app.py

import streamlit as st
import pandas as pd
import yfinance as yf
from ta.volatility import BollingerBands
from ta.trend import MACD, EMAIndicator, SMAIndicator
from ta.momentum import RSIIndicator
import datetime
from datetime import date
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
from xgboost import XGBRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.metrics import r2_score, mean_absolute_error,accuracy_score


st.title('Stock Price Analysis')
st.sidebar.info('Welcome to the Stock Price Prediction App. Choose your options below')


def main():
    option = st.sidebar.selectbox('Make a choice', ['Visualize','Recent Data', 'Predict'])
    if option == 'Visualize':
        tech_indicators()
    elif option == 'Recent Data':
        dataframe()
    else:
        predict()



@st.cache_resource
def download_data(op, start_date, end_date):
    df = yf.download(op, start=start_date, end=end_date, progress=False)
    return df



option = st.sidebar.text_input('Enter a Stock Symbol', value='SPY')
option = option.upper()
today = datetime.date.today()
duration = st.sidebar.number_input('Enter the duration', value=3000)
before = today - datetime.timedelta(days=duration)
start_date = st.sidebar.date_input('Start Date', value=before)
end_date = st.sidebar.date_input('End date', today)
if st.sidebar.button('Send'):
    if start_date < end_date:
        st.sidebar.success('Start date: `%s`\n\nEnd date: `%s`' %(start_date, end_date))
        download_data(option, start_date, end_date)
    else:
        st.sidebar.error('Error: End date must fall after start date')




data = download_data(option, start_date, end_date)
scaler = StandardScaler()

def tech_indicators():
    st.header('Technical Indicators')
    option = st.radio('Choose a Technical Indicator to Visualize', ['Close', 'BB', 'MACD', 'RSI', 'SMA', 'EMA'])

    bb_indicator = BollingerBands(data.Close)
    bb = data
    bb['bb_h'] = bb_indicator.bollinger_hband()
    bb['bb_l'] = bb_indicator.bollinger_lband()
    bb = bb[['Close', 'bb_h', 'bb_l']]
    macd = MACD(data.Close).macd()
    rsi = RSIIndicator(data.Close).rsi()
    sma = SMAIndicator(data.Close, window=14).sma_indicator()
    ema = EMAIndicator(data.Close).ema_indicator()

    if option == 'Close':
        st.write('Close Price')
        st.line_chart(data.Close)
    elif option == 'BB':
        st.write('BollingerBands')
        st.line_chart(bb)
    elif option == 'MACD':
        st.write('Moving Average Convergence Divergence')
        st.line_chart(macd)
    elif option == 'RSI':
        st.write('Relative Strength Indicator')
        st.line_chart(rsi)
    elif option == 'SMA':
        st.write('Simple Moving Average')
        st.line_chart(sma)
    else:
        st.write('Expoenetial Moving Average')
        st.line_chart(ema)


def dataframe():
    st.header('Recent Data')
    st.dataframe(data.tail(10))



def predict():
    model = st.radio('Choose a model', ['LinearRegression', 'RandomForestRegressor', 'ExtraTreesRegressor', 'KNeighborsRegressor', 'XGBoostRegressor'])
    num = st.number_input('How many days forecast?', value=5)
    num = int(num)
    if st.button('Predict'):
        if model == 'LinearRegression':
            engine = LinearRegression()
            model_engine(engine, num)
        elif model == 'RandomForestRegressor':
            engine = RandomForestRegressor()
            model_engine(engine, num)
        elif model == 'ExtraTreesRegressor':
            engine = ExtraTreesRegressor()
            model_engine(engine, num)
        elif model == 'KNeighborsRegressor':
            engine = KNeighborsRegressor()
            model_engine(engine, num)
        else:
            engine = XGBRegressor()
            model_engine(engine, num)


def model_engine(model, num):
    df = data[['Close']]
    df['preds'] = data.Close.shift(-num)
    x = df.drop(['preds'], axis=1).values
    x = scaler.fit_transform(x)
    x_forecast = x[-num:]

    x = x[:-num]

    y = df.preds.values

    y = y[:-num]


    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.2, random_state=7)
    model.fit(x_train, y_train)
    preds = model.predict(x_test)
    st.text(f'R2_SCORE: {r2_score(y_test, preds)} \
            \nMAE: {mean_absolute_error(y_test, preds)}'
          )
    
    
    forecast_pred = model.predict(x_forecast)
    day = 1
    for i in forecast_pred:
        st.text(f'Day {day}: {i}')
        day += 1

if __name__ == '__main__':
    main()