diff --git a/README.md b/README.md
index c1a96567d..f6fa43b50 100644
--- a/README.md
+++ b/README.md
@@ -614,7 +614,7 @@ Intraday bars: 1m / 5m / 15m / 30m / 1H / 4H / 1D. 15 metrics + benchmark compar
-Quant Library 286 tested functions across 19 modules, callable from every transport
+Quant Library 289 tested functions across 19 modules, callable from every transport
`src/quantlib` holds one tested implementation of each piece of finance math the
agent needs. Skills **import** these rather than carrying formulas inside
diff --git a/README_ar.md b/README_ar.md
index 48bae5d04..fc57c335d 100644
--- a/README_ar.md
+++ b/README_ar.md
@@ -610,7 +610,7 @@ LONGBRIDGE_ACCESS_TOKEN=...
-Quant Library 286 دالة مختبَرة عبر 19 وحدة، قابلة للاستدعاء من كل المسارات
+Quant Library 289 دالة مختبَرة عبر 19 وحدة، قابلة للاستدعاء من كل المسارات
يحتفظ `src/quantlib` بتنفيذ مختبَر **واحد فقط** لكل قطعة من الرياضيات المالية التي
يحتاجها الـ agent. صارت الـ skills **تستورد** هذه الدوال بدلاً من حمل الصيغ داخل كتل
diff --git a/README_es.md b/README_es.md
index ab9c70de6..61dfd478d 100644
--- a/README_es.md
+++ b/README_es.md
@@ -615,7 +615,7 @@ Barras intradía: 1m / 5m / 15m / 30m / 1H / 4H / 1D. 15 métricas + comparació
-Quant Library 286 funciones probadas en 19 módulos, invocables desde cualquier transporte
+Quant Library 289 funciones probadas en 19 módulos, invocables desde cualquier transporte
`src/quantlib` contiene una implementación probada de cada pieza de matemática
financiera que el agente necesita. Las skills **importan** estas funciones en
diff --git a/README_ja.md b/README_ja.md
index 494a2ca08..2a17454da 100644
--- a/README_ja.md
+++ b/README_ja.md
@@ -610,7 +610,7 @@ Intraday bars: 1m / 5m / 15m / 30m / 1H / 4H / 1D. 15 metrics + benchmark compar
-Quant Library 19 モジュール・286 個のテスト済み関数、すべての経路から呼び出し可能
+Quant Library 19 モジュール・289 個のテスト済み関数、すべての経路から呼び出し可能
`src/quantlib` は、agent が必要とする金融数学のそれぞれについて、テスト済みの実装を
**1 つだけ**保持します。skill はこれらを **import** するようになり、markdown コード
diff --git a/README_ko.md b/README_ko.md
index 092f4db1d..ebbfb288f 100644
--- a/README_ko.md
+++ b/README_ko.md
@@ -610,7 +610,7 @@ Intraday bars: 1m / 5m / 15m / 30m / 1H / 4H / 1D. 15 metrics + benchmark compar
-Quant Library 19개 모듈 286개의 테스트된 함수, 모든 경로에서 호출 가능
+Quant Library 19개 모듈 289개의 테스트된 함수, 모든 경로에서 호출 가능
`src/quantlib`는 agent가 필요로 하는 각 금융 수학에 대해 테스트된 구현을 **하나씩만**
보유합니다. skill은 이제 이 함수들을 **import**하며, markdown 코드 블록 안에 수식을
diff --git a/README_zh.md b/README_zh.md
index 61a55db26..38abad5de 100644
--- a/README_zh.md
+++ b/README_zh.md
@@ -607,7 +607,7 @@ LONGBRIDGE_ACCESS_TOKEN=...
-Quant Library 19 个模块 286 个经测试的函数，四条通路皆可调用
+Quant Library 19 个模块 289 个经测试的函数，四条通路皆可调用
`src/quantlib` 为 agent 需要的每一块金融数学各提供**一份**经测试的实现。skill 现在是
**import** 这些函数，而不再把公式抄在 markdown 代码块里——如果你在某个 `SKILL.md`
diff --git a/agent/src/quantlib/volatility.py b/agent/src/quantlib/volatility.py
new file mode 100644
index 000000000..3b9def497
--- /dev/null
+++ b/agent/src/quantlib/volatility.py
@@ -0,0 +1,181 @@
+"""Heston (1993) stochastic volatility option pricing model.
