AlphaModel

class qf_lib.backtesting.alpha_model.alpha_model.AlphaModel(risk_estimation_factor: float, data_provider: qf_lib.data_providers.data_provider.DataProvider)

Bases: object

Base class for all alpha models.

Parameters

• risk_estimation_factor – float value which estimates the risk level of the specific AlphaModel. Corresponds to the level at which the stop-loss should be placed.

• data_provider (DataProvider) – DataProvider which provides data for the ticker. For the backtesting purposes, in order to avoid looking into the future, use DataHandler wrapper.

Methods

calculate_exposure(ticker, current_exposure)	Returns the expected Exposure, which is the key part of a generated Signal.
calculate_fraction_at_risk(ticker)	Returns the float value which determines the risk factor for an AlphaModel and a specified Ticker, may be used to calculate the position size.
get_signal(ticker, current_exposure, …)	Returns the Signal calculated for a specific AlphaModel and a set of data for a specified Ticker

abstract calculate_exposure(ticker: qf_lib.common.tickers.tickers.Ticker, current_exposure: qf_lib.backtesting.alpha_model.exposure_enum.Exposure) → qf_lib.backtesting.alpha_model.exposure_enum.Exposure

Returns the expected Exposure, which is the key part of a generated Signal. Exposure suggests the trend direction for managing the trading position. Uses DataHandler passed when the AlphaModel (child) is initialized - all required data is provided in the child class.

Parameters

• ticker (Ticker) – Ticker for which suggested signal exposure is calculated.

• current_exposure (Exposure) – The actual exposure, based on which the AlphaModel should return its Signal. Can be different from previous Signal suggestions, but it should correspond with the current trading position

calculate_fraction_at_risk(ticker: qf_lib.common.tickers.tickers.Ticker) → float

Returns the float value which determines the risk factor for an AlphaModel and a specified Ticker, may be used to calculate the position size.

For example: Value of 0.02 means that we should place a Stop Loss 2% below the latest available price of the instrument. This value should always be positive

Parameters

ticker (Ticker) – Ticker for which the calculation should be made

Returns

percentage_at_risk value for an AlphaModel and a Ticker, calculated as Normalized Average True Range multiplied by the risk_estimation_factor, being a property of each AlphaModel: fraction_at_risk = ATR / last_close * risk_estimation_factor

Return type

float

get_signal(ticker: qf_lib.common.tickers.tickers.Ticker, current_exposure: qf_lib.backtesting.alpha_model.exposure_enum.Exposure, current_time: Optional[datetime.datetime] = None, frequency: qf_lib.common.enums.frequency.Frequency = <Frequency.DAILY: 252>) → qf_lib.backtesting.signals.signal.Signal

Returns the Signal calculated for a specific AlphaModel and a set of data for a specified Ticker

Parameters

• ticker (Ticker) – A ticker of an asset for which the Signal should be generated

• current_exposure (Exposure) – The actual exposure, based on which the AlphaModel should return its Signal. Can be different from previous Signal suggestions, but it should correspond with the current trading position

• current_time (Optional[datetime]) – current time, which is afterwards recorded inside each of the Signals. The parameter is optional and if not provided, defaults to the current user time.

• frequency (Optional[Frequency]) – frequency of trading. Optional parameter, with the default value being equal to daily frequency. Used to obtain the last available price.

Returns

Signal being the suggestion for the next trading period

Return type

Signal