btgsolutions-dataservices-python-client 4.8.0

Python package containing several classes and data for extracting and manipulating market and trading data.

BTG Solutions - Data Services

Real time and historical Financial Market Data, News, Corporate Events and more. More information at https://dataservices.btgpactualsolutions.com/.

Installation

pip3 install btgsolutions-dataservices-python-client

Documentation

The official documentation is hosted at https://python-client-docs.dataservices.btgpactualsolutions.com/

Examples

Real Time Data

Market Data Stream (optimized for performance)

import btgsolutions_dataservices as btg
ws = btg.MarketDataFeed(api_key='YOUR_API_KEY', data_type='books', data_subtype='stocks')
ws.run()
ws.subscribe(['PETR4'])

## The following is optional to keep the program running in a .py file:
# from time import sleep
# while True:
#   sleep(1)

Market Data Stream

Books

import btgsolutions_dataservices as btg
ws = btg.MarketDataWebSocketClient(api_key='YOUR_API_KEY', data_type='books', instruments=['PETR4', 'VALE3'])
ws.run(on_message=lambda message: print(message))

## The following is optional to keep the program running in a .py file:
# from time import sleep
# while True:
#   sleep(1)

Books, Top Of Book (n=1)

import btgsolutions_dataservices as btg
ws = btg.MarketDataWebSocketClient(api_key='YOUR_API_KEY', data_type='books')
ws.run(on_message=lambda message: print(message))
ws.subscribe(['PETR4', 'VALE3'], n=1)

## The following is optional to keep the program running in a .py file:
# from time import sleep
# while True:
#   sleep(1)

Trades

import btgsolutions_dataservices as btg
ws = btg.MarketDataWebSocketClient(api_key='YOUR_API_KEY', data_type='trades', instruments=['PETR4', 'VALE3'])
ws.run(on_message=lambda message: print(message))

## The following is optional to keep the program running in a .py file:
# from time import sleep
# while True:
#   sleep(1)

Trades, delayed (15 minutes delay)

import btgsolutions_dataservices as btg
ws = btg.MarketDataWebSocketClient(api_key='YOUR_API_KEY', data_type='trades', stream_type='delayed', instruments=['PETR4', 'VALE3'])
ws.run(on_message=lambda message: print(message))

## The following is optional to keep the program running in a .py file:
# from time import sleep
# while True:
#   sleep(1)

Books, throttle (1 second throttle)

import btgsolutions_dataservices as btg
ws = btg.MarketDataWebSocketClient(api_key='YOUR_API_KEY', data_type='books', stream_type='throttle', instruments=['PETR4', 'VALE3'])
ws.run(on_message=lambda message: print(message))

## The following is optional to keep the program running in a .py file:
# from time import sleep
# while True:
#   sleep(1)

Trades, NASDAQ (US)

import btgsolutions_dataservices as btg
ws = btg.MarketDataWebSocketClient(api_key='YOUR_API_KEY', exchange='nasdaq', data_type='trades')
ws.run(on_message=lambda message: print(message))
ws.subscribe(['AMZN', 'GOOG', 'TSLA'])

## The following is optional to keep the program running in a .py file:
# from time import sleep
# while True:
#   sleep(1)

Trades, BMV (MX)

import btgsolutions_dataservices as btg
ws = btg.MarketDataWebSocketClient(api_key='YOUR_API_KEY', exchange='bmv', data_type='trades')
ws.run(on_message=lambda message: print(message))

## The following is optional to keep the program running in a .py file:
# from time import sleep
# while True:
#   sleep(1)

Security Status

import btgsolutions_dataservices as btg
ws = btg.MarketDataWebSocketClient(api_key='YOUR_API_KEY', data_type='instrument_status', data_subtype='stocks')
ws.run(on_message=lambda message: print(message))
ws.instrument_status('PETR4')
ws.instrument_status_history('PETR4')

## The following is optional to keep the program running in a .py file:
# from time import sleep
# while True:
#   sleep(1)

Settlement Price

import btgsolutions_dataservices as btg
ws = btg.MarketDataWebSocketClient(api_key='YOUR_API_KEY', data_type='settlement-price', instruments=['ABEVOU25', 'WINV25'])
ws.run(on_message=lambda message: print(message))

