The Trading Strategy using Moving Averages 9x21 🐍

Introduction

The objective of this code is to be able to reduce the work of analysing a large number of stocks. The idea is that the investor already have selected a list of stocks he wants to analyse, and he will use this code to decide which ones to buy, to hold or to stop ("close the position"). He can and should combine Fundamental Analysis and Technical Analysis.

He should use Fundamental Analysis to generate the list of companies he would like to invest on - this part is not developed as a code in this Jupyter NoteBook (JNB), but is briefly discussed below.

Fundamental Analysis

The idea is to get a list of companies that are solid and could be interesting to hold for some time. In the beginning of the code, we provide a list of stocks traded in the Brazilian Market (called B3) based on the IDIV Index. This index gathers stocks that pay good dividends and have good liquidity. It's composition can be downloaded at B3 site: https://www.b3.com.br/pt_br/market-data-e-indices/indices/indices-de-segmentos-e-setoriais/indice-dividendos-idiv-composicao-da-carteira.htm

Therefore, using the IDIV is a good way to select good companies and then we can use Technical Analysis to decide WHEN to: buy, hold or stop.

Technical Analysis

The list will be be tested by a classic Technical Indicator: the combination of two Moving Averages (MAs). In this code I use the MAs of 9 and 21 days, as it is a classic. But the code can be easily adapted for any other combination of MAs.

Why using MAs?

MAs are used to identify trends. The idea is that if the "fast" one (9 periods) crosses the "slow" one (21 periods) upwards, the stock is in a bullish trend, so we should buy it; if it crosses downwards, we should sell it (in this case we will use it as a STOP sign, we should "close the position" (that is, sell all the stocks we have). We could also open a short position on that specific stock, however we are not this risk-taker and, due to liquidity issues in the Brazilian market, that might be not a good idea.

Hence, the idea is that this code should be run daily. </p> It will give you the list of stocks classified in 4 cases:

1. if MA9 crossed MA21 up on the previous day: buy
2. if MA9 is higher than MA21 but it was already higher on the day before: hold (if you already bought it before, otherwise wait)
3. if MA9 crossed MA21 down: sell (if you already bought it before, otherwise wait) obs: If we sell above the price we bought, it is considered "Take Profit" (TP), if we sell below the price we paid, it is considered "Stop Loss" (SL).
4. if MA9 is lower than MA21 and it was already lower on the day before: wait (you should not have a stock in this classification in your portfolio) (for more on this topic: https://www.daytradetheworld.com/trading-blog/alexander-elder-trade-market-beginners-2/#5_-_Create_a_money_Management_Plan)

As we can see, the movement of crossing is used as signal for buying or selling. However, I think it should not be used alone, you should combine it with a few more technical indicators. I use this code to reduce the number of stocks I will analyse - I open a full Graph of the stocks in situation i) and check a few other indicators, as the RSI, for example (I like to use TradingView website for it). Then I decide to buy it or not.

However, it is strongly advised to use iii) as a STOP signal!

Stocks in situation ii) are more complicated - you can chose to use another criteria to make a partial TP. That could be a RSI > 70, for example. Stocks in situation iv): if you have time you can check on them, maybe one of them is "giving" a signal of reversing the tendency (for example: a hammer candle) that the criteria MA9x21 will not identify. However, if you buy a stock classified on "wait" it is a riskier trade and you should define another criteria for SL - as MA9 is already below MA21 this code will not give you a SL signal.

