mirror of
https://gitee.com/fasiondog/hikyuu.git
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215 lines
8.4 KiB
C++
215 lines
8.4 KiB
C++
/*
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* Copyright (c) 2024 hikyuu.org
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*
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* Created on: 2024-03-13
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* Author: fasiondog
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*/
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#include <hikyuu/trade_sys/factor/build_in.h>
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#include "../pybind_utils.h"
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namespace py = pybind11;
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using namespace hku;
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class PyMultiFactor : public MultiFactorBase {
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PY_CLONE(PyMultiFactor, MultiFactorBase)
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public:
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using MultiFactorBase::MultiFactorBase;
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PyMultiFactor(const MultiFactorBase& base) : MultiFactorBase(base) {}
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IndicatorList _calculate(const vector<IndicatorList>& all_stk_inds) {
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// PYBIND11_OVERLOAD_PURE_NAME(IndicatorList, MultiFactorBase, "_calculate", _calculate,
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// all_stk_inds);
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auto self = py::cast(this);
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auto func = self.attr("_calculate")();
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auto py_all_stk_inds = vector_to_python_list<IndicatorList>(all_stk_inds);
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auto py_ret = func(py_all_stk_inds);
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return py_ret.cast<IndicatorList>();
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}
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};
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void export_MultiFactor(py::module& m) {
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py::class_<ScoreRecord>(m, "ScoreRecord", "")
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.def(py::init<>())
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.def(py::init<const Stock&, ScoreRecord::value_t>())
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.def("__str__", to_py_str<ScoreRecord>)
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.def("__repr__", to_py_str<ScoreRecord>)
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.def_readwrite("stock", &ScoreRecord::stock, "时间")
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.def_readwrite("value", &ScoreRecord::value, "时间");
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size_t null_size = Null<size_t>();
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py::class_<MultiFactorBase, MultiFactorPtr, PyMultiFactor>(m, "MultiFactorBase",
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R"(市场环境判定策略基类
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自定义市场环境判定策略接口:
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- _calculate : 【必须】子类计算接口
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- _clone : 【必须】克隆接口
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- _reset : 【可选】重载私有变量)")
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.def(py::init<>())
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.def(py::init<const MultiFactorBase&>())
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.def("__str__", to_py_str<MultiFactorBase>)
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.def("__repr__", to_py_str<MultiFactorBase>)
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.def_property("name", py::overload_cast<>(&MultiFactorBase::name, py::const_),
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py::overload_cast<const string&>(&MultiFactorBase::name),
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py::return_value_policy::copy, "名称")
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.def("get_query", &MultiFactorBase::getQuery, py::return_value_policy::copy, R"(查询条件)")
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.def("get_param", &MultiFactorBase::getParam<boost::any>, R"(get_param(self, name)
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获取指定的参数
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:param str name: 参数名称
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:return: 参数值
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:raises out_of_range: 无此参数)")
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.def("set_param", &MultiFactorBase::setParam<boost::any>, R"(set_param(self, name, value)
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设置参数
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:param str name: 参数名称
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:param value: 参数值
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:raises logic_error: Unsupported type! 不支持的参数类型)")
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.def("have_param", &MultiFactorBase::haveParam, "是否存在指定参数")
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.def("get_ref_stock", &MultiFactorBase::getRefStock, py::return_value_policy::copy,
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"获取参考证券")
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.def("get_datetime_list", &MultiFactorBase::getDatetimeList, py::return_value_policy::copy,
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"获取参考日期列表(由参考证券通过查询条件获得)")
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.def("get_stock_list", &MultiFactorBase::getStockList, py::return_value_policy::copy,
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"获取创建时指定的证券列表")
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.def("get_stock_list_num", &MultiFactorBase::getStockListNumber,
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"获取创建时指定的证券列表中证券数量")
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.def("get_ref_indicators", &MultiFactorBase::getRefIndicators, py::return_value_policy::copy,
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"获取创建时输入的原始因子列表")
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.def("get_factor", &MultiFactorBase::getFactor, py::return_value_policy::copy,
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py::arg("stock"), R"(get_factor(self, stock)
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获取指定证券合成后的新因子
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:param Stock stock: 指定证券)")
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.def("get_all_factors", &MultiFactorBase::getAllFactors, py::return_value_policy::copy,
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R"(get_all_factors(self)
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获取所有证券合成后的因子列表
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:return: [factor1, factor2, ...] 顺序与参考证券顺序相同)")
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.def("get_ic", &MultiFactorBase::getIC, py::arg("ndays") = 0, R"(get_ic(self[, ndays=0])
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获取合成因子的IC, 长度与参考日期同
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ndays 对于使用 IC/ICIR 加权的新因子,最好保持好 ic_n 一致,
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但对于等权计算的新因子,不一定非要使用 ic_n 计算。
