def _output(self):
Output = {}
for i, iAccount in enumerate(self.Accounts):
iOutput = iAccount.output()
if iOutput: Output[str(i)+"-"+iAccount.Name] = iOutput
AccountValueSeries, CashSeries, DebtSeries, InitCash, DebtRecord, CashRecord = 0, 0, 0, 0, None, None
for iAccount in self.Accounts:
AccountValueSeries += iAccount.getAccountValueSeries()
CashSeries += iAccount.getCashSeries()
DebtSeries += iAccount.getDebtSeries()
InitCash += iAccount.InitCash
DebtRecord = iAccount.DebtRecord.append(DebtRecord)
CashRecord = iAccount.CashRecord.append(CashRecord)
DebtRecord = DebtRecord[DebtRecord["融资"]!=0]
StrategyOutput = genAccountOutput(InitCash, CashSeries, DebtSeries, AccountValueSeries, CashRecord, DebtRecord, self._Model.DateIndexSeries)
StrategyOutput["统计数据"] = StrategyOutput["统计数据"].rename(columns={"账户价值": "策略表现", "无杠杆价值": "无杠杆表现"})
if self.Benchmark.FactorTable is not None:# 设置了基准
BenchmarkPrice = self.Benchmark.FactorTable.readData(factor_names=[self.Benchmark.PriceFactor], dts=AccountValueSeries.index.tolist(), ids=[self.Benchmark.BenchmarkID]).iloc[0,:,0]
BenchmarkOutput = pd.DataFrame(calcYieldSeq(wealth_seq=BenchmarkPrice.values), index=BenchmarkPrice.index, columns=["基准收益率"])
BenchmarkOutput["基准累计收益率"] = BenchmarkOutput["基准收益率"].cumsum()
BenchmarkOutput["基准净值"] = BenchmarkPrice / BenchmarkPrice.iloc[0]
if DebtRecord.shape[0]>0:
LYield = (StrategyOutput["日期序列"]["无杠杆收益率"].values if "时间序列" not in StrategyOutput else StrategyOutput["时间序列"]["无杠杆收益率"].values)
else:
LYield = (StrategyOutput["日期序列"]["收益率"].values if "时间序列" not in StrategyOutput else StrategyOutput["时间序列"]["收益率"].values)
if not self.Benchmark.RebalanceDTs: RebalanceIndex = None
else:
RebalanceIndex = pd.Series(np.arange(BenchmarkOutput["基准收益率"].shape[0]), index=BenchmarkOutput["基准收益率"].index, dtype=int)
RebalanceIndex = RebalanceIndex.loc[RebalanceIndex.index.intersection(self.Benchmark.RebalanceDTs)].values.tolist()
BenchmarkOutput["相对收益率"] = calcLSYield(long_yield=LYield, short_yield=BenchmarkOutput["基准收益率"].values, rebalance_index=RebalanceIndex)
BenchmarkOutput["相对累计收益率"] = BenchmarkOutput["相对收益率"].cumsum()
BenchmarkOutput["相对净值"] = (1 + BenchmarkOutput["相对收益率"]).cumprod()
if "时间序列" in StrategyOutput:
StrategyOutput["时间序列"] = pd.merge(StrategyOutput["时间序列"], BenchmarkOutput, left_index=True, right_index=True)
BenchmarkOutput = BenchmarkOutput.iloc[self._Model.DateIndexSeries.values]
BenchmarkOutput["基准收益率"] = BenchmarkOutput["基准净值"].values / np.r_[1, BenchmarkOutput["基准净值"].iloc[:-1].values] - 1
BenchmarkOutput["基准累计收益率"] = BenchmarkOutput["基准收益率"].cumsum()
BenchmarkOutput["相对收益率"] = BenchmarkOutput["相对净值"].values / np.r_[1, BenchmarkOutput["相对净值"].iloc[:-1].values] - 1
BenchmarkOutput["相对累计收益率"] = BenchmarkOutput["相对收益率"].cumsum()
BenchmarkOutput.index = StrategyOutput["日期序列"].index
