ImGabreuw
722 lines
25 KiB
Plaintext
722 lines
25 KiB
Plaintext
{
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"cells": [
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"# Estratégia Magic Formula de Joel Greenblatt\n",
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"\n",
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"A Magic Formula é uma estratégia de investimento que classifica ações com base em dois critérios principais:\n",
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"\n",
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"- **Qualidade**: medida pelo Retorno sobre o Capital Investido (ROIC)\n",
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"- **Valor**: medida pelo Rendimento de Lucros (Earnings Yield)"
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],
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"id": "f0f9bbafd34ec9e"
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},
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{
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"cell_type": "code",
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"ExecuteTime": {
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"end_time": "2025-04-13T20:27:04.058395Z",
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"start_time": "2025-04-13T20:26:52.206786Z"
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}
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},
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"source": "!pip install pandas numpy requests beautifulsoup4",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/usr/bin/bash: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8): No such file or directory\r\n",
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"Collecting pandas\r\n",
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" Using cached pandas-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (89 kB)\r\n",
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"Collecting numpy\r\n",
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" Using cached numpy-2.2.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (62 kB)\r\n",
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"Collecting yfinance\r\n",
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" Using cached yfinance-0.2.55-py2.py3-none-any.whl.metadata (5.8 kB)\r\n",
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"Requirement already satisfied: requests in /home/geron/Projects/python-for-finances/.venv/lib/python3.13/site-packages (2.32.3)\r\n",
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"Requirement already satisfied: beautifulsoup4 in /home/geron/Projects/python-for-finances/.venv/lib/python3.13/site-packages (4.13.3)\r\n",
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"Requirement already satisfied: python-dateutil>=2.8.2 in /home/geron/Projects/python-for-finances/.venv/lib/python3.13/site-packages (from pandas) (2.9.0.post0)\r\n",
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"Collecting pytz>=2020.1 (from pandas)\r\n",
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" Using cached pytz-2025.2-py2.py3-none-any.whl.metadata (22 kB)\r\n",
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"Collecting tzdata>=2022.7 (from pandas)\r\n",
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" Using cached tzdata-2025.2-py2.py3-none-any.whl.metadata (1.4 kB)\r\n",
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"Collecting multitasking>=0.0.7 (from yfinance)\r\n",
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" Using cached multitasking-0.0.11-py3-none-any.whl.metadata (5.5 kB)\r\n",
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"Requirement already satisfied: platformdirs>=2.0.0 in /home/geron/Projects/python-for-finances/.venv/lib/python3.13/site-packages (from yfinance) (4.3.7)\r\n",
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"Collecting frozendict>=2.3.4 (from yfinance)\r\n",
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" Using cached frozendict-2.4.6-py313-none-any.whl.metadata (23 kB)\r\n",
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"Collecting peewee>=3.16.2 (from yfinance)\r\n",
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" Using cached peewee-3.17.9-cp313-cp313-linux_x86_64.whl\r\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /home/geron/Projects/python-for-finances/.venv/lib/python3.13/site-packages (from requests) (3.4.1)\r\n",
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"Requirement already satisfied: idna<4,>=2.5 in /home/geron/Projects/python-for-finances/.venv/lib/python3.13/site-packages (from requests) (3.10)\r\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /home/geron/Projects/python-for-finances/.venv/lib/python3.13/site-packages (from requests) (2.4.0)\r\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /home/geron/Projects/python-for-finances/.venv/lib/python3.13/site-packages (from requests) (2025.1.31)\r\n",
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"Requirement already satisfied: soupsieve>1.2 in /home/geron/Projects/python-for-finances/.venv/lib/python3.13/site-packages (from beautifulsoup4) (2.6)\r\n",