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所以,ndays 增加了一个特殊值 0, 表示直接使用 ic_n 参数计算 IC
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:rtype: Indicator)")
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.def("get_icir", &MultiFactorBase::getICIR, py::arg("ir_n"), py::arg("ic_n") = 0,
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R"(get_icir(self, ir_n[, ic_n=0])
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获取合成因子的 ICIR
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:param int ir_n: 计算 IR 的 n 窗口
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:param int ic_n: 计算 IC 的 n 窗口 (同 get_ic 中的 ndays))")
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.def("clone", &MultiFactorBase::clone, "克隆操作")
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.def(
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"get_score",
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[](MultiFactorBase& self, const Datetime& date, size_t start, size_t end) {
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return self.getScore(date, start, end);
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},
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py::arg("datet"), py::arg("start") = 0, py::arg("end") = null_size,
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R"(get_score(self, date[, start=0, end=Null])
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获取指定日期截面的所有因子值,已经降序排列,相当于各证券日期截面评分。
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:param Datetime date: 指定日期
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:param int start: 取当日排名开始
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:param int end: 取当日排名结束(不包含本身)
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:rtype: ScoreRecordList)")
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.def("get_all_scores", &MultiFactorBase::getAllScores, py::return_value_policy::copy,
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R"(get_all_scores(self)
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获取所有日期的所有评分,长度与参考日期相同
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:return: 每日 ScoreRecordList 结果的 list)")
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.def("get_all_src_factors", &MultiFactorBase::getAllSrcFactors)
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DEF_PICKLE(MultiFactorPtr);
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m.def(
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"MF_EqualWeight",
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[](const py::sequence& inds, const py::sequence& stks, const KQuery& query,
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const Stock& ref_stk, int ic_n) {
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IndicatorList c_inds = python_list_to_vector<Indicator>(inds);
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StockList c_stks = python_list_to_vector<Stock>(stks);
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return MF_EqualWeight(c_inds, c_stks, query, ref_stk, ic_n);
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},
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py::arg("inds"), py::arg("stks"), py::arg("query"), py::arg("ref_stk"), py::arg("ic_n") = 5,
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R"(MF_EqualWeight(inds, stks, query, ref_stk[, ic_n=5])
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等权重合成因子
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:param sequense(Indicator) inds: 原始因子列表
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:param sequense(stock) stks: 计算证券列表
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:param Query query: 日期范围
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:param Stock ref_stk: 参考证券
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:param int ic_n: 默认 IC 对应的 N 日收益率
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:rtype: MultiFactor)");
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m.def(
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"MF_ICWeight",
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[](const py::sequence& inds, const py::sequence& stks, const KQuery& query,
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const Stock& ref_stk, int ic_n, int ic_rolling_n) {
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// MF_EqualWeight
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IndicatorList c_inds = python_list_to_vector<Indicator>(inds);
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StockList c_stks = python_list_to_vector<Stock>(stks);
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return MF_ICWeight(c_inds, c_stks, query, ref_stk, ic_n, ic_rolling_n);
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},
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py::arg("inds"), py::arg("stks"), py::arg("query"), py::arg("ref_stk"), py::arg("ic_n") = 5,
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py::arg("ic_rolling_n") = 120,
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R"(MF_EqualWeight(inds, stks, query, ref_stk[, ic_n=5, ic_rolling_n=120])
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滚动IC权重合成因子
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:param sequense(Indicator) inds: 原始因子列表
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:param sequense(stock) stks: 计算证券列表
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:param Query query: 日期范围
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:param Stock ref_stk: 参考证券
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:param int ic_n: 默认 IC 对应的 N 日收益率
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:param int ic_rolling_n: IC 滚动周期
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:rtype: MultiFactor)");
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m.def(
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"MF_ICIRWeight",
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[](const py::sequence& inds, const py::sequence& stks, const KQuery& query,
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const Stock& ref_stk, int ic_n, int ic_rolling_n) {
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// MF_EqualWeight
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IndicatorList c_inds = python_list_to_vector<Indicator>(inds);
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StockList c_stks = python_list_to_vector<Stock>(stks);
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return MF_ICIRWeight(c_inds, c_stks, query, ref_stk, ic_n, ic_rolling_n);
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},
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py::arg("inds"), py::arg("stks"), py::arg("query"), py::arg("ref_stk"), py::arg("ic_n") = 5,
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py::arg("ic_rolling_n") = 120,
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R"(MF_EqualWeight(inds, stks, query, ref_stk[, ic_n=5, ic_rolling_n=120])
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滚动ICIR权重合成因子
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:param sequense(Indicator) inds: 原始因子列表
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:param sequense(stock) stks: 计算证券列表
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:param Query query: 日期范围
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:param Stock ref_stk: 参考证券
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:param int ic_n: 默认 IC 对应的 N 日收益率
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:param int ic_rolling_n: IC 滚动周期
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:rtype: MultiFactor)");
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} |