# importing libraries we'll need to get and treat data
import yfinance as yf
import pandas as pd

# creating the list of tickers: IDIV index as of 23/mar/22
idiv = ['ABCB4.SA', 'BRSR6.SA', 'BBSE3.SA', 'BBDC3.SA', 'BBDC4.SA', 'BRAP4.SA', 'BBAS3.SA', 'AGRO3.SA', 'CCRO3.SA', 'CMIG3.SA', 'CMIG4.SA', 'CESP6.SA', 'CSMG3.SA', 'CPLE3.SA', 'CPLE6.SA', 'CPFE3.SA', 'CYRE3.SA', 'DIRR3.SA', 'ELET3.SA', 'ELET6.SA', 'ENAT3.SA', 'ENBR3.SA', 'EGIE3.SA', 'ROMI3.SA', 'MYPK3.SA', 'ITSA4.SA', 'ITUB3.SA', 'ITUB4.SA', 'JHSF3.SA', 'MRVE3.SA', 'PSSA3.SA', 'QUAL3.SA', 'SAPR4.SA', 'SANB11.SA', 'CSNA3.SA', 'SYNE3.SA', 'TAEE11.SA', 'TGMA3.SA', 'VIVT3.SA', 'TRPL4.SA', 'UNIP6.SA', 'WIZS3.SA']

# getting the quotes for the list and saving it as a Pandas dataframe (df)
# we will get just the "Adj Close" because that is the one used by Technical Indicators / to build Graphs
dfidiv = yf.download(idiv, start="2022-01-01", end = "2022-03-22")["Adj Close"]
# checking the df
dfidiv.head()

[*********************100%***********************]  42 of 42 completed

	ABCB4.SA	AGRO3.SA	BBAS3.SA	BBDC3.SA	BBDC4.SA	BBSE3.SA	BRAP4.SA	BRSR6.SA	CCRO3.SA	CESP6.SA	...	ROMI3.SA	SANB11.SA	SAPR4.SA	SYNE3.SA	TAEE11.SA	TGMA3.SA	TRPL4.SA	UNIP6.SA	VIVT3.SA	WIZS3.SA
Date
2022-01-03	15.55	28.360001	28.264404	16.500881	19.636387	19.813507	24.650000	9.202440	11.44	20.889999	...	16.920689	29.763933	3.68	7.26	36.480000	14.74	24.230000	101.699997	47.707657	7.66
2022-01-04	15.74	27.400000	28.293827	16.577982	19.765133	19.765415	24.879999	9.289621	11.43	20.370001	...	15.979017	30.192051	3.65	7.30	36.660000	13.97	24.260000	101.959999	47.707657	7.37
2022-01-05	15.02	27.490000	27.823082	16.468193	19.625380	19.524960	24.940001	9.124947	11.26	19.990000	...	15.684743	29.958532	3.61	6.93	36.290001	13.31	23.900000	96.430000	47.647785	7.10
2022-01-06	15.25	28.270000	28.048647	16.647846	19.904888	19.659616	25.650000	9.183066	11.01	19.670000	...	15.106006	30.201782	3.51	6.70	36.209999	13.03	23.790001	94.410004	46.879433	7.12
2022-01-07	15.36	28.059999	28.078068	16.717712	20.194376	19.044050	26.660000	9.134633	11.26	19.750000	...	15.164861	30.299080	3.56	6.76	36.240002	13.06	23.660000	95.449997	46.560116	7.14

5 rows × 42 columns

# checking if we got all quotes for all the tickers on the list
print(len(idiv))
dfidiv.shape

42

(56, 42)

# ok! 42 tickers on the list and 45 columns on the df

dfidiv.tail()

	ABCB4.SA	AGRO3.SA	BBAS3.SA	BBDC3.SA	BBDC4.SA	BBSE3.SA	BRAP4.SA	BRSR6.SA	CCRO3.SA	CESP6.SA	...	ROMI3.SA	SANB11.SA	SAPR4.SA	SYNE3.SA	TAEE11.SA	TGMA3.SA	TRPL4.SA	UNIP6.SA	VIVT3.SA	WIZS3.SA
Date
2022-03-16	16.230000	32.090000	33.480000	17.410000	21.100000	22.799999	33.580002	10.326107	12.36	22.100000	...	12.528887	34.150002	3.97	6.24	41.349998	15.02	24.730000	104.760002	51.490002	7.62
2022-03-17	16.400000	33.200001	33.790001	17.540001	21.180000	23.299999	34.619999	10.310000	12.78	22.969999	...	13.170635	34.750000	3.99	6.45	41.750000	14.85	25.049999	106.400002	52.250000	7.80
2022-03-18	16.879999	33.500000	33.720001	17.500000	21.129999	23.340000	35.209999	10.300000	12.85	23.510000	...	13.555683	34.980000	3.97	6.76	42.419998	15.54	25.370001	107.400002	53.049999	8.16
2022-03-21	16.700001	33.730000	34.410000	17.830000	21.629999	23.639999	36.439999	10.490000	12.81	23.600000	...	13.220000	35.919998	3.99	6.90	42.430000	14.92	25.719999	107.559998	53.369999	8.38
2022-03-22	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

