### Install Fundamentus Library

Source: https://context7.com/mv/fundamentus-api/llms.txt

Installs the Fundamentus Python library using pip. This is the first step to using the library's functionalities for accessing financial data.

```bash
pip install fundamentus
```

--------------------------------

### Get Raw Company Financial Multiples (Python)

Source: https://github.com/mv/fundamentus-api/blob/main/README.md

Fetches a DataFrame with raw financial multiples, preserving original column names from the Fundamentus website. These names are in Portuguese, may contain spaces and accents, requiring explicit filtering.

```python
>>> import fundamentus
>>> df = fundamentus.get_resultado_raw()
>>> print(df.columns)

Index(['Cotação', 'P/L', 'P/VP', 'PSR', 'Div.Yield', 'P/Ativo', 'P/Cap.Giro',
       'P/EBIT', 'P/Ativ Circ.Liq', 'EV/EBIT', 'EV/EBITDA', 'Mrg Ebit',
       'Mrg. Líq.', 'Liq. Corr.', 'ROIC', 'ROE', 'Liq.2meses', 'Patrim. Líq',
       'Dív.Brut/ Patrim.', 'Cresc. Rec.5a'],
      dtype='object', name='Multiples')

>>> print( df[ df['P/L'] > 0] )

    papel   Cotação    P/L  P/VP  ...  Dív.Brut/ Patrim.  Cresc. Rec.5a
    ABCB4     15.81   9.66  0.83  ...               0.00        -0.5287
    ABEV3     15.95  28.87  3.22  ...               0.09         0.0455
    AEDU11    37.35  20.13  1.13  ...               0.30         0.2090
    ...         ...    ...   ...  ...                ...            ...
    WIZS3      7.95   5.96  3.61  ...               0.00         0.1737
    WSON33    45.45  34.29  1.34  ...               1.11         0.0131
    YDUQ3     33.26  39.71  3.10  ...               1.45         0.0449

# filter on DataFrame
df = df[ df['P/L'] > 0   ]
df = df[ df['P/L'] < 100 ]
df = df[ df['P/VP'] > 0  ]
```

--------------------------------

### Get Company Financial Multiples (Python)

Source: https://github.com/mv/fundamentus-api/blob/main/README.md

Fetches a DataFrame containing financial multiples for companies. The column names are simplified for easier filtering. It handles filtering based on these simplified names.

```python
>>> import fundamentus
>>> df = fundamentus.get_resultado()
>>> print(df.columns)

  Index(['cotacao', 'pl', 'pvp', 'psr', 'dy', 'pa', 'pcg', 'pebit', 'pacl',
         'evebit', 'evebitda', 'mrgebit', 'mrgliq', 'roic', 'roe', 'liqc',
         'liq2m', 'patrliq', 'divbpatr', 'c5y'],
        dtype='object', name='Multiples')

>>> print( df[ df.pl > 0] )

    papel   cotacao     pl   pvp  ...  divbpatr     c5y
    ABCB4     15.81   9.66  0.83  ...      0.00 -0.5287
    ABEV3     15.95  28.87  3.22  ...      0.09  0.0455
    AEDU11    37.35  20.13  1.13  ...      0.30  0.2090
    ...         ...    ...   ...  ...       ...     ...
    WIZS3      7.95   5.96  3.61  ...      0.00  0.1737
    WSON33    45.45  34.29  1.34  ...      1.11  0.0131
    YDUQ3     33.26  39.71  3.10  ...      1.45  0.0449

# filter on DataFrame
df = df[ df.pl  > 0   ]
df = df[ df.pl  < 100 ]
df = df[ df.pvp > 0   ]
```