## Getting the last event (settlement-price) of ABEVOU25:
# ws.get_last_event(['ABEVOU25'])

## The following is optional to keep the program running in a .py file:
# from time import sleep
# while True:
#   sleep(1)

Broker Analytics

import btgsolutions_dataservices as btg

ws = btg.BrokerAnalyticsWebSocketClient(api_key='YOUR_API_KEY')
ws.run(on_message=lambda message: print(message))

ws.available_tickers()
ws.available_brokers()
ws.subscribe_top_tickers(n=10, brokers=['85'])
ws.subscribe_top_brokers(n=5, tickers=['SNFF11'])
ws.subscribed_to()
ws.get_last_event(analytics_type='top_tickers', n=3, brokers=['85', '3'])
ws.get_last_event(analytics_type='top_brokers', n=100, tickers=['SNFF11'])
ws.unsubscribe_top_tickers(brokers=['85'])
ws.unsubscribe_top_brokers(tickers=['SNFF11'])

## The following is optional to keep the program running in a .py file:
# from time import sleep
# while True:
#   sleep(1)

Intraday Candles

import btgsolutions_dataservices as btg
int_candles = btg.IntradayCandles(api_key='YOUR_API_KEY')
int_candles.get_intraday_candles(market_type='stocks', tickers=['PETR4', 'VALE3'], candle_period='1m', delay='delayed', mode='relative', timezone='UTC', market_status='regular', raw_data=True)

Intraday Tick Data

import btgsolutions_dataservices as btg
intra_tickdata = btg.IntradayTickData(api_key='YOUR_API_KEY')
intra_tickdata.get_trades(ticker='PETR4')

Quotes

import btgsolutions_dataservices as btg
quotes = btg.Quotes(api_key='YOUR_API_KEY')
quotes.get_quote(market_type = 'stocks', tickers = ['PETR4', 'VALE3'])

Ticker Last Trade

import btgsolutions_dataservices as btg
last_event = btg.TickerLastEvent(api_key='YOUR_API_KEY')
last_event.get_trades(data_type='equities', ticker='VALE3')

Ticker Last Top of Book

import btgsolutions_dataservices as btg
last_event = btg.TickerLastEvent(api_key='YOUR_API_KEY')
last_event.get_tobs(data_type='equities')

Ticker Last Trading Status

import btgsolutions_dataservices as btg
last_event = btg.TickerLastEvent(api_key='YOUR_API_KEY')
last_event.get_status(tickers=['PETR4','VALE3'])

Ticker Last Polling - Top of Books

import btgsolutions_dataservices as btg
last_event = btg.TickerLastEventPolling(api_key='YOUR_API_KEY', data_type='top-of-books', data_subtype='stocks')
last_event.get()

Historical Data

Historical Candles

Interday

import btgsolutions_dataservices as btg
hist_candles = btg.HistoricalCandles(api_key='YOUR_API_KEY')
hist_candles.get_interday_history_candles(ticker='PETR4',  market_type='stocks', corporate_events_adj=True, start_date='2023-10-01', end_date='2023-10-13', rmv_after_market=True, timezone='UTC', raw_data=False, round=False)

Intraday

import btgsolutions_dataservices as btg
hist_candles = btg.HistoricalCandles(api_key='YOUR_API_KEY')
hist_candles.get_intraday_history_candles(ticker='PETR4',  market_type='stocks', corporate_events_adj=True, date='2023-10-06', candle='1m', rmv_after_market=True, timezone='UTC', raw_data=False, round=True)

Interday Batch

import btgsolutions_dataservices as btg
hist_candles = btg.HistoricalCandles(api_key='YOUR_API_KEY')
hist_candles.get_interday_history_candles_batch(market_type='stocks', tickers=['PETR4', 'VALE3'], start_date='2023-10-01', end_date='2023-10-13', corporate_events_adj=True, rmv_after_market=True, timezone='UTC', raw_data=False, round=True)

Available Tickers

import btgsolutions_dataservices as btg
hist_candles = btg.HistoricalCandles(api_key='YOUR_API_KEY')
hist_candles.get_available_tickers(market_type='stocks', date='2025-05-29')

Plot Candles

import btgsolutions_dataservices as btg
hist_candles = btg.HistoricalCandles(api_key='YOUR_API_KEY')
hist_candles.get_intraday_history_candles(ticker='PETR4',  market_type='stocks', corporate_events_adj=True, date='2023-10-06', candle='1m', rmv_after_market=True, timezone='UTC', raw_data=False).plot(x='candle_time', y='close_price', kind='scatter')