5 rows × 42 columns

# deleting the null lines
dfidiv = dfidiv.dropna(axis = 0, how ='any')
# and visualizing the last lines
dfidiv.tail()

	ABCB4.SA	AGRO3.SA	BBAS3.SA	BBDC3.SA	BBDC4.SA	BBSE3.SA	BRAP4.SA	BRSR6.SA	CCRO3.SA	CESP6.SA	...	ROMI3.SA	SANB11.SA	SAPR4.SA	SYNE3.SA	TAEE11.SA	TGMA3.SA	TRPL4.SA	UNIP6.SA	VIVT3.SA	WIZS3.SA
Date
2022-03-15	15.720000	32.080002	33.040001	17.120001	20.799999	22.459999	32.810001	10.045190	11.78	22.500000	...	12.094474	33.290001	3.92	6.40	41.009998	14.52	24.590000	101.430000	50.869999	7.23
2022-03-16	16.230000	32.090000	33.480000	17.410000	21.100000	22.799999	33.580002	10.326107	12.36	22.100000	...	12.528887	34.150002	3.97	6.24	41.349998	15.02	24.730000	104.760002	51.490002	7.62
2022-03-17	16.400000	33.200001	33.790001	17.540001	21.180000	23.299999	34.619999	10.310000	12.78	22.969999	...	13.170635	34.750000	3.99	6.45	41.750000	14.85	25.049999	106.400002	52.250000	7.80
2022-03-18	16.879999	33.500000	33.720001	17.500000	21.129999	23.340000	35.209999	10.300000	12.85	23.510000	...	13.555683	34.980000	3.97	6.76	42.419998	15.54	25.370001	107.400002	53.049999	8.16
2022-03-21	16.700001	33.730000	34.410000	17.830000	21.629999	23.639999	36.439999	10.490000	12.81	23.600000	...	13.220000	35.919998	3.99	6.90	42.430000	14.92	25.719999	107.559998	53.369999	8.38

5 rows × 42 columns

dfidiv.shape

(54, 42)

# so we cut 2 lines, before it was 56 lines...

# we'll make a copy of this df in order to keep the original if we need to check anything...
# the copy will be used to calculate the MAs 9 and 21
dfidivb = dfidiv.copy()
dfidivb.head()

	ABCB4.SA	AGRO3.SA	BBAS3.SA	BBDC3.SA	BBDC4.SA	BBSE3.SA	BRAP4.SA	BRSR6.SA	CCRO3.SA	CESP6.SA	...	ROMI3.SA	SANB11.SA	SAPR4.SA	SYNE3.SA	TAEE11.SA	TGMA3.SA	TRPL4.SA	UNIP6.SA	VIVT3.SA	WIZS3.SA
Date
2022-01-03	15.55	28.360001	28.264404	16.500881	19.636387	19.813507	24.650000	9.202440	11.44	20.889999	...	16.920689	29.763933	3.68	7.26	36.480000	14.74	24.230000	101.699997	47.707657	7.66
2022-01-04	15.74	27.400000	28.293827	16.577982	19.765133	19.765415	24.879999	9.289621	11.43	20.370001	...	15.979017	30.192051	3.65	7.30	36.660000	13.97	24.260000	101.959999	47.707657	7.37
2022-01-05	15.02	27.490000	27.823082	16.468193	19.625380	19.524960	24.940001	9.124947	11.26	19.990000	...	15.684743	29.958532	3.61	6.93	36.290001	13.31	23.900000	96.430000	47.647785	7.10
2022-01-06	15.25	28.270000	28.048647	16.647846	19.904888	19.659616	25.650000	9.183066	11.01	19.670000	...	15.106006	30.201782	3.51	6.70	36.209999	13.03	23.790001	94.410004	46.879433	7.12
2022-01-07	15.36	28.059999	28.078068	16.717712	20.194376	19.044050	26.660000	9.134633	11.26	19.750000	...	15.164861	30.299080	3.56	6.76	36.240002	13.06	23.660000	95.449997	46.560116	7.14

