Libraries Installation

In [73]:

!pip install yfinance -q
!pip install pandas -q
!pip install numpy -q
!pip install matplotlib -q

[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m23.0.1[0m[39;49m -> [0m[32;49m23.2.1[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m

[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m23.0.1[0m[39;49m -> [0m[32;49m23.2.1[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m

[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m23.0.1[0m[39;49m -> [0m[32;49m23.2.1[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m

[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m A new release of pip is available: [0m[31;49m23.0.1[0m[39;49m -> [0m[32;49m23.2.1[0m
[1m[[0m[34;49mnotice[0m[1;39;49m][0m[39;49m To update, run: [0m[32;49mpip install --upgrade pip[0m

In [50]:

import yfinance as yf
import pandas as pd
import numpy as np

Setup portfolio

In [ ]:

In [33]:

buy_orders = {
    "VALE3.SA": 1400,
    "WEGE3.SA": 1300,
    "BPAC11.SA": 1100,
    "KNRI11.SA": 1000,
    "SMAL11.SA": 800,
    "AAPL34.SA": 700,
    "IVVB11.SA": 500,
    "PETR4.SA": 1000,
    "COCA34.SA": 1000,
}

In [34]:

assets = list(buy_orders.keys())

In [35]:

assets

Out [35]:

['VALE3.SA',
 'WEGE3.SA',
 'BPAC11.SA',
 'KNRI11.SA',
 'SMAL11.SA',
 'AAPL34.SA',
 'IVVB11.SA',
 'PETR4.SA',
 'COCA34.SA']

In [36]:

total_invested = sum(buy_orders.values())

In [37]:

total_invested

Out [37]:

Import financial assets data

In [38]:

start = "2020-01-01"

assets_history_price = yf.download(assets, start=start, progress=False)["Adj Close"]

Dummy portfolio simulation

In [47]:

first_line = assets_history_price.iloc[0]
first_line

Out [47]:

AAPL34.SA     14.815145
BPAC11.SA     17.714905
COCA34.SA     34.120262
IVVB11.SA    140.600006
KNRI11.SA    178.510574
PETR4.SA      12.180102
SMAL11.SA    139.500000
VALE3.SA      39.578941
WEGE3.SA      16.768709
Name: 2020-01-02 00:00:00, dtype: float64

In [42]:

buy_orders_df = pd.Series(data = buy_orders, index=assets)

In [52]:

number_of_shares = round(buy_orders_df / first_line, 0)

In [56]:

net_worth = assets_history_price * number_of_shares

In [58]:

net_worth["Total net worth"] = net_worth.sum(axis=1)

In [59]:

net_worth

Out [59]:

	AAPL34.SA	BPAC11.SA	COCA34.SA	IVVB11.SA	KNRI11.SA	PETR4.SA	SMAL11.SA	VALE3.SA	WEGE3.SA	Total net worth
Date
2020-01-02	696.311793	1098.324097	989.487602	562.400024	1071.063446	998.768393	837.000000	1385.262947	1307.959316	8946.577619
2020-01-03	697.689195	1101.511440	975.998394	562.799988	1071.063446	990.635223	845.399963	1375.064182	1291.256996	8911.418828
2020-01-06	701.132250	1099.917828	978.863392	564.799988	1062.370239	1002.347118	834.600037	1366.905117	1294.597252	8905.533220
2020-01-07	702.853845	1104.306175	978.863392	565.200012	1066.501923	998.443075	836.399963	1376.848869	1304.618465	8934.035720
2020-01-08	708.764479	1094.519222	978.378849	566.200012	1006.133972	992.261967	829.199982	1377.103748	1255.254627	8807.816858
...	...	...	...	...	...	...	...	...	...	...
2023-08-02	2173.280079	2059.019943	1445.940018	960.799988	962.399963	2503.460056	678.000000	2348.149872	3122.339905	16253.389824
2023-08-03	2201.949928	2070.179962	1457.830013	979.000000	971.100037	2535.440006	677.880020	2362.850075	3153.540024	16409.770065
2023-08-04	2086.800072	2042.900047	1423.320053	966.799988	972.120026	2460.000000	683.099991	2364.599915	3186.299881	16185.939972
2023-08-07	NaN	NaN	NaN	979.960022	NaN	NaN	680.400009	NaN	NaN	1660.360031
2023-08-08	2061.420029	2055.919991	1438.690018	972.119995	985.019989	2487.059994	679.679993	2357.600021	3156.660095	16194.170124