Historical Candles Crypto

Interday

import btgsolutions_dataservices as btg
hist_candles_crypto = btg.HistoricalCandlesCrypto(api_key='YOUR_API_KEY')
hist_candles_crypto.get_interday_history_candles(ticker='BTC', currency='BRL', exchange='consolidated', start_date='2025-06-01', end_date='2025-07-01', timezone='UTC', raw_data=False)

Intraday

import btgsolutions_dataservices as btg
hist_candles_crypto = btg.HistoricalCandlesCrypto(api_key='YOUR_API_KEY')
hist_candles_crypto.get_intraday_history_candles(ticker='BTC', currency='BRL', exchange='consolidated', date='2025-06-01', candle='1h', timezone='America/Sao_Paulo', raw_data=False)

Available Tickers

import btgsolutions_dataservices as btg
hist_candles_crypto = btg.HistoricalCandlesCrypto(api_key='YOUR_API_KEY')
hist_candles_crypto.get_available_tickers(exchange='coinbase', date='2023-01-13')

Historical Tick Data (Bulk Data)

Available Tickers

import btgsolutions_dataservices as btg
bulk_data = btg.BulkData(api_key='YOUR_API_KEY')
bulk_data.get_available_tickers(date='2023-07-03', data_type='trades', prefix='PETR')

Get Data

import btgsolutions_dataservices as btg
bulk_data = btg.BulkData(api_key='YOUR_API_KEY')
bulk_data.get_data(ticker='DI1F18', date='2017-01-02', data_type='trades')
# bulk_data.get_data(ticker='PETR4', date='2024-01-22', data_type='books')
# bulk_data.get_data(ticker='VALE3', date='2024-04-01', data_type='trades-and-book-events')
# bulk_data.get_data(ticker='PETR4', date='2025-05-07', data_type='instrument-status')

Get Data With Billing Headers

import btgsolutions_dataservices as btg
bulk_data = btg.BulkData(api_key='YOUR_API_KEY')
df, billing_headers = bulk_data.get_data(ticker='PETR4', date='2025-06-20', data_type='trades', return_billing_headers=True)
# billing_headers = bulk_data.get_data(ticker='PETR4', date='2025-06-20', data_type='trades', dry_run=True)

Security List

import btgsolutions_dataservices as btg
bulk_data = btg.BulkData(api_key='YOUR_API_KEY')
bulk_data.get_security_list(date='2025-05-07')

Market Data Channels

import btgsolutions_dataservices as btg
bulk_data = btg.BulkData(api_key='YOUR_API_KEY')
bulk_data.get_market_data_channels(date='2026-01-30')

Compressed Data (PCAP files)

import btgsolutions_dataservices as btg
bulk_data = btg.BulkData(api_key='YOUR_API_KEY')
bulk_data.get_compressed_data(channel='98', date='2026-01-30', data_type='instruments')
# bulk_data.get_compressed_data(channel='98', date='2026-01-30', data_type='incremental', feed='feedA')
# bulk_data.get_compressed_data(channel='98', date='2026-01-30', data_type='snapshot')

Alternative Data

High Frequency News Stream

import btgsolutions_dataservices as btg
ws = btg.HFNWebSocketClient(api_key='YOUR_API_KEY')
ws.run(on_message=lambda message: print(message))

# Subscribe to live economy news in Portuguese
ws.subscribe(settings={'feed': 'economy', 'text_language': 'portuguese'})

# Request latest news on demand (without subscribing to the live stream)
ws.latest_news(settings={'feed': 'crypto', 'limit': '10'})

# Get available filter values
ws.available_filters(settings={})

# Stop receiving broadcasts (keeps connection open)
ws.unsubscribe()

ws.close()

## The following is optional to keep the program running in a .py file:
# from time import sleep
# while True:
#   sleep(1)

High Frequency News

import btgsolutions_dataservices as btg
hfn = btg.HighFrequencyNews(api_key='YOUR_API_KEY')