StrategyOutput["日期序列"] = pd.merge(StrategyOutput["日期序列"], BenchmarkOutput, left_index=True, right_index=True)
BenchmarkStatistics = summaryStrategy(BenchmarkOutput[["基准净值", "相对净值"]].values, list(BenchmarkOutput.index), init_wealth=[1, 1])
BenchmarkStatistics.columns = ["基准", "相对表现"]
StrategyOutput["统计数据"] = pd.merge(StrategyOutput["统计数据"], BenchmarkStatistics, left_index=True, right_index=True)
Output["Strategy"] = StrategyOutput
return Output
def genAccountOutput(init_cash, cash_series, debt_series, account_value_series,
debt_record, date_index):
Output = {}
# 以时间点为索引的序列
Output["时间序列"] = pd.DataFrame(cash_series, columns=["现金"])
Output["时间序列"]["负债"] = debt_series
Output["时间序列"]["证券"] = account_value_series - (cash_series - debt_series)
Output["时间序列"]["账户价值"] = account_value_series
AccountEarnings = account_value_series.diff()
AccountEarnings.iloc[0] = account_value_series.iloc[0] - init_cash
Output["时间序列"]["收益"] = AccountEarnings
PreAccountValue = np.r_[init_cash, account_value_series.values[:-1]]
AccountReturn = AccountEarnings / np.abs(PreAccountValue)
AccountReturn[AccountEarnings == 0] = 0.0
Output["时间序列"]["收益率"] = AccountReturn
AccountReturn[np.isinf(AccountReturn)] = np.nan
Output["时间序列"]["累计收益率"] = AccountReturn.cumsum()
Output["时间序列"]["净值"] = (AccountReturn + 1).cumprod()
DebtDelta = debt_record.groupby(by=["时间点"]).sum()["融资"]
PreUnleveredValue = pd.Series(np.r_[init_cash,
(account_value_series.values +
debt_series.values)[:-1]],
index=AccountEarnings.index)
PreUnleveredValue[DebtDelta.index] += DebtDelta.clip(0, np.inf)
UnleveredReturn = AccountEarnings / np.abs(PreUnleveredValue)
UnleveredReturn[AccountEarnings == 0] = 0.0
Output["时间序列"]["无杠杆收益率"] = UnleveredReturn
UnleveredReturn[np.isinf(UnleveredReturn)] = np.nan
Output["时间序列"]["无杠杆累计收益率"] = UnleveredReturn.cumsum()
Output["时间序列"]["无杠杆净值"] = (1 + UnleveredReturn).cumprod()
# 以日期为索引的序列
if date_index.shape[0] == Output["时间序列"].shape[
0]: # 判断回测序列为日级别, 直接将时间点索引更改为日期索引
Output["日期序列"] = Output.pop("时间序列")
Output["日期序列"].index = date_index.index
else:
Output["日期序列"] = Output["时间序列"].iloc[date_index.values].copy()
Output["日期序列"].index = date_index.index
AccountValueSeries = Output["日期序列"]["账户价值"]
DebtSeries = Output["日期序列"]["负债"]
AccountEarnings = AccountValueSeries.diff()
AccountEarnings.iloc[0] = AccountValueSeries.iloc[0] - init_cash
Output["日期序列"]["收益"] = AccountEarnings
PreAccountValue = np.append(np.array(init_cash),
AccountValueSeries.values[:-1])
AccountReturn = AccountEarnings / np.abs(PreAccountValue)
AccountReturn[AccountEarnings == 0] = 0.0
Output["日期序列"]["收益率"] = AccountReturn
AccountReturn[np.isinf(AccountReturn)] = np.nan
Output["日期序列"]["累计收益率"] = AccountReturn.cumsum()
Output["日期序列"]["净值"] = (AccountReturn + 1).cumprod()
debt_record = debt_record.copy()
