app.py

import plotly.graph_objects as go
import plotly.express as px
import streamlit as st
import pandas as pd
import urllib.parse
import pandas as pd
import requests
import json



# Settings:
## Extra CSS:
st.set_page_config(page_title='Upstox Portfolio Viewer', page_icon=':bar_chart:', layout='wide')
hide_st_style = '''
    <style>
        #MainMenu {visibility: hidden;}
        footer {visibility: hidden;}
        header {visibility: hidden;}
    </style>
'''
st.markdown(hide_st_style, unsafe_allow_html=True)



# @st.cache_data
# def load_instruments():
#     data = pd.read_csv('NSE.csv')
#     return data


def connect():
    conf = get_details()
    redirect_url = urllib.parse.quote(conf['rurl'], safe='')
    uri = f"https://api-v2.upstox.com/login/authorization/dialog?response_type=code&client_id={conf['apiKey']}&redirect_uri={redirect_url}"

    st.markdown(f'[Authorize with Upstox]({uri})')


def login(code):
    conf = get_details()
    conf['code'] = code

    store_details(conf)

    conf = get_details()

    url = "https://api-v2.upstox.com/login/authorization/token"

    headers = {
        "accept": "application/json",
        "Api-Version": "2.0",
        "Content-Type": "application/x-www-form-urlencoded",
    }

    data = {
        "code": conf['code'],
        "client_id": conf['apiKey'],
        "client_secret": conf['secretKey'],
        "redirect_uri": conf['rurl'],
        "grant_type": "authorization_code",
    }

    response = requests.post(url, headers=headers, data=data)
    json_response = response.json()
    try:
        access_token = json_response['access_token']
        conf['access_token'] = access_token
    except Exception:
        pass
        # uri = f"https://upstoxapi.streamlit.app"
        # st.markdown(f'[Something went wrong!!! Please restart the app]({uri})')
        # st.stop()

    store_details(conf)


def get_details():
    with open('config.json') as f:
        data = json.load(f)
    return data


def store_details(conf):
    with open('config.json', 'w') as f:
        json.dump(conf, f)


def get_profile():
    conf = get_details()

    url = 'https://api-v2.upstox.com/user/profile'
    
    headers = {
        "accept": "application/json",
        "Api-Version": "2.0",
        "Authorization": f"Bearer {conf['access_token']}",
    }

    response = requests.get(url, headers=headers)
    json_response = response.json()

    return json_response


def pnl(data):
    net_pnl = 0
    initial = sum([avg_price['average_price']*avg_price['quantity'] for avg_price in data])
    
    for pnl in data:
        net_pnl += pnl['pnl']

    current = initial + net_pnl

    per = (current - initial) / initial
    st.metric(label=f"NET PNL of Rs. {initial:.2f} /-", value=f"{current:.2f} /-", delta=f"{per*100:.2f}%")


def plot_pnl(data):
    labels = [name['company_name'] for name in data]
    values = [price['pnl'] for price in data]

    df1 = pd.DataFrame(list(zip(labels, values)), columns=['Companies -->', 'PNLs -->'])
    df1.sort_values(by=['PNLs -->'], inplace=True)
    fig1 = px.bar(
        df1,
        x = 'PNLs -->',
        y = 'Companies -->',
        orientation="h",
        title="<b>PNL per Company:</b>",
    )
    fig1.update_layout(
        plot_bgcolor="rgba(0,0,0,0)",
        xaxis=(dict(showgrid=True))
    )
    st.plotly_chart(fig1, use_container_width=True)
    st.markdown(f'Net PNL: {sum(values):.2f} /-')



def get_holdings():
    conf = get_details()

    url = 'https://api-v2.upstox.com/portfolio/long-term-holdings'
    
    headers = {
        "accept": "application/json",
        "Api-Version": "2.0",
        "Authorization": f"Bearer {conf['access_token']}",
    }

    response = requests.get(url, headers=headers)
    json_response = response.json()

    return json_response


def get_investments_plot_by_price(data):
    labels = [name['company_name'] for name in data]
    values = [avg_price['average_price']*avg_price['quantity'] for avg_price in data]
    qts = [qt['quantity'] for qt in data]

    fig1 = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
    fig1.update_layout(plot_bgcolor='rgba(0,0,0,0)', title="<b>Investments per Company by Price Weightage:</b>")