--------------------------------

### Get Specific Stock Data (Python)

Source: https://github.com/mv/fundamentus-api/blob/main/README.md

Fetches detailed financial data for a specific stock ticker or a list of tickers. The returned DataFrame includes various financial metrics for the requested companies.

```python
>>> import fundamentus

>>> df = fundamentus.get_papel('WEGE3')  ## or...
>>> df = fundamentus.get_papel(['ITSA4','WEGE3'])

>>> print(df)

        Tipo       Empresa        Setor ... Receita_Liquida_3m    EBIT_3m Lucro_Liquido_3m
ITSA4  PN N1  ITAÚSA PN N1  Financeiros ...         1778000000  257000000       1784000000
WEGE3  ON N1  WEG SA ON N1  Máquinas e  ...         4801260000  946670000        644246000
```

--------------------------------

### Get Market Screening Results (Raw Columns)

Source: https://context7.com/mv/fundamentus-api/llms.txt

Retrieves the market screening data with original Portuguese column names directly from the HTML source. This is useful when the exact original naming convention is required or for compatibility with external tools expecting these labels. Returns a Pandas DataFrame.

```python
import fundamentus

# Get raw data with original Portuguese column names
df = fundamentus.get_resultado_raw()

print(df.columns)

# Filter using original Portuguese column names (requires bracket notation)
filtered = df[df['P/L'] > 0]
filtered = filtered[filtered['P/L'] < 100]
filtered = filtered[filtered['P/VP'] > 0]

print(filtered[['Cotação', 'P/L', 'P/VP', 'Div.Yield', 'ROE']].head())
```

--------------------------------

### Get Market Screening Results (Simplified Columns)

Source: https://context7.com/mv/fundamentus-api/llms.txt

Retrieves a market screening table with simplified, Python-friendly column names. This function is useful for quick analysis and filtering of stocks based on financial multiples. It returns a Pandas DataFrame indexed by stock ticker.

```python
import fundamentus

# Get all stocks with financial multiples
df = fundamentus.get_resultado()

# Available columns:
# cotacao (price), pl (P/L), pvp (P/VP), psr (PSR), dy (Div.Yield),
# pa (P/Ativo), pcg (P/Cap.Giro), pebit (P/EBIT), pacl (P/Ativ Circ.Liq),
# evebit (EV/EBIT), evebitda (EV/EBITDA), mrgebit (Mrg Ebit), mrgliq (Mrg. Líq.),
# roic (ROIC), roe (ROE), liqc (Liq. Corr.), liq2m (Liq.2meses),
# patrliq (Patrim. Líq), divbpatr (Dív.Brut/Patrim.), c5y (Cresc. Rec.5a)

print(df.columns)

# Filter for value stocks with positive earnings
value_stocks = df[df.pl > 0]
value_stocks = value_stocks[value_stocks.pl < 15]
value_stocks = value_stocks[value_stocks.pvp < 1.5]
value_stocks = value_stocks[value_stocks.roe > 0.10]

print(value_stocks[['cotacao', 'pl', 'pvp', 'dy', 'roe']].head(10))
```

--------------------------------

### Get Specific Stock Details

Source: https://context7.com/mv/fundamentus-api/llms.txt

Retrieves detailed financial information for one or more specific stock tickers. This function fetches company details, balance sheet data, income statement items, and financial indicators from the company's detail page on Fundamentus. Returns a Pandas DataFrame.

```python
import fundamentus

# Get details for a single stock
df = fundamentus.get_papel('WEGE3')

print(df.T)  # Transpose for better readability

# Get details for multiple stocks at once
stocks = ['ITSA4', 'WEGE3', 'VALE3', 'PETR4']
df = fundamentus.get_papel(stocks)

print(df[['Tipo', 'Setor', 'Cotacao', 'PL', 'PVP', 'ROE']])
```

--------------------------------

### Display Available Sectors

Source: https://context7.com/mv/fundamentus-api/llms.txt

Prints a formatted table listing all available sectors, including their labels, descriptions, and numeric IDs.

```python
import fundamentus
fundamentus.print_setores()
```

