B5: complete the Python type stubs for all 25 indicators
The .pyi shipped stubs for only 9 of the 25 exported classes, so with py.typed set, type checkers flagged DEMA, TEMA, HMA, KAMA, CCI, ROC, WilliamsR, ADX, MFI, TRIX, PSAR, Keltner, Donchian, VWAP, AwesomeOscillator and Aroon as missing. All 16 are now stubbed with signatures matching python/src/lib.rs (constructor defaults, update return types, batch array shapes, lifecycle methods). Verified: the stub set equals the 25 registered classes and mypy type-checks a script exercising every class with no issues.
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kingchenc
@@ -135,3 +135,202 @@ class OBV:
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def warmup_period(self) -> int: ...
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@property
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def value(self) -> Optional[float]: ...
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class DEMA:
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def __init__(self, period: int) -> None: ...
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def update(self, value: float) -> Optional[float]: ...
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def batch(self, prices: NDArray[np.float64]) -> NDArray[np.float64]: ...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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@property
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def period(self) -> int: ...
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class TEMA:
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def __init__(self, period: int) -> None: ...
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def update(self, value: float) -> Optional[float]: ...
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def batch(self, prices: NDArray[np.float64]) -> NDArray[np.float64]: ...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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@property
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def period(self) -> int: ...
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class HMA:
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def __init__(self, period: int) -> None: ...
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def update(self, value: float) -> Optional[float]: ...
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def batch(self, prices: NDArray[np.float64]) -> NDArray[np.float64]: ...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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@property
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def period(self) -> int: ...
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class KAMA:
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def __init__(self, er_period: int = 10, fast: int = 2, slow: int = 30) -> None: ...
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def update(self, value: float) -> Optional[float]: ...
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def batch(self, prices: NDArray[np.float64]) -> NDArray[np.float64]: ...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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class CCI:
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def __init__(self, period: int = 20) -> None: ...
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def update(self, candle: CandleLike) -> Optional[float]: ...
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def batch(
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self,
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high: NDArray[np.float64],
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low: NDArray[np.float64],
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close: NDArray[np.float64],
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) -> NDArray[np.float64]: ...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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@property
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def period(self) -> int: ...
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class ROC:
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def __init__(self, period: int) -> None: ...
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def update(self, value: float) -> Optional[float]: ...
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def batch(self, prices: NDArray[np.float64]) -> NDArray[np.float64]: ...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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@property
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def period(self) -> int: ...
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class WilliamsR:
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def __init__(self, period: int = 14) -> None: ...
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def update(self, candle: CandleLike) -> Optional[float]: ...
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def batch(
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self,
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high: NDArray[np.float64],
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low: NDArray[np.float64],
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close: NDArray[np.float64],
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) -> NDArray[np.float64]: ...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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class ADX:
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def __init__(self, period: int = 14) -> None: ...
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def update(self, candle: CandleLike) -> Optional[Tuple[float, float, float]]: ...
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def batch(
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self,
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high: NDArray[np.float64],
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low: NDArray[np.float64],
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close: NDArray[np.float64],
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) -> NDArray[np.float64]:
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"""Returns shape ``(n, 3)`` with columns ``[plus_di, minus_di, adx]``."""
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...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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class MFI:
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def __init__(self, period: int = 14) -> None: ...
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def update(self, candle: CandleLike) -> Optional[float]: ...
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def batch(
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self,
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high: NDArray[np.float64],
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low: NDArray[np.float64],
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close: NDArray[np.float64],
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volume: NDArray[np.float64],
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) -> NDArray[np.float64]: ...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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class TRIX:
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def __init__(self, period: int) -> None: ...
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def update(self, value: float) -> Optional[float]: ...
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def batch(self, prices: NDArray[np.float64]) -> NDArray[np.float64]: ...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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class PSAR:
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def __init__(
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self, af_start: float = 0.02, af_step: float = 0.02, af_max: float = 0.20
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) -> None: ...
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def update(self, candle: CandleLike) -> Optional[float]: ...
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def batch(
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self,
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high: NDArray[np.float64],
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low: NDArray[np.float64],
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close: NDArray[np.float64],
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) -> NDArray[np.float64]: ...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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class Keltner:
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def __init__(
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self, ema_period: int = 20, atr_period: int = 10, multiplier: float = 2.0
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) -> None: ...
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def update(self, candle: CandleLike) -> Optional[Tuple[float, float, float]]: ...
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def batch(
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self,
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high: NDArray[np.float64],
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low: NDArray[np.float64],
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close: NDArray[np.float64],
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) -> NDArray[np.float64]:
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"""Returns shape ``(n, 3)`` with columns ``[upper, middle, lower]``."""
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...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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class Donchian:
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def __init__(self, period: int = 20) -> None: ...
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def update(self, candle: CandleLike) -> Optional[Tuple[float, float, float]]: ...
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def batch(
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self,
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high: NDArray[np.float64],
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low: NDArray[np.float64],
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) -> NDArray[np.float64]:
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"""Returns shape ``(n, 3)`` with columns ``[upper, middle, lower]``."""
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...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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class VWAP:
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def __init__(self) -> None: ...
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def update(self, candle: CandleLike) -> Optional[float]: ...
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def batch(
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self,
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high: NDArray[np.float64],
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low: NDArray[np.float64],
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close: NDArray[np.float64],
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volume: NDArray[np.float64],
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) -> NDArray[np.float64]: ...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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class AwesomeOscillator:
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def __init__(self, fast: int = 5, slow: int = 34) -> None: ...
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def update(self, candle: CandleLike) -> Optional[float]: ...
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def batch(
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self,
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high: NDArray[np.float64],
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low: NDArray[np.float64],
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) -> NDArray[np.float64]: ...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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class Aroon:
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def __init__(self, period: int = 14) -> None: ...
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def update(self, candle: CandleLike) -> Optional[Tuple[float, float]]: ...
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def batch(
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self,
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high: NDArray[np.float64],
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low: NDArray[np.float64],
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) -> NDArray[np.float64]:
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"""Returns shape ``(n, 2)`` with columns ``[up, down]``."""
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...
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def reset(self) -> None: ...
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def is_ready(self) -> bool: ...
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def warmup_period(self) -> int: ...
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