使用QuantLib从债券结算日而非估值日提取折现因子

# 假设 curve 已构建，bond.settlementDate() 和 row['date'] 已定义
# 尝试直接获取从结算日到现金流日期的折现因子（可能不符合预期）
# row['DiscFactor (Dirty Price)'] = round(curve.discount(bond.settlementDate(), row['date']), 9)

import QuantLib as ql
import pandas as pd

# 假设已初始化QuantLib环境，并定义了相关参数
# 例如：
# today = ql.Date(1, ql.January, 2023)
# ql.Settings.instance().evaluationDate = today
# calendar = ql.UnitedStates()
# day_count = ql.Actual360()
# curve = ql.DiscountCurve(...) # 假设 curve 已经通过 bootstrapping 构建完成
# bond = ql.FixedRateBond(...) # 假设 bond 已经创建，并包含 cashflows

# 模拟 QuantLib 环境和对象
today = ql.Date(15, ql.January, 2024)
ql.Settings.instance().evaluationDate = today
calendar = ql.UnitedStates()
day_count = ql.Actual360()

# 模拟收益率曲线 (示例，实际中应通过bootstrap构建)
dates = [today, today + ql.Period(6, ql.Months), today + ql.Period(1, ql.Years), today + ql.Period(2, ql.Years)]
rates = [0.03, 0.032, 0.035, 0.04]
curve_handle = ql.YieldTermStructureHandle(
    ql.ZeroSpreadedTermStructure(
        ql.RelinkableHandle(), # 这里通常是原始曲线
        ql.Handle(ql.FlatForward(today, 0.0, day_count)), # 简化示例，实际应是 bootstrapped curve
        ql.Compounded, ql.Annual, ql.Period(0, ql.Days)
    )
)
# 更真实的曲线构建示例 (略)
# 例如：
# helpers = [ql.DepositRateHelper(...), ql.FraRateHelper(...), ql.FuturesRateHelper(...), ql.SwapRateHelper(...)]
# curve = ql.PiecewiseLogLinearDiscountCurve(today, helpers, day_count)
# curve_handle = ql.YieldTermStructureHandle(curve)

# 为了示例可运行，我们直接使用一个简化的FlatForward曲线
curve = ql.FlatForward(today, 0.035, day_count, ql.Compounded, ql.Annual)
curve_handle = ql.YieldTermStructureHandle(curve)


# 模拟债券及其现金流
issue_date = ql.Date(15, ql.January, 2023)
maturity_date = ql.Date(15, ql.January, 2026)
settlement_days = 2
face_amount = 100.0
coupon_rate = 0.04
schedule = ql.Schedule(issue_date, maturity_date, ql.Period(ql.Semiannual), calendar,
                       ql.Unadjusted, ql.Unadjusted, ql.DateGeneration.Backward, False)
bond = ql.FixedRateBond(settlement_days, face_amount, schedule, [coupon_rate], day_count, ql.Following)

# 获取债券结算日
bond_settlement_date = calendar.advance(today, settlement_days, ql.Days)
# 确保结算日不早于估值日
if bond_settlement_date < today:
    bond_settlement_date = today

# 提取现金流信息并计算折现因子
fields = ['accrualStartDate', 'accrualEndDate', 'date', 'nominal', 'rate',
          'amount', 'accrualDays', 'accrualPeriod']
BondCashflows = []

# 计算从估值日到结算日的折现因子，用于后续调整
df_eval_to_settlement = curve_handle.discount(bond_settlement_date)

for cf in list(map(ql.as_fixed_rate_coupon, bond.cashflows())):
    # 过滤掉已经支付的现金流，或者只处理未来现金流
    if cf.date() < today:
        continue # 跳过过去的现金流

    row = {fld: getattr(cf, fld)() for fld in fields if hasattr(cf, fld)} # 使用getattr更健壮
    row['AccrualPeriod'] = round((row['accrualEndDate'] - row['accrualStartDate']) / 365, 4)

    # 1. 计算基于估值日的折现因子 (用于NPV)
    row['ZeroRate (NPV)'] = round(curve_handle.zeroRate(row['date'], day_count, ql.Compounded, ql.Annual).rate(), 9)
    row['DiscFactor (NPV)'] = round(curve_handle.discount(row['date']), 9)
    row['NPV'] = round(row['DiscFactor (NPV)'] * row['amount'], 9)

    # 2. 计算基于结算日的折现因子 (用于Dirty Price)
    # 首先获取从估值日到当前现金流日期的折现因子
    df_eval_to_cashflow = curve_handle.discount(row['date'])
    # 然后进行调整
    row['DiscFactor (Dirty Price)'] = round(df_eval_to_cashflow / df_eval_to_settlement, 9)

    # 这里的ZeroRate (Dirty Price) 实际上是 Forward Rate
    # 从结算日到现金流日期的远期零利率
    row['ZeroRate (Dirty Price)'] = round(
        curve_handle.forwardRate(bond_settlement_date, row['date'], day_count, ql.Compounded, ql.Annual).rate(), 9
    )
    row['Dirty Price'] = round(row['DiscFactor (Dirty Price)'] * row['amount'], 9)

    BondCashflows.append(row)

BondCashflows_df = pd.DataFrame(BondCashflows)
print(BondCashflows_df)