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"Requirement already satisfied: typing-extensions>=4.0.0 in /home/geron/Projects/python-for-finances/.venv/lib/python3.13/site-packages (from beautifulsoup4) (4.13.2)\r\n",
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"Requirement already satisfied: six>=1.5 in /home/geron/Projects/python-for-finances/.venv/lib/python3.13/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\r\n",
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"Using cached pandas-2.2.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.7 MB)\r\n",
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"Using cached numpy-2.2.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.1 MB)\r\n",
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"Using cached yfinance-0.2.55-py2.py3-none-any.whl (109 kB)\r\n",
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"Using cached frozendict-2.4.6-py313-none-any.whl (16 kB)\r\n",
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"Using cached multitasking-0.0.11-py3-none-any.whl (8.5 kB)\r\n",
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"Using cached pytz-2025.2-py2.py3-none-any.whl (509 kB)\r\n",
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"Using cached tzdata-2025.2-py2.py3-none-any.whl (347 kB)\r\n",
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"Installing collected packages: pytz, peewee, multitasking, tzdata, numpy, frozendict, pandas, yfinance\r\n",
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"Successfully installed frozendict-2.4.6 multitasking-0.0.11 numpy-2.2.4 pandas-2.2.3 peewee-3.17.9 pytz-2025.2 tzdata-2025.2 yfinance-0.2.55\r\n"
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"end_time": "2025-04-13T20:27:40.137379Z",
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"start_time": "2025-04-13T20:27:39.725248Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import requests\n",
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"from bs4 import BeautifulSoup"
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],
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"id": "737ea007e532eace",
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"outputs": [],
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"execution_count": 2
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-13T20:55:39.461968Z",
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"start_time": "2025-04-13T20:55:39.453184Z"
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}
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},
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"cell_type": "code",
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"source": [
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"def obter_acoes_b3():\n",
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" url = \"https://www.fundamentus.com.br/resultado.php\"\n",
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" headers = {\n",
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" 'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64; rv:136.0) Gecko/20100101 Firefox/136.0'\n",
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" }\n",
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"\n",
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" response = requests.get(url, headers=headers)\n",
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" soup = BeautifulSoup(response.text, 'html.parser')\n",
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"\n",
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" tabela = soup.find('table', {'id': 'resultado'})\n",
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" dados = []\n",
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"\n",
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" for linha in tabela.find_all('tr')[1:]:\n",
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" colunas = linha.find_all('td')\n",
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" ticker = colunas[0].text.strip()\n",
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"\n",
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" # Extrair métricas fundamentalistas\n",
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" ev_ebit = colunas[10].text.strip().replace('.', '').replace(',', '.')\n",
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" roic = colunas[16].text.strip().replace('.', '').replace(',', '.')\n",
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" liquidez = colunas[18].text.strip().replace('.', '').replace(',', '.')\n",
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" divida_pl = colunas[19].text.strip().replace('.', '').replace(',', '.')\n",
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"\n",
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" # Converter para float, tratando valores inválidos\n",
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" try:\n",
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" ev_ebit = float(ev_ebit) if ev_ebit != '-' else np.nan\n",
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" roic = float(roic[:-1]) if '%' in roic else float(roic)\n",
