app.py

import streamlit as st
import pandas as pd
import json
from datetime import datetime
from datetime import timedelta

st.set_page_config(page_title='CCIC 4.0: Group J',
                   page_icon='favicon.ico', layout='wide')

@st.cache(allow_output_mutation=True)
def get_data():
    return pd.read_csv(r'csv_files/news_scraped.csv')


@st.cache
def get_groww():
    f = open('json_files/groww_all.json')
    data = json.load(f)
    return data


@st.cache
def get_capital():
    f = open(r'json_files/capital_fo.json')
    data = json.load(f)
    return data


@st.cache
def get_upfo():
    f = open(r'json_files/upstox_fo.json')
    data = json.load(f)
    return data


@st.cache
def get_upmo():
    f = open(r'json_files/upstox_morning.json')
    data = json.load(f)
    return data


@st.cache
def get_nse():
    f = open(r'index_json_files/nseindia.json')
    data = json.load(f)
    return data


@st.cache
def get_cashmc():
    f = open(r'index_json_files/cash_moneycontrol.json')
    data = json.load(f)
    return data


@st.cache
def get_fnomc():
    f = open(r'index_json_files/fno_moneycontrol.json')
    data = json.load(f)
    return data


@st.cache
def get_eqsis():
    f = open(r'index_json_files/eqsis.json')
    data = json.load(f)
    return data


@st.cache
def get_invest():
    f = open(r'index_json_files/investing.json')
    data = json.load(f)
    return data


today = datetime.today()
dayno = today.weekday()
day = today.strftime("%d %B %Y")
yesterday = today - timedelta(days=1)
day1 = yesterday.strftime("%d %B")
yesterday1 = today - timedelta(days=2)
day2 = yesterday1.strftime("%d %B")
yesterday2 = today - timedelta(days=3)
day3 = yesterday2.strftime("%d %B")
yesterday3 = today - timedelta(days=4)
day4 = yesterday3.strftime("%d %B")

if dayno == 0:
    yesterday = day3
    yesterday1 = day4

if dayno == 6:
    yesterday = day2
    yesterday1 = day3

# add day
st.markdown("<h1 style='text-align: center;'><b>CCIC 4.0 : Group J</b></h1>",
            unsafe_allow_html=True)
st.write("")
st.write("")

col1, col2, col3 = st.columns((1, 4, 1))

with col1:
    st.write("")


with col2:
    st.write("Date: " + day)
    st.header("Market Commentary 📻")
    st.write("")
    grow = get_groww()
    capital = get_capital()
    upfo = get_upfo()
    upmo = get_upmo()

    gsector = grow["news"][0]['description']
    gsector = gsector.split('\n')
    gnews = grow["news"][1]['description']
    gnews = gnews.split('\n')
    summary = grow["introduction"]["content"].split('\n')
    summary = " ".join(summary[1:])
    derivatives_cap = capital['fno']
    globalm = upfo['asian']
    index = upfo['index']
    fiidii = upfo['fiidii']
    indiavix = upfo['indiavix']
    stock = upfo['stock']
    stock = stock.split('.')
    disc = upfo['disclaimer']
    news1 = upmo['news1']
    news2 = upmo['news2']
    news3 = upmo['news3']

    summary
    st.subheader("Global Markets 🌎")
    st.write(globalm)
    st.subheader("Index Action 📈📉")
    st.write(index)
    st.subheader("General NEWS 🗞️")
    for upd in gsector:
        st.write("⭐️ " + upd[1:])
    # st.write(gsector)
    st.write(news1)
    st.write(news2)
    st.write(news3)
    st.subheader("FII/FPI and DII 💰")
    st.write(fiidii)
    st.subheader("Derivatives 💵")
    st.write(derivatives_cap)
    st.subheader("Stock Futures 📊")
    st.write("**Long Build-up**" + stock[0][13:])
    st.write("**Short Build-up**" + stock[1][16:])
    if(len(stock[2][15:]) > 4):
        st.write("**Under F&O Ban**" + stock[2][15:])
    st.info(disc, icon="ℹ️")