--------------------------------

### print_setores

Source: https://context7.com/mv/fundamentus-api/llms.txt

Prints a formatted table of all available sectors with their labels, descriptions, and IDs.

```APIDOC
## GET /print_setores

### Description
Prints a formatted table of all available sectors with their labels, descriptions, and IDs.

### Method
GET

### Endpoint
/print_setores

### Parameters
None

### Request Example
```python
import fundamentus

fundamentus.print_setores()
```

### Response
#### Success Response (200)
- **Formatted Table** - A string containing a formatted table of sectors, descriptions, and IDs printed to standard output.

#### Response Example
```
label      | desc                                       |   id
-----------+--------------------------------------------+-----
agro       | Agropecuária                               |    1
saneamento | Água e Saneamento                          |    2
...        | ...                                        | ...
```
```

--------------------------------

### Implement Magic Formula Strategy with Fundamentus

Source: https://context7.com/mv/fundamentus-api/llms.txt

This script retrieves market data, applies fundamental filters (P/L, ROIC, liquidity), removes financial sector stocks, and ranks the remaining assets based on the Magic Formula (EV/EBIT and ROIC). It outputs the top 20 ranked stocks for investment consideration.

```python
import pandas as pd
import fundamentus

def filter_out_finance(data):
    """Remove financial sector stocks from analysis."""
    df = data.copy()
    exclude = []
    exclude += fundamentus.list_papel_setor(fundamentus.get_setor_id('financeiro'))
    exclude += fundamentus.list_papel_setor(fundamentus.get_setor_id('prev-seguros'))

    for ticker in exclude:
        try:
            df = df.drop(ticker)
        except KeyError:
            pass
    return df

def magic_formula_ranking(data):
    """Apply Magic Formula ranking: EV/EBIT + ROIC."""
    magic = data.copy()

    # Rank by EV/EBIT (lower is better)
    rank_evebit = data.sort_values('evebit', ascending=True).index
    df_rank1 = pd.DataFrame({'rank': range(1, len(data) + 1)}, index=rank_evebit)
    magic['rank_evebit'] = df_rank1['rank']

    # Rank by ROIC (higher is better)
    rank_roic = data.sort_values('roic', ascending=False).index
    df_rank2 = pd.DataFrame({'rank': range(1, len(data) + 1)}, index=rank_roic)
    magic['rank_roic'] = df_rank2['rank']

    # Combined Magic Formula rank
    magic['rank_magic'] = magic['rank_evebit'] + magic['rank_roic']
    return magic.sort_values('rank_magic')

# Get all stocks
data = fundamentus.get_resultado()

# Apply filters
filtered = data[data.pl > 0]
filtered = filtered[filtered.pl < 30]
filtered = filtered[filtered.roic > 0]
filtered = filtered[filtered.evebit > 0]
filtered = filtered[filtered.divbpatr < 3]
filtered = filtered[filtered.liq2m > 100000]

# Remove financial stocks
filtered = filter_out_finance(filtered)

# Apply Magic Formula ranking
ranked = magic_formula_ranking(filtered)

# Top 20 stocks by Magic Formula
top20 = ranked.head(20)
print(top20[['cotacao', 'pl', 'evebit', 'roic', 'rank_evebit', 'rank_roic', 'rank_magic']])
```

--------------------------------

### List All Available Stocks

Source: https://context7.com/mv/fundamentus-api/llms.txt

Retrieves a complete list of all stock tickers currently available on the Fundamentus platform.

```python
import fundamentus
all_stocks = fundamentus.list_papel_all()
print(f"Total stocks available: {len(all_stocks)}")
```

