Spaces:
Runtime error
Runtime error
File size: 2,620 Bytes
2bb47bc 0733e8d c152034 0733e8d 2bb47bc c152034 2bb47bc |
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 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
# pip install streamlit fbprophet yfinance plotly
# !pip install yfinance
# !pip install prophet
import streamlit as st
from datetime import date, datetime, timedelta
import pandas as pd
import yfinance as yf
from prophet import Prophet
from prophet.plot import plot_plotly
from plotly import graph_objs as go
# TODAY = date.today().strftime("%Y-%m-%d")
TODAY = datetime.today()
st.title('Stock Forecast')
st.markdown('This app is built to predict the stock market performance')
stocks = ('TSLA', 'FB', 'NVDA', 'BABA', 'GOOG', 'AAPL', 'MSFT', 'GME', 'AMZN', 'XIACF')
selected_stock = st.selectbox('Select dataset for prediction', stocks)
n_years = st.slider('Years of prediction:', 1, 4)
period = n_years * 365
new_resolution = st.radio(
"Do you want to get the higher resolution or short time interval, please choose one:",
('In 1 day', 'In 1 hour', 'In 5 minutes'))
if new_resolution == 'In 5 minutes':
new_interval = "5m"
START = TODAY - timedelta(days=30)
elif new_resolution == 'In 1 hour':
new_interval = "1h"
START = TODAY - timedelta(days=365)
else:
new_interval = "1d"
START = "2018-01-01"
@st.cache
def load_data(ticker):
data = yf.download(ticker, START, TODAY, interval = new_interval)
data.reset_index(inplace=True)
return data
data_load_state = st.text('Loading data...')
data = load_data(selected_stock)
data_load_state.text('... Data loaded, well done!')
@st.cache
def convert_df(df):
# IMPORTANT: Cache the conversion to prevent computation on every rerun
return df.to_csv().encode('utf-8')
csv = convert_df(data)
st.download_button(
label="Download data as CSV",
data=csv,
file_name='stock_data.csv',
mime='text/csv',
)
st.subheader('Raw data')
st.write(data.tail())
# Plot raw data
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 with Rangeslider', xaxis_rangeslider_visible=True)
st.plotly_chart(fig)
plot_raw_data()
# Predict forecast with Prophet.
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)
# Show and plot forecast
st.subheader('Forecast data')
st.write(forecast.tail())
st.write(f'Forecast plot for {n_years} years')
fig1 = plot_plotly(m, forecast)
st.plotly_chart(fig1)
st.write("Forecast components")
fig2 = m.plot_components(forecast)
st.write(fig2)
|