    st.write("")
    st.write("")

    st.header("Nifty200 NEWS 📰")
    st.write("")
    newsdf = get_data()
    for i in range(len(newsdf)):
        company = newsdf.loc[i, 'company'] + " : "
        title = newsdf.loc[i, 'title']
        st.write("👉" + f"{company} {title}")

    for i in range(len(gnews)):
        st.write("👉" + gnews[i])

    st.write("")
    st.write("")

    st.header("Index level Reporting 🪙")
    st.write("")
    
    st.subheader("Market Turnover 📝")
    st.write("")
    nse = get_nse()
    # st.json(nse)
    equity_prev = nse["equities"]["previous"]["volume"]
    equity_tod = nse["equities"]["today"]["volume"]
    delta_eq = ((equity_tod-equity_prev)/equity_prev)*100
    indexfut_prev = nse["index_futures"]["previous"]["volume"]
    indexfut_tod = nse["index_futures"]["today"]["volume"]
    indexfut_oi = int(nse["index_futures"]["previous"]["open_interest"])
    delta_indexfut = ((indexfut_tod-indexfut_prev)/indexfut_prev)*100
    indexopt_prev = nse["index_options"]["previous"]["volume"]
    indexopt_tod = nse["index_options"]["today"]["volume"]
    indexopt_oi = int(nse["index_options"]["previous"]["open_interest"])
    delta_indexopt = ((indexopt_tod-indexopt_prev)/indexopt_prev)*100
    stockfut_prev = nse["stock_futures"]["previous"]["volume"]
    stockfut_tod = nse["stock_futures"]["today"]["volume"]
    stockfut_oi = int(nse["stock_futures"]["previous"]["open_interest"])
    delta_stockfut = ((stockfut_tod-stockfut_prev)/stockfut_prev)*100
    stockopt_prev = nse["stock_options"]["previous"]["volume"]
    stockopt_tod = nse["stock_options"]["today"]["volume"]
    stockopt_oi = int(nse["stock_options"]["previous"]["open_interest"])
    delta_stockopt = ((stockopt_tod-stockopt_prev)/stockopt_prev)*100
    data = [[equity_tod, delta_eq, '-'], [indexfut_tod, delta_indexfut, indexfut_oi], [indexopt_tod, delta_indexopt, indexopt_oi], [stockfut_tod, delta_stockfut, stockfut_oi], [stockopt_tod, delta_stockopt, stockopt_oi]]
    df = pd.DataFrame(data, columns=['Previous Volume(shares/contracts)', '🛆 Volume (%)', 'Previous OI(contracts)'], index=[
                      'Cash Equity', 'Index Futures', 'Index Options', 'Stock Futures', 'Stock Options'])
    df.reset_index(inplace=True)
    df.rename(columns={'index': 'Product'}, inplace=True)
    # s1 = dict(selector='th', props=[('text-align', 'center')])
    # s2 = dict(selector='td', props=[('text-align', 'center')])
    # # you can include more styling paramteres, check the pandas docs
    # table = df.style.set_table_styles([s1, s2]).hide(axis=0).to_html()
    # st.write(f'{table}', unsafe_allow_html=True)
    st.dataframe(df, use_container_width=True)
    st.write("")

    st.subheader("FII and DII Activity(₹crores) 🏤")
    st.write("")
    cashmc = get_cashmc()
    sum_purchase_fii = 0
    sum_sales_fii = 0
    sum_purchase_dii = 0
    sum_sales_dii = 0
    count = 0
    for i in cashmc['dates']:
        sum_purchase_fii += i["fii"]["gross_purchase"]
        sum_sales_fii += i["fii"]["gross_sales"]
        sum_purchase_dii += i["dii"]["gross_purchase"]
        sum_sales_dii += i["dii"]["gross_sales"]
        count += 1