debt_record["时间点"] = [
iDateTime.date() for iDateTime in debt_record["时间点"]
]
DebtDelta = debt_record.groupby(by=["时间点"]).sum()["融资"]
PreUnleveredValue = pd.Series(np.append(np.array(init_cash),
(AccountValueSeries.values +
DebtSeries.values)[:-1]),
index=AccountEarnings.index)
PreUnleveredValue[DebtDelta.index] += DebtDelta.clip(0, np.inf)
UnleveredReturn = AccountEarnings / np.abs(PreUnleveredValue)
UnleveredReturn[AccountEarnings == 0] = 0.0
Output["日期序列"]["无杠杆收益率"] = UnleveredReturn
UnleveredReturn[np.isinf(UnleveredReturn)] = np.nan
Output["日期序列"]["无杠杆累计收益率"] = UnleveredReturn.cumsum()
Output["日期序列"]["无杠杆净值"] = (1 + UnleveredReturn).cumprod()
# 统计数据
Output["统计数据"] = summaryStrategy(Output["日期序列"][["净值", "无杠杆净值"]].values,
list(Output["日期序列"].index),
init_wealth=[1, 1])
Output["统计数据"].columns = ["账户价值", "无杠杆价值"]
return Output
def genAccountOutput(init_cash,
cash_series,
debt_series,
account_value_series,
cash_record,
debt_record,
date_index,
risk_free_rate=0.0):
Output = {}
# 以时间点为索引的序列
Output["时间序列"] = pd.DataFrame(cash_series, columns=["现金"])
Output["时间序列"]["负债"] = debt_series
Output["时间序列"]["证券"] = account_value_series - (cash_series - debt_series)
Output["时间序列"]["账户价值"] = account_value_series
AccountEarnings = account_value_series.diff()
AccountEarnings.iloc[0] = account_value_series.iloc[0] - init_cash
# 现金流调整
CashDelta = cash_record.loc[:, ["时间点", "现金流"]].groupby(
by=["时间点"]).sum()["现金流"]
CashDelta = CashDelta[CashDelta != 0]
if CashDelta.shape[0] > 0:
Output["时间序列"]["累计资金投入"] = init_cash + CashDelta.loc[
Output["时间序列"].index].fillna(0).cumsum()
else:
Output["时间序列"]["累计资金投入"] = init_cash
AccountEarnings[CashDelta.index] -= CashDelta
Output["时间序列"]["收益"] = AccountEarnings
PreAccountValue = np.r_[init_cash, account_value_series.values[:-1]]
AccountReturn = AccountEarnings / np.abs(PreAccountValue)
AccountReturn[AccountEarnings == 0] = 0.0
Output["时间序列"]["收益率"] = AccountReturn
AccountReturn[np.isinf(AccountReturn)] = np.nan
Output["时间序列"]["累计收益率"] = AccountReturn.cumsum()
Output["时间序列"]["净值"] = (AccountReturn + 1).cumprod()
if CashDelta.shape[0] > 0:
Output["时间序列"]["考虑资金投入的累计收益率"] = Output["时间序列"]["账户价值"] / Output[
"时间序列"]["累计资金投入"] - 1
Output["时间序列"]["考虑资金投入的净值"] = Output["时间序列"]["考虑资金投入的累计收益率"] + 1
# 负债调整
debt_record = debt_record[debt_record["融资"] != 0]
if debt_record.shape[0] > 0:
DebtDelta = debt_record.loc[:, ["时间点", "融资"]].groupby(
by=["时间点"]).sum()["融资"]
PreUnleveredValue = pd.Series(np.r_[init_cash,
(account_value_series.values +
debt_series.values)[:-1]],
index=AccountEarnings.index)
PreUnleveredValue[DebtDelta.index] += DebtDelta.clip(0, np.inf)
UnleveredReturn = AccountEarnings / np.abs(PreUnleveredValue)
UnleveredReturn[AccountEarnings == 0] = 0.0
Output["时间序列"]["无杠杆收益率"] = UnleveredReturn
UnleveredReturn[np.isinf(UnleveredReturn)] = np.nan
