Portfolio Overview

Gather Portfolio Data

• Select Tickers, stock Names and number of shares owned

import datetime as dt
import matplotlib.pyplot as plt
from pandas_datareader import data as web
import pandas as pd

# Import and clean data
tickers = ['NIO', 'WKHS', 'CRSR', 'IWDA.AS']
stock_names = ['NIO', 'WorkHorse', 'Corsair', 'iShares World']
shares = [6, 8, 8, 19]
prices = []
total = []

for ticker in tickers:
    df = web.DataReader(ticker, 'yahoo', dt.datetime(2019,8,10), dt.datetime.now())
    price = df[-1:]['Close'][0]
    prices.append(price)
    index = prices.index(price)
    total.append(price * shares[index])

stocks_tickers = ['NIO', 'WKHS', 'CRSR', 'IWDA.AS', 'SXR8.DE']
stocks = web.DataReader(stocks_tickers, 'yahoo', dt.datetime(2019,8,10), dt.datetime.now())
    
total_str = ['$'+str(round(t,2)) for t in total]

stock_names_and_total = stock_names.copy()
stock_names_and_total.append('Total')
total_portfolio = total.copy()
total_portfolio.append(sum(total))
total_portfolio_str = ['$'+str(round(t,2)) for t in total_portfolio]

stocks.drop(columns=['Adj Close','High','Low','Open','Volume'], inplace=True)

stocks_close = stocks.Close.reset_index().copy()

My Portfolio treatment

# Current Portfolio value
current_portfolio = sum(total)

# stocks_tickers = ['NIO', 'WKHS', 'CRSR', 'IWDA.AS', 'SXR8.DE']
avg_price = [31.65, 21.5, 37.9, 55.0842]
shares = [6, 8, 8, 19]

initial_purchase = [shares[i]*avg_price[i] for i in range(len(avg_price))]
initial_portfolio = sum(initial_purchase)
initial_purchase_str = ['$'+str(round(t)) for t in initial_purchase]

# Current Weights in My Portfolio
weights = [price/initial_portfolio for price in initial_purchase]

# Calculating Return
todays_price = list(stocks_close[-1:][tickers].values.flatten())
returns = [todays_price[i]/avg_price[i]-1 for i in range(len(avg_price))]

# Portfolio Total Return
total_return = sum([returns[i]*weights[i] for i in range(len(returns))])
total_mkt_ret = 254

myportfolio = pd.DataFrame({'Stocks': stock_names,'Tickers': tickers, '# Shares': shares, 
                            'Weights': weights, 'Returns': returns, 'Price Today': todays_price, 
                            'Initial Purchase Price': avg_price, 'Initial Purchase per Stock': initial_purchase, 
                            'Current Value': total})
myportfolio

	Stocks	Tickers	# Shares	Weights	Returns	Price Today	Initial Purchase Price	Initial Purchase per Stock	Current Value
0	NIO	NIO	6	0.110942	0.846445	58.439999	31.6500	189.9000	350.639992
1	WorkHorse	WKHS	8	0.100485	0.105116	23.760000	21.5000	172.0000	190.080002
2	Corsair	CRSR	8	0.177134	-0.004486	37.730000	37.9000	303.2000	301.839996
3	iShares World	IWDA.AS	19	0.611439	0.112297	61.270000	55.0842	1046.5998	1164.130009

stocks_close['My Portfolio'] = (stocks_close['NIO']*shares[0]+stocks_close['WKHS']*shares[1]+stocks_close['CRSR']*shares[2]+
                                stocks_close['IWDA.AS']*shares[3])
stocks_close_dropna = stocks_close.dropna().copy()
stocks_close_dropna