    prev_purchase_fii = cashmc['dates'][0]['fii']['gross_purchase']
    prev_purchase_dii = cashmc['dates'][0]['dii']['gross_purchase']
    prev_sale_fii = cashmc['dates'][0]['fii']['gross_sales']
    prev_sale_dii = cashmc['dates'][0]['dii']['gross_sales']
    prev1_purchase_fii = cashmc['dates'][1]['fii']['gross_purchase']
    prev1_purchase_dii = cashmc['dates'][1]['dii']['gross_purchase']
    prev1_sale_fii = cashmc['dates'][1]['fii']['gross_sales']
    prev1_sale_dii = cashmc['dates'][1]['dii']['gross_sales']
    avg_purchase_fii = sum_purchase_fii/count
    avg_sale_fii = sum_sales_fii/count
    avg_purchase_dii = sum_purchase_dii/count
    avg_sale_dii = sum_sales_dii/count
    month_change_fii = sum_purchase_fii-sum_sales_fii
    month_change_dii = sum_purchase_dii-sum_sales_dii


    fnomc = get_fnomc()
    sum_purchase_fii_indexfut = 0
    sum_sales_fii_indexfut = 0
    sum_purchase_fii_indexopt = 0
    sum_sales_fii_indexopt = 0
    count = 0
    for i in fnomc["index"]['dates']:
        sum_purchase_fii_indexfut += i["fii_index_put"]["gross_purchase"]
        sum_sales_fii_indexfut += i["fii_index_put"]["gross_sales"]
        sum_purchase_fii_indexopt += i["fii_index_opt"]["gross_purchase"]
        sum_sales_fii_indexopt += i["fii_index_opt"]["gross_sales"]
        count += 1

    prev_purchase_fii_indexfut = fnomc["index"]['dates'][0]['fii_index_put']['gross_purchase']
    prev_purchase_fii_indexopt = fnomc["index"]['dates'][0]['fii_index_opt']['gross_purchase']
    prev_sale_fii_indexfut = fnomc["index"]['dates'][0]['fii_index_put']['gross_sales']
    prev_sale_fii_indexopt = fnomc["index"]['dates'][0]['fii_index_opt']['gross_sales']
    prev1_purchase_fii_indexfut = fnomc["index"]['dates'][0]['fii_index_put']['gross_purchase']
    prev1_purchase_fii_indexopt = fnomc["index"]['dates'][0]['fii_index_opt']['gross_purchase']
    prev1_sale_fii_indexfut = fnomc["index"]['dates'][0]['fii_index_put']['gross_sales']
    prev1_sale_fii_indexopt = fnomc["index"]['dates'][0]['fii_index_opt']['gross_sales']
    avg_purchase_fii_indexfut = sum_purchase_fii_indexfut/count
    avg_sale_fii_indexfut = sum_sales_fii_indexfut/count
    avg_purchase_fii_indexopt = sum_purchase_fii_indexopt/count
    avg_sale_fii_indexopt = sum_sales_fii_indexopt/count
    month_change_fii_indexfut = sum_purchase_fii_indexfut-sum_sales_fii_indexfut
    month_change_fii_indexopt = sum_purchase_fii_indexopt-sum_sales_fii_indexopt

    sum_purchase_fii_stockfut = 0
    sum_sales_fii_stockfut = 0
    sum_purchase_fii_stockopt = 0
    sum_sales_fii_stockopt = 0
    count = 0
    for i in fnomc["stock"]['dates']:
        sum_purchase_fii_stockfut += i["fii_stock_put"]["gross_purchase"]
        sum_sales_fii_stockfut += i["fii_stock_put"]["gross_sales"]
        sum_purchase_fii_stockopt += i["fii_stock_opt"]["gross_purchase"]
        sum_sales_fii_stockopt += i["fii_stock_opt"]["gross_sales"]
        count += 1

