nnew.py

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

# from flask import Flask , render_template,request
# app=Flask(__name__)
# @app.route('/')
# @app.route('/home')
# def home():
#     return render_template("p.html")
# @app.route('/result')
# def getvalue():
#     output=request.form.to_dict()
#     name=output["name"]
#     return render_template("p.html")
# LOADING DATA FROM THE WEB
# company=getvalue()
company="TSLA"
start = dt.datetime(2018, 1, 1)
end = dt.datetime(2022, 3, 24)

data = web.DataReader(company, 'yahoo', start, end)

# Preparing 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))

# BUILDING THE MODEL FOR THE GRAPH
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 price

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'''

# Loading test data
test_start = dt.datetime(2020, 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']))

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 prediction on the graph
plt.plot(actual_prices, color="red", label=f"Actual {company}Price")
plt.plot(predicted_prices, color="green", label=f"Predicted {company}Price")
plt.title(label=f"{company} Share Price")
plt.xlabel('Time')
plt.ylabel(f'{company} Share Price')
plt.legend()
plt.show()

# prediction of share price of 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}')