5 rows × 42 columns

dfidivb.tail()

	ABCB4.SA	AGRO3.SA	BBAS3.SA	BBDC3.SA	BBDC4.SA	BBSE3.SA	BRAP4.SA	BRSR6.SA	CCRO3.SA	CESP6.SA	...	ROMI3.SA	SANB11.SA	SAPR4.SA	SYNE3.SA	TAEE11.SA	TGMA3.SA	TRPL4.SA	UNIP6.SA	VIVT3.SA	WIZS3.SA
Date
2022-03-15	15.720000	32.080002	33.040001	17.120001	20.799999	22.459999	32.810001	10.045190	11.78	22.500000	...	12.094474	33.290001	3.92	6.40	41.009998	14.52	24.590000	101.430000	50.869999	7.23
2022-03-16	16.230000	32.090000	33.480000	17.410000	21.100000	22.799999	33.580002	10.326107	12.36	22.100000	...	12.528887	34.150002	3.97	6.24	41.349998	15.02	24.730000	104.760002	51.490002	7.62
2022-03-17	16.400000	33.200001	33.790001	17.540001	21.180000	23.299999	34.619999	10.310000	12.78	22.969999	...	13.170635	34.750000	3.99	6.45	41.750000	14.85	25.049999	106.400002	52.250000	7.80
2022-03-18	16.879999	33.500000	33.720001	17.500000	21.129999	23.340000	35.209999	10.300000	12.85	23.510000	...	13.555683	34.980000	3.97	6.76	42.419998	15.54	25.370001	107.400002	53.049999	8.16
2022-03-21	16.700001	33.730000	34.410000	17.830000	21.629999	23.639999	36.439999	10.490000	12.81	23.600000	...	13.220000	35.919998	3.99	6.90	42.430000	14.92	25.719999	107.559998	53.369999	8.38

5 rows × 42 columns

# In this step we will calculate de Mas 9 and 21 and their difference (MA09 minus MA21), and will 
# replace the value of every cell with this difference (this will generate null values for the first 21 lines)
for (columnName) in dfidivb:
    dfidivb[columnName] = (dfidivb[columnName].rolling(9).mean()) - (dfidivb[columnName].rolling(21).mean())

# having a look at this new df
dfidivb.head(25)

	ABCB4.SA	AGRO3.SA	BBAS3.SA	BBDC3.SA	BBDC4.SA	BBSE3.SA	BRAP4.SA	BRSR6.SA	CCRO3.SA	CESP6.SA	...	ROMI3.SA	SANB11.SA	SAPR4.SA	SYNE3.SA	TAEE11.SA	TGMA3.SA	TRPL4.SA	UNIP6.SA	VIVT3.SA	WIZS3.SA
Date
2022-01-03	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-04	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-05	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-06	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-07	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-10	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-11	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-12	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-13	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-14	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-17	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-18	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-19	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-20	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-21	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-24	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-25	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-26	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-27	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-28	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2022-01-31	0.100635	-0.428730	1.410994	0.603307	0.851148	0.897241	0.490635	0.522625	0.573492	0.669365	...	0.025846	0.651979	0.150635	0.156825	0.880793	0.414603	0.179206	-2.365397	0.787359	0.283333
2022-02-01	0.093651	-0.356825	1.408348	0.626409	0.896038	1.040751	0.240159	0.508018	0.673651	0.591905	...	0.166443	0.654661	0.168730	0.199841	0.923174	0.607143	0.178254	-1.442699	0.803832	0.393492
2022-02-02	0.119206	-0.333968	1.352774	0.642217	0.923205	1.178154	0.064286	0.478189	0.701905	0.479524	...	0.221717	0.688563	0.166667	0.222222	0.880634	0.650794	0.187619	-0.464921	0.846756	0.442698
2022-02-03	0.126508	-0.268730	1.288482	0.679269	0.989051	1.304564	-0.033016	0.429140	0.699365	0.404921	...	0.227789	0.696797	0.172698	0.243333	0.802222	0.657143	0.161428	0.200793	0.930544	0.465873
2022-02-04	0.149682	-0.070793	1.214850	0.707452	1.028920	1.398151	0.030476	0.380706	0.675555	0.379365	...	0.204901	0.761500	0.166508	0.247778	0.683174	0.678730	0.129524	0.837777	0.981546	0.486508