# Latest news with filters
hfn.get_latest_news(feed='economy', text_language='portuguese', limit=10)

# Latest news by ticker tags
hfn.get_latest_news(tags=['PETR4', 'VALE3'])

# Historical news for a date range
hfn.get_historical_news(
    start_date='2026-05-01T00:00:00.000Z',
    end_date='2026-05-08T23:59:59.999Z',
    feed='economy',
)

# Available filter values
hfn.get_available_filters()

OPA

import btgsolutions_dataservices as btg
public_sources = btg.PublicSources(api_key='YOUR_API_KEY')
public_sources.get_opas(start_date='2022-10-01', end_date='2024-10-01')

STOCK LOAN

import btgsolutions_dataservices as btg
stock_loan = btg.StockLoan(api_key='YOUR_API_KEY')
stock_loan.get_trades()
stock_loan.get_paginated_trades(page=1, limit=1000, ticker ='PETR4')
stock_loan.get_available_tickers()

Company Fundamentals

Company General Information

import btgsolutions_dataservices as btg
company_data = btg.CompanyData(api_key='YOUR_API_KEY')
company_data.general_info(ticker='PETR4')

Income Statement

import btgsolutions_dataservices as btg
company_data = btg.CompanyData(api_key='YOUR_API_KEY')
company_data.income_statement(ticker='PETR4')

Balance Sheet

import btgsolutions_dataservices as btg
company_data = btg.CompanyData(api_key='YOUR_API_KEY')
company_data.balance_sheet(ticker='PETR4')

Cash Flow

import btgsolutions_dataservices as btg
company_data = btg.CompanyData(api_key='YOUR_API_KEY')
company_data.cash_flow(ticker='PETR4')

Valuation

import btgsolutions_dataservices as btg
company_data = btg.CompanyData(api_key='YOUR_API_KEY')
company_data.valuation(ticker='PETR4')

Ratios

import btgsolutions_dataservices as btg
company_data = btg.CompanyData(api_key='YOUR_API_KEY')
company_data.ratios(ticker='PETR4')

Growth

import btgsolutions_dataservices as btg
company_data = btg.CompanyData(api_key='YOUR_API_KEY')
company_data.growth(ticker='PETR4')

Interims

import btgsolutions_dataservices as btg
company_data = btg.CompanyData(api_key='YOUR_API_KEY')
company_data.interims(ticker='PETR4')

All Financial Tables

import btgsolutions_dataservices as btg
company_data = btg.CompanyData(api_key='YOUR_API_KEY')
company_data.all_financial_tables(ticker='PETR4')

REST Technical Catalogs

Technical endpoint descriptions for REST APIs are exported by the package so MCPs and other connectors can reuse the same source of truth instead of duplicating endpoint semantics locally.

from btgsolutions_dataservices.rest import (
    DATASERVICES_ENDPOINTS,
    DATASERVICES_ENDPOINT_RELATIONSHIPS,
    get_dataservices_tool_description,
)

print(get_dataservices_tool_description("get_quotes"))
print(get_dataservices_tool_description("get_book_scope"))

The Data Services catalog covers quotes, candles, intraday trades, last events, reference data, corporate events, company fundamental data, HFN news, stock-loan data, bulk market data, broker reference, broker analytics and book-scope endpoints. It documents parameters, endpoint relationships such as available-ticker discovery before market-data calls, broker-reference discovery before broker analytics, HFN filter discovery before news queries, corporate events before adjusted price analysis, and book-scope constraints for microstructure analysis.

The REST catalogs also document cross-domain relationships between Public Sources and market-data services: company directory resolution feeds ticker reference, quotes, candles, stock-loan, broker analytics and book-scope tools; sector classification feeds peer-market comparisons; fund holdings feed underlying ticker market analysis; ownership/free-float feeds liquidity context; and disclosures, official notices, HFN news and corporate events feed event window analysis.