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" liquidez = float(liquidez)\n",
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" divida_pl = float(divida_pl)\n",
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"\n",
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" # Calcular Earnings Yield (inverso do EV/EBIT)\n",
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" earnings_yield = 1 / ev_ebit if ev_ebit > 0 else np.nan\n",
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"\n",
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" dados.append({\n",
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" 'Ticker': ticker,\n",
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" 'ROIC': roic,\n",
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" 'EV/EBIT': ev_ebit,\n",
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" 'Earnings Yield': earnings_yield,\n",
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" 'Liquidez Media': liquidez,\n",
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" 'Divida Bruta/PL': divida_pl\n",
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" })\n",
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" except (ValueError, ZeroDivisionError):\n",
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" continue\n",
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"\n",
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" return pd.DataFrame(dados)"
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],
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"id": "5a0af3f299da432",
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"outputs": [],
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"execution_count": 33
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}
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},
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"cell_type": "code",
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"source": [
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"acoes_df = obter_acoes_b3()\n",
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"acoes_df"
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],
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"id": "eee764781bd7c385",
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"outputs": [
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{
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"data": {
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" Ticker ROIC EV/EBIT Earnings Yield Liquidez Media Divida Bruta/PL\n",
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"0 PORP4 -2.08 0.00 NaN 2.239900e+07 0.00\n",
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"1 POPR4 19.93 0.00 NaN 5.458030e+08 0.82\n",
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"2 MNSA3 145.70 0.00 NaN -9.105000e+06 -6.52\n",
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"3 CFLU4 32.15 0.00 NaN 6.035100e+07 0.06\n",
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"4 CSTB4 20.11 0.00 NaN 8.420670e+09 0.14\n",
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".. ... ... ... ... ... ...\n",
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"986 UBBR4 0.33 0.00 NaN 1.031720e+10 0.00\n",
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"987 VSTE3 0.06 25.50 0.039216 1.043280e+09 0.33\n",
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"988 UBBR11 0.33 0.00 NaN 1.031720e+10 0.00\n",
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"989 UBBR3 0.33 0.00 NaN 1.031720e+10 0.00\n",
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"990 LJQQ3 0.03 4.86 0.205761 5.459970e+08 0.98\n",
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"\n",
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"[991 rows x 6 columns]"
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Ticker</th>\n",
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" <th>ROIC</th>\n",
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" <th>EV/EBIT</th>\n",
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" <th>Earnings Yield</th>\n",
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" <th>Liquidez Media</th>\n",
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" <th>Divida Bruta/PL</th>\n",
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" <th>0</th>\n",
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" <td>PORP4</td>\n",
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" <td>-2.08</td>\n",
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" <td>0.00</td>\n",
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" <td>NaN</td>\n",
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" <td>2.239900e+07</td>\n",
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" <td>0.00</td>\n",
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" </tr>\n",
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" <th>1</th>\n",
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" <td>POPR4</td>\n",
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" <td>19.93</td>\n",
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" <td>0.00</td>\n",