    prev_purchase_fii_stockfut = fnomc["stock"]['dates'][0]['fii_stock_put']['gross_purchase']
    prev_purchase_fii_stockopt = fnomc["stock"]['dates'][0]['fii_stock_opt']['gross_purchase']
    prev_sale_fii_stockfut = fnomc["stock"]['dates'][0]['fii_stock_put']['gross_sales']
    prev_sale_fii_stockopt = fnomc["stock"]['dates'][0]['fii_stock_opt']['gross_sales']
    prev1_purchase_fii_stockfut = fnomc["stock"]['dates'][0]['fii_stock_put']['gross_purchase']
    prev1_purchase_fii_stockopt = fnomc["stock"]['dates'][0]['fii_stock_opt']['gross_purchase']
    prev1_sale_fii_stockfut = fnomc["stock"]['dates'][0]['fii_stock_put']['gross_sales']
    prev1_sale_fii_stockopt = fnomc["stock"]['dates'][0]['fii_stock_opt']['gross_sales']
    avg_purchase_fii_stockfut = sum_purchase_fii_stockfut/count
    avg_sale_fii_stockfut = sum_sales_fii_stockfut/count
    avg_purchase_fii_stockopt = sum_purchase_fii_stockopt/count
    avg_sale_fii_stockopt = sum_sales_fii_stockopt/count
    month_change_fii_stockfut = sum_purchase_fii_stockfut-sum_sales_fii_stockfut
    month_change_fii_stockopt = sum_purchase_fii_stockopt-sum_sales_fii_stockopt

    data = [
        [prev_purchase_fii - prev_sale_fii, prev1_purchase_fii - prev1_sale_fii, avg_purchase_fii - avg_sale_fii], 
        [prev_purchase_dii - prev_sale_dii, prev1_purchase_dii - prev1_sale_dii, avg_purchase_dii - avg_sale_dii],
        [prev_purchase_fii_indexfut - prev_sale_fii_indexfut, prev1_purchase_fii_indexfut - prev1_sale_fii_indexfut, avg_purchase_fii_indexfut - avg_sale_fii_indexfut],
        [prev_purchase_fii_indexopt - prev_sale_fii_indexopt, prev1_purchase_fii_indexopt - prev1_sale_fii_indexopt, avg_purchase_fii_indexopt - avg_sale_fii_indexopt], 
        [prev_purchase_fii_stockfut - prev_sale_fii_stockfut, prev1_purchase_fii_stockfut - prev1_sale_fii_stockfut, avg_purchase_fii_stockfut - avg_sale_fii_stockfut],
        [prev_purchase_fii_stockopt - prev_sale_fii_stockopt, prev1_purchase_fii_stockopt - prev1_sale_fii_stockopt, avg_purchase_fii_stockopt - avg_sale_fii_stockopt], 
    ]
    df = pd.DataFrame(data, columns=[yesterday, yesterday1,'30D Average Net Purchase'], 
                        index=['FII Cash', 'DII Cash', 'FII Index Futures', 'FII Index Options', 'FII Stock Futures', 'FII Stock Options'])
    df.reset_index(inplace=True)
    df.rename(columns={'index': 'Category'}, inplace=True)
    st.dataframe(df, use_container_width=True)
    st.write("")

    st.subheader("Options Snapshot 📸")
    st.write("")
    eqsis = get_eqsis()
    long_fii_ic = eqsis["index_call"]["data"][1]["long"]
    short_fii_ic = eqsis["index_call"]["data"][1]["short"]
    long_dii_ic = eqsis["index_call"]["data"][2]["long"]
    short_dii_ic = eqsis["index_call"]["data"][2]["short"]
    long_fii_ip = eqsis["index_put"]["data"][1]["long"]
    short_fii_ip = eqsis["index_put"]["data"][1]["short"]
    long_dii_ip = eqsis["index_put"]["data"][2]["long"]
    short_dii_ip = eqsis["index_put"]["data"][2]["short"]

