algo_engine.utils

Utility functions for time-series generation and test data creation.

Time-Series Utilities

algo_engine.utils.ts_indices(market_date: date | SessionDate = None, interval: timedelta | float = 60.0, session_start: time | SessionTime = None, session_end: time | SessionTime = None, session_breaks: list[tuple[time, time]] | SessionBreak = None, ts_mode: Literal['start', 'end', 'both'] | str = 'end', ts_format='timestamp') → list[float]

algo_engine.utils.ts_indices(market_date: date | SessionDate = None, interval: timedelta | float = 60.0, session_start: time | SessionTime = None, session_end: time | SessionTime = None, session_breaks: list[tuple[time, time]] | SessionBreak = None, ts_mode: Literal['start', 'end', 'both'] | str = 'end', ts_format='datetime') → list[datetime]

algo_engine.utils.ts_indices(market_date: date | SessionDate = None, interval: timedelta | float = 60.0, session_start: time | SessionTime = None, session_end: time | SessionTime = None, session_breaks: list[tuple[time, time]] | SessionBreak = None, ts_mode: Literal['start', 'end', 'both'] | str = 'end', ts_format='session_time') → list[SessionTime]

Fake Data Generators

algo_engine.utils.fake_data(market_date: date, p0: float = 100.0, volatility: float = 0.2, interval: float = 60.0, **kwargs) → DataFrame

algo_engine.utils.fake_daily_data(start_date: date, end_date: date, p0: float = 100.0, volatility: float = 0.2, calendar: list[date] = None, **kwargs) → DataFrame