feat(quantlib): add finite-difference sensitivity Greeks for barrier options

README.md

@@ -614,7 +614,7 @@ Intraday bars: 1m / 5m / 15m / 30m / 1H / 4H / 1D. 15 metrics + benchmark compar
 </details>
 
 <details>
-<summary><b>Quant Library</b> <sub>286 tested functions across 19 modules, callable from every transport</sub></summary>
+<summary><b>Quant Library</b> <sub>287 tested functions across 19 modules, callable from every transport</sub></summary>
 
 `src/quantlib` holds one tested implementation of each piece of finance math the
 agent needs. Skills **import** these rather than carrying formulas inside

README_ar.md

@@ -610,7 +610,7 @@ LONGBRIDGE_ACCESS_TOKEN=...
 </details>
 
 <details>
-<summary><b>Quant Library</b> <sub>286 دالة مختبَرة عبر 19 وحدة، قابلة للاستدعاء من كل المسارات</sub></summary>
+<summary><b>Quant Library</b> <sub>287 دالة مختبَرة عبر 19 وحدة، قابلة للاستدعاء من كل المسارات</sub></summary>
 
 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

README_es.md

@@ -615,7 +615,7 @@ Barras intradía: 1m / 5m / 15m / 30m / 1H / 4H / 1D. 15 métricas + comparació
 </details>
 
 <details>
-<summary><b>Quant Library</b> <sub>286 funciones probadas en 19 módulos, invocables desde cualquier transporte</sub></summary>
+<summary><b>Quant Library</b> <sub>287 funciones probadas en 19 módulos, invocables desde cualquier transporte</sub></summary>
 
 `src/quantlib` contiene una implementación probada de cada pieza de matemática
 financiera que el agente necesita. Las skills **importan** estas funciones en

README_ja.md

@@ -610,7 +610,7 @@ Intraday bars: 1m / 5m / 15m / 30m / 1H / 4H / 1D. 15 metrics + benchmark compar
 </details>
 
 <details>
-<summary><b>Quant Library</b> <sub>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</sub></summary>
+<summary><b>Quant Library</b> <sub>19 モジュール・287 個のテスト済み関数、すべての経路から呼び出し可能</sub></summary>
 
 `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

README_ko.md

@@ -610,7 +610,7 @@ Intraday bars: 1m / 5m / 15m / 30m / 1H / 4H / 1D. 15 metrics + benchmark compar
 </details>
 
 <details>
-<summary><b>Quant Library</b> <sub>19개 모듈 286개의 테스트된 함수, 모든 경로에서 호출 가능</sub></summary>
+<summary><b>Quant Library</b> <sub>19개 모듈 287개의 테스트된 함수, 모든 경로에서 호출 가능</sub></summary>
 
 `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

README_zh.md

@@ -607,7 +607,7 @@ LONGBRIDGE_ACCESS_TOKEN=...
 </details>
 
 <details>
-<summary><b>Quant Library</b> <sub>19 个模块 286 个经测试的函数，四条通路皆可调用</sub></summary>
+<summary><b>Quant Library</b> <sub>19 个模块 287 个经测试的函数，四条通路皆可调用</sub></summary>
 
 `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`

agent/src/quantlib/options.py

@@ -32,6 +32,7 @@
 
 __all__ = [
     "BARRIER_TYPES",
+    "barrier_greeks",
     "barrier_option_price",
     "bs_greeks",
     "bs_price",
@@ -633,3 +634,69 @@ def barrier_option_price(
             raise ValueError(f"Unhandled barrier type {b_type}")
 
