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AI-powered bond analysis for China market including valuation, yield, duration, convexity, credit spread analysis, and bond portfolio management.
---
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.
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