Pratik Bhadane
602d675749
Implements all phases of the derivatives expansion plan: Rust core (crates/ferro_ta_core/src/options/, src/futures/): - BSM and Black-76 pricing (scalar + vectorized batch) - Greeks: delta, gamma, vega, theta, rho - Implied volatility solver (Newton + bisection fallback) - Smile/skew metrics: ATM IV, 25-delta RR/BF, skew slope, convexity - Chain helpers: moneyness labels, strike selection by offset or delta - Synthetic forwards, basis, annualized basis, implied carry, carry spread - Continuous contract stitching: weighted, back-adjusted, ratio-adjusted - Curve analytics: calendar spreads, slope, contango/backwardation summary PyO3 bindings (src/options/, src/futures/): - All Rust functions registered and exposed via _ferro_ta extension Python API (python/ferro_ta/analysis/): - options.py: pricing, greeks, IV, smile, chain, legacy iv_rank/percentile/zscore - futures.py: basis, carry, curve, roll, synthetic, continuous contracts - options_strategy.py: typed strategy schemas (expiry/strike selectors, leg presets, risk controls, simulation limits) - derivatives_payoff.py: multi-leg payoff aggregation and Greeks aggregation Bug fix: wrap _to_f64 calls in iv_rank/iv_percentile/iv_zscore to raise FerroTAInputError (not plain ValueError) for 2D array input. Docs: derivatives.rst, derivatives-analytics.md, options-volatility.md, quickstart.rst, index.rst, api/analysis.rst all updated. Tests: 2053 pass, 12 skipped. All CI checks pass locally. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
109 lines
2.7 KiB
Python
109 lines
2.7 KiB
Python
"""
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Derivatives benchmark hooks.
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These are intentionally optional and skip when `py_vollib` is unavailable.
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Run with:
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uv run pytest benchmarks/test_derivatives_speed.py --benchmark-only -v
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"""
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from __future__ import annotations
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import importlib.util
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import numpy as np
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import pytest
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from ferro_ta.analysis.options import implied_volatility, option_price
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def _sample_chain(n: int = 1000) -> tuple[np.ndarray, ...]:
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spot = np.linspace(90.0, 110.0, n)
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strike = np.full(n, 100.0)
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rate = np.full(n, 0.02)
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time_to_expiry = np.full(n, 0.5)
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volatility = np.full(n, 0.2)
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return spot, strike, rate, time_to_expiry, volatility
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def test_ferro_ta_option_price_speed(benchmark):
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spot, strike, rate, time_to_expiry, volatility = _sample_chain()
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benchmark.pedantic(
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lambda: option_price(
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spot,
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strike,
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rate,
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time_to_expiry,
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volatility,
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option_type="call",
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model="bsm",
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),
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iterations=5,
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rounds=20,
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warmup_rounds=2,
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)
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def test_ferro_ta_implied_vol_speed(benchmark):
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spot, strike, rate, time_to_expiry, volatility = _sample_chain()
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prices = option_price(
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spot,
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strike,
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rate,
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time_to_expiry,
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volatility,
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option_type="call",
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model="bsm",
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)
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benchmark.pedantic(
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lambda: implied_volatility(
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prices,
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spot,
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strike,
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rate,
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time_to_expiry,
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option_type="call",
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model="bsm",
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),
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iterations=5,
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rounds=20,
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warmup_rounds=2,
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)
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@pytest.mark.skipif(
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importlib.util.find_spec("py_vollib") is None,
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reason="py_vollib is optional",
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)
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def test_py_vollib_scalar_loop_baseline(benchmark):
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from py_vollib.black_scholes_merton import black_scholes_merton as py_vollib_bsm
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from py_vollib.black_scholes_merton.implied_volatility import (
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implied_volatility as py_vollib_iv,
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)
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spot, strike, rate, time_to_expiry, volatility = _sample_chain(250)
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prices = [
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py_vollib_bsm("c", float(s), float(k), float(t), float(r), float(vol), 0.0)
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for s, k, r, t, vol in zip(spot, strike, rate, time_to_expiry, volatility)
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]
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benchmark.pedantic(
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lambda: [
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py_vollib_iv(
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float(price),
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"c",
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float(s),
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float(k),
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float(t),
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float(r),
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0.0,
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)
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for price, s, k, r, t in zip(prices, spot, strike, rate, time_to_expiry)
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],
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iterations=3,
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rounds=10,
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warmup_rounds=1,
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)
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