Skill: gechengling/security-bond-analysis (version 3.0.1)

--- name: Bond Analysis Expert slug: security-bond-analysis description: AI-powered bond analysis expert for China market — covers bond valuation, yield analysis, duration/convexity calculation, credit spread analysis, and bond portfolio management. Built for fixed income analysts, bond traders, and portfolio managers. Keywords: bond analysis, yield curve, duration, convexity, credit spread, China bonds,利率债, 信用债, 国债, 企业债, 城投债, 债券估值, 收益率曲线, 久期, 凸性, 信用利差, 固收, 利率风险, 债券组合. version: "3.0.1" --- # Bond Analysis Expert / 债券分析专家 > **English:** AI-powered bond analysis expert — covers bond valuation, yield analysis, duration/convexity calculation, credit spread analysis, and bond portfolio management. Built for fixed income professionals. > > **中文:** 债券分析专家——覆盖债券估值、收益率分析、久期/凸性计算、信用利差分析、债券组合管理。适用：固收分析师、债券交易员、组合管理人。 --- ### 证券监管最新动态 [2026-05-25更新] | 动态类型 | 内容摘要 | 影响范围 | |---------|---------|---------| | 证券监管 | 2026年Q1：信用债市场分化加剧，信用风险识别要求提升 | 债券分析框架需纳入理财新规和地方债务化解动态 | | 证券监管 | 银行理财'三清'推进，债券投资偏好可能调整 | 债券分析框架需纳入理财新规和地方债务化解动态 | | 证券监管 | 地方债务化解持续推进，城投债分析逻辑需更新 | 债券分析框架需纳入理财新规和地方债务化解动态 | > **数据截止**: 2026-05-25 | 来源：证监会、NFRA、中证协、安永Q1分析 > **声明**: 以上动态供参考，具体以官方最新发布为准 ## Industry Pain Points / 行业痛点 | Pain Point / 痛点 | Impact / 影响 | Solution / 本Skill解决方案 | |------------------|-------------|------------------------| | **收益率计算复杂** | 多种收益率指标容易混淆 | 标准化收益率计算框架 | | **信用分析耗时** | 发行主体众多，分析量大 | 模板化信用分析框架 | | **利率风险难测** | 久期/凸性概念抽象 | 可视化风险分析 | | **城投债信仰** | 城投刚兑预期与违约现实冲突 | 区域财政分析模型 | | **久期管理难** | 利率变动对组合影响大 | 久期匹配优化工具 | --- ## Trigger Keywords / 触发关键词 **English Triggers:** bond analysis, yield curve, duration, convexity, credit spread, China bonds, fixed income, bond valuation, interest rate risk, credit risk **中文触发词（优先）：** 债券分析 / 收益率曲线 / 久期 / 凸性 / 信用利差 / 中国债券 / 固收 / 债券估值 / 利率风险 / 信用风险 / 国债 / 企业债 / 城投债 / 金融债 / 可转债 / 债券回购 / 债券评级 / YTM / 即期收益率 / 到期收益率 --- ## Core Capabilities / 核心能力 ### 1. Bond Valuation Engine / 债券估值引擎 ```python import numpy as np import pandas as pd class BondAnalyzer: """债券分析引擎""" @staticmethod def calculate_price(face_value: float, coupon_rate: float, ytm: float, years: int, frequency: int = 2) -> float: """ 债券定价 Args: face_value: 面值（元） coupon_rate: 年票面利率 ytm: 到期收益率 years: 剩余期限（年） frequency: 付息频率（1=年付，2=半年付） """ n = years * frequency r_per_period = ytm / frequency c_per_period = (face_value * coupon_rate) / frequency # 现金流现值 pv_coupons = sum([c_per_period / (1 + r_per_period) ** t for t in range(1, n + 1)]) pv_face = face_value / (1 + r_per_period) ** n return pv_coupons + pv_face @staticmethod def calculate_ytm(price: float, face_value: float, coupon_rate: float, years: float, frequency: int = 2) -> float: """ 计算到期收益率（YTM）- 牛顿迭代法 """ n = years * frequency c = (face_value * coupon_rate) / frequency # 初始猜测 ytm = coupon_rate for _ in range(100): pv = sum([c / (1 + ytm/frequency) ** t for t in range(1, n + 1)]) + face_value / (1 + ytm/frequency) ** n diff = price - pv # 导数（久期近似） duration = BondAnalyzer.calculate_duration( price, face_value, coupon_rate, ytm, years, frequency ) dv = -duration / (1 + ytm/frequency) * diff ytm = ytm + diff / dv * 0.5 if abs(diff) < 1e-6: break return ytm @staticmethod def calculate_duration(price: float, face_value: float, coupon_rate: float, ytm: float, years: float, frequency: int = 2) -> float: """ 计算久期（Macauley Duration） """ n = int(years * frequency) c = (face_value * coupon_rate) / frequency r = ytm / frequency # 加权平均到期时间 weighted_time = sum([t * c / (1 + r) ** t for t in range(1, n + 1)]) weighted_time += n * face_value / (1 + r) ** n return weighted_time / price / frequency # 转换为年 @staticmethod def calculate_convexity(price: float, face_value: float, coupon_rate: float, ytm: float, years: float, frequency: int = 2) -> float: """ 计算凸性 """ n = int(years * frequency) c = (face_value * coupon_rate) / frequency r = ytm / frequency weighted_sq = sum([t * (t + 1) * c / (1 + r) ** (t + 2) for t in range(1, n + 1)]) weighted_sq += n * (n + 1) * face_value / (1 + r) ** (n + 2) return weighted_sq / price / (frequency ** 2) @staticmethod def price_change_estimate(duration: float, convexity: float, rate_change: float) -> dict: """ 利率变动对价格的影响估算 """ # 久期效应 duration_effect = -duration * rate_change # 凸性效应 convexity_effect = 0.5 * convexity * (rate_change ** 2) total_effect = duration_effect + convexity_effect return { "duration_effect": round(duration_effect * 100, 4), "convexity_effect": round(convexity_effect * 100, 4), "total_effect": round(total_effect * 100, 4), "approximate_new_price_pct": round((1 + total_effect) * 100, 4) } ``` ### 2. Credit Analysis Framework / 信用分析框架 ```markdown ## 信用债分析模板 ### 一、发债主体概况 | 项目 | 内容 | |-----|------| | 公司名称 | | | 实际控制人 | | | 主体评级 | | | 行业分类 | | | 主营业务 | | ### 二、财务分析 ```python CREDIT_ANALYSIS_RATIOS = { "盈利能力": { "毛利率": ">30%为优质", "净利率": ">15%为优质", "ROE": ">10%为优质" }, "偿债能力": { "资产负债率": "<70%为稳健", "流动比率": ">1.5为稳健", "利息保障倍数": ">3倍为稳健" }, "现金流": { "经营现金流/带息债务": ">15%为稳健", "经营现金流/资本支出": ">100%为稳健" } } ``` ### 三、信用利差分析 ```python def analyze_credit_spread(bond_yield: float, treasury_yield: float, rating: str) -> dict: """ 信用利差分析 """ spread = bond_yield - treasury_yield # 评级对应利差参考 SPREAD_REFERENCE = { "AAA": 0.50, # 50bp "AA+": 0.80, "AA": 1.20, "AA-": 1.50, "A+": 2.00, "A": 2.50 } reference = SPREAD_REFERENCE.get(rating, 2.0) return { "credit_spread": round(spread * 100, 2), "reference_spread": reference, "relative_value": "低估" if spread < reference else "高估", "spread_premium": round((spread - reference) * 100, 2) } ``` ``` ### 3. Bond Portfolio Management / 债券组合管理 ```python class BondPortfolio: """债券组合管理""" def __init__(self): self.bonds = [] def add_bond(self, bond: dict): """添加债券""" # 计算关键指标 bond["price"] = self._calculate_bond_price(bond) bond["ytm"] = self._calculate_ytm(bond) bond["duration"] = self._calculate_duration(bond) bond["convexity"] = self._calculate_convexity(bond) bond["dv01"] = bond["duration"] * bond["price"] / 100 / 100 # 每bp变化 self.bonds.append(bond) def portfolio_duration(self) -> float: """组合久期""" total_value = sum(b["market_value"] for b in self.bonds) weighted_duration = sum( b["duration"] * b["market_value"] for b in self.bonds ) / total_value return weighted_duration def portfolio_credit_breakdown(self) -> dict: """组合信用分布""" breakdown = {} for bond in self.bonds: rating = bond.get("rating", "Unknown") if rating not in breakdown: breakdown[rating] = {"count": 0, "value": 0} breakdown[rating]["count"] += 1 breakdown[rating]["value"] += bond.get("market_value", 0) return breakdown def interest_rate_risk(self, rate_shock: float) -> dict: """利率风险分析""" port_duration = self.portfolio_duration() # 纯久期效应 duration_pnl = -port_duration * rate_shock / 100 # 凸性调整 port_convexity = sum( b["convexity"] * b["market_value"] for b in self.bonds ) / sum(b["market_value"] for b in self.bonds) convexity_pnl = 0.5 * port_convexity * (rate_shock/100) ** 2 return { "portfolio_duration": round(port_duration, 3), "rate_shock_bp": round(rate_shock * 100, 0), "duration_pnl_pct": round(duration_pnl * 100, 2), "convexity_pnl_pct": round(convexity_pnl * 100, 2), "total_pnl_pct": round((duration_pnl + convexity_pnl) * 100, 2) } ``` --- ## Quick Command Templates / 快速指令模板 **债券定价：** ``` 计算债券价格： - 面值：100元 - 票面利率：4% - 到期收益率：3.5% - 剩余期限：5年 - 付息频率：年付 ``` **久期分析：** ``` 分析债券组合的久期风险： - 组合总规模：1000万 - 利率上升50bp时的损益 ``` --- ## Disclaimer Bond analysis involves various risks including interest rate risk and credit risk. This skill provides analysis tools for educational purposes only and does not constitute investment advice.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)