    df1 = pd.DataFrame(list(zip(labels, values)), columns=['Companies -->', 'Amounts -->'])
    df1.sort_values(by=['Amounts -->'], inplace=True)
    fig2 = px.bar(
        df1,
        x = 'Amounts -->',
        y = 'Companies -->',
        orientation="h",
        title="<b>Investments per Company by Amount:</b>",
    )
    fig2.update_layout(
        plot_bgcolor="rgba(0,0,0,0)",
        xaxis=(dict(showgrid=True))
    )

    df2 = pd.DataFrame(list(zip(labels, qts)), columns=['Companies -->', 'Quantity -->'])
    df2.sort_values(by=['Quantity -->'], inplace=True)
    fig3 = px.bar(
        df2,
        x = 'Quantity -->',
        y = 'Companies -->',
        orientation="h",
        title="<b>Shares per Company by Quantity:</b>",
    )
    fig3.update_layout(
        plot_bgcolor="rgba(0,0,0,0)",
        xaxis=(dict(showgrid=True))
    )

    st.subheader('Distribution by Invested Amount')
    l, r = st.columns(2)
    with l:
        st.plotly_chart(fig1, use_container_width=True)
    with r:
        st.plotly_chart(fig3, use_container_width=True)

    st.plotly_chart(fig2, use_container_width=True)
    st.markdown(f'Total Amount Invested: {sum(values):.2f} /-')


def get_sell_charges(ins_token, quan, price, prod, temp=None):
    conf = get_details()
    url = 'https://api-v2.upstox.com/charges/brokerage'    
    
    headers = {
        "accept": "application/json",
        "Api-Version": "2.0",
        "Authorization": f"Bearer {conf['access_token']}",
    }
    params = {
        "instrument_token": ins_token,
        "quantity": quan,
        "product": prod,
        "transaction_type": "SELL",
        "price": price
    }

    response = requests.get(url, headers=headers, params=params)
    json_response = response.json()
    
    dp_charge = 18.5
    grace = 20
    
    return json_response['data']['charges']['total'] + dp_charge + grace


def get_all_sell_estimates(data):
    labels = [name['company_name'] for name in data]
    ins_tokens = [tok['instrument_token'] for tok in data]
    qts = [qt['quantity'] for qt in data]
    ltp = [lt['last_price'] for lt in data]
    byp = [bp['average_price'] for bp in data]
    prod = [p['product'] for p in data]

    ttax = 0
    for l, a, b, c, p, bp in zip(labels, ins_tokens, qts, ltp, prod, byp):
        sell_charge = get_sell_charges(a, b, c, p, l)
        ttax += sell_charge

    net_pnl = 0
    initial = sum([avg_price['average_price']*avg_price['quantity'] for avg_price in data])
    
    for pnl in data:
        net_pnl += pnl['pnl']

    current = initial + net_pnl - ttax

    per = (current - initial) / initial
    st.write(f"NET PNL (with other charges {ttax}) of Rs. {initial:.2f}/- is: {(current - initial):.2f}/- (PORTFOLIO VALUE: {current:.2f} | {per*100:.2f}%)")


# def get_sell_charges(ins_token, quan, price):
#     conf = get_details()

#     url = 'https://api-v2.upstox.com/charges/brokerage'    
    
#     headers = {
#         "accept": "application/json",
#         "Api-Version": "2.0",
#         "Authorization": f"Bearer {conf['access_token']}",
#     }
#     params = {
#         "instrument_token": ins_token,
#         "quantity": quan,
#         "product": "D",
#         "transaction_type": "SELL",
#         "price": price
#     }

#     response = requests.get(url, headers=headers, params=params)
#     json_response = response.json()
#     st.write(json_response)

#     return json_response['data']['charges']['total']


# def get_all_sell_estimates(data):
#     labels = [name['company_name'] for name in data]
#     ins_tokens = [tok['instrument_token'] for tok in data]
#     qts = [qt['quantity'] for qt in data]
#     ltp = [lt['last_price'] for lt in data]
#     charges = []