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" <td>NaN</td>\n",
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" <td>5.458030e+08</td>\n",
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" <td>0.82</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>MNSA3</td>\n",
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" <td>145.70</td>\n",
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" <td>0.00</td>\n",
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" <td>NaN</td>\n",
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" <td>-9.105000e+06</td>\n",
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" <td>-6.52</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>CFLU4</td>\n",
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" <td>32.15</td>\n",
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" <td>0.00</td>\n",
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" <td>NaN</td>\n",
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" <td>6.035100e+07</td>\n",
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" <td>0.06</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>CSTB4</td>\n",
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" <td>20.11</td>\n",
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" <td>0.00</td>\n",
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" <td>NaN</td>\n",
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" <td>8.420670e+09</td>\n",
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" <td>0.14</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>986</th>\n",
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" <td>UBBR4</td>\n",
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" <td>0.33</td>\n",
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" <td>0.00</td>\n",
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" <td>NaN</td>\n",
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" <td>1.031720e+10</td>\n",
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" <td>0.00</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>987</th>\n",
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" <td>VSTE3</td>\n",
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" <td>0.06</td>\n",
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" <td>25.50</td>\n",
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" <td>0.039216</td>\n",
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" <td>1.043280e+09</td>\n",
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" <td>0.33</td>\n",
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" <th>988</th>\n",
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" <td>UBBR11</td>\n",
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" <td>0.00</td>\n",
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" <tr>\n",
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" <th>989</th>\n",
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" <td>UBBR3</td>\n",
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" <td>0.33</td>\n",
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" <td>0.00</td>\n",
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" <td>NaN</td>\n",
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" <td>1.031720e+10</td>\n",
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" <td>0.00</td>\n",
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" <th>990</th>\n",
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" <td>LJQQ3</td>\n",
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" <td>0.03</td>\n",
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" <td>4.86</td>\n",
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" <td>0.205761</td>\n",
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" <td>5.459970e+08</td>\n",
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" <td>0.98</td>\n",
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"execution_count": 34
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}
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},
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"cell_type": "code",
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"source": [
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"def filtrar_acoes(df):\n",
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" \"\"\"Filtra ações conforme recomendação de Greenblatt\"\"\"\n",
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"\n",
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" # Remover empresas com baixa liquidez (menor que 1M)\n",
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" df_filtrado = df[df['Liquidez Media'] > 1_000_000]\n",
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"\n",
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" # Remover empresas com dívida muito alta\n",
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" df_filtrado = df_filtrado[(df_filtrado['Divida Bruta/PL'] > 0) & (df_filtrado['Divida Bruta/PL'] < 4)]\n",
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"\n",
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" return df_filtrado"
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],