896 rows × 10 columns

Comparison with IBOV

In [88]:

ibov = yf.download("^BVSP", start=start, progress=False)["Adj Close"]

In [89]:

ibov_df = pd.DataFrame(ibov)

In [90]:

ibov_df.rename(columns={"Adj Close": "IBOV"}, inplace=True)

In [98]:

comparison = pd.merge(ibov_df, net_worth, how = "inner", on = "Date")

In [101]:

comparison

Out [101]:

	IBOV	AAPL34.SA	BPAC11.SA	COCA34.SA	IVVB11.SA	KNRI11.SA	PETR4.SA	SMAL11.SA	VALE3.SA	WEGE3.SA	Total net worth
Date
2020-01-02	118573.000000	696.311793	1098.324097	989.487602	562.400024	1071.063446	998.768393	837.000000	1385.262947	1307.959316	8946.577619
2020-01-03	117707.000000	697.689195	1101.511440	975.998394	562.799988	1071.063446	990.635223	845.399963	1375.064182	1291.256996	8911.418828
2020-01-06	116878.000000	701.132250	1099.917828	978.863392	564.799988	1062.370239	1002.347118	834.600037	1366.905117	1294.597252	8905.533220
2020-01-07	116662.000000	702.853845	1104.306175	978.863392	565.200012	1066.501923	998.443075	836.399963	1376.848869	1304.618465	8934.035720
2020-01-08	116247.000000	708.764479	1094.519222	978.378849	566.200012	1006.133972	992.261967	829.199982	1377.103748	1255.254627	8807.816858
...	...	...	...	...	...	...	...	...	...	...	...
2023-08-01	121248.000000	2201.949928	2095.599953	1429.699978	970.599976	968.339996	2509.200031	678.539978	2386.999893	3109.080048	16350.009781
2023-08-02	120859.000000	2173.280079	2059.019943	1445.940018	960.799988	962.399963	2503.460056	678.000000	2348.149872	3122.339905	16253.389824
2023-08-03	120586.000000	2201.949928	2070.179962	1457.830013	979.000000	971.100037	2535.440006	677.880020	2362.850075	3153.540024	16409.770065
2023-08-04	119508.000000	2086.800072	2042.900047	1423.320053	966.799988	972.120026	2460.000000	683.099991	2364.599915	3186.299881	16185.939972
2023-08-08	119163.382812	2061.420029	2055.919991	1438.690018	972.119995	985.019989	2487.059994	679.679993	2357.600021	3156.660095	16194.170124

894 rows × 11 columns

Data normalization (data scaling)

In [99]:

comparison = comparison.dropna()
comparison_scaling = comparison / comparison.iloc[0]

In [100]:

comparison_scaling

Out [100]:

	IBOV	AAPL34.SA	BPAC11.SA	COCA34.SA	IVVB11.SA	KNRI11.SA	PETR4.SA	SMAL11.SA	VALE3.SA	WEGE3.SA	Total net worth
Date
2020-01-02	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2020-01-03	0.992696	1.001978	1.002902	0.986367	1.000711	1.000000	0.991857	1.010036	0.992638	0.987230	0.996070
2020-01-06	0.985705	1.006923	1.001451	0.989263	1.004267	0.991884	1.003583	0.997133	0.986748	0.989784	0.995412
2020-01-07	0.983883	1.009395	1.005447	0.989263	1.004979	0.995741	0.999674	0.999283	0.993926	0.997446	0.998598
2020-01-08	0.980383	1.017884	0.996536	0.988773	1.006757	0.939378	0.993486	0.990681	0.994110	0.959705	0.984490
...	...	...	...	...	...	...	...	...	...	...	...
2023-08-01	1.022560	3.162305	1.907998	1.444889	1.725818	0.904092	2.512294	0.810681	1.723138	2.377046	1.827516
2023-08-02	1.019279	3.121131	1.874692	1.461302	1.708393	0.898546	2.506547	0.810036	1.695093	2.387184	1.816716
2023-08-03	1.016977	3.162305	1.884853	1.473318	1.740754	0.906669	2.538567	0.809892	1.705705	2.411038	1.834195
2023-08-04	1.007885	2.996933	1.860016	1.438442	1.719061	0.907621	2.463033	0.816129	1.706968	2.436085	1.809177
2023-08-08	1.004979	2.960484	1.871870	1.453975	1.728521	0.919665	2.490127	0.812043	1.701915	2.413424	1.810097

894 rows × 11 columns

In [102]:

comparison_scaling.plot();

In [104]:

comparison_scaling[["IBOV", "Total net worth"]].plot();