    long_fii_ic1 = eqsis["index_call"]["data"][5]["long"]
    short_fii_ic1 = eqsis["index_call"]["data"][5]["short"]
    long_dii_ic1 = eqsis["index_call"]["data"][6]["long"]
    short_dii_ic1 = eqsis["index_call"]["data"][6]["short"]
    long_fii_ip1 = eqsis["index_put"]["data"][5]["long"]
    short_fii_ip1 = eqsis["index_put"]["data"][5]["short"]
    long_dii_ip1 = eqsis["index_put"]["data"][6]["long"]
    short_dii_ip1 = eqsis["index_put"]["data"][6]["short"]
    
    data = [
        [long_fii_ic-short_fii_ic, long_fii_ic1-short_fii_ic1, (long_fii_ic-short_fii_ic)-(long_fii_ic1-short_fii_ic1)],
        [long_dii_ic-short_dii_ic, long_dii_ic1-short_dii_ic1, (long_dii_ic-short_dii_ic)-(long_dii_ic1-short_dii_ic1)],
        [long_fii_ip-short_fii_ip, long_fii_ip1-short_fii_ip1, (long_fii_ip-short_fii_ip)-(long_fii_ip1-short_fii_ip1)],
        [long_dii_ip-short_dii_ip, long_dii_ip1-short_dii_ip1, (long_dii_ip-short_dii_ip)-(long_dii_ip1-short_dii_ip1)],
    ]

    df = pd.DataFrame(data, columns=[yesterday, yesterday1, "1D change"],
                      index=['FII Index Call Options', 'DII Index Call Options', 'FII Index Put Options', 'DII Index Put Options'])
    df.reset_index(inplace=True)
    df.rename(columns={'index': 'Category'}, inplace=True)
    st.dataframe(df, use_container_width=True)
    st.write("")

    st.subheader("ADR moves 🏦")
    st.write("")
    invest = get_invest()
    df = pd.DataFrame.from_dict(invest, orient='index')
    df.reset_index(inplace=True)
    df.rename(columns={'index': 'Name', 'chg_pct' : '1D % change', 'volume': 'Volume', 'month_chg_pct': '30D % change'}, inplace=True)
    st.dataframe(df, use_container_width=True)

    st.write("")
    st.write("")

    st.header("Methodology ✍️")
    st.write("")
    st.write("We have made a concerted effort to ensure that the news stories we present to our users are from reputable and reliable sources. In order to achieve this, we have carefully selected a number of prominent news outlets to serve as our sources, and have implemented a web scraper to gather news stories from these sources on a regular basis.")
    st.write("With that said, in order to maintain the integrity and relevance of our news feed, we have implemented a filter that removes any news stories that does not meet the following criteria: ")
    st.write("👉  The company is listed in the NIFTY200 index.")
    st.write("👉  The news story was published yesterday.")
    st.write("👉  The news story was published over the weekend (if we are viewing the news feed on a Monday).")
    st.write("This filter helps us to provide our users with the most current and relevant news stories, and ensures that our news feed is as useful as possible.")
    st.write("In order to provide our users with up-to-date and accurate Index level reporting, we have implemented a similar process for gathering and displaying data. Specifically, we have scraped data from reliable sources and used it to create a web app using Streamlit. This app allows users to easily view and interact with the data, and we have hosted it on Streamlit Cloud for convenient access.")
    st.write("In order to make this process as efficient as possible, we have also set up a workflow using GitHub Actions that runs the code to update the content every day at 7:30 AM IST. This ensures that the data is always current and accurate, without requiring us to manually update it on a daily basis.")
    st.write("Overall, we believe that this combination of reliable data sources, a user-friendly web app, and automated updates allows our users to have the most comprehensive and up-to-date overview of the market before the start of the trading session.")

    st.write("")
    st.write("")
    st.info("Sources: MoneyControl, NSE India, Eqsis, Groww, Upstox, Capital Market", icon="💁🏻")


with col3:
    st.write("")