--------------------------------

### list_papel_all

Source: https://context7.com/mv/fundamentus-api/llms.txt

Returns a list of all stock tickers available on Fundamentus.

```APIDOC
## GET /list_papel_all

### Description
Returns a list of all stock tickers available on Fundamentus.

### Method
GET

### Endpoint
/list_papel_all

### Parameters
None

### Request Example
```python
import fundamentus

all_stocks = fundamentus.list_papel_all()
print(f"Total stocks available: {len(all_stocks)}")
print(all_stocks[:10])
```

### Response
#### Success Response (200)
- **stocks** (list of strings) - A list of all available stock ticker symbols.

#### Response Example
```json
[
  "AALR3",
  "ABCB4",
  "ABEV3",
  "AERI3",
  "AESB3",
  "AFLT3",
  "AGRO3",
  "AGXY3",
  "AHEB3",
  "ALLD3"
]
```
```

--------------------------------

### Retrieve Raw Stock Details

Source: https://context7.com/mv/fundamentus-api/llms.txt

Fetches raw HTML tables for a specific stock ticker. Returns a list of DataFrames containing summary, market value, indicators, and balance sheet data.

```python
import fundamentus
tables = fundamentus.get_detalhes_raw('VALE3')
print(f"Number of tables: {len(tables)}")
print(tables[0])
```

--------------------------------

### Output Data as CSV or Table

Source: https://context7.com/mv/fundamentus-api/llms.txt

Utility functions to format and print pandas DataFrames as either CSV strings or human-readable ASCII tables.

```python
import fundamentus
df = fundamentus.get_resultado()
filtered = df[df.pl > 0][df.pl < 15]
selected_cols = filtered[['cotacao', 'pl', 'pvp', 'dy', 'roe']]
fundamentus.print_csv(selected_cols.head(5))
fundamentus.print_table(selected_cols.head(5))
```

--------------------------------

### print_csv and print_table

Source: https://context7.com/mv/fundamentus-api/llms.txt

Utility functions to output DataFrames in CSV format or as formatted ASCII tables.

```APIDOC
## Utility Functions: print_csv, print_table

### Description
Utility functions to output DataFrames in CSV format or as formatted ASCII tables.

### Method
Various (internal utility functions, typically called after retrieving data with other functions like `get_resultado`)

### Endpoint
N/A (These are utility functions)

### Parameters
#### `print_csv(dataframe)`
- **dataframe** (pandas DataFrame) - The DataFrame to be printed in CSV format.

#### `print_table(dataframe)`
- **dataframe** (pandas DataFrame) - The DataFrame to be printed as a formatted table.

### Request Example
```python
import fundamentus

df = fundamentus.get_resultado()
filtered = df[df.pl > 0][df.pl < 15]
selected_cols = filtered[['cotacao', 'pl', 'pvp', 'dy', 'roe']]

# Output as CSV
fundamentus.print_csv(selected_cols.head(5))

# Output as formatted table
fundamentus.print_table(selected_cols.head(5))
```

### Response
#### Success Response (200)
- **CSV Output** - Data printed to standard output in CSV format.
- **Table Output** - Data printed to standard output as a formatted ASCII table.

#### Response Example (CSV)
```csv
papel,cotacao,pl,pvp,dy,roe
ABCB4,15.8100,9.6600,0.8300,0.0528,0.0861
BBAS3,27.5000,4.8200,0.7800,0.0812,0.1620
```

#### Response Example (Table)
```
 papel   |   cotacao |     pl |    pvp |      dy |     roe
---------+-----------+--------+--------+---------+--------
 ABCB4   |     15.81 |   9.66 |   0.83 | 0.0528  | 0.0861
 BBAS3   |     27.5  |   4.82 |   0.78 | 0.0812  | 0.1620
```
```

--------------------------------

### Map Sector Labels to IDs

Source: https://context7.com/mv/fundamentus-api/llms.txt

Converts a human-readable sector label string into the corresponding numeric ID required by the Fundamentus website.

```python
import fundamentus
finance_id = fundamentus.get_setor_id('financeiro')
print(f"Finance sector ID: {finance_id}")
```

--------------------------------

### List Stocks by Sector

Source: https://context7.com/mv/fundamentus-api/llms.txt

Retrieves a list of stock tickers associated with a specific sector ID. Requires the numeric sector ID as input.

```python
import fundamentus
mining_stocks = fundamentus.list_papel_setor(27)
print(mining_stocks)
```

