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import streamlit as st | ||
import pandas as pd | ||
import base64 | ||
import matplotlib.pyplot as plt | ||
import seaborn as sns | ||
import numpy as np | ||
import yfinance as yf | ||
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st.title('S&P 500 App') | ||
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st.markdown(""" | ||
This app retrieves the list of the **S&P 500** (from Wikipedia) and its corresponding **stock closing price** (year-to-date)! | ||
* **Python libraries:** base64, pandas, streamlit, numpy, matplotlib, seaborn | ||
* **Data source:** [Wikipedia](https://www.wikipedia.org/). | ||
""") | ||
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st.sidebar.header('User Input Features') | ||
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# Web scraping of S&P 500 data | ||
# | ||
@st.cache | ||
def load_data(): | ||
url = 'https://en.wikipedia.org/wiki/List_of_S%26P_500_companies' | ||
html = pd.read_html(url, header = 0) | ||
df = html[0] | ||
return df | ||
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df = load_data() | ||
sector = df.groupby('GICS Sector') | ||
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# Sidebar - Sector selection | ||
sorted_sector_unique = sorted( df['GICS Sector'].unique() ) | ||
selected_sector = st.sidebar.multiselect('Sector', sorted_sector_unique, sorted_sector_unique) | ||
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# Filtering data | ||
df_selected_sector = df[ (df['GICS Sector'].isin(selected_sector)) ] | ||
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st.header('Display Companies in Selected Sector') | ||
st.write('Data Dimension: ' + str(df_selected_sector.shape[0]) + ' rows and ' + str(df_selected_sector.shape[1]) + ' columns.') | ||
st.dataframe(df_selected_sector) | ||
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# Download S&P500 data | ||
# https://discuss.streamlit.io/t/how-to-download-file-in-streamlit/1806 | ||
def filedownload(df): | ||
csv = df.to_csv(index=False) | ||
b64 = base64.b64encode(csv.encode()).decode() # strings <-> bytes conversions | ||
href = f'<a href="data:file/csv;base64,{b64}" download="SP500.csv">Download CSV File</a>' | ||
return href | ||
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st.markdown(filedownload(df_selected_sector), unsafe_allow_html=True) | ||
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# https://pypi.org/project/yfinance/ | ||
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data = yf.download( | ||
tickers = list(df_selected_sector[:10].Symbol), | ||
period = "ytd", | ||
interval = "1d", | ||
group_by = 'ticker', | ||
auto_adjust = True, | ||
prepost = True, | ||
threads = True, | ||
proxy = None | ||
) | ||
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# Plot Closing Price of Query Symbol | ||
def price_plot(symbol): | ||
df = pd.DataFrame(data[symbol].Close) | ||
df['Date'] = df.index | ||
plt.fill_between(df.Date, df.Close, color='skyblue', alpha=0.3) | ||
plt.plot(df.Date, df.Close, color='skyblue', alpha=0.8) | ||
plt.xticks(rotation=90) | ||
plt.title(symbol, fontweight='bold') | ||
plt.xlabel('Date', fontweight='bold') | ||
plt.ylabel('Closing Price', fontweight='bold') | ||
return st.pyplot() | ||
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num_company = st.sidebar.slider('Number of Companies', 1, 5) | ||
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if st.button('Show Plots'): | ||
st.header('Stock Closing Price') | ||
for i in list(df_selected_sector.Symbol)[:num_company]: | ||
price_plot(i) |