_quote.filter(['Close'])
#Get the last 60 daya closing price values and conver the dataframe to an array
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)
#Create an empty list
X_test = []
#Append the past 60 days
X_test.append(last_60_days_scaled)
#Convert the x_test data set to a numpy array
X_test = np.array(X_test)
#Reshape the data
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
#Get the predicted scaled price
pred_price = model.predict(X_test)
#Undo the scaling
pred_price = scaler.inverse_transform(pred_price)
print(pred_price)
#Ger the Quote
apple_quote2 = web.DataReader('AAPL', data_source='yahoo', start='2019-12-18', end='2019-12-18')
print(apple_quote2['Close'])
# Description: This program uses an artificial neural network called Long Short Term Memory (LSTM)
# to predict the closing stock price of a corporation (Apple Inc.) using the past 60 days stock price.
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')
# Get the stock quote
df = web.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2019-12-17')
# Show the data
print(df)
# Visualize the closing price history
plt.plot(figsize=(16,8))
plt.title('Close Price History')
plt.plot(df['Close'])
plt.xlabel('Data', fontsize=18)
plt.ylabel('Close Price USD($)', fontsize=18)
plt.show()
#Create a new dataframe with only the 'Close column'
data = df.filter(['Close'])
#Convert the dataframe to a numpy array
dataset = data.values
#Get the number of rows to train the model on
training_data_len = math.ceil ( len(dataset) * .8 )
print(training_data_len)
#Scale the data
scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(dataset)
print(scaled_data)
#Create the training dataset
#Create the scaled training data set
train_data = scaled_data[0:training_data_len , :]
#Split the data into x_train and y_traim data sets
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()
#Convert the x_train and y_train to numpy arrays
x_train, y_train = np.array(x_train), np.array(y_train)
#Reshape the data
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
print(x_train.shape)
#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))
#Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')
#Train the model
model.fit(x_train, y_train, batch_size=1, epochs=1)
#Create the testong data set
#Create a neq array containing scaled values from indez 1543 to 2003
test_data = scaled_data[training_data_len - 60: , :]
#Create the data sets x_test and y_test
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])
#Convert the data to an numpy array
x_test = np.array(x_test)
#Reshape the data
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1 ))
#Get the models predicted price values
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)
#Get the root mean squared error (RMSE)
rmse = np.sqrt( np.mean( predictions - y_test )** 2 )
print(rmse)
#Plot the data
train = data[:training_data_len]
valid = data[training_data_len:]
valid['Predictions'] = predictions
#Visualize the data
plt.plot(figsize=(16,8))
plt.title('Model')
plt.xlabel('Date', fontsize=18)
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()
#Show the valid and predicted price
print(valid)
#Get the quote
apple_quote = web.DataReader('AAPL', data_source='yahoo', start='2012-01-01', end='2019-12-17')
#Create a new Dataframe
new_df = apple_quote.filter(['Close'])
#Get the last 60 daya closing price values and conver the dataframe to an array
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)
#Create an empty list
X_test = []
#Append the past 60 days
X_test.append(last_60_days_scaled)
#Convert the x_test data set to a numpy array
X_test = np.array(X_test)
#Reshape the data
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
#Get the predicted scaled price
pred_price = model.predict(X_test)
#Undo the scaling
pred_price = scaler.inverse_transform(pred_price)
print(pred_price)
#Ger the Quote
apple_quote2 = web.DataReader('AAPL', data_source='yahoo', start='2019-12-18', end='2019-12-18')
print(apple_quote2['Close'])
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pandas_datareader as web
import datetime as dt
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM
#Load Data
company = 'FB'
start = dt.datetime(2012,1,1)
end = dt.datetime(2020,1,1)
data = web.DataReader(company, 'yahoo', start, end)
#Prepare Data
scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1,1))
prediction_days = 60
x_train = []
y_train = []
for x in range(prediction_days, len(scaled_data)):
x_train.append(scaled_data[x-prediction_days:x,0])
y_train.append(scaled_data[x, 0])
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))
#Build The Model
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1], 1)))
model.add(Dropout(0.2))
model.add(LSTM(units=50, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50))
model.add(Dropout(0.2))
model.add(Dense(units=1)) #Prediction of the next closing value
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(x_train, y_train, epochs=25, batch_size=32)
''' Test The Model Accuracy on Existing Data '''
#Load Test Data
test_start = dt.datetime(2012,1,1)
test_end = dt.datetime.now()
test_data = web.DataReader(company, 'yahoo', test_start, test_end)
actual_prices = test_data['Close'].values
total_dataset = pd.concat((data['Close'], test_data['Close']), axis=0)
#model_inputs=total_dataset[len(total_dataset)-len(test_data)-prediction_days:].values
model_inputs = total_dataset[len(total_dataset) - len(test_data) - prediction_days:].values
model_inputs = model_inputs.reshape(-1, 1)
model_inputs = scaler.transform(model_inputs)
#Make Prediction on Test Data
x_test = []
for x in range(prediction_days, len(model_inputs)):
x_test.append(model_inputs[x-prediction_days:x, 0])
x_test = np.array(x_test)
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
predicted_prices = model.predict(x_test)
predicted_prices = scaler.inverse_transform(predicted_prices)
# Plot The Test Predictions
plt.plot(actual_prices, color="black", label=f"Actual {company} Price")
plt.plot(predicted_prices, color="green", label=f"Predicted {company} Price")
plt.title(f"{company} Share Price")
plt.xlabel('Time')
plt.ylabel(f"{company} Share Price")
plt.legend()
plt.show()
# Predict Next Day
real_data = [model_inputs[len(model_inputs) + 1 - prediction_days:len(model_inputs+1), 0]]
real_data = np.array(real_data)
real_data = np.reshape(real_data, (real_data.shape[0], real_data.shape[1],1))
prediction = model.predict(real_data)
prediction = scaler.inverse_transform(prediction)
print(f"Prediction: {prediction}")
$ conda info --envs
$ conda.bat activate tf250
(tf250)$ conda update --all
(tf250)$ pip install --upgrade pip
(tf250)$ pip install wheel
(tf250)$ pip uninstall numpy
(tf250)$ conda install numpy=1.19.2
(tf250)$ pip install pandas
(tf250)$ pip install sklearn
(tf250)$ pip install matplotlib
(tf250)$ pip install pandas_datareader
(tf250)$ pip install --default-timeout=100 tensorflow
(tf250)$ conda deactivate
$ exit
Jika muncul masalah dengan Microsoft Visual C++ v14.0 seperti dibawah ini maka siapkan space kira 10GB silahkan lihat cara download dan instalnya di Visual-Studio.
Sekian bahasan untuk topik Trend Analysis berikutnya kita akan bahas Graphics Media.