+
+Implements semi-analytical European option pricing under the Heston model
+via numerical quadrature of the characteristic function (Lewis 2001 / Albrecher et al. 2007 formulation).
+
+Model dynamics:
+ dS_t = (r - q) * S_t * dt + sqrt(V_t) * S_t * dW_1,t
+ dV_t = kappa * (theta - V_t) * dt + sigma_v * sqrt(V_t) * dW_2,t
+ d_t = rho * dt
+
+Parameters:
+ S0: Initial spot price (> 0)
+ K: Strike price (> 0)
+ T: Time to expiration in years (> 0)
+ r: Continuously compounded risk-free rate
+ q: Continuously compounded dividend yield
+ v0: Initial variance (> 0)
+ kappa: Mean-reversion rate (> 0)
+ theta: Long-term variance (> 0)
+ sigma_v: Volatility of variance (> 0)
+ rho: Correlation between spot and variance Brownian motions in [-1, 1]
+"""
+
+from __future__ import annotations
+
+import cmath
+import math
+
+from scipy.integrate import quad
+from src.quantlib.options import normalise_option_type
+
+__all__ = [
+ "heston_characteristic_function",
+ "heston_price",
+ "heston_feller_condition",
+]
+
+
+def heston_feller_condition(kappa: float, theta: float, sigma_v: float) -> dict[str, float | bool]:
+ """Check Feller condition (2 * kappa * theta > sigma_v**2).
+
+ When satisfied, variance process V_t strictly stays positive (never reaches zero).
+
+ Args:
+ kappa: Mean reversion rate.
+ theta: Long-term variance.
+ sigma_v: Volatility of variance.
+
+ Returns:
+ dict with keys:
+ * ``feller_ratio`` (float): 2 * kappa * theta / (sigma_v**2)
+ * ``is_satisfied`` (bool): True if feller_ratio > 1.0.
+ """
+ if sigma_v <= 0.0:
+ raise ValueError(f"sigma_v must be strictly positive, got {sigma_v}")
+ if kappa <= 0.0 or theta <= 0.0:
+ raise ValueError(f"kappa and theta must be strictly positive, got kappa={kappa}, theta={theta}")
+
+ ratio = float((2.0 * kappa * theta) / (sigma_v**2))
+ return {
+ "feller_ratio": ratio,
+ "is_satisfied": bool(ratio > 1.0),
+ }
+
+
+def heston_characteristic_function(
+ u: complex | float,
+ S0: float,
+ T: float,
+ r: float,
+ q: float,
+ v0: float,
+ kappa: float,
+ theta: float,
+ sigma_v: float,
+ rho: float,
+) -> complex:
+ """Evaluate the Heston characteristic function phi(u) for log(S_T).
+
+ Uses the Albrecher et al. (2007) formulation (Little Heston Trap stable branch).
+ """
+ i = 1j
+ x0 = math.log(S0)
+ sigma_sq = sigma_v**2
+
+ # Continuous branch under risk-neutral measure
+ term = kappa - i * rho * sigma_v * u
+ d = cmath.sqrt(term**2 + sigma_sq * (i * u + u**2))
+ g = (term - d) / (term + d)
+
+ exp_neg_dt = cmath.exp(-d * T)
+ one_minus_g_exp = 1.0 - g * exp_neg_dt
+ one_minus_g = 1.0 - g
+
+ C = (r - q) * i * u * T + (kappa * theta / sigma_sq) * (
+ (term - d) * T - 2.0 * cmath.log(one_minus_g_exp / one_minus_g)
+ )
+ D = ((term - d) / sigma_sq) * ((1.0 - exp_neg_dt) / one_minus_g_exp)
+
+ return cmath.exp(C + D * v0 + i * u * x0)
+
+
+def heston_price(
+ S0: float,
+ K: float,
+ T: float,
+ r: float,
+ v0: float,
+ kappa: float,
+ theta: float,
+ sigma_v: float,
+ rho: float,
+ option_type: str = "call",
+ q: float = 0.0,
+ integration_limit: float = 200.0,
+) -> float:
+ """Price a European option under the Heston stochastic volatility model.
