Utilities#

Universe

Defines a timeseries of constituents of a some grouping of instruments (for example constituents of S&P 500, or all stocks in a region, or something else)

UniverseFilterTs

A class implementing a universe filter timeseries.

UniverseGenerator

Universe generator from SIGMaster to define universes over a data range.

StrategySingleStockFilter

A class implementing a full set of features for single stock filters.

StrategySingleStockFilterTs

A class generating single stock universes based on customized filters.

SingleStockFilter

A class implementing a single stock filter.

FirmLevelSingleStockFilter

A class implementing a single stock filter (firm level).

SIGMaster

SIGMaster using FrameworkObject

LiquidityIndicators

Collection of liquidity indicators used to rank and select stocks.

EquityGroup

A class representing an equity group.

FactorExposures

A class implementing estimation methods for exposures to factors for a selection of instruments, i.e. performing a series of multivariate time-series regressions based on a configuration.

EquityUniverseFilter

Class designed to filter stocks by fundamentals, e.g.
sigtech.framework.strategies.equity_factor_basket.default_factors_to_weight_function(factors: pandas.core.frame.DataFrame, factor_weights: Optional[dict] = None, z_score: bool = False, proportion: Optional[float] = None, long_only: bool = False, **kwargs) → pandas.core.series.Series

Default factor to weight function - This creates a linear combination of factors.

If proportion is not passed, stock weights are proportional to the sum of the input factor loadings. If proportion is passed, the top proportion % of stocks are given weights of 1 and the bottom proportion % are given weights of -1. If long_only=True (default False) the negative weights are truncated.

Parameters

  • factors – Factor indexed pd.DataFrame of stock loadings. Stocks as columns.

  • factor_weights – Optional dictionary of factor weights. Keys should match factors index.

  • z_score – Boolean of whether to normalise stock-factor loadings before combination. This allows different factors to be put onto the same scale. Default is False.

  • proportion – Optional, proportion for long/short allocations.

  • long_only – If proportion is passed with long_only=True, only the top proportion % are returned with positive values and the negative mirror image is removed. If long_only=False both the positive and negative weights are returned. If proportion is not passed this parameter has no effect.

Returns

Series of stock weights. Index will be (possibly subset of) columns of factors.

sigtech.framework.strategies.equity_factor_basket.factor_weighted_factors_to_weight_function(factors: pandas.core.frame.DataFrame, factor_weights: Optional[dict] = None, z_score: bool = False, proportion: Optional[float] = None, long_only: bool = False, **kwargs) → pandas.core.series.Series

Similar to default_factors_to_weight_function but weight the stocks proportionally to their summed factor scores.

sigtech.framework.instruments.equities.get_liquidity_df(liquidity_function, tickers: Optional[Union[str, list[str]]] = None, exchange_ticker: Optional[str] = None, dt: Optional[datetime.date] = None, rolling_window: int = 60, min_periods_ratio: float = 0.75, filters: Optional[dict] = None) → pandas.core.frame.DataFrame

Return a DataFrame containing the liquidity values for all the tickers in the given universe of listings. This information can be used to compute the most liquid listing.

Parameters

  • liquidity_function – The liquidity function to be used.

  • tickers – Tradable ticker used to find all relevant listings or list of tradable tickers. If not provided, exchange_ticker must be provided.

  • exchange_ticker – Exchange ticker used to find all relevant listings. If not provided, tickers must be provided.

  • dt – Date used to compute the liquidity metric among the listings. If not provided, will be equal to the environment date.

  • rolling_window – Rolling window value (default is 60).

  • min_periods_ratio – Minimum number of observations in window required to have a value, as percentage of rolling_window (e.g. 0.75 for 75%). Default is 0.75.

  • filters – Dict of additional SIGMaster filters.

Returns

pandas DataFrame.

Example:

from sigtech.framework.equities.liquidity_indicators import LiquidityIndicators, get_liquidity_df

# All listings with exchange ticker 'AAPL' are used as input.
get_liquidity_df(exchange_ticker='AAPL',
                 rolling_window=30,
                 liquidity_function=LiquidityIndicators.rolling_dollar_vol)
sigtech.framework.instruments.equities.get_most_liquid_stock(liquidity_function, tickers: Optional[Union[str, list[str]]] = None, exchange_ticker: Optional[str] = None, dt: Optional[datetime.date] = None, rolling_window: int = 60, min_periods_ratio: float = 0.75, filters: Optional[dict] = None) → Optional[str]

Return the most liquid listing given a tradable ticker or exchange ticker.

Parameters

  • liquidity_function – The liquidity function to be used.

  • tickers – Tradable ticker used to find all relevant listings or list of tradable tickers. If not provided, exchange_ticker must be provided.

  • exchange_ticker – Exchange ticker used to find all relevant listings. If not provided, tickers must be provided.

  • dt – Date used to compute the liquidity metric among the listings. If not provided, will be equal to the environment date.

  • rolling_window – Rolling window value (default is 60).

  • min_periods_ratio – Minimum number of observations in window required to have a value, as percentage of rolling_window (e.g. 0.75 for 75%). Default is 0.75.

  • filters – Dict of additional SIGMaster filters.

Returns

Ticker of the stock with the highest median of rolling dollar volume for the input date or None if no data is available.

Example:

from sigtech.framework.equities.liquidity_indicators import LiquidityIndicators

# All listings with exchange ticker 'AAPL' are used as input.
sig.get_most_liquid_stock(exchange_ticker='AAPL',
                          rolling_window=30,
                          liquidity_function=LiquidityIndicators.rolling_dollar_vol,
                          filters={'EXCHANGE_COUNTRY_ISO2': ['US']})

# All listings with same exchange ticker as '1000045.SINGLE_STOCK.TRADABLE' are used as input.
sig.get_most_liquid_stock(tickers='1000045.SINGLE_STOCK.TRADABLE',
                          liquidity_function=LiquidityIndicators.rolling_dollar_vol,
                          filters={'EXCHANGE_COUNTRY_ISO2': ['EUROPE']})

# *Only* the listings provided in the list are used as input.
sig.get_most_liquid_stock(tickers=['1000045.SINGLE_STOCK.TRADABLE', '1000046.SINGLE_STOCK.TRADABLE'],
                          liquidity_function=LiquidityIndicators.rolling_dollar_vol)