Libraries Installation

In [31]:

!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 [33]:

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

Setup portfolio

In [50]:

amount_to_invest = 50_000.0
assets = ["PETR3.SA", "CMIG3.SA", "CPLE6.SA", "RANI3.SA", "TAEE11.SA", "FESA4.SA", "UNIP6.SA"]
weights = [32.5183, 18.4078, 12.6776, 11.1030, 10.3605, 9.1889, 5.7439]

In [51]:

if sum(weights) != 100.0:
    sum(weights)

In [52]:

buy_orders = {
    asset: (weights / 100) * amount_to_invest
    for asset, weights in zip(assets, weights)
}

In [53]:

buy_orders

Out [53]:

{'PETR3.SA': 16259.150000000003,
 'CMIG3.SA': 9203.900000000001,
 'CPLE6.SA': 6338.8,
 'RANI3.SA': 5551.5,
 'TAEE11.SA': 5180.25,
 'FESA4.SA': 4594.45,
 'UNIP6.SA': 2871.95}

In [54]:

if sum(buy_orders.values()) != amount_to_invest:
    sum(buy_orders.values())

In [55]:

assets = list(buy_orders.keys())

In [56]:

assets

Out [56]:

['PETR3.SA',
 'CMIG3.SA',
 'CPLE6.SA',
 'RANI3.SA',
 'TAEE11.SA',
 'FESA4.SA',
 'UNIP6.SA']

In [57]:

total_invested = sum(buy_orders.values())

In [58]:

total_invested

Out [58]:

50000.0

Import financial assets data

In [59]:

start = "2017-02-01"

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

Dummy portfolio simulation

In [60]:

first_line = assets_history_price.iloc[0]
first_line

Out [60]:

CMIG3.SA      4.455539
CPLE6.SA      1.778329
FESA4.SA      6.051053
PETR3.SA      6.434494
RANI3.SA      2.162255
TAEE11.SA    11.382806
UNIP6.SA      2.045254
Name: 2017-02-01 00:00:00, dtype: float64

In [61]:

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

In [62]:

number_of_shares = round(buy_orders_df / first_line, 0)

In [63]:

net_worth = assets_history_price * number_of_shares

In [64]:

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

In [65]:

net_worth

Out [65]:

	CMIG3.SA	CPLE6.SA	FESA4.SA	PETR3.SA	RANI3.SA	TAEE11.SA	UNIP6.SA	Total net worth
Date
2017-02-01	9205.144042	6337.965883	4592.748901	16259.967590	5550.509322	5179.176650	2871.536605	49997.048993
2017-02-02	9615.273934	6465.322914	4614.160777	16038.055955	5550.509322	5222.037277	2855.359305	50360.719485
2017-02-03	9906.921354	6517.062172	4603.454839	16481.878019	5550.509322	5231.563163	2851.314646	51142.703515
2017-02-06	9788.441837	6415.574983	4571.338111	16108.668256	5530.758809	5164.888473	2891.758229	50471.428698
2017-02-07	9742.872944	6495.171587	4603.454839	15906.927777	5550.509322	5131.551127	2871.536605	50302.024201
...	...	...	...	...	...	...	...	...
2023-08-04	39171.358109	30579.119728	38784.898842	83087.762699	27004.221810	16191.813316	114720.838715	349540.013219
2023-08-07	38572.220158	30757.320408	37570.500000	83643.696144	28085.397628	16125.270538	113007.957001	347762.361876
2023-08-08	39150.701576	30792.961224	36963.300579	83593.164627	27312.880881	16066.050625	112320.000000	346199.059512
2023-08-09	40183.701576	30935.521088	36750.778610	84553.417686	27595.250000	15783.949375	112221.720428	348024.338764
2023-08-11	40948.119370	32325.478912	34299.208958	84578.693085	27466.899510	15847.650833	108725.763428	344191.814095

1625 rows × 8 columns

Comparison with IBOV

In [66]:

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

In [67]:

ibov_df = pd.DataFrame(ibov)

In [68]:

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

In [69]:

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

In [70]:

comparison

Out [70]:

	IBOV	CMIG3.SA	CPLE6.SA	FESA4.SA	PETR3.SA	RANI3.SA	TAEE11.SA	UNIP6.SA	Total net worth
Date
2017-02-01	64836.000000	9205.144042	6337.965883	4592.748901	16259.967590	5550.509322	5179.176650	2871.536605	49997.048993
2017-02-02	64578.000000	9615.273934	6465.322914	4614.160777	16038.055955	5550.509322	5222.037277	2855.359305	50360.719485
2017-02-03	64954.000000	9906.921354	6517.062172	4603.454839	16481.878019	5550.509322	5231.563163	2851.314646	51142.703515
2017-02-06	63993.000000	9788.441837	6415.574983	4571.338111	16108.668256	5530.758809	5164.888473	2891.758229	50471.428698
2017-02-07	64199.000000	9742.872944	6495.171587	4603.454839	15906.927777	5550.509322	5131.551127	2871.536605	50302.024201
...	...	...	...	...	...	...	...	...	...
2023-08-04	119508.000000	39171.358109	30579.119728	38784.898842	83087.762699	27004.221810	16191.813316	114720.838715	349540.013219
2023-08-07	119380.000000	38572.220158	30757.320408	37570.500000	83643.696144	28085.397628	16125.270538	113007.957001	347762.361876
2023-08-08	119090.000000	39150.701576	30792.961224	36963.300579	83593.164627	27312.880881	16066.050625	112320.000000	346199.059512
2023-08-09	118409.000000	40183.701576	30935.521088	36750.778610	84553.417686	27595.250000	15783.949375	112221.720428	348024.338764
2023-08-11	118065.140625	40948.119370	32325.478912	34299.208958	84578.693085	27466.899510	15847.650833	108725.763428	344191.814095

1617 rows × 9 columns

Data normalization (data scaling)

In [71]:

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

In [72]:

comparison_scaling

Out [72]:

	IBOV	CMIG3.SA	CPLE6.SA	FESA4.SA	PETR3.SA	RANI3.SA	TAEE11.SA	UNIP6.SA	Total net worth
Date
2017-02-01	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
2017-02-02	0.996021	1.044554	1.020094	1.004662	0.986352	1.000000	1.008276	0.994366	1.007274
2017-02-03	1.001820	1.076238	1.028258	1.002331	1.013648	1.000000	1.010115	0.992958	1.022914
2017-02-06	0.986998	1.063367	1.012245	0.995338	0.990695	0.996442	0.997241	1.007042	1.009488
2017-02-07	0.990175	1.058416	1.024804	1.002331	0.978288	1.000000	0.990804	1.000000	1.006100
...	...	...	...	...	...	...	...	...	...
2023-08-04	1.843235	4.255377	4.824753	8.444812	5.109959	4.865179	3.126330	39.951028	6.991213
2023-08-07	1.841261	4.190290	4.852869	8.180395	5.144149	5.059968	3.113481	39.354524	6.955658
2023-08-08	1.836788	4.253133	4.858493	8.048187	5.141041	4.920788	3.102047	39.114946	6.924390
2023-08-09	1.826285	4.365353	4.880986	8.001913	5.200098	4.971661	3.047579	39.080721	6.960898
2023-08-11	1.820981	4.448395	5.100292	7.468122	5.201652	4.948537	3.059878	37.863269	6.884243

1616 rows × 9 columns

In [73]:

comparison_scaling.plot();

In [74]:

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

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