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"id": "f91f75a047ff57bf",
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"outputs": [],
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"execution_count": 35
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},
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{
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"ExecuteTime": {
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"end_time": "2025-04-13T20:55:42.184602Z",
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}
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},
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"cell_type": "code",
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"source": "acoes_df = filtrar_acoes(acoes_df)",
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"id": "17276b69637e2cbe",
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"outputs": [],
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"execution_count": 36
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-04-13T20:55:42.796804Z",
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}
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},
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"cell_type": "code",
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"source": [
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"def magic_formula(df):\n",
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" # Remover dados inválidos\n",
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" df = df[df['Earnings Yield'].notna()].copy()\n",
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"\n",
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" # Classificar as ações (ranking)\n",
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" df['ROIC_Rank'] = df['ROIC'].rank(ascending=False)\n",
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" df['EY_Rank'] = df['Earnings Yield'].rank(ascending=False)\n",
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"\n",
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" # Calcular o ranking combinado\n",
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" df['Magic_Rank'] = df['ROIC_Rank'] + df['EY_Rank']\n",
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"\n",
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" # Ordenar pelo ranking combinado (do menor para o maior)\n",
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" df_result = df.sort_values('Magic_Rank')\n",
|
||
"\n",
|
||
" return df_result"
|
||
],
|
||
"id": "32611e00190bf868",
|
||
"outputs": [],
|
||
"execution_count": 37
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-13T20:55:43.475901Z",
|
||
"start_time": "2025-04-13T20:55:43.454407Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"resultado = magic_formula(acoes_df)\n",
|
||
"\n",
|
||
"# Selecionar as 15 melhores ações\n",
|
||
"top_acoes = resultado.head(15)\n",
|
||
"top_acoes"
|
||
],
|
||
"id": "625cd15e9bf429eb",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
" Ticker ROIC EV/EBIT Earnings Yield Liquidez Media Divida Bruta/PL \\\n",
|
||
"453 AHEB3 61.47 2.17 0.460829 2.444130e+08 0.02 \n",
|
||
"455 AHEB5 61.47 2.48 0.403226 2.444130e+08 0.02 \n",
|
||
"459 AHEB6 61.47 2.80 0.357143 2.444130e+08 0.02 \n",
|
||
"367 CSPC4 45.33 1.43 0.699301 2.126670e+09 1.48 \n",
|
||
"371 CSPC3 45.33 1.43 0.699301 2.126670e+09 1.48 \n",
|
||
"433 SYNE3 50.87 2.41 0.414938 1.073780e+09 0.76 \n",
|
||
"435 CPFG4 36.99 0.95 1.052632 3.614430e+09 0.42 \n",
|
||
"437 CPFG3 36.99 0.97 1.030928 3.614430e+09 0.42 \n",
|
||
"396 PALF11 46.56 2.84 0.352113 3.055110e+09 3.00 \n",
|
||
"403 PALF5 46.56 2.87 0.348432 3.055110e+09 3.00 \n",
|
||
"405 PALF3 46.56 2.88 0.347222 3.055110e+09 3.00 \n",
|
||
"380 ENER3 51.72 3.09 0.323625 1.167120e+09 3.89 \n",
|
||
"381 ENER6 51.72 3.09 0.323625 1.167120e+09 3.89 \n",
|
||
"379 ENER5 51.72 3.09 0.323625 1.167120e+09 3.89 \n",
|
||
"382 TEFC11 46.72 3.45 0.289855 4.585600e+10 2.95 \n",
|
||
"\n",
|
||
" ROIC_Rank EY_Rank Magic_Rank \n",
|
||
"453 7.0 25.0 32.0 \n",
|
||
"455 7.0 30.0 37.0 \n",
|
||
"459 7.0 32.0 39.0 \n",
|
||
"367 23.5 16.5 40.0 \n",
|
||
"371 23.5 16.5 40.0 \n",
|
||
"433 16.0 28.0 44.0 \n",
|
||
"435 37.5 7.0 44.5 \n",
|
||
"437 37.5 8.0 45.5 \n",
|
||
"396 19.0 33.0 52.0 \n",
|
||
"403 19.0 34.0 53.0 \n",
|
||
"405 19.0 35.0 54.0 \n",
|
||
"380 11.0 44.0 55.0 \n",
|
||
"381 11.0 44.0 55.0 \n",
|
||
"379 11.0 44.0 55.0 \n",
|
||
"382 17.0 52.0 69.0 "
|
||
],
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Ticker</th>\n",
|
||
" <th>ROIC</th>\n",
|
||
" <th>EV/EBIT</th>\n",
|
||
" <th>Earnings Yield</th>\n",
|
||
" <th>Liquidez Media</th>\n",
|
||
" <th>Divida Bruta/PL</th>\n",
|
||
" <th>ROIC_Rank</th>\n",
|
||
" <th>EY_Rank</th>\n",
|
||
" <th>Magic_Rank</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>453</th>\n",
|
||
" <td>AHEB3</td>\n",
|
||
" <td>61.47</td>\n",
|
||
" <td>2.17</td>\n",
|
||
" <td>0.460829</td>\n",
|
||
" <td>2.444130e+08</td>\n",
|
||
" <td>0.02</td>\n",
|
||
" <td>7.0</td>\n",
|
||
" <td>25.0</td>\n",
|
||
" <td>32.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>455</th>\n",