+
+ Uses Lewis (2001) / Carr-Madan single-integral formulation with stable characteristic function.
+
+ Args:
+ S0: Current spot price (> 0).
+ K: Strike price (> 0).
+ T: Time to expiry in years (> 0).
+ r: Risk-free rate.
+ v0: Initial variance (> 0).
+ kappa: Mean reversion speed (> 0).
+ theta: Long-term variance (> 0).
+ sigma_v: Volatility of variance (> 0).
+ rho: Spot-variance correlation in [-1.0, 1.0].
+ option_type: 'call' or 'put'.
+ q: Continuous dividend yield.
+ integration_limit: Upper limit for numerical quadrature.
+
+ Returns:
+ Option price as a non-negative float.
+ """
+ if S0 <= 0.0 or K <= 0.0:
+ raise ValueError(f"Spot S0 and strike K must be positive, got S0={S0}, K={K}")
+ if T <= 0.0:
+ opt_type = normalise_option_type(option_type)
+ return float(max(0.0, S0 - K) if opt_type == "call" else max(0.0, K - S0))
+ if v0 < 0.0 or kappa <= 0.0 or theta <= 0.0 or sigma_v <= 0.0:
+ raise ValueError("v0 must be >= 0, and kappa, theta, sigma_v must be strictly positive")
+ if not (-1.0 <= rho <= 1.0):
+ raise ValueError(f"rho must be in [-1.0, 1.0], got {rho}")
+
+ opt_type = normalise_option_type(option_type)
+ k = math.log(S0 / K) + (r - q) * T
+
+ def integrand(u: float) -> float:
+ if u == 0.0:
+ return 0.0
+ # Centered characteristic function for u - i/2
+ phi = heston_characteristic_function(
+ u=u - 0.5j,
+ S0=1.0,
+ T=T,
+ r=0.0,
+ q=0.0,
+ v0=v0,
+ kappa=kappa,
+ theta=theta,
+ sigma_v=sigma_v,
+ rho=rho,
+ )
+ val = cmath.exp(-1j * u * k) * phi / (u**2 + 0.25)
+ return float(val.real)
+
+ integ, _ = quad(integrand, 0.0, integration_limit, limit=2000)
+ prefactor = (1.0 / math.pi) * math.sqrt(S0 * K) * math.exp(-0.5 * (r + q) * T)
+ call = float(S0 * math.exp(-q * T) - prefactor * integ)
+ call = float(max(0.0, call))
+
+ if opt_type == "call":
+ return call
+ else:
+ discounted_F = S0 * math.exp(-q * T)
+ discounted_K = K * math.exp(-r * T)
+ put = call - discounted_F + discounted_K
+ return float(max(0.0, put))
diff --git a/agent/src/tools/quantlib_tool.py b/agent/src/tools/quantlib_tool.py
index cdcff3638..e22c5ca12 100644
--- a/agent/src/tools/quantlib_tool.py
+++ b/agent/src/tools/quantlib_tool.py
@@ -82,6 +82,7 @@
"valuation.threestatement": "src.quantlib.valuation.threestatement",
"valuation.artifact": "src.quantlib.valuation.artifact",
"valuation.contracts": "src.quantlib.valuation.contracts",
+ "volatility": "src.quantlib.volatility",
}
#: Exported names refused because they write to a caller-supplied path. This
diff --git a/agent/tests/quantlib/test_volatility.py b/agent/tests/quantlib/test_volatility.py
new file mode 100644
index 000000000..843fd28ac
--- /dev/null
+++ b/agent/tests/quantlib/test_volatility.py
@@ -0,0 +1,95 @@
+"""Unit and property tests for Heston (1993) stochastic volatility model."""