Alternative Data - Metadata

Technical endpoint descriptions for the public-sources alternative-data APIs are exported by the package so MCPs and other connectors can reuse the same source of truth:

from btgsolutions_dataservices.rest import (
    PUBLIC_SOURCES_ENDPOINTS,
    PUBLIC_SOURCES_ENDPOINT_RELATIONSHIPS,
    get_public_sources_tool_description,
)

print(get_public_sources_tool_description("get_company_board"))

The catalog documents endpoint parameters, known data gaps, excluded endpoints, and relationships such as: company metadata resolving identifiers for governance and ownership calls; macro indicator metadata feeding macro observations; asset metadata feeding maximum-theoretical-margin calls; financial statement type metadata feeding statement queries; assemblies and ownership notices feeding notice summaries; and fund holdings/asset fund holders linking funds with company tickers.

import btgsolutions_dataservices as btg
meta = btg.AlternativeDataMetadata(api_key='YOUR_API_KEY')
meta.get_company_directory(query='PETROBRAS', jurisdiction='BR')
meta.list_companies(query='PETROBRAS', jurisdiction='BR')
meta.list_etfs(query='BOVA')
meta.get_company_sector(identifier='PETR4')
meta.get_taxonomy(system='b3')
meta.get_cnae(code='6422100')
meta.get_sector_companies(sector='Petróleo, Gás e Biocombustíveis')
meta.get_sectors_summary()
meta.get_financial_statement_types()
meta.get_datasets()
meta.get_available_assets(dataset='maximum_theoretical_margin', prefix='PETR')
meta.get_available_indicators()

Alternative Data - Companies

import btgsolutions_dataservices as btg
companies = btg.AlternativeDataCompanies(api_key='YOUR_API_KEY')
companies.list_companies(query='PETROBRAS', jurisdiction='BR')
companies.get_board(company_id='VALE3', body='board')
companies.get_governance_summary(company_id='ITUB4')
companies.get_governance_history(company_id='PETR4', start_date='2023-01-01', end_date='2024-12-31')
companies.get_governance_documents(company_id='PETR4', start_date='2024-01-01', end_date='2024-12-31')
companies.get_governance_compensation(company_id='VALE3', fiscal_year='2024')
companies.get_governance_related_party(company_id='ITUB4')
companies.get_governance_beneficial_ownership(company_id='AAPL')  # UK PSC / US SEC proxy data; not BR listed-company ownership
companies.get_corporate_registry(company_id='PETR4', direction='partners')
companies.get_corporate_registry(company_id='PETR4', direction='investees')
companies.get_insider_trades(company_id='AAPL', start_date='2024-01-01', end_date='2024-12-31')
companies.get_board_changes(company_id='VALE3', event='elected')
companies.get_assemblies(company_id='PINE4')
companies.get_financial_statements(company_id='PETR4', statement='income_statement', quarter='4T24')
companies.get_financial_notes(company_id='VALE3', quarter='4T24')
companies.get_disclosures(company_id='PETR4', document_type='repurchase')
companies.get_disclosures(company_id='PETR4', document_type='insider')

For Brazilian governance endpoints, current board and summary responses prefer the latest raw FRE filing when available. Returned governance summaries can include latest_reference_date, latest_version, latest_document_id and structure_source; alternate B3 tickers/units such as BPAC11 can resolve to the underlying listed company when covered by the source metadata.

Alternative Data - People

import btgsolutions_dataservices as btg
people = btg.AlternativeDataPeople(api_key='YOUR_API_KEY')
people.get_appointments(person_id='slug:Jean Paul Lemann', active_only=True)
people.get_appointments(person_id='slug:Jean Paul Lemann', group_by='company')

Alternative Data - Funds

import btgsolutions_dataservices as btg
funds = btg.AlternativeDataFunds(api_key='YOUR_API_KEY')
funds.list_etfs(query='BOVA')
funds.get_holdings(fund_id='BOVA11', reference_date='2024-12-31')
funds.get_exposures(fund_id='BOVA11', exposure_type='asset_class')
funds.get_history(fund_id='BOVA11', start_date='2024-01-01', end_date='2024-12-31')
funds.get_lookthrough(fund_id='BOVA11')
# Manager aggregation requires an exact covered manager CNPJ/name, not ETF issuer slugs, ETF tickers or fund CNPJs.
# funds.get_manager_aggregate_holdings(manager_id='...')