#     for a, b, c in zip(ins_tokens, qts, ltp):
#         charges.append(get_sell_charges(a, b, c))

#     st.write(labels)
#     st.write(ltp)
#     st.write(charges)


def get_ltps(symbs):
    to_fetch = ','.join(symbs)
    conf = get_details()

    url = 'https://api-v2.upstox.com/market-quote/ltp' 
    
    headers = {
        "accept": "application/json",
        "Api-Version": "2.0",
        "Authorization": f"Bearer {conf['access_token']}",
    }
    params = {
        "symbol": to_fetch
    }

    response = requests.get(url, headers=headers, params=params)
    json_response = response.json()

    return json_response


def get_wannabe_investments_plot_by_price(data, symbs, quantity):
    # labels = [name['company_name'] for name in data]
    # values = [price['last_price']*(quantity - price['quantity']) if (price['last_price']*(quantity - price['quantity'])) > 0 else 0 for price in data]
    # qts = [quantity - qt['quantity'] if (quantity - qt['quantity']) > 0 else 0 for qt in data]
    labels = []
    values = []
    qts = []
    for name in data:
        if name['company_name'] in symbs:
            labels.append(name['company_name'])
            values.append(name['last_price']*(quantity - name['quantity']) if (name['last_price']*(quantity - name['quantity'])) > 0 else 0)
            qts.append(quantity - name['quantity'] if (quantity - name['quantity']) > 0 else 0)
    
    extra_labels = set(symbs) - set(labels)
    if extra_labels:
        ltps = get_ltps(extra_labels)
        # st.write(ltps)

        for k, v in ltps['data']:
            if k in extra_labels:
                labels.append(k)
                values.append(quantity*v['last_price'])

    fig1 = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
    fig1.update_layout(plot_bgcolor='rgba(0,0,0,0)', title="<b>Investments per Company by Price Weightage Required:</b>")


    df1 = pd.DataFrame(list(zip(labels, values)), columns=['Companies -->', 'Amounts -->'])
    df1.sort_values(by=['Amounts -->'], inplace=True)
    fig2 = px.bar(
        df1,
        x = 'Amounts -->',
        y = 'Companies -->',
        orientation="h",
        title="<b>Investments per Company by Amount Required:</b>",
    )
    fig2.update_layout(
        plot_bgcolor="rgba(0,0,0,0)",
        xaxis=(dict(showgrid=True))
    )

    df2 = pd.DataFrame(list(zip(labels, qts)), columns=['Companies -->', 'Quantity -->'])
    df2.sort_values(by=['Quantity -->'], inplace=True)
    fig3 = px.bar(
        df2,
        x = 'Quantity -->',
        y = 'Companies -->',
        orientation="h",
        title="<b>Shares per Company by Quantity Required:</b>",
    )
    fig3.update_layout(
        plot_bgcolor="rgba(0,0,0,0)",
        xaxis=(dict(showgrid=True))
    )

    st.subheader('Distribution by Amount Required')
    l, r = st.columns(2)
    with r:
        st.plotly_chart(fig1, use_container_width=True)
    with l:
        st.plotly_chart(fig3, use_container_width=True)

    st.plotly_chart(fig2, use_container_width=True)
    st.markdown(f'Total Amount Required: {sum(values):.2f} /-')



response = st.experimental_get_query_params()
if 'code' in response:
    st.sidebar.markdown('In case of any errors: [restart-app](https://upstoxapi.streamlit.app)')
    login(response['code'][0])
    st.success('Login Successfull!')

    # ins_data = load_instruments()

    data = get_holdings()
    # st.write(data)

    profile = get_profile()
    # st.write(profile)

    st.header(f"Welcome {profile['data']['user_name']}")
    pnl(data['data'])
    plot_pnl(data['data'])
    st.markdown('##')

    with st.expander('Show Holdings'):
        get_investments_plot_by_price(data['data'])
        st.markdown('##')
        # get_all_sell_estimates(data['data'])

    # with st.expander('Show Goals'):
    st.subheader('Set Goals Here:')

    # ops = list(ins_data['tradingsymbol'].unique())
    # ops.extend([name['company_name'] for name in data['data']])
    symbs = st.multiselect(
        'Select The Appropriate Symbols:',
        options= [name['company_name'] for name in data['data']],
        default= [name['company_name'] for name in data['data']]
    )
    quantity = st.slider('Quantity(s) [ALL]:', 1, 100, value=10, step=1)

    get_wannabe_investments_plot_by_price(data['data'], symbs, quantity)
    st.markdown('##')

    get_all_sell_estimates(data['data'])
    st.markdown('##')

else:
    connect()