     return float(max(0.0, price))
+
+
+
+def barrier_greeks(
+    S: float,
+    K: float,
+    H: float,
+    T: float,
+    r: float,
+    sigma: float,
+    barrier_type: str,
+    option_type: str = "call",
+    q: float = 0.0,
+    rebate: float = 0.0,
+) -> dict[str, float]:
+    """Compute sensitivity Greeks for single-barrier options via central finite differences.
+
+    Matches the conventions of :func:`bs_greeks`:
+      * delta: per 1.0 of spot
+      * gamma: per 1.0 of spot squared
+      * theta: per calendar day (1/365)
+      * vega: per 1 percentage point of volatility (0.01)
+      * rho: per 1 percentage point of interest rate (0.01)
+
+    Args:
+        S, K, H, T, r, sigma, barrier_type, option_type, q, rebate: Standard barrier inputs.
+
+    Returns:
+        dict with keys: ``delta``, ``gamma``, ``theta``, ``vega``, ``rho``.
+    """
+    p = barrier_option_price(S, K, H, T, r, sigma, barrier_type, option_type, q, rebate)
+
+    dS = max(1e-4, 1e-4 * S)
+    p_up = barrier_option_price(S + dS, K, H, T, r, sigma, barrier_type, option_type, q, rebate)
+    p_down = barrier_option_price(S - dS, K, H, T, r, sigma, barrier_type, option_type, q, rebate)
+
+    delta = float((p_up - p_down) / (2.0 * dS))
+    gamma = float((p_up - 2.0 * p + p_down) / (dS**2))
+
+    # Theta (per calendar day): time decay moves forward, so T - dt
+    dt = 1.0 / 365.0
+    if T > dt:
+        p_dt = barrier_option_price(S, K, H, T - dt, r, sigma, barrier_type, option_type, q, rebate)
+        theta = float(p_dt - p)
+    else:
+        theta = 0.0
+
+    # Vega (per 1 percentage point = 0.01)
+    dvol = 1e-4
+    p_vol_up = barrier_option_price(S, K, H, T, r, sigma + dvol, barrier_type, option_type, q, rebate)
+    p_vol_down = barrier_option_price(S, K, H, T, r, max(1e-6, sigma - dvol), barrier_type, option_type, q, rebate)
+    vega = float((p_vol_up - p_vol_down) / (2.0 * dvol) * 0.01)
+
+    # Rho (per 1 percentage point = 0.01)
+    dr = 1e-4
+    p_r_up = barrier_option_price(S, K, H, T, r + dr, sigma, barrier_type, option_type, q, rebate)
+    p_r_down = barrier_option_price(S, K, H, T, r - dr, sigma, barrier_type, option_type, q, rebate)
+    rho = float((p_r_up - p_r_down) / (2.0 * dr) * 0.01)
+
+    return {
+        "delta": delta,
+        "gamma": gamma,
+        "theta": theta,
+        "vega": vega,
+        "rho": rho,
+    }

agent/tests/quantlib/test_options.py

@@ -20,12 +20,11 @@
 import pytest
 
 from src.quantlib.options import (
-    BARRIER_TYPES,
+    barrier_greeks,
     barrier_option_price,
     bs_greeks,
     bs_price,
     implied_volatility,
-    normalise_barrier_type,
     normalise_option_type,
 )
 
@@ -560,6 +559,17 @@ def test_already_breached_barrier_behavior(self):
         assert barrier_option_price(85.0, 100.0, 90.0, 0.5, 0.05, 0.20, "down-and-in", "call") == pytest.approx(
             vanilla
         )
+    def test_barrier_greeks_far_from_barrier_matches_bs_greeks(self):
+        # When barrier H is far away (e.g. down barrier H=10 when S=100), barrier call greeks ~ vanilla call greeks
+        S, K, H, T, r, sigma = 100.0, 100.0, 10.0, 1.0, 0.05, 0.20
+        bg = barrier_greeks(S, K, H, T, r, sigma, "down-and-out", "call")
+        vg = bs_greeks(S, K, T, r, sigma, "call")
+
+        assert bg["delta"] == pytest.approx(vg["delta"], abs=5e-3)
+        assert bg["gamma"] == pytest.approx(vg["gamma"], abs=5e-3)
+        assert bg["vega"] == pytest.approx(vg["vega"], abs=5e-3)
+        assert bg["theta"] == pytest.approx(vg["theta"], abs=5e-3)
+        assert bg["rho"] == pytest.approx(vg["rho"], abs=5e-3)
 
     def test_barrier_option_input_validation(self):
         with pytest.raises(ValueError, match="strictly positive"):