minitrade.datasource¤

minitrade.datasource.base ¤

QuoteSource ¤

Bases: ABC

QuoteSource is a base class that returns quote data for instruments. Extend this class to add a concrete implementation to get data from particular data source.

At a minimum, the following methods should be implemented:

class MyQuoteSource(QuoteSource):
    def _ticker_timezone(self, ticker: str) -> str:
        pass

    def _ticker_calendar(self, ticker: str) -> str:
        pass

    def _daily_bar(self, ticker: str, start: str, end: str) -> pd.DataFrame:
        pass

SYSTEM_SOURCES `class-attribute` `instance-attribute` ¤

SYSTEM_SOURCES = sorted(['Yahoo', 'EODHistoricalData', 'TwelveData', 'Alpaca', 'EastMoney', 'Tiingo', 'InteractiveBrokers', 'CboeIndex', 'CboeFutures'])

A list of names for supported quote sources as input to QuoteSource.get_source().

_daily_bar `abstractmethod` ¤

_daily_bar(ticker: str, start: str, end: str) -> DataFrame

Same as daily_bar() for only one ticker.

This should be overridden in subclass to provide an implemention.

Returns:

Type	Description
`DataFrame`	A dataframe with columns 'Open', 'High', 'Low', 'Close', 'Volume' indexed by datetime

_minute_bar `abstractmethod` ¤

_minute_bar(ticker: str, start: str, end: str, interval: int) -> DataFrame

Same as minute_bar() for only one ticker.

This should be overridden in subclass to provide an implemention.

Returns:

Type	Description
`DataFrame`	A dataframe with columns 'Open', 'High', 'Low', 'Close', 'Volume' indexed by datetime

_spot `abstractmethod` ¤

_spot(tickers: list[str]) -> Series

Return spot prices for a list of tickers.

_ticker_calendar `abstractmethod` ¤

_ticker_calendar(ticker: str) -> str

Get the calendar name of a ticker as recognized by pandas_market_calendars.

_ticker_timezone `abstractmethod` ¤

_ticker_timezone(ticker: str) -> str

Get the timezone of a ticker.

daily_bar ¤

daily_bar(tickers: list[str] | str, start: str = '2000-01-01', end: str = None, align: bool = True, normalize: bool = False, num_workers: int = 1) -> DataFrame

Read end-of-day OHLCV data for a list of tickers starting from start date and ending on end date (both inclusive).

Parameters:

Name	Type	Description	Default
`tickers`	`list[str] \| str`	Tickers as a list of string or a comma separated string without space	required
`start`	`str`	Start date in string format 'YYYY-MM-DD'	`'2000-01-01'`
`end`	`str`	End date in string format 'YYYY-MM-DD'	`None`
`align`	`bool`	True to align data to start on the same date, i.e. drop leading days when not all tickers have data available.	`True`
`normalize`	`bool`	True to normalize the close price on the start date to 1 for all tickers and scale all price data accordingly.	`False`
`num_workers`	`int`	Number of parallel workers to use for fetching data.	`1`

Returns:

Type	Description
`DataFrame`	A dataframe with 2-level columns, first level being the tickers, and the second level being columns 'Open', 'High', 'Low', 'Close', 'Volume'. The dataframe is indexed by datetime.

get_source `staticmethod` ¤

get_source(name: str, **kwargs: dict[str, Any]) -> QuoteSource

Get quote source by name

Parameters:

Name	Type	Description	Default
`name`	`str`	Quote source name	required
`kwargs`	`dict[str, Any]`	Keyword arguments to be passed to the quote source constructor	`{}`

Returns:

Type	Description
`QuoteSource`	A QuoteSource instance

Raises:

Type	Description
`ValueError`	If the asked data source is not supported

is_trading_now ¤

is_trading_now(ticker: str) -> bool

Check if a ticker is trading now.

list `staticmethod` ¤

list() -> list[str]

List all available quote sources.

minute_bar ¤

minute_bar(tickers: list[str] | str, start: str = None, end: str = None, interval: int = 1, num_workers: int = 1) -> DataFrame

Read minute OHLCV data for a ticker starting from start date and ending on end date (both inclusive).

Parameters:

Name	Type	Description	Default
`tickers`	`list[str] \| str`	Tickers as a list or comma-separated string	required
`start`	`str`	Start date in string format 'YYYY-MM-DD'	`None`
`end`	`str`	End date in string format 'YYYY-MM-DD'	`None`
`interval`	`int`	Interval in minutes	`1`
`num_workers`	`int`	Number of parallel workers to use for fetching data.	`1`

Returns:

Type	Description
`DataFrame`	A dataframe with columns 'Open', 'High', 'Low', 'Close', 'Volume' indexed by datetime

monthly_bar ¤

monthly_bar(tickers: list[str] | str, start: str = '2020-01-01', end: str = None, align: bool = True, normalize: bool = False, num_workers: int = 1) -> DataFrame

Read monthly OHLCV data for a list of tickers starting from start date and ending on end date (both inclusive).

Parameters:

Name	Type	Description	Default
`tickers`	`list[str] \| str`	Tickers as a list of string or a comma separated string without space	required
`start`	`str`	Start date in string format 'YYYY-MM'	`'2020-01-01'`
`end`	`str`	End date in string format 'YYYY-MM'	`None`
`align`	`bool`	True to align data to start on the same date, i.e. drop leading days when not all tickers have data available.	`True`
`normalize`	`bool`	True to normalize the close price on the start date to 1 for all tickers and scale all price data accordingly.	`False`
`num_workers`	`int`	Number of parallel workers to use for fetching data.	`1`

Returns:

Type	Description
`DataFrame`	A dataframe with 2-level columns, first level being the tickers, and the second level being columns 'Open', 'High', 'Low', 'Close', 'Volume'. The dataframe is indexed by last day of month.

spot ¤

spot(tickers: list[str] | str) -> Series

Read current quote for a list of tickers.

Parameters:

Name	Type	Description	Default
`tickers`	`list[str] \| str`	Tickers as a list of string or a comma separated string without space	required

Returns:

Type	Description
`Series`	Current quotes indexed by ticker as a pandas Series

ticker_calendar ¤

ticker_calendar(ticker: str) -> str

Get the calendar name of a ticker.

ticker_timezone ¤

ticker_timezone(ticker: str) -> str

Get the timezone of a ticker.

today ¤

today(ticker: str) -> datetime

Get today's date in a ticker's local timezone.

minitrade.datasource¤