+
+import math
+import pytest
+from src.quantlib.volatility import (
+ heston_characteristic_function,
+ heston_price,
+ heston_feller_condition,
+)
+from src.quantlib.options import bs_price
+
+
+class TestHestonModel:
+ def test_feller_condition(self):
+ # 2 * 1.5 * 0.04 = 0.12, sigma_v**2 = 0.575**2 = 0.330625 -> ratio ~ 0.363 (< 1, violated)
+ res = heston_feller_condition(kappa=1.5, theta=0.04, sigma_v=0.575)
+ assert not res["is_satisfied"]
+ assert pytest.approx(res["feller_ratio"], abs=1e-3) == 0.363
+
+ # Satisfied case: kappa=2.0, theta=0.1, sigma_v=0.2 -> 2*2*0.1 / 0.04 = 10.0 (> 1)
+ res_sat = heston_feller_condition(kappa=2.0, theta=0.1, sigma_v=0.2)
+ assert res_sat["is_satisfied"]
+ assert pytest.approx(res_sat["feller_ratio"], rel=1e-7) == 10.0
+
+ def test_characteristic_function_at_zero(self):
+ # phi(0) = E[e^0] = 1.0
+ cf = heston_characteristic_function(
+ u=0.0,
+ S0=100.0,
+ T=1.0,
+ r=0.05,
+ q=0.02,
+ v0=0.04,
+ kappa=1.5,
+ theta=0.04,
+ sigma_v=0.3,
+ rho=-0.5,
+ )
+ assert pytest.approx(cf.real, abs=1e-7) == 1.0
+ assert pytest.approx(cf.imag, abs=1e-7) == 0.0
+
+ def test_heston_moodley_benchmark(self):
+ # Moodley (2005) Table 2: S0=100, K=100, T=0.5, r=0.0, q=0.0, v0=0.04, kappa=1.5, theta=0.04, sigma_v=0.575, rho=-0.5711
+ # Call price = 5.0272...
+ price = heston_price(
+ S0=100.0,
+ K=100.0,
+ T=0.5,
+ r=0.0,
+ v0=0.04,
+ kappa=1.5,
+ theta=0.04,
+ sigma_v=0.575,
+ rho=-0.5711,
+ option_type="call",
+ )
+ assert pytest.approx(price, abs=1e-3) == 5.027
+
+ def test_put_call_parity(self):
+ S0, K, T, r, q = 100.0, 95.0, 1.0, 0.04, 0.01
+ v0, kappa, theta, sigma_v, rho = 0.04, 2.0, 0.04, 0.3, -0.7
+ call = heston_price(S0, K, T, r, v0, kappa, theta, sigma_v, rho, option_type="call", q=q)
+ put = heston_price(S0, K, T, r, v0, kappa, theta, sigma_v, rho, option_type="put", q=q)
+ # Parity: Call - Put = S0*exp(-qT) - K*exp(-rT)
+ parity = S0 * math.exp(-q * T) - K * math.exp(-r * T)
+ assert pytest.approx(call - put, abs=1e-4) == parity
+
+ def test_convergence_to_black_scholes_zero_vol_of_vol(self):
+ # When sigma_v -> 0, Heston approaches Black-Scholes with constant variance v0=theta
+ S0, K, T, r, q = 100.0, 100.0, 1.0, 0.05, 0.0
+ vol = 0.2
+ v0 = theta = vol**2
+ h_price = heston_price(
+ S0=S0,
+ K=K,
+ T=T,
+ r=r,
+ v0=v0,
+ kappa=1.0,
+ theta=theta,
+ sigma_v=1e-4,
+ rho=0.0,
+ option_type="call",
+ q=q,
+ )
+ bs = bs_price(S=S0, K=K, T=T, r=r, sigma=vol, option_type="call", q=q)
+ assert pytest.approx(h_price, abs=1e-3) == bs
+
+ def test_invalid_parameters_raise(self):
+ with pytest.raises(ValueError):
+ heston_price(S0=-100.0, K=100.0, T=1.0, r=0.05, v0=0.04, kappa=1.0, theta=0.04, sigma_v=0.3, rho=0.0)
+ with pytest.raises(ValueError):
+ heston_price(S0=100.0, K=100.0, T=1.0, r=0.05, v0=0.04, kappa=1.0, theta=0.04, sigma_v=0.3, rho=1.5)
+ with pytest.raises(ValueError):
+ heston_feller_condition(kappa=-1.0, theta=0.04, sigma_v=0.3)