|
||
" <td>AHEB5</td>\n",
|
||
" <td>61.47</td>\n",
|
||
" <td>2.48</td>\n",
|
||
" <td>0.403226</td>\n",
|
||
" <td>2.444130e+08</td>\n",
|
||
" <td>0.02</td>\n",
|
||
" <td>7.0</td>\n",
|
||
" <td>30.0</td>\n",
|
||
" <td>37.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>459</th>\n",
|
||
" <td>AHEB6</td>\n",
|
||
" <td>61.47</td>\n",
|
||
" <td>2.80</td>\n",
|
||
" <td>0.357143</td>\n",
|
||
" <td>2.444130e+08</td>\n",
|
||
" <td>0.02</td>\n",
|
||
" <td>7.0</td>\n",
|
||
" <td>32.0</td>\n",
|
||
" <td>39.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>367</th>\n",
|
||
" <td>CSPC4</td>\n",
|
||
" <td>45.33</td>\n",
|
||
" <td>1.43</td>\n",
|
||
" <td>0.699301</td>\n",
|
||
" <td>2.126670e+09</td>\n",
|
||
" <td>1.48</td>\n",
|
||
" <td>23.5</td>\n",
|
||
" <td>16.5</td>\n",
|
||
" <td>40.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>371</th>\n",
|
||
" <td>CSPC3</td>\n",
|
||
" <td>45.33</td>\n",
|
||
" <td>1.43</td>\n",
|
||
" <td>0.699301</td>\n",
|
||
" <td>2.126670e+09</td>\n",
|
||
" <td>1.48</td>\n",
|
||
" <td>23.5</td>\n",
|
||
" <td>16.5</td>\n",
|
||
" <td>40.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>433</th>\n",
|
||
" <td>SYNE3</td>\n",
|
||
" <td>50.87</td>\n",
|
||
" <td>2.41</td>\n",
|
||
" <td>0.414938</td>\n",
|
||
" <td>1.073780e+09</td>\n",
|
||
" <td>0.76</td>\n",
|
||
" <td>16.0</td>\n",
|
||
" <td>28.0</td>\n",
|
||
" <td>44.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>435</th>\n",
|
||
" <td>CPFG4</td>\n",
|
||
" <td>36.99</td>\n",
|
||
" <td>0.95</td>\n",
|
||
" <td>1.052632</td>\n",
|
||
" <td>3.614430e+09</td>\n",
|
||
" <td>0.42</td>\n",
|
||
" <td>37.5</td>\n",
|
||
" <td>7.0</td>\n",
|
||
" <td>44.5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>437</th>\n",
|
||
" <td>CPFG3</td>\n",
|
||
" <td>36.99</td>\n",
|
||
" <td>0.97</td>\n",
|
||
" <td>1.030928</td>\n",
|
||
" <td>3.614430e+09</td>\n",
|
||
" <td>0.42</td>\n",
|
||
" <td>37.5</td>\n",
|
||
" <td>8.0</td>\n",
|
||
" <td>45.5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>396</th>\n",
|
||
" <td>PALF11</td>\n",
|
||
" <td>46.56</td>\n",
|
||
" <td>2.84</td>\n",
|
||
" <td>0.352113</td>\n",
|
||
" <td>3.055110e+09</td>\n",
|
||
" <td>3.00</td>\n",
|
||
" <td>19.0</td>\n",
|
||
" <td>33.0</td>\n",
|
||
" <td>52.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>403</th>\n",
|
||
" <td>PALF5</td>\n",
|
||
" <td>46.56</td>\n",
|
||
" <td>2.87</td>\n",
|
||
" <td>0.348432</td>\n",
|
||
" <td>3.055110e+09</td>\n",
|
||
" <td>3.00</td>\n",
|
||
" <td>19.0</td>\n",
|
||
" <td>34.0</td>\n",
|
||
" <td>53.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>405</th>\n",
|
||
" <td>PALF3</td>\n",
|
||
" <td>46.56</td>\n",
|
||
" <td>2.88</td>\n",
|
||
" <td>0.347222</td>\n",
|
||
" <td>3.055110e+09</td>\n",
|
||
" <td>3.00</td>\n",
|
||
" <td>19.0</td>\n",
|
||
" <td>35.0</td>\n",
|
||
" <td>54.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>380</th>\n",
|
||
" <td>ENER3</td>\n",
|
||
" <td>51.72</td>\n",
|
||
" <td>3.09</td>\n",
|
||
" <td>0.323625</td>\n",
|
||
" <td>1.167120e+09</td>\n",
|
||
" <td>3.89</td>\n",
|
||
" <td>11.0</td>\n",
|
||
" <td>44.0</td>\n",
|
||
" <td>55.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>381</th>\n",
|
||
" <td>ENER6</td>\n",
|
||
" <td>51.72</td>\n",
|
||
" <td>3.09</td>\n",
|
||
" <td>0.323625</td>\n",
|
||
" <td>1.167120e+09</td>\n",
|
||
" <td>3.89</td>\n",
|
||
" <td>11.0</td>\n",
|
||
" <td>44.0</td>\n",
|
||
" <td>55.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>379</th>\n",
|
||
" <td>ENER5</td>\n",
|
||
" <td>51.72</td>\n",
|
||
" <td>3.09</td>\n",
|
||
" <td>0.323625</td>\n",
|
||
" <td>1.167120e+09</td>\n",
|
||
" <td>3.89</td>\n",
|
||
" <td>11.0</td>\n",
|
||
" <td>44.0</td>\n",
|
||
" <td>55.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>382</th>\n",
|
||
" <td>TEFC11</td>\n",
|
||
" <td>46.72</td>\n",
|
||
" <td>3.45</td>\n",
|
||
" <td>0.289855</td>\n",
|
||
" <td>4.585600e+10</td>\n",
|
||
" <td>2.95</td>\n",
|
||
" <td>17.0</td>\n",
|
||
" <td>52.0</td>\n",
|
||
" <td>69.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
]
|
||
},
|
||
"execution_count": 38,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"execution_count": 38
|
||
},
|
||
{
|
||
"metadata": {
|
||
"ExecuteTime": {
|
||
"end_time": "2025-04-13T20:55:47.816488Z",
|
||
"start_time": "2025-04-13T20:55:47.812631Z"
|
||
}
|
||
},
|
||
"cell_type": "code",
|
||
"source": [
|
||
"# Salvar resultados em um CSV\n",
|
||
"top_acoes.to_csv('magic_formula_b3_resultado.csv', index=False)\n",
|
||
"print(\"\\nResultados salvos em 'magic_formula_b3_resultado.csv'\")"
|
||
],
|
||
"id": "605685dca910a47c",
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Resultados salvos em 'magic_formula_b3_resultado.csv'\n"
|
||
]
|
||
}
|
||
],
|
||
"execution_count": 39
|
||
},
|
||
{
|
||
"metadata": {},
|
||
"cell_type": "code",
|
||
"outputs": [],
|
||
"execution_count": null,
|
||
"source": "",
|
||
"id": "cdcab25ed5f48c3b"
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 2
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython2",
|
||
"version": "2.7.6"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|