main.py

import streamlit as st
from datetime import date

import yfinance as yf
from fbprophet import Prophet
from fbprophet.plot import plot_plotly
from plotly import graph_objs as go
import pandas as pd

@st.cache
def get_data():
    path = 'stock.csv'
    return pd.read_csv(path, low_memory=False)

df = get_data()
df = df.drop_duplicates(subset="Name", keep="first")

START = "2015-01-01"
TODAY = date.today().strftime("%Y-%m-%d")

st.title("Stock Prediction")
st.write("###")

stocks = df['Name']
# stocks = ("AAPL", "NFLX", "GOOG", "MSFT", "INFY", "RPOWER.NS", "BAJFINANCE.NS", "YESBANK.NS", "RCOM.NS", "EXIDEIND.NS", "TATACHEM.NS", "TATAMOTORS.NS", "RUCHI.NS")
selected_stock = st.selectbox("Select dataset and years for prediction", stocks)

index = df[df["Name"]==selected_stock].index.values[0]
symbol = df["Symbol"][index]

n_years = st.slider("", 1, 5)
period = n_years * 365

@st.cache
def load_data(ticker):
    data = yf.download(ticker, START, TODAY)
    data.reset_index(inplace=True)
    return data

data_load_state = st.text("Load data ...")
data = load_data(symbol)
data_load_state.text("Loading data ... Done!")

st.write("###")

st.subheader("Raw data")
st.write(data.tail())

def plot_raw_data():
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=data['Date'], y=data['Open'], name='stock_open'))
    fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'], name='stock_close'))
    fig.layout.update(title_text = "Time Series Data", xaxis_rangeslider_visible = True)
    st.plotly_chart(fig)

plot_raw_data()

#Forecasting
df_train = data[['Date', 'Close']]
df_train = df_train.rename(columns={"Date": "ds", "Close": "y"})

m = Prophet()
m.fit(df_train)

future = m.make_future_dataframe(periods=period)
forecast = m.predict(future)

st.write("***")
st.write("###")

st.subheader("Forecast data")
st.write(forecast.tail())

fig1 = plot_plotly(m, forecast)
st.plotly_chart(fig1)

st.subheader("Forecast Components")
fig2 = m.plot_components(forecast)
st.write(fig2)