--------------------------------

### get_detalhes_raw

Source: https://context7.com/mv/fundamentus-api/llms.txt

Retrieves raw HTML tables from the stock detail page without processing. Returns a list of DataFrames corresponding to each HTML table on the page, useful for advanced processing or debugging.

```APIDOC
## GET /detalhes_raw

### Description
Retrieves raw HTML tables from the stock detail page without processing. Returns a list of DataFrames corresponding to each HTML table on the page, useful for advanced processing or debugging.

### Method
GET

### Endpoint
/detalhes_raw

### Parameters
#### Query Parameters
- **ticker** (string) - Required - The stock ticker symbol (e.g., 'VALE3').

### Request Example
```python
import fundamentus

tables = fundamentus.get_detalhes_raw('VALE3')
print(f"Number of tables: {len(tables)}")
```

### Response
#### Success Response (200)
- **tables** (list of DataFrames) - A list where each element is a pandas DataFrame representing an HTML table from the stock detail page.

#### Response Example
```json
[
  // DataFrame 0: Summary header
  // DataFrame 1: Market value and shares info
  // DataFrame 2: Price oscillations and fundamental indicators
  // DataFrame 3: Balance sheet data
  // DataFrame 4: Income statement
]
```
```

--------------------------------

### List Companies by Sector (Python)

Source: https://github.com/mv/fundamentus-api/blob/main/README.md

Retrieves a list of stock tickers belonging to a specific sector, identified by a sector code. This function is useful for obtaining all companies within a particular industry.

```python
>>> import fundamentus

>>> fin = fundamentus.list_papel_setor(35)  # finance
>>> seg = fundamentus.list_papel_setor(38)  # seguradoras

>>> print(fin)
   ['ABCB4', 'BBAS3', 'BBDC3', 'BBDC4', ... ]

>>> print(seg)
   ['BBSE3', 'IRBR3', 'SULA4', 'WIZS3', ... ]
```

--------------------------------

### list_papel_setor

Source: https://context7.com/mv/fundamentus-api/llms.txt

Returns a list of stock tickers belonging to a specific sector. Sectors are identified by their numeric ID from the Fundamentus website.

```APIDOC
## GET /list_papel_setor

### Description
Returns a list of stock tickers belonging to a specific sector. Sectors are identified by their numeric ID from the Fundamentus website.

### Method
GET

### Endpoint
/list_papel_setor

### Parameters
#### Query Parameters
- **setor_id** (integer) - Required - The numeric ID of the sector.

### Request Example
```python
import fundamentus

mining_stocks = fundamentus.list_papel_setor(27) # Mining sector
print(mining_stocks)
```

### Response
#### Success Response (200)
- **stocks** (list of strings) - A list of stock ticker symbols belonging to the specified sector.

#### Response Example
```json
[
  "VALE3",
  "CMIN3",
  "CSNA3"
]
```
```

--------------------------------

### get_setor_id

Source: https://context7.com/mv/fundamentus-api/llms.txt

Converts a sector label to its numeric ID used by the Fundamentus website. Labels use simplified Portuguese identifiers.

```APIDOC
## GET /get_setor_id

### Description
Converts a sector label to its numeric ID used by the Fundamentus website. Labels use simplified Portuguese identifiers.

### Method
GET

### Endpoint
/get_setor_id

### Parameters
#### Query Parameters
- **label** (string) - Required - The simplified Portuguese sector label (e.g., 'financeiro', 'mineracao').

### Request Example
```python
import fundamentus

finance_id = fundamentus.get_setor_id('financeiro')
print(f"Finance sector ID: {finance_id}")
```

### Response
#### Success Response (200)
- **sector_id** (integer) - The numeric ID of the sector.

#### Response Example
```json
{
  "sector_id": 20
}
```
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.