technical_analysis_app.py

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

# set offline mode for cufflinks
cf.go_offline()

## Data Functions
@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")


## UI Functions

# inputs for downloading data
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")


# inputs for technical analysis
st.sidebar.header("Technical Analysis Parameters")

volume_flag = st.sidebar.checkbox(label="Add volume")

# Simple Moving Average
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
)

# Bollinger Bands
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)

# Relative Strength Index
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)


## Main Panel
st.title("A simple web app for technical analysis")
st.write("""
### User manual
* you can select any of the companies that is a component of the S&P index
* you can select the time period of your interest
* you can download the selected data as a CSV file
* you can add the following Technical Indicators to the plot: Simple Moving 
Average, Bollinger Bands, Relative Strength Index
* you can experiment with different parameters of the indicators
""")

df = load_data(ticker, start_date, end_date)

# data preview
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",
)

# technical analysis plot
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)