低价效应策略
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Split-adjusted stock price and the cross-section of US stock returns [点击查看论文]
策略概要
投资范围包括在纽约证券交易所上市的市值高于中位数的股票，以及来自CRSP数据库的月度回报数据和来自Compustat的会计数据。每个月，投资者根据其拆分调整后的价格将股票分为十分位数，并创建一个零投资组合。该策略涉及买入底部十分位数的股票，卖出顶部十分位数的股票。投资组合按价值加权，这意味着股票根据其市值进行加权。投资组合每月重新平衡，以反映更新的股票价格。
II. 策略合理性
证券的价格由其预期回报乘以贴现因子决定，这意味着预期回报与价格之间存在关系。由于已实现的回报通常与预期回报呈正相关，因此拆分调整后的价格与未来已实现的回报之间应该存在负相关关系。
回测表现
波动率19.24%
夏普比率0.74
索提诺比率0.934
胜率48%
完整 Python 代码
from AlgorithmImports import *
class LowPriceEffect(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
market:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
self.weight:Dict[Symbol, float] = {}
self.fundamental_count:int = 500
self.fundamental_sorting_key = lambda x: x.DollarVolume
self.quantile:int = 10
self.leverage:int = 5
self.selection_flag:bool = False
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.FundamentalSelectionFunction)
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
self.Schedule.On(self.DateRules.MonthStart(market), self.TimeRules.AfterMarketOpen(market), self.Selection)
self.settings.daily_precise_end_time = False
def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
for security in changes.AddedSecurities:
security.SetFeeModel(CustomFeeModel())
security.SetLeverage(self.leverage)
def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
if not self.selection_flag:
return Universe.Unchanged
selected:List[Fundamental] = [x for x in fundamental if x.HasFundamentalData and x.Market == 'usa' and x.SecurityReference.ExchangeId == "NYS" and x.MarketCap != 0]
if len(selected) > self.fundamental_count:
selected = [x for x in sorted(selected, key=self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]]
long:List[Fundamental] = []
short:List[Fundamental] = []
# Store stock adjusted price.
price:Dict[Fundamental, float] = { x : x.AdjustedPrice for x in selected }
if len(price) >= self.quantile:
sorted_by_price:List[Fundamental] = sorted(price, key = price.get, reverse = True)
quantile:int = int(len(sorted_by_price) / self.quantile)
long = sorted_by_price[-quantile:]
short = sorted_by_price[:quantile]
# Market cap weighting.
for i, portfolio in enumerate([long, short]):
mc_sum:float = sum(map(lambda x: x.MarketCap, portfolio))
for stock in portfolio:
self.weight[stock.Symbol] = ((-1) ** i) * stock.MarketCap / mc_sum
return list(self.weight.keys())
def OnData(self, data: Slice) -> None:
if not self.selection_flag:
return
self.selection_flag = False
# Trade execution.
portfolio:List[PortfolioTarget] = [PortfolioTarget(symbol, w) for symbol, w in self.weight.items() if symbol in data and data[symbol]]
self.SetHoldings(portfolio, True)
self.weight.clear()
def Selection(self) -> None:
self.selection_flag = True
# Custom fee model.
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))