25 rows × 42 columns

dfidivb.tail()

	ABCB4.SA	AGRO3.SA	BBAS3.SA	BBDC3.SA	BBDC4.SA	BBSE3.SA	BRAP4.SA	BRSR6.SA	CCRO3.SA	CESP6.SA	...	ROMI3.SA	SANB11.SA	SAPR4.SA	SYNE3.SA	TAEE11.SA	TGMA3.SA	TRPL4.SA	UNIP6.SA	VIVT3.SA	WIZS3.SA
Date
2022-03-15	-0.312381	1.452222	-0.554421	-0.326853	-0.291391	-0.367302	2.353016	-0.071652	-0.449048	0.005556	...	-1.041997	0.002857	0.010952	0.046508	1.261747	0.073175	0.318889	1.667460	-0.014605	-0.334127
2022-03-16	-0.235714	1.125714	-0.708029	-0.289761	-0.224869	-0.399207	2.329683	-0.034903	-0.367619	-0.051111	...	-1.069266	0.226826	-0.003016	0.022381	1.250000	0.047619	0.262540	2.053809	0.201743	-0.272222
2022-03-17	-0.122064	0.879048	-0.739355	-0.201564	-0.086285	-0.311429	2.180794	-0.003790	-0.224762	-0.057460	...	-1.008931	0.590000	-0.017937	-0.049524	1.106508	-0.006032	0.167302	2.568095	0.482537	-0.194921
2022-03-18	0.054603	0.545715	-0.540768	-0.048614	0.106742	-0.183175	1.877302	0.097109	-0.027778	-0.026826	...	-0.811940	1.051746	-0.016825	-0.066984	1.096349	0.037778	0.163333	3.309048	0.774685	-0.041905
2022-03-21	0.190635	0.468254	-0.316636	0.116868	0.315632	-0.014286	1.762857	0.163944	0.103175	0.010635	...	-0.644569	1.578730	-0.008889	-0.049048	1.121905	0.038095	0.196190	4.051429	1.083152	0.123175

5 rows × 42 columns

dfidivb.shape

(54, 42)

# deleting the null lines
dfidivb = dfidivb.dropna(axis = 0, how ='any')
# and visualizing the df
dfidivb.shape

(34, 42)

# Creating the messages that will be displayed for the tickers (regarding the 4 cases explained in the introduction of this JNB)
# The messages will be based in the values in the last 2 lines of the df (if we call the day you are checking d, they will be d-1 and d-2)
# The expresison: "d-1 > 0" will be used to represent the value on d-1 is positive, etc.
# i) if d-1 > 0  and  d-2 < 0 : BUY
# ii) if d-1 < 0  and  d-2 <0 : STOP
# iii) if d-1 > 0  and  d-2 > 0 : HOLD
# iv) if d-1 < 0  and  d-2 < 0 : WAIT

# creating functions for colored messages to be used
def prRed(skk): print("\033[91m {}\033[00m" .format(skk))
def prGreen(skk): print("\033[92m {}\033[00m" .format(skk))
def prYellow(skk): print("\033[93m {}\033[00m" .format(skk))
def prCyan(skk): print("\033[96m {}\033[00m" .format(skk))
def prLightGray(skk): print("\033[97m {}\033[00m" .format(skk))