Symbols	Date	NIO	WKHS	CRSR	IWDA.AS	SXR8.DE	My Portfolio
287	2020-09-23	17.959999	22.469999	14.250000	54.235001	283.760010	1431.985001
288	2020-09-24	17.850000	22.129999	15.580000	53.619999	280.279999	1427.559975
289	2020-09-25	18.320000	24.809999	17.250000	54.000000	282.720001	1472.399994
291	2020-09-29	20.850000	27.100000	18.780001	54.639999	286.239990	1530.299999
292	2020-09-30	21.219999	25.280001	20.100000	54.959999	289.260010	1534.599987
293	2020-10-01	21.760000	25.080000	20.950001	55.084999	289.679993	1545.414989
294	2020-10-02	21.180000	23.620001	20.900000	54.889999	287.829987	1526.149994
299	2020-10-09	21.469999	26.770000	18.110001	56.195000	295.140015	1555.564999
304	2020-10-16	28.480000	22.809999	19.200001	56.825001	300.700012	1586.635014
309	2020-10-23	27.160000	20.340000	24.670000	55.650002	293.459991	1580.390030
314	2020-10-30	30.580000	15.380000	24.040001	53.514999	281.989990	1515.624996
319	2020-11-06	41.630001	17.290001	27.080000	56.505001	297.250000	1678.335033
324	2020-11-13	44.560001	19.219999	29.180000	57.970001	303.440002	1755.990028
329	2020-11-20	49.250000	25.780001	40.330002	58.529999	304.190002	1936.449997
333	2020-11-27	54.000000	27.180000	38.770000	59.209999	306.730011	1976.589989
338	2020-12-04	43.040001	22.070000	35.680000	59.154999	306.720001	1844.184982
343	2020-12-11	41.980000	21.780001	33.259998	58.730000	303.399994	1808.069981
348	2020-12-18	46.720001	21.150000	38.700001	59.285000	305.079987	1885.535007
357	2021-01-04	53.490002	21.420000	34.750000	59.305000	304.350006	1897.095016
358	2021-01-05	53.200001	22.430000	35.900002	59.419998	305.070007	1914.819984
359	2021-01-06	50.500000	23.650000	37.889999	60.270000	309.769989	1940.450001
360	2021-01-07	54.279999	27.600000	39.430000	60.799999	312.799988	2017.119984
361	2021-01-08	58.919998	25.565001	38.310001	61.099998	314.010010	2025.419975
366	2021-01-15	56.270000	23.340000	38.130001	61.259998	315.200012	1993.319981
367	2021-01-19	58.439999	23.760000	37.730000	61.270000	315.200012	2006.689999

stocks_close_dropna['Portfolio Return'] = stocks_close_dropna['My Portfolio'] / stocks_close_dropna['My Portfolio'].shift(1) - 1
stocks_close_dropna['Market Return'] = stocks_close_dropna['SXR8.DE'] / stocks_close_dropna['SXR8.DE'].shift(1) - 1
stocks_close_dropna

Symbols	Date	NIO	WKHS	CRSR	IWDA.AS	SXR8.DE	My Portfolio	Portfolio Return	Market Return
287	2020-09-23	17.959999	22.469999	14.250000	54.235001	283.760010	1431.985001	NaN	NaN
288	2020-09-24	17.850000	22.129999	15.580000	53.619999	280.279999	1427.559975	-0.003090	-0.012264
289	2020-09-25	18.320000	24.809999	17.250000	54.000000	282.720001	1472.399994	0.031410	0.008706
291	2020-09-29	20.850000	27.100000	18.780001	54.639999	286.239990	1530.299999	0.039324	0.012450
292	2020-09-30	21.219999	25.280001	20.100000	54.959999	289.260010	1534.599987	0.002810	0.010551
293	2020-10-01	21.760000	25.080000	20.950001	55.084999	289.679993	1545.414989	0.007047	0.001452
294	2020-10-02	21.180000	23.620001	20.900000	54.889999	287.829987	1526.149994	-0.012466	-0.006386
299	2020-10-09	21.469999	26.770000	18.110001	56.195000	295.140015	1555.564999	0.019274	0.025397
304	2020-10-16	28.480000	22.809999	19.200001	56.825001	300.700012	1586.635014	0.019973	0.018839
309	2020-10-23	27.160000	20.340000	24.670000	55.650002	293.459991	1580.390030	-0.003936	-0.024077
314	2020-10-30	30.580000	15.380000	24.040001	53.514999	281.989990	1515.624996	-0.040980	-0.039085
319	2020-11-06	41.630001	17.290001	27.080000	56.505001	297.250000	1678.335033	0.107355	0.054115
324	2020-11-13	44.560001	19.219999	29.180000	57.970001	303.440002	1755.990028	0.046269	0.020824
329	2020-11-20	49.250000	25.780001	40.330002	58.529999	304.190002	1936.449997	0.102768	0.002472
333	2020-11-27	54.000000	27.180000	38.770000	59.209999	306.730011	1976.589989	0.020729	0.008350
338	2020-12-04	43.040001	22.070000	35.680000	59.154999	306.720001	1844.184982	-0.066987	-0.000033
343	2020-12-11	41.980000	21.780001	33.259998	58.730000	303.399994	1808.069981	-0.019583	-0.010824
348	2020-12-18	46.720001	21.150000	38.700001	59.285000	305.079987	1885.535007	0.042844	0.005537
357	2021-01-04	53.490002	21.420000	34.750000	59.305000	304.350006	1897.095016	0.006131	-0.002393
358	2021-01-05	53.200001	22.430000	35.900002	59.419998	305.070007	1914.819984	0.009343	0.002366
359	2021-01-06	50.500000	23.650000	37.889999	60.270000	309.769989	1940.450001	0.013385	0.015406
360	2021-01-07	54.279999	27.600000	39.430000	60.799999	312.799988	2017.119984	0.039511	0.009781
361	2021-01-08	58.919998	25.565001	38.310001	61.099998	314.010010	2025.419975	0.004115	0.003868
366	2021-01-15	56.270000	23.340000	38.130001	61.259998	315.200012	1993.319981	-0.015849	0.003790
367	2021-01-19	58.439999	23.760000	37.730000	61.270000	315.200012	2006.689999	0.006707	0.000000

