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predictpage.py
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predictpage.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_datareader as data
from keras.models import load_model
import streamlit as st
import requests
from datetime import date
from datetime import timedelta
VIRTUAL_TWILIO_NUMBER = "+17633738499"
VERIFIED_NUMBER = "+917398816950"
STOCK_NAME = ''
STOCK_ENDPOINT = "https://www.alphavantage.co/query"
NEWS_ENDPOINT = "https://newsapi.org/v2/everything"
SYMBOL_ENDPOINT = "https://api.iextrading.com/1.0/ref-data/symbols#"
STOCK_API_KEY = "E4FRAX6OLKKJ2WJV"
NEWS_API_KEY = "18887424b625474d9d4ff26f0f133e1a"
TWILIO_SID = "ACb28949638a23ee329a3f81c746fd427f"
TWILIO_AUTH_TOKEN = "743fd998dbe5404d89ad74b9e12cb242"
def show_predict_page():
start = '2010-01-01'
today = date.today()
end = today - timedelta(days = 1)
st.title('Stock Trend Prediction')
symbol_response = requests.get(SYMBOL_ENDPOINT)
symbol_json = symbol_response.json()
symbol_list = [''] + [x['name'] for x in symbol_json]
symbol_tuple = tuple(symbol_list)
COMPANY_NAME = st.selectbox('Select Stock name:',(symbol_tuple))
company_json = symbol_response.json()
for x in company_json:
if x['name'] == COMPANY_NAME:
STOCK_NAME = x['symbol']
break
# user_input = st.text_input('Enter Stock Ticket' , 'AAPL')
user_input = STOCK_NAME
df = data.DataReader(user_input,'yahoo',start, end)
#Describing Data
st.subheader('Data from 2010 - to present')
st.write(df.describe())
# visualization
st.subheader('Closing Price Vs Time Chart')
fig = plt.figure(figsize = (12,6))
plt.plot(df.Close)
st.pyplot(fig)
st.subheader('Closing Price Vs Time Chart with 100MA')
ma100 = df.Close.rolling(100).mean()
fig = plt.figure(figsize = (12,6))
plt.plot(ma100)
plt.plot(df.Close)
st.pyplot(fig)
st.subheader('Closing Price Vs Time Chart with 100MA and 200MA')
ma100 = df.Close.rolling(100).mean()
ma200 = df.Close.rolling(200).mean()
fig = plt.figure(figsize = (12,6))
plt.plot(ma100 ,'r')
plt.plot(ma200 , 'g')
plt.plot(df.Close, 'b')
st.pyplot(fig)
# Splitting data into x_train and y_train
data_training = pd.DataFrame(df['Close'][0:int(len(df)*0.70)])
data_testing = pd.DataFrame(df['Close'][int(len(df)*.70):int(len(df))])
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler(feature_range = (0,1))
data_training_array = scaler.fit_transform(data_training)
# Load my model
model = load_model('keras_model.h5')
# Testing Part
past_100_days = data_training.tail(100)
final_df = past_100_days.append(data_testing,ignore_index = True)
input_data = scaler.fit_transform(final_df)
x_test = []
y_test = []
for i in range(100, input_data.shape[0]):
x_test.append(input_data[i-100:i])
y_test.append(input_data[i,0])
x_test,y_test = np.array(x_test), np.array(y_test)
y_predicted = model.predict(x_test)
scaler = scaler.scale_
scale_factor = 1/scaler[0]
y_predicted = y_predicted*scale_factor
y_test = y_test*scale_factor
# Final Graph
# st.subheader('Prediction vs Original')
# fig2 = plt.figure(figsize=(12,6))
# plt.plot(y_test ,'b' , label = 'Original Price')
# plt.plot(y_predicted ,'r' , label = 'Predicted Price')
# plt.xlabel('Time')
# plt.ylabel('Price')
# plt.legend()
# st.pyplot(fig2)