# creating the function to classify the tickers and print the messages
def alertacolor (acao):
    if (dfidivb.at[dfidivb.index[-1],acao] > 0) and (dfidivb.at[dfidivb.index[-2],acao] < 0):
        prGreen('  Buy!  : ) ')
    elif (dfidivb.at[dfidivb.index[-1],acao] < 0) and (dfidivb.at[dfidivb.index[-2],acao] > 0):
        prRed('  Stoooop!!')
    elif (dfidivb.at[dfidivb.index[-1],acao] > 0) and (dfidivb.at[dfidivb.index[-2],acao] > 0):
        prGreen('  Keep Calm and... Hold  : )')
    else:
        prYellow('  Wait...')

# running this function for the list of tickers
for papel in idiv:
    prCyan (papel+': ') 
    alertacolor(papel)
    print(' ')

 ABCB4.SA: 
   Keep Calm and... Hold  : )
 
 BRSR6.SA: 
   Keep Calm and... Hold  : )
 
 BBSE3.SA: 
   Wait...
 
 BBDC3.SA: 
   Buy!  : ) 
 
 BBDC4.SA: 
   Keep Calm and... Hold  : )
 
 BRAP4.SA: 
   Keep Calm and... Hold  : )
 
 BBAS3.SA: 
   Wait...
 
 AGRO3.SA: 
   Keep Calm and... Hold  : )
 
 CCRO3.SA: 
   Buy!  : ) 
 
 CMIG3.SA: 
   Keep Calm and... Hold  : )
 
 CMIG4.SA: 
   Keep Calm and... Hold  : )
 
 CESP6.SA: 
   Buy!  : ) 
 
 CSMG3.SA: 
   Wait...
 
 CPLE3.SA: 
   Keep Calm and... Hold  : )
 
 CPLE6.SA: 
   Keep Calm and... Hold  : )
 
 CPFE3.SA: 
   Keep Calm and... Hold  : )
 
 CYRE3.SA: 
   Wait...
 
 DIRR3.SA: 
   Wait...
 
 ELET3.SA: 
   Wait...
 
 ELET6.SA: 
   Wait...
 
 ENAT3.SA: 
   Keep Calm and... Hold  : )
 
 ENBR3.SA: 
   Keep Calm and... Hold  : )
 
 EGIE3.SA: 
   Keep Calm and... Hold  : )
 
 ROMI3.SA: 
   Wait...
 
 MYPK3.SA: 
   Wait...
 
 ITSA4.SA: 
   Buy!  : ) 
 
 ITUB3.SA: 
   Buy!  : ) 
 
 ITUB4.SA: 
   Buy!  : ) 
 
 JHSF3.SA: 
   Wait...
 
 MRVE3.SA: 
   Wait...
 
 PSSA3.SA: 
   Wait...
 
 QUAL3.SA: 
   Buy!  : ) 
 
 SAPR4.SA: 
   Wait...
 
 SANB11.SA: 
   Keep Calm and... Hold  : )
 
 CSNA3.SA: 
   Wait...
 
 SYNE3.SA: 
   Wait...
 
 TAEE11.SA: 
   Keep Calm and... Hold  : )
 
 TGMA3.SA: 
   Keep Calm and... Hold  : )
 
 VIVT3.SA: 
   Keep Calm and... Hold  : )
 
 TRPL4.SA: 
   Keep Calm and... Hold  : )
 
 UNIP6.SA: 
   Keep Calm and... Hold  : )
 
 WIZS3.SA: 
   Buy!  : ) 
 

ok!

References

Youtube Video (in Portuguese): "PYTHON PARA INVESTIMENTOS #4: Simulando uma CARTEIRA DE AÇÕES e comparando com o IBOVESPA"

https://youtu.be/TiNLwmLN-iE?list=PLCAhGm8nJ9CBn51o0x3j1p1LuMRqpeqCy

https://www.geeksforgeeks.org/print-colors-python-terminal/

last update: 23.mar.22

to go back to my GitHub Page: https://rafsz.github.io/