Pratik Bhadane
263 lines
8.1 KiB
Python
263 lines
8.1 KiB
Python
"""
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Batch Execution API — run indicators on multiple series in a single call.
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This module provides a 2-D batch API that accepts a 2-D numpy array
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(n_samples × n_series) and applies an indicator to every column, returning
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a 2-D output array of the same shape.
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For the most common indicators — SMA, EMA, RSI — the 2-D path is handled
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entirely in Rust (a single GIL release for all columns). The generic
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``batch_apply`` is available for other indicators that do not have a Rust
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batch implementation.
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Functions
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---------
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batch_sma — SMA on every column of a 2-D array (Rust fast path for 2-D)
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batch_ema — EMA on every column of a 2-D array (Rust fast path for 2-D)
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batch_rsi — RSI on every column of a 2-D array (Rust fast path for 2-D)
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batch_apply — Generic batch wrapper (Python loop) for any arbitrary indicator
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Usage
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-----
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>>> import numpy as np
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>>> from ferro_ta.data.batch import batch_sma
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>>> data = np.random.rand(100, 5) # 100 bars, 5 symbols
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>>> result = batch_sma(data, timeperiod=14)
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>>> result.shape
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(100, 5)
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"""
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from __future__ import annotations
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from collections.abc import Callable
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import numpy as np
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from numpy.typing import ArrayLike
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from ferro_ta._ferro_ta import (
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batch_adx as _rust_batch_adx,
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)
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from ferro_ta._ferro_ta import (
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batch_atr as _rust_batch_atr,
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)
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from ferro_ta._ferro_ta import (
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batch_ema as _rust_batch_ema,
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)
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from ferro_ta._ferro_ta import (
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batch_rsi as _rust_batch_rsi,
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)
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from ferro_ta._ferro_ta import (
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batch_sma as _rust_batch_sma,
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)
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from ferro_ta._ferro_ta import (
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batch_stoch as _rust_batch_stoch,
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)
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from ferro_ta.indicators.momentum import RSI
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from ferro_ta.indicators.overlap import EMA, SMA
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__all__ = [
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"batch_sma",
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"batch_ema",
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"batch_rsi",
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"batch_apply",
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]
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def batch_apply(
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data: ArrayLike,
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fn: Callable[..., np.ndarray],
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**kwargs,
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) -> np.ndarray:
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"""Apply any single-series indicator *fn* to every column of *data*.
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This is the generic fallback batch executor — it calls *fn* once per
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column in a Python loop. For the common indicators SMA, EMA, and RSI
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prefer the dedicated :func:`batch_sma`, :func:`batch_ema`, and
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:func:`batch_rsi` functions, which use a Rust-side loop and avoid
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per-column Python round-trips.
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Parameters
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----------
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data : array-like, shape (n_samples,) or (n_samples, n_series)
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Input data. If 1-D, the function is called directly on the array
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and the result is returned without adding a column dimension.
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fn : callable
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Single-series indicator function (e.g. ``SMA``, ``EMA``, ``RSI``).
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It must accept a 1-D array as first positional argument and return
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a 1-D array of the same length.
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**kwargs
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Extra keyword arguments forwarded to *fn* (e.g. ``timeperiod=14``).
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Returns
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-------
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numpy.ndarray
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Same shape as *data*. Leading values are ``NaN`` for the warm-up
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period, identical to calling *fn* on each column individually.
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Examples
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--------
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>>> import numpy as np
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>>> from ferro_ta import SMA
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>>> from ferro_ta.data.batch import batch_apply
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>>> data = np.random.rand(50, 3)
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>>> out = batch_apply(data, SMA, timeperiod=5)
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>>> out.shape
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(50, 3)
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"""
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arr = np.asarray(data, dtype=np.float64)
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if arr.ndim == 1:
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return fn(arr, **kwargs)
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if arr.ndim != 2:
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raise ValueError(f"batch_apply expects 1-D or 2-D input; got {arr.ndim}-D")
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n_samples, n_series = arr.shape
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result = np.empty((n_samples, n_series), dtype=np.float64)
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for j in range(n_series):
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result[:, j] = fn(arr[:, j], **kwargs)
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return result
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def batch_sma(
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data: ArrayLike,
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timeperiod: int = 30,
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parallel: bool = True,
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) -> np.ndarray:
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"""Simple Moving Average on every column of *data*.
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For 2-D inputs uses a Rust-side column loop (single GIL release).
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When *parallel* is ``True`` (default), columns are processed in parallel
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via Rayon across all available CPU cores.
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1-D input is passed directly to the single-series SMA.
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Parameters
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----------
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data : array-like, shape (n_samples,) or (n_samples, n_series)
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timeperiod : int, default 30
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parallel : bool, default True
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Enable multi-threaded parallel column processing via Rayon.