Output["时间序列"]["无杠杆累计收益率"] = UnleveredReturn.cumsum()
Output["时间序列"]["无杠杆净值"] = (1 + UnleveredReturn).cumprod()
# 以日期为索引的序列
if date_index.shape[0] == Output["时间序列"].shape[
0]: # 判断回测序列为日级别, 直接将时间点索引更改为日期索引
Output["日期序列"] = Output.pop("时间序列")
Output["日期序列"].index = date_index.index
else:
Output["日期序列"] = Output["时间序列"].iloc[date_index.values].copy()
Output["日期序列"].index = date_index.index
AccountValueSeries = Output["日期序列"]["账户价值"]
DebtSeries = Output["日期序列"]["负债"]
AccountEarnings = AccountValueSeries.diff()
AccountEarnings.iloc[0] = AccountValueSeries.iloc[0] - init_cash
# 现金流调整
cash_record = cash_record.copy()
cash_record["时间点"] = [
iDateTime.date() for iDateTime in cash_record["时间点"]
]
CashDelta = cash_record.loc[:, ["时间点", "现金流"]].groupby(
by=["时间点"]).sum()["现金流"]
CashDelta = CashDelta[CashDelta != 0]
if CashDelta.shape[0] > 0:
Output["日期序列"]["累计资金投入"] = init_cash + CashDelta.loc[
Output["日期序列"].index].fillna(0).cumsum()
else:
Output["日期序列"]["累计资金投入"] = init_cash
AccountEarnings[CashDelta.index] -= CashDelta
Output["日期序列"]["收益"] = AccountEarnings
PreAccountValue = np.append(np.array(init_cash),
AccountValueSeries.values[:-1])
AccountReturn = AccountEarnings / np.abs(PreAccountValue)
AccountReturn[AccountEarnings == 0] = 0.0
Output["日期序列"]["收益率"] = AccountReturn
AccountReturn[np.isinf(AccountReturn)] = np.nan
Output["日期序列"]["累计收益率"] = AccountReturn.cumsum()
Output["日期序列"]["净值"] = (AccountReturn + 1).cumprod()
if CashDelta.shape[0] > 0:
Output["日期序列"]["考虑资金投入的累计收益率"] = Output["日期序列"]["账户价值"] / Output[
"日期序列"]["累计资金投入"] - 1
Output["日期序列"]["考虑资金投入的净值"] = Output["日期序列"]["考虑资金投入的累计收益率"] + 1
# 负债调整
if debt_record.shape[0] > 0:
debt_record = debt_record.copy()
debt_record["时间点"] = [
iDateTime.date() for iDateTime in debt_record["时间点"]
]
DebtDelta = debt_record.loc[:, ["时间点", "融资"]].groupby(
by=["时间点"]).sum()["融资"]
PreUnleveredValue = pd.Series(np.append(
np.array(init_cash),
(AccountValueSeries.values + DebtSeries.values)[:-1]),
index=AccountEarnings.index)
PreUnleveredValue[DebtDelta.index] += DebtDelta.clip(0, np.inf)
UnleveredReturn = AccountEarnings / np.abs(PreUnleveredValue)
UnleveredReturn[AccountEarnings == 0] = 0.0
Output["日期序列"]["无杠杆收益率"] = UnleveredReturn
UnleveredReturn[np.isinf(UnleveredReturn)] = np.nan
Output["日期序列"]["无杠杆累计收益率"] = UnleveredReturn.cumsum()
Output["日期序列"]["无杠杆净值"] = (1 + UnleveredReturn).cumprod()
# 统计数据
TargetCols = ["净值"] + ["考虑资金投入的净值"] * (
CashDelta.shape[0] > 0) + ["无杠杆净值"] * (debt_record.shape[0] > 0)
Output["统计数据"] = summaryStrategy(Output["日期序列"][TargetCols].values,
list(Output["日期序列"].index),
init_wealth=[1] * len(TargetCols),
risk_free_rate=risk_free_rate)
Output["统计数据"].columns = ["绝对表现"] + ["考虑资金投入的表现"] * (
CashDelta.shape[0] > 0) + ["无杠杆表现"] * (debt_record.shape[0] > 0)
return Output