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main.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_datareader as web
import datetime as dt
from sklearn.manifold import trustworthiness
from sklearn.preprocessing import MinMaxScaler
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Dropout, LSTM
# Load Data
print("""
If ticker of Indian Stock Market, add ".NS" at last
For example -- "ADANIPOWER.NS" for Adani Power
-- "TATAMOTORS.NS" for Tata Motors
""")
company = input("Enter ticker symbol :" ).upper()
start = dt.datetime(2012,1,1)
end = dt.datetime(2022,1,1)
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 = []
print("Collecting data...")
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])
# Converting 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))
print("Initializing...")
# Training 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, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50))
model.add(Dropout(0.2))
model.add(Dense(units=1)) # Prediction for next price
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(x_train, y_train, epochs=5, batch_size=32)
# Testing
test_start = dt.datetime(2020,1,1)
test_end = dt.datetime(2022,1,1)
test_data = web.DataReader(company, 'yahoo', test_start, test_end)
actual_price = 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 = model_inputs.reshape(-1,1)
model_inputs = scaler.transform(model_inputs)
# Prediction
x_test = []
print("Predicting...")
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)
# Prediction for 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} ")
# Ploting the prediction
plt.plot(actual_price, color="blue", label=f"Actual {company} price...")
plt.plot(predicted_prices, color="green", label=f"Predicted {company} price...")
plt.title(f'{company} share prices...')
plt.xlabel("Time")
plt.ylabel(f"{company} share price")
plt.legend()
plt.show()