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import yfinance as yf
from statsmodels.tsa.stattools import adfuller
from sklearn.metrics import mean_squared_error,r2_score
from statsmodels.tsa.arima.model import ARIMA
import numpy as np
from sklearn.preprocessing import StandardScaler
from datetime import datetime, timedelta
import pandas as pd
import streamlit as st
import plotly.graph_objects as go
import plotly.express as px
st.set_page_config(
page_title="Stock Prediction",
page_icon="chart_with_upwords_trend",
layout= "wide"
)
st.title("Stock Prediction")
col1,clo2,clo3 = st.columns(3)
with col1:
ticker = st.text_input('Stock Ticker','AAPL')
def get_data(ticker):
stock_data = yf.download(ticker,start='2024-01-01')
return stock_data[['Close']]
def get_rollng_mean(close_price):
rolling_price = close_price.rolling(window=7).mean().dropna()
return rolling_price
def stationary_check(close_price):
adf_test=adfuller(close_price)
p_value=round(adf_test[1],3)
return p_value
def get_differencing_order(close_price):
p_value = stationary_check(close_price)
d=0
while True:
if p_value > 0.05:
d=d+1
close_price=close_price.diff().dropna()
p_value = stationary_check(close_price)
else:
break
return d
def scaling(close_price):
scaler = StandardScaler()
scaled_data = scaler.fit_transform(np.array(close_price).reshape(-1,1))
return scaled_data,scaler
rmse =0
st.subheader('Predicting Next 30 days Close Price for :-'+ticker)
close_price = get_data(ticker)
rolling_price = get_rollng_mean(close_price)
differencing_order= get_differencing_order(rolling_price)
def fit_model(data,differencing_order):
model =ARIMA(data,order=(7,differencing_order,7))
model_fit =model.fit()
forcast_steps=30
forcast = model_fit.get_forecast(steps=forcast_steps)
predictions = forcast.predicted_mean
return predictions
def evaluate_model(original_price,differencing_order):
train_data, test_data =original_price[:-30], original_price[-30:]
predictions = fit_model(train_data,differencing_order)
rmse= np.sqrt(mean_squared_error(test_data,predictions))
return round(rmse,2)
scaled_data,scaler=scaling(rolling_price)
rmse = evaluate_model(scaled_data,differencing_order)
def get_forecast(orignal_price,differencing_order):
predictions = fit_model(orignal_price,differencing_order)
start_date = datetime.now().strftime('%Y-%m-%d')
end_date = (datetime.now() + timedelta(days=29)).strftime('%Y-%m-%d')
forecast_index = pd.date_range(start=start_date,end=end_date,freq='D')
forecast_df= pd.DataFrame(predictions,index=forecast_index,columns= ['Close'])
return forecast_df
def inverse_scaling(scaler,scaled_data):
close_price = scaler.inverse_transform(np.array(scaled_data).reshape(-1,1))
return close_price
st.write("**Model RMSE Score**",rmse)
forecast= get_forecast(scaled_data,differencing_order)
forecast['Close']= inverse_scaling(scaler,forecast['Close'])
st.write('#### Forecast Data (Next 30 days)')
df_1=forecast.sort_index(ascending=True).round(3)
st.dataframe(df_1.style.set_properties(**{'font-size': '20px'}),width=1000)
forecast = pd.concat([rolling_price,forecast])
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