Pratik Bhadane
207 lines
6.3 KiB
Python
207 lines
6.3 KiB
Python
"""
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ferro_ta.options — Options and Implied Volatility Helpers
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=========================================================
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Optional module that provides helpers for options/IV analysis when supplied
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with an implied-volatility series (IV series as input). All heavy compute
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delegates to Rust via ``ferro_ta`` core; this module is a thin orchestration
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layer.
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.. note::
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Options support is **optional** and does not require any additional
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third-party libraries beyond ``numpy``. For advanced option-pricing
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functionality (e.g. Black-Scholes, Greeks) install the optional
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``ferro_ta[options]`` extra which may pull in additional dependencies.
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See ``docs/options-volatility.md`` for the full design doc.
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Quick start
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-----------
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>>> import numpy as np
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>>> from ferro_ta.analysis.options import iv_rank, iv_percentile
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>>>
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>>> # Synthetic IV series (e.g. VIX or single-name IV)
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>>> rng = np.random.default_rng(42)
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>>> iv = rng.uniform(10, 40, 252)
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>>>
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>>> rank = iv_rank(iv, window=252)
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>>> pct = iv_percentile(iv, window=252)
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API
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---
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iv_rank(iv_series, window)
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Rolling IV rank: where is today's IV relative to min/max over *window* bars?
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Returns values in [0, 1] (NaN during warm-up).
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iv_percentile(iv_series, window)
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Rolling IV percentile: fraction of observations over *window* bars that are
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≤ today's IV. Returns values in [0, 1] (NaN during warm-up).
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iv_zscore(iv_series, window)
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Rolling IV z-score: (IV - rolling_mean) / rolling_std over *window* bars.
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Returns z-score values (NaN during warm-up).
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"""
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from __future__ import annotations
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import numpy as np
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from numpy.lib.stride_tricks import sliding_window_view
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from numpy.typing import ArrayLike, NDArray
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from ferro_ta.core.exceptions import FerroTAInputError, FerroTAValueError
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__all__ = [
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"iv_rank",
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"iv_percentile",
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"iv_zscore",
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]
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def _validate_iv(iv_series: NDArray[np.float64], window: int) -> NDArray[np.float64]:
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"""Validate and convert iv_series; check window."""
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arr = np.asarray(iv_series, dtype=np.float64)
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if arr.ndim != 1:
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raise FerroTAInputError("iv_series must be a 1-D array.")
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if len(arr) == 0:
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raise FerroTAInputError("iv_series must not be empty.")
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if window < 1:
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raise FerroTAValueError(f"window must be >= 1, got {window}.")
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return arr
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def iv_rank(
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iv_series: ArrayLike,
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window: int = 252,
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) -> NDArray[np.float64]:
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"""Compute rolling IV rank.
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IV rank measures where today's IV sits relative to the min/max of IV over
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the look-back *window*. A value of 1.0 means current IV is at its
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highest, 0.0 means it is at its lowest.
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Parameters
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----------
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iv_series : array-like
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1-D series of implied volatility values (e.g. VIX daily closes or
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single-name option IV). Any positive numeric values are accepted.
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window : int
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Look-back period in bars (default 252 ≈ 1 trading year).
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Returns
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-------
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ndarray of float64
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Rolling IV rank in [0, 1]. NaN for bars where the window is not yet
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full (i.e. the first ``window - 1`` bars).
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Examples
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--------
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>>> import numpy as np
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>>> from ferro_ta.analysis.options import iv_rank
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>>> iv = np.array([20.0, 25.0, 30.0, 15.0, 22.0])
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>>> iv_rank(iv, window=3)
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array([ nan, nan, 1. , 0. , 0.46666667])
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"""
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arr = _validate_iv(np.asarray(iv_series, dtype=np.float64), window)
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n = len(arr)
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out = np.full(n, np.nan, dtype=np.float64)
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if window > n:
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return out
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windows = sliding_window_view(arr, window_shape=window)
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lower = np.nanmin(windows, axis=1)
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upper = np.nanmax(windows, axis=1)
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current = arr[window - 1 :]
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spread = upper - lower
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out[window - 1 :] = np.where(spread == 0.0, 0.0, (current - lower) / spread)
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return out
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def iv_percentile(
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iv_series: ArrayLike,
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window: int = 252,
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) -> NDArray[np.float64]:
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"""Compute rolling IV percentile.
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IV percentile measures the fraction of days over the look-back *window*
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for which IV was *at or below* today's level. Unlike IV rank (which only
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considers min/max), IV percentile uses the full distribution of values.
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Parameters
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----------
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iv_series : array-like
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1-D series of implied volatility values.
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window : int
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Look-back period in bars (default 252).
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Returns
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-------
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ndarray of float64
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Rolling IV percentile in [0, 1]. NaN for bars before the window fills.
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Examples
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--------
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>>> import numpy as np
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>>> from ferro_ta.analysis.options import iv_percentile
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>>> iv = np.array([20.0, 25.0, 30.0, 15.0, 22.0])
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>>> iv_percentile(iv, window=3)
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array([ nan, nan, 1. , 0. , 0.33333333])
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"""
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arr = _validate_iv(np.asarray(iv_series, dtype=np.float64), window)
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n = len(arr)
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out = np.full(n, np.nan, dtype=np.float64)
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if window > n:
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return out
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windows = sliding_window_view(arr, window_shape=window)
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current = arr[window - 1 :, None]
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out[window - 1 :] = np.sum(windows <= current, axis=1, dtype=np.int64) / window
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return out
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def iv_zscore(
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iv_series: ArrayLike,
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window: int = 252,
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) -> NDArray[np.float64]:
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"""Compute rolling IV z-score.
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Measures how many standard deviations today's IV is above (positive) or
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below (negative) the rolling mean over *window* bars.
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Parameters
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----------
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iv_series : array-like
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1-D series of implied volatility values.
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window : int
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Look-back period in bars (default 252).
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Returns
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-------
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ndarray of float64
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Rolling z-score. NaN during warm-up (first ``window - 1`` bars) and
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when the rolling standard deviation is zero.
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Examples
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--------
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>>> import numpy as np
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>>> from ferro_ta.analysis.options import iv_zscore
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>>> iv = np.array([20.0, 25.0, 30.0, 15.0, 22.0])
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>>> z = iv_zscore(iv, window=3)
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>>> z[2] # (30 - 25) / std([20, 25, 30])
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np.float64(1.2247...)
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"""
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arr = _validate_iv(np.asarray(iv_series, dtype=np.float64), window)
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n = len(arr)
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out = np.full(n, np.nan, dtype=np.float64)
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if window > n:
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return out
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windows = sliding_window_view(arr, window_shape=window)
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mean = np.nanmean(windows, axis=1)
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std = np.nanstd(windows, axis=1, ddof=0)
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current = arr[window - 1 :]
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out[window - 1 :] = np.where(std == 0.0, np.nan, (current - mean) / std)
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return out
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