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Set to ``False`` for small inputs where thread overhead dominates.
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Returns
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-------
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numpy.ndarray — same shape as *data*.
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Examples
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--------
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>>> import numpy as np
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>>> from ferro_ta.data.batch import batch_sma
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>>> data = np.arange(1.0, 101.0).reshape(100, 1).repeat(3, axis=1)
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>>> out = batch_sma(data, timeperiod=10)
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>>> out.shape
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(100, 3)
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"""
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arr = np.ascontiguousarray(data, dtype=np.float64)
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if arr.ndim == 1:
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return SMA(arr, timeperiod=timeperiod)
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if arr.ndim != 2:
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raise ValueError(f"batch_sma expects 1-D or 2-D input; got {arr.ndim}-D")
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return np.asarray(_rust_batch_sma(arr, timeperiod, parallel))
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def batch_ema(
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data: ArrayLike,
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timeperiod: int = 30,
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parallel: bool = True,
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) -> np.ndarray:
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"""Exponential Moving Average on every column of *data*.
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For 2-D inputs uses a Rust-side column loop (single GIL release).
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When *parallel* is ``True`` (default), columns are processed in parallel
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via Rayon across all available CPU cores.
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Parameters
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----------
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data : array-like, shape (n_samples,) or (n_samples, n_series)
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timeperiod : int, default 30
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parallel : bool, default True
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Enable multi-threaded parallel column processing via Rayon.
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Returns
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-------
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numpy.ndarray — same shape as *data*.
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"""
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arr = np.ascontiguousarray(data, dtype=np.float64)
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if arr.ndim == 1:
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return EMA(arr, timeperiod=timeperiod)
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if arr.ndim != 2:
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raise ValueError(f"batch_ema expects 1-D or 2-D input; got {arr.ndim}-D")
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return np.asarray(_rust_batch_ema(arr, timeperiod, parallel))
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def batch_rsi(
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data: ArrayLike,
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timeperiod: int = 14,
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parallel: bool = True,
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) -> np.ndarray:
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"""Relative Strength Index on every column of *data*.
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For 2-D inputs uses a Rust-side column loop (single GIL release).
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When *parallel* is ``True`` (default), columns are processed in parallel
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via Rayon across all available CPU cores.
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Parameters
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----------
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data : array-like, shape (n_samples,) or (n_samples, n_series)
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timeperiod : int, default 14
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parallel : bool, default True
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Enable multi-threaded parallel column processing via Rayon.
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Returns
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-------
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numpy.ndarray — same shape as *data*. Values in [0, 100].
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"""
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arr = np.ascontiguousarray(data, dtype=np.float64)
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if arr.ndim == 1:
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return RSI(arr, timeperiod=timeperiod)
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if arr.ndim != 2:
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raise ValueError(f"batch_rsi expects 1-D or 2-D input; got {arr.ndim}-D")
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return np.asarray(_rust_batch_rsi(arr, timeperiod, parallel))
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def batch_atr(
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high: ArrayLike,
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low: ArrayLike,
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close: ArrayLike,
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timeperiod: int = 14,
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parallel: bool = True,
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) -> np.ndarray:
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h = np.ascontiguousarray(high, dtype=np.float64)
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low_arr = np.ascontiguousarray(low, dtype=np.float64)
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c = np.ascontiguousarray(close, dtype=np.float64)
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return np.asarray(_rust_batch_atr(h, low_arr, c, timeperiod, parallel))
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def batch_stoch(
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high: ArrayLike,
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low: ArrayLike,
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close: ArrayLike,
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fastk_period: int = 5,
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slowk_period: int = 3,
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slowd_period: int = 3,
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parallel: bool = True,
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) -> tuple[np.ndarray, np.ndarray]:
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h = np.ascontiguousarray(high, dtype=np.float64)
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low_arr = np.ascontiguousarray(low, dtype=np.float64)
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c = np.ascontiguousarray(close, dtype=np.float64)
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k, d = _rust_batch_stoch(
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h, low_arr, c, fastk_period, slowk_period, slowd_period, parallel
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)
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return np.asarray(k), np.asarray(d)
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def batch_adx(
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high: ArrayLike,
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low: ArrayLike,
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close: ArrayLike,
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timeperiod: int = 14,
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parallel: bool = True,
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) -> np.ndarray:
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h = np.ascontiguousarray(high, dtype=np.float64)
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low_arr = np.ascontiguousarray(low, dtype=np.float64)
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c = np.ascontiguousarray(close, dtype=np.float64)
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return np.asarray(_rust_batch_adx(h, low_arr, c, timeperiod, parallel))
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