Spaces:
Sleeping
Sleeping
File size: 2,259 Bytes
0d4d5de 0d2ddff |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
#importing libraries
import streamlit as st
import yfinance as yf
from datetime import date
from prophet import Prophet
from prophet.plot import plot_plotly
from plotly import graph_objs as go
#main function
def main():
START="2017-01-01"
TODAY=date.today().strftime("%Y-%m-%d")
st.title("Stock Forecast App")
st.write("**Disclaimer:** The stock price predictions generated by this app should not be considered financial advice. Always consult with a qualified financial advisor before making investment decisions.")
stocks= ("MSFT","AAPL","GOOG")
st.write("""
***Stock Tickers:***
- AAPL : Apple Inc.
- MSFT : Microsoft Corporation.
- GOOG : Alphabet Inc.(Google)
""")
selected_stocks=st.selectbox('select dataset for prediction',stocks)
n_year=st.slider("**Select Year of prediction**",1,4)
period=n_year * 365
#download Dataset
@st.cache_data
def load_data(ticker):
data=yf.download(ticker,START,TODAY)
data.reset_index(inplace=True)
return data
data_load_state=st.text('Loading data..')
data=load_data(selected_stocks)
data_load_state.text("Done!!")
st.subheader("Raw data")
st.write(data.tail())
#plot the Raw Data
def plot_rawdata():
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="Forecast Data")
fig.update_xaxes(rangeslider_visible=True)
st.plotly_chart(fig)
plot_rawdata()
#Forecasting
df_train=data[["Date",'Close']]
df_train=df_train.rename(columns={'Date':'ds','Close':'y'})
model=Prophet()
model.fit(df_train)
future=model.make_future_dataframe(periods=period)
forecast=model.predict(future)
#show and plot the feature
st.subheader("Forecasted dataset")
st.write(forecast.tail(5))
st.write("Forecast plot")
fig1=plot_plotly(model,forecast)
st.plotly_chart(fig1)
st.write("Forecast components")
fig2=model.plot_components(forecast)
st.write(fig2)
if __name__=="__main__":
main() |