SP500companyTA.py

import ssl
ssl._create_default_https_context = ssl._create_unverified_context

import yfinance as yf
import streamlit as st
import datetime
import pandas as pd
import cufflinks as cf
from plotly.offline import iplot
cf.go_offline()

@st.cache_data
def get_sp500_components():
    df = pd.read_html("https://en.wikipedia.org/wiki/List_of_S%26P_500_companies")
    df = df[0]
    tickers = df["Symbol"].to_list()
    tickers_companies_dict = dict(
    zip(df["Symbol"], df["Security"])
    )
    return tickers, tickers_companies_dict

@st.cache_data
def load_data(symbol, start, end):
    return yf.download(symbol, start, end)

@st.cache_data
def convert_df_to_csv(df):
    return df.to_csv().encode("utf-8")

st.sidebar.header("Stock Parameters")
available_tickers, tickers_companies_dict = get_sp500_components()
ticker = st.sidebar.selectbox(
    "Ticker",
    available_tickers,
    format_func=tickers_companies_dict.get
)
start_date = st.sidebar.date_input(
    "Start date",
    datetime.date(2019, 1, 1)
)
end_date = st.sidebar.date_input(
    "End date",
    datetime.date.today()
)
if start_date > end_date:
    st.sidebar.error("The end date must fall after the start date")

st.sidebar.header("Technical Analysis Parameters")
volume_flag = st.sidebar.checkbox(label="Add volume")

exp_sma = st.sidebar.expander("SMA")
sma_flag = exp_sma.checkbox(label="Add SMA")
sma_periods= exp_sma.number_input(
    label="SMA Periods",
    min_value=1,
    max_value=50,
    value=20,
    step=1
)

exp_bb = st.sidebar.expander("Bollinger Bands")
bb_flag = exp_bb.checkbox(label="Add Bollinger Bands")
bb_periods= exp_bb.number_input(label="BB Periods",
    min_value=1, max_value=50,
    value=20, step=1)
bb_std= exp_bb.number_input(label="# of standard deviations",
    min_value=1, max_value=4,
    value=2, step=1)

exp_rsi = st.sidebar.expander("Relative Strength Index")
rsi_flag = exp_rsi.checkbox(label="Add RSI")
rsi_periods= exp_rsi.number_input(
    label="RSI Periods",
    min_value=1,
    max_value=50,
    value=20,
    step=1
)
rsi_upper= exp_rsi.number_input(label="RSI Upper",
    min_value=50,
    max_value=90, value=70,
    step=1)
rsi_lower= exp_rsi.number_input(label="RSI Lower",
    min_value=10,
    max_value=50, value=30,
    step=1)

st.title("S&P 500 technical analysis")
st.write("""
    ### User manual
    * you can select any company from the S&P 500 constituents
""")

df = load_data(ticker, start_date, end_date)

data_exp = st.expander("Preview data")
available_cols = df.columns.tolist()
columns_to_show = data_exp.multiselect(
    "Columns",
    available_cols,
    default=available_cols
)

data_exp.dataframe(df[columns_to_show])
csv_file = convert_df_to_csv(df[columns_to_show])
data_exp.download_button(
    label="Download selected as CSV",
    data=csv_file,
    file_name=f"{ticker}_stock_prices.csv",
    mime="text/csv",
)


title_str = f"{tickers_companies_dict[ticker]}'s stock price"
qf = cf.QuantFig(df, title=title_str)
if volume_flag:
    qf.add_volume()
if sma_flag:
    qf.add_sma(periods=sma_periods)
if bb_flag:
    qf.add_bollinger_bands(periods=bb_periods,
        boll_std=bb_std)
if rsi_flag:
    qf.add_rsi(periods=rsi_periods,
        rsi_upper=rsi_upper,
        rsi_lower=rsi_lower,
        showbands=True)
fig = qf.iplot(asFigure=True)
st.plotly_chart(fig)