Creating a Stock Market Analyser in Python

We shall be using Streamlit.

Prateek Majumder
Apr 19 · 5 min read

Streamlit can be used to create simple and easy web apps in Python. Streamlit makes deveopment and deployment of Web Apps very easy. Check out the official streamlit website.

Understanding some Stock Market Terms

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Stock Codes

Sample Stock Market Data.

Then, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.

Moving Averages

A simple moving average (SMA) is a calculation that takes the arithmetic mean of a given set of prices over the specific number of days in the past; for example, over the previous 15, 30, 100, or 200 days.

OHLC Candle Stick Graph

Many algorithms are based on the same price information shown in candlestick charts. Trading is often dictated by emotion, which can be read in candlestick charts.

Getting into the Code

import warnings
warnings.filterwarnings('ignore') # Hide warnings
import datetime as dt
import pandas as pd
pd.core.common.is_list_like = pd.api.types.is_list_like
import as web
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from PIL import Image
import os
from mpl_finance import candlestick_ohlc
import matplotlib.dates as mdates
import streamlit as st

Pandas datareader will be used to scrape the data. From mpl_finance, we shall be using the Candlestick charts.

Next, we will take the Stock code of the company, and also the starting date of the stock and the end date of the stock.

st.title('Stock Market App')
st.write("Developed by Prateek Majumder")
image ='STOCK.png'))
com = st.text_input("Enter the Stock Code of company","AAPL")'You Enterted the company code: ', comst_date= st.text_input("Enter Starting date as YYYY-MM-DD", "2000-01-10")'You Enterted the starting date: ', st_dateend_date= st.text_input("Enter Ending date as YYYY-MM-DD", "2000-01-20")'You Enterted the ending date: ', end_date

Next, we will use to gather the stock market data from Yahoo Finance.

df = web.DataReader(com, 'yahoo', st_date, end_date)  # Collects data
df.set_index("Date", inplace=True)

Next, we will plot some of the data we got from yahoo finance.

st.title('Stock Market Data')
'The Complete Stock Data as extracted from Yahoo Finance: '
'1. The Stock Open Values over time: '
'2. The Stock Close Values over time: '

Take note, that in the code, I had given AAPL (Apple) and sample dates, these can be modified in the app GUI.

GUI for data entry.
Data from Yahoo Finance.

Next, we code for displaying the moving averages.

mov_avg= st.text_input("Enter number of days Moving Average:", "50")'You Enterted the Moving Average: ', mov_avgdf["mov_avg_close"] = df['Close'].rolling(window=int(mov_avg),min_periods=0).mean()'1. Plot of Stock Closing Value for '+ mov_avg+ " Days of Moving Average"
' Actual Closing Value also Present'
df["mov_avg_open"] = df['Open'].rolling(window=int(mov_avg),min_periods=0).mean()'2. Plot of Stock Open Value for '+ mov_avg+ " Days of Moving Average"
' Actual Opening Value also Present'

It will look somewhat like this.

Moving Average.

Now, we do the code for the OHLC Candle Stick Graph.

ohlc_day= st.text_input("Enter number of days for Resampling for OHLC CandleStick Chart", "50")# Resample to get open-high-low-close (OHLC) on every n days of data
df_ohlc = df.Close.resample(ohlc_day+'D').ohlc()
df_volume = df.Volume.resample(ohlc_day+'D').sum()
df_ohlc.Date =
# Create and visualize candlestick charts
'OHLC Candle Stick Graph for '+ ohlc_day+ " Days"ax1 = plt.subplot2grid((6,1), (0,0), rowspan=5, colspan=1)
candlestick_ohlc(ax1, df_ohlc.values, width=2, colorup='g')
plt.ylabel('Stock Candle Sticks')

Other code and images can be added to the webapp for creating a better experience and better understanding.

Running the streamlit app.

streamlit run [filename]

Read More :

App Deployed in Heroku :

procfile, requirements.txt, runtime.txt,

Read More :

Check out the full project on my Github Page :

Do check out the live deployed Link :

Do connect with me on Linkedin :

Thank You.

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Prateek Majumder

Written by

Electrical Engineering | IEM Kolkata | Varied Interests | Trying to understand the World.

Analytics Vidhya

Analytics Vidhya is a community of Analytics and Data Science professionals. We are building the next-gen data science ecosystem

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