Complete Portfolio Overview using Dash and Plotly

from plotly.subplots import make_subplots
import plotly.graph_objects as go
import plotly.express as px

import dash  # (version 1.12.0) pip install dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import datetime

# app = dash.Dash(__name__)

fig = make_subplots(
    rows=1, cols=2,
    specs=[[{"type": "xy"}, {"type": "domain"}]])


fig.add_trace(go.Bar(y=total_portfolio, x=stock_names_and_total, name='', showlegend=False,
                    text = total_portfolio_str, textposition='outside'),
              row=1, col=1)

fig.update_yaxes(range=[0,sum(total)+500], row=1, col=1)

fig.add_trace(go.Pie(values=total, labels=stock_names, hole=.3, showlegend=True,
                     text=total_str, name='Stock', titleposition='middle center', 
                     customdata=shares,
                     hovertemplate = "%{label}: <br>Size: %{percent} </br> %{text} </br> Shares: %{customdata}",
                     textfont=dict(
                         #family="sans serif",
                         size=14,
                         #color="LightSeaGreen"
                     )
                    ),
              row=1, col=2)

fig.update_layout(title_text='PORTFOLIO OVERVIEW', showlegend=True)

fig.show()

fig2 = make_subplots(
    rows=1, cols=2,
    specs=[[{"type": "xy"}, {"type": "xy"}]])

fig2.add_trace(go.Scatter(x=stocks_close_dropna["Date"], 
                         y=stocks_close_dropna['Portfolio Return'], 
                         name='Portfolio Return'), row=1, col=1)

fig2.add_trace(go.Scatter(x=stocks_close_dropna["Date"], 
                         y=stocks_close_dropna['Market Return'], 
                         name='Market Return'), row=1, col=1)
                              
fig2.add_trace(go.Bar(name='Initial Investment', x=myportfolio['Stocks'], 
                      y=myportfolio['Initial Purchase per Stock'],
                      text=initial_purchase_str, textposition='outside', 
                      showlegend=True), row=1, col=2) #marker_color='midnightblue')

fig2.add_trace(go.Bar(name='Current Value', x=myportfolio['Stocks'], 
                      y=myportfolio['Current Value'], 
                      text=['$'+str(round(i)) for i in total_portfolio], textposition='outside', 
                      showlegend=True), row=1, col=2) #marker_color=' darkred')

fig2.update_yaxes(range=[0,max(total)+100], row=1, col=2)

fig2.update_layout(height=500, title_text='PORTFOLIO vs MARKET (S&P 500) RETURNS', showlegend=True)

fig2.show()

app.layout = html.Div(children=[
    html.H1(children='My Portfolio Dash Visualization'),

    html.Div(children='''
        Dash: A web application framework for Python.
    '''),

    dcc.Graph(
        id='example-graph',
        figure=fig
    ),

    dcc.Graph(
        id='example-graph2',
        figure=fig2
    )
])

if __name__ == '__main__':
    app.run_server(debug=True, use_reloader=False)

Dash is running on http://127.0.0.1:8050/

Dash is running on http://127.0.0.1:8050/

Dash is running on http://127.0.0.1:8050/

Dash is running on http://127.0.0.1:8050/

Dash is running on http://127.0.0.1:8050/

Dash is running on http://127.0.0.1:8050/

Dash is running on http://127.0.0.1:8050/

Dash is running on http://127.0.0.1:8050/

 * Serving Flask app "__main__" (lazy loading)
 * Environment: production
   WARNING: This is a development server. Do not use it in a production deployment.
   Use a production WSGI server instead.
 * Debug mode: on

Individual Graphs

Generate a Time Graph comparing Portfolio with S&P500

import plotly.express as px

fig = px.line(stocks_close_dropna, x="Date", y=['Portfolio Return', 'Market Return'],
              title='Portfolio vs Market(S&P 500)')

fig.update_xaxes(
    rangeslider_visible=True,
    rangeselector=dict(
        buttons=list([
            dict(count=1, label="1m", step="month", stepmode="backward"),
            dict(count=6, label="6m", step="month", stepmode="backward"),
            dict(count=1, label="YTD", step="year", stepmode="todate"),
            dict(count=1, label="1y", step="year", stepmode="backward"),
            dict(step="all")
        ])
    )
)
fig.show()

Create a Bar chart with Portfolio Evolution

import plotly.graph_objects as go

fig = go.Figure(data=[
    go.Bar(name='Initial Investment', x=myportfolio['Stocks'], 
           y=myportfolio['Initial Purchase per Stock']), #marker_color='midnightblue'),
    go.Bar(name='Current Value', x=myportfolio['Stocks'], 
           y=myportfolio['Current Value']) #marker_color=' darkred')
])

# Change the bar mode
fig.update_layout(title='Portfolio Evolution', barmode='group')
fig.show()