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data_analysis.py
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data_analysis.py
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import xlrd
import pandas as pd
import numpy as np
import streamlit as st
import yfinance as yf
import seaborn as sns
import plotly.graph_objs as go
#####################################################################################################################
companies = {}
xls = xlrd.open_workbook("cname.xls")
sh = xls.sheet_by_index(0)
for i in range(505):
cell_value_class = sh.cell(i, 0).value
cell_value_id = sh.cell(i, 1).value
companies[cell_value_class] = cell_value_id
############################################################################
def company_name():
company = st.sidebar.selectbox("Companies", list(companies.keys()), 0)
return company
# company = company_name()
############################################################################
def show_data():
show = st.sidebar.selectbox("Options", ["Graphs", "Company Data"], 0)
return show
# show_data = show_data()
############################################################################
def data_analysis():
company = company_name()
def data_download():
data = yf.download(tickers=companies[company], period='3650d', interval='1d')
def divide(j):
j = j / 1000000
return j
data['Volume'] = data['Volume'].apply(divide)
data.rename(columns={'Volume': 'Volume (in millions)'}, inplace=True)
return data
data = data_download()
show = show_data()
df1 = data
if show == "Graphs":
st.header('Visualization for ' + company)
check = st.checkbox("Show Moving Average")
if check:
ma = st.radio("Moving Average Days", [10,50,100,200])
df1['MA'] = df1.Close.rolling(ma).mean()
fig = go.Figure(data=[go.Candlestick(x=df1.index,
open=df1['Open'],
high=df1['High'],
low=df1['Low'],
close=df1['Close'],
name='Market Data'),
go.Scatter(x=list(df1.index), y=list(df1.MA), line=dict(color='blue', width=2), name='Moving Average')])
fig.update_layout(
title='Live share price evolution',
yaxis_title='Stock Price (USD per shares)', width=850, height=550)
fig.update_xaxes(rangeslider_visible=True,
rangeselector=dict(
buttons=list([
dict(count=30, label="30D", step="day", stepmode="backward"),
dict(count=60, label="60D", step="day", stepmode="backward"),
dict(count=90, label="90D", step="day", stepmode="backward"),
dict(count=180, label="180D", step="day", stepmode="backward"),
dict(count=365, label="365D", step="day", stepmode="backward"),
dict(step="all")
])
))
st.plotly_chart(fig)
else:
# df1['MA'] = df1.Close.rolling(ma).mean()
fig = go.Figure(data=[go.Candlestick(x=df1.index,
open=df1['Open'],
high=df1['High'],
low=df1['Low'],
close=df1['Close'],
name='Market Data')
])
fig.update_layout(
title='Live share price evolution',
yaxis_title='Stock Price (USD per shares)', width=850, height=550)
fig.update_xaxes(rangeslider_visible=True,
rangeselector=dict(
buttons=list([
dict(count=30, label="30D", step="day", stepmode="backward"),
dict(count=60, label="60D", step="day", stepmode="backward"),
dict(count=90, label="90D", step="day", stepmode="backward"),
dict(count=180, label="180D", step="day", stepmode="backward"),
dict(count=365, label="365D", step="day", stepmode="backward"),
dict(step="all")
])
))
st.plotly_chart(fig)
# ma = st.slider('Slide to select days for Moving Average', min_value=5, max_value=100)
# df1['MA'] = df1.Close.rolling(ma).mean()
# fig = go.Figure(data=[go.Candlestick(x=df1.index,
# open=df1['Open'],
# high=df1['High'],
# low=df1['Low'],
# close=df1['Close'],
# name='Market Data'),
# go.Scatter(x=list(df1.index), y=list(df1.MA), line=dict(color='blue', width=2), name='Moving Average')])
# fig.update_layout(
# title='Live share price evolution',
# yaxis_title='Stock Price (USD per shares)', width=850, height=550)
# fig.update_xaxes(rangeslider_visible=True,
# rangeselector=dict(
# buttons=list([
# dict(count=30, label="30D", step="day", stepmode="backward"),
# dict(count=60, label="60D", step="day", stepmode="backward"),
# dict(count=90, label="90D", step="day", stepmode="backward"),
# dict(count=120, label="120D", step="day", stepmode="backward"),
# dict(count=150, label="150D", step="day", stepmode="backward"),
# dict(step="all")
# ])
# ))
# st.plotly_chart(fig)
# ma = st.slider('Slide to select days for Moving Average', min_value=5, max_value=100)
# df1 = yf.download(tickers=companies[company], period='1460d', interval='1d')
# df1['MA'] = df1.Close.rolling(ma).mean()
# fig0 = go.Figure()
# fig0.add_trace(go.Scatter(x=list(df1.index), y=list(df1.MA)))
# fig0.update_layout(title_text="Volume of the stock in millions")
# fig0.update_xaxes(rangeslider_visible=True)
# st.plotly_chart(fig0)
st.markdown("### Volume of the stocks")
st.markdown("Trading volume is a measure of how much of a given financial asset has traded in a period of "
"time. For stocks, volume is measured in the number of shares traded and, for futures and options, "
"it is based on how many contracts have changed hands.")
