Module serve

Classes

class Period (*args, **kwds)

Create a collection of name/value pairs.

Example enumeration:

>>> class Color(Enum):
...     RED = 1
...     BLUE = 2
...     GREEN = 3

Access them by:

• attribute access:

Color.RED

• value lookup:

Color(1)

• name lookup:

Color['RED']

Enumerations can be iterated over, and know how many members they have:

>>> len(Color)
3

>>> list(Color)
[<Color.RED: 1>, <Color.BLUE: 2>, <Color.GREEN: 3>]

Methods can be added to enumerations, and members can have their own attributes – see the documentation for details.

Parent

• enum.Enum

Class variables

var DAILY

var HOURLY

var MONTHLY

var QUARTERLY

var WEEKLY

var YEARLY

class StatCalculator (state: StateSignals)

Class analog to the BacktestResults class. However, this class is used to calculate the stats in real time. It calculates the statistics based on a one-year moving window when possible. For some indicators, it is not possible because it would cause a difference in calculation of that metric between the inference and the backtest. Those metrics are marked as All-time in the comments.

Static methods

def prepare_data(state: StateSignals) ‑> (typing.List[str], )

Extracts the relevent up-to-date data from the state object and returns it as an array.

def strided_arr(a: numpy.ndarray, window: int)

Create a strided array to compute the rolling window of an array

Args:

a: The array
window: The window size

Returns:

The strided array. Shape(len(a) - window + 1, window)

Methods

def compute_calmar_ratio(self, yearly_max_drawdown: pandas.core.series.Series) ‑> Optional[float]

Compute the calmar ratio of the strategy

Args:

yearly_max_drawdown: The yearly maximum drawdown of the strategy

Returns:

The calmar ratio of the strategy

def compute_sharp_ratio(self, risk_free_rate) ‑> float

Compute the sharp ratio of the strategy on a weekly basis

Args:

risk_free_rate: The risk free rate or MAR (Minimum acceptable return)

Returns:

The sharp ratio of the strategy

def compute_sortino_ratio(self, risk_free_rate) ‑> float

Compute the sortino ratio of the strategy on a weekly basis

Args:

risk_free_rate: The risk free rate or MAR (Minimum acceptable return)

Returns:

The sortino ratio of the strategy

def get_drawdown(self, equity_history: numpy.ndarray, equity_timestamps: numpy.ndarray, window: Union[datetime.timedelta, int] = datetime.timedelta(days=365)) ‑> numpy.ndarray[typing.Any, numpy.dtype[numpy.float32]]

Get the maximum drawdown of the strategy for each timestep (Causally - so looking back in time)

Args:

equity_history: The equity history of the strategy (Worth of the portoflio evolution over time)
equity_timestamps: The timestamps of the equity history
window: The lookback window to use to compute the drawdown

Returns:

A time series of the maximum drawdown of the strategy for each time steps

def get_ohlc(self, period: Period)

Return a OHLC dataframe of the equity history corresponding to the period.

Args:

period: The period to use to make the OHLC dataframe

Returns:

The OHLC dataframe