Alternative Data - Ownership

import btgsolutions_dataservices as btg
ownership = btg.AlternativeDataOwnership(api_key='YOUR_API_KEY')
ownership.get_top_shareholders(company_id='VALE3', limit=10)
ownership.get_ownership_current(company_id='ITUB4')
ownership.get_ownership_history(company_id='PETR4', start_date='2023-01-01', end_date='2024-12-31')
ownership.get_ownership_change_events(company_id='VALE3', start_date='2024-01-01', end_date='2024-12-31')
ownership.get_ownership_official_notices(company_id='PETR4')
ownership.get_notice_summary(url='https://www.rad.cvm.gov.br/ENETWEB/frmGerenciaPastaDeArquivos.aspx?numProtocolo=1234567')
ownership.get_ownership_control_group(company_id='VALE3')
ownership.get_ownership_free_float(company_id='PETR4')
ownership.get_shareholder_holdings(shareholder_id='00.000.000/0001-91')
ownership.get_institutional_holders(identifier='VALE3')
ownership.get_fund_holders(identifier='PETR4', identifier_type='b3_ticker')

For Brazilian ownership/free-float endpoints, current control and free-float responses use the latest FRE source document/version for the selected reference date when available. Repeated older source documents are deduplicated where possible, while distinct rows for the same holder across classes, roles or ownership categories are preserved. Zero free-float or independence percentages can be valid source values, not missing data.

Alternative Data - Macro & Markets

import btgsolutions_dataservices as btg
macro = btg.AlternativeDataMacroMarkets(api_key='YOUR_API_KEY')
macro.get_macro_indicators(indicator='selic')
macro.get_macro_indicators(indicator='ipca_contributions', start_date='2024-01', end_date='2024-12')
macro.get_macro_indicators(indicator='gdp', type='yoy')
macro.get_macro_indicators(indicator='comexstat', year='2024', state='SP')
macro.get_macro_indicators(indicator='comexstat', aggregate='states', start_date='2024-01', end_date='2024-12')
macro.get_macro_indicators(indicator='comexstat', aggregate='timeline', group_by='country', state='SP')
macro.get_macro_indicators(indicator='rreo', year='2024', period='6')
macro.get_maximum_theoretical_margin(asset='PETR4', report_date='2024-12-31')
macro.get_dpmfi(start_date='2024-01', end_date='2024-12', status='dados_oficiais')
macro.get_dpmfi(snapshot_date='2026-06-24', limit=10)  # reproducible scoped snapshot
macro.get_dpmfi_composition(bond_type='IPCA')

AlternativeDataMacroMarkets exposes the public-sources market-data endpoint for B3 maximum theoretical margin. The direct market-data investor-categories endpoint is intentionally not exposed in this package.

Reference Data

Corporate Events

import btgsolutions_dataservices as btg
corporate_events = btg.CorporateEvents(api_key='YOUR_API_KEY')
corporate_events.get(start_date='2024-05-01', end_date='2024-05-31')
# corporate_events.get(start_date='2024-05-01', end_date='2024-05-31', tickers=['VALE3'])

Broker Reference

import btgsolutions_dataservices as btg
broker_reference = btg.BrokerReference(api_key='YOUR_API_KEY')
broker_reference.get()

Book Scope

import btgsolutions_dataservices as btg
book_scope = btg.BookScope(api_key='YOUR_API_KEY')

result = book_scope.get(
    symbol='DOLM26',
    market_type='derivatives',
    start_time='2026-05-28T14:12:00Z',
    end_time='2026-05-28T14:15:00Z',
    select=['trades', 'book_snapshot', 'book_incremental'],  # choose one, two, or all three
)

single_file_result = book_scope.get(
    symbol='DOLM26',
    market_type='derivatives',
    start_time='2026-05-28T14:12:00Z',
    end_time='2026-05-28T14:15:00Z',
    select=['trades', 'book_snapshot', 'book_incremental'],
    aggregate_info=True,
)

Broker Analytics

import btgsolutions_dataservices as btg
broker_analytics = btg.BrokerAnalytics(api_key='YOUR_API_KEY', market_type='stocks')
summary = broker_analytics.get_summary(brokers=['85', '3'], tickers=['PETR4', 'ABCB4'])
top_brokers = broker_analytics.get_top_brokers(n=10)
top_tickers = broker_analytics.get_top_tickers(n=10, brokers=['85', '3'])

Ticker Reference Data

import btgsolutions_dataservices as btg
ref = btg.ReferenceData(api_key='YOUR_API_KEY')
ref.ticker_reference(tickers=['VALE3','PETR4'])

Algo Hunter

import btgsolutions_dataservices as btg
algohunter = AlgoHunter(api_key='YOUR_API_KEY')
algohunter.get_thermometer()
algohunter.get_recently_detected(ticker='PETR4')