# fig1 = go.Figure()
# fig1.add_trace(go.Scatter(x=list(data.index), y=list(data['Volume (in millions)'])))
fig1 = go.Figure([go.Bar(x=data.index, y=data['Volume (in millions)'])])
fig1.update_layout(title_text="Volume of the stock in millions", width=850, height=550)
fig1.update_xaxes(rangeslider_visible=True,
rangeselector=dict(
buttons=list([
dict(count=30, label="30D", step="day", stepmode="backward"),
dict(count=60, label="60D", step="day", stepmode="backward"),
dict(count=90, label="90D", step="day", stepmode="backward"),
dict(count=180, label="180D", step="day", stepmode="backward"),
dict(count=365, label="365D", step="day", stepmode="backward"),
dict(step="all")
])
))
st.plotly_chart(fig1)
st.markdown("### Opening prices of the stock")
st.markdown("The opening price is the price at which a security first trades upon the opening of an exchange "
"on a trading day; for example, the National Stock Exchange (NSE) opens at precisely 9:00 a.m. "
"Eastern time. The price of the first trade for any listed stock is its daily opening price. The "
"opening price is an important marker for that day's trading activity, particularly for those "
"interested in measuring short-term results such as day traders.")
fig2 = go.Figure()
fig2.add_trace(go.Scatter(x=list(data.index), y=list(data.Open)))
fig2.update_layout(title_text="Opening price of the stock", width=850, height=550)
fig2.update_xaxes(rangeslider_visible=True,
rangeselector=dict(
buttons=list([
dict(count=30, label="30D", step="day", stepmode="backward"),
dict(count=60, label="60D", step="day", stepmode="backward"),
dict(count=90, label="90D", step="day", stepmode="backward"),
dict(count=180, label="180D", step="day", stepmode="backward"),
dict(count=365, label="365D", step="day", stepmode="backward"),
dict(step="all")
])
))
st.plotly_chart(fig2)
st.markdown("### High price for the stock")
st.markdown("Today's high refers to a company's intraday high trading price. Today's high is the highest "
"price at which a stock traded during the course of the trading day. Today's high is typically "
"higher than the closing or opening price. More often than not this is higher than the closing "
"price.")
fig3 = go.Figure()
fig3.add_trace(go.Scatter(x=list(data.index), y=list(data.High)))
fig3.update_layout(title_text="High price of the stock", width=850, height=550)
fig3.update_xaxes(rangeslider_visible=True,
rangeselector=dict(
buttons=list([
dict(count=30, label="30D", step="day", stepmode="backward"),
dict(count=60, label="60D", step="day", stepmode="backward"),
dict(count=90, label="90D", step="day", stepmode="backward"),
dict(count=180, label="180D", step="day", stepmode="backward"),
dict(count=365, label="365D", step="day", stepmode="backward"),
dict(step="all")
])
))
st.plotly_chart(fig3)
st.markdown("### Lowest price for the stock")
st.markdown("Today’s low is a security's intraday low trading price. Today's low is the lowest price at which a"
" stock trades over the course of a trading day. Today's low is typically lower than the opening or"
" closing price, as it is unusual that the lowest price of the day would happen to occur at those "
"particular moments.")
fig4 = go.Figure()
fig4.add_trace(go.Scatter(x=list(data.index), y=list(data.Low)))
fig4.update_layout(title_text="Low price of the stock", width=850, height=550)
fig4.update_xaxes(rangeslider_visible=True,
rangeselector=dict(
buttons=list([
dict(count=30, label="30D", step="day", stepmode="backward"),
dict(count=60, label="60D", step="day", stepmode="backward"),
dict(count=90, label="90D", step="day", stepmode="backward"),
dict(count=180, label="180D", step="day", stepmode="backward"),
dict(count=365, label="365D", step="day", stepmode="backward"),
dict(step="all")
])
))
st.plotly_chart(fig4)
st.markdown("### Closing price of the stock")
st.markdown("The closing price of a stock is the price at which the share closes at the end of trading hours "
"of the stock market. In simple terms, the closing price is the weighted average of all prices "
"during the last 30 minutes of the trading hours.")
fig5 = go.Figure()
fig5.add_trace(go.Scatter(x=list(data.index), y=list(data.Close)))
fig5.update_layout(title_text="Closing price of the stock", width=850, height=550)
fig5.update_xaxes(rangeslider_visible=True,
rangeselector=dict(
buttons=list([
dict(count=30, label="30D", step="day", stepmode="backward"),
dict(count=60, label="60D", step="day", stepmode="backward"),
dict(count=90, label="90D", step="day", stepmode="backward"),
dict(count=180, label="180D", step="day", stepmode="backward"),
dict(count=365, label="365D", step="day", stepmode="backward"),
dict(step="all")
])
))
st.plotly_chart(fig5)
######################################################################################
elif show == "Company Data":
symbolticker = companies[company]
dataticker = yf.Ticker(symbolticker)
st.header('Information of company ' + company)
st.markdown(dataticker.info)
st.markdown("### Stock Price Data")
st.dataframe(data)
st.markdown("### International Securities Identification Number")
st.markdown(dataticker.isin)
# st.markdown("### Sustainability")
st.dataframe(dataticker.sustainability)
st.markdown("### Major Holders")
st.dataframe(dataticker.major_holders)
st.markdown("### Institutional Holders")
st.dataframe(dataticker.institutional_holders)
st.markdown("### Calendar")
st.dataframe(dataticker.calendar)
st.markdown("### Recommendations")
st.dataframe(dataticker.recommendations)
#############################################################################
if __name__ == "__main__":
data_analysis()