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Nearness to 52-Week Low Strategy

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Academic paper

Nearness to the 52-Week High and Low Prices, Past Returns, and Average Stock Returns

AuthorsLi‐Wen Chen; Hsin‐Yi Yu

Institute
  • TWNational Chung Cheng University
  • TWNational University of Kaohsiung

Paper linkhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=2797149

Strategy in a nutshell

Investment universe: Common stocks listed on NYSE, AMEX, and NASDAQ with monthly price data from CRSP.

Sorting measure (LOW): Calculated as the stock’s nearness to its 52-week low over the prior month.

Portfolio formation:

Positions are held for one month, with a one-month skip between formation and holding to avoid look-ahead bias.

Go long on stocks in the lowest 5% of LOW (closest to 52-week lows).

Go short on the remaining 95%.

Portfolios are value-weighted.

Economic rationale

Although the source study does not provide a definitive explanation, it shows that 52-week low proximity, 52-week high, and momentum strategies each contain unique, unpriced information relevant for cross-sectional stock returns.

This suggests that nearness to 52-week extremes reflects investor behavior not fully captured by traditional risk factors, potentially tied to anchoring, reference points, or market sentiment.

Backtest performance

Annualised return7.67%
Volatility11.44%
Beta0.272
Sharpe ratio0.67
Sortino ratio0.095
Win rate30%

Full Python code

from AlgorithmImports import *
from pandas.core.frame import DataFrame
class Nearnessto52WeekLow(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
market:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol

self.fundamental_count:int = 3000
self.fundamental_sorting_key = lambda x: x.MarketCap

self.weight:Dict[Symbol, float] = {}
self.data:Dict[Symbol, SymbolData] = {}

self.period:int = 52 * 5 + 4*5
self.quantile:int = 20
self.leverage:int = 5
self.min_share_price:float = 5.
self.exchange_codes:List[str] = ['NYS', 'NAS', 'ASE']

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]:
# Update the rolling window every day.
for stock in fundamental:
    symbol:Symbol = stock.Symbol
    if symbol in self.data:
        # Store daily price.
        self.data[symbol].update(stock.AdjustedPrice)
    
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 in self.exchange_codes and \
    x.MarketCap != 0 and x.Price >= self.min_share_price
]
if len(selected) > self.fundamental_count:
    selected = [x for x in sorted(selected, key=self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]]
LOW:Dict[Fundamental, float] = {}
# Warmup price rolling windows.
for stock in selected:
    symbol:Symbol = stock.Symbol
    if symbol not in self.data:
        self.data[symbol] = SymbolData(self.period)
        history:DataFrame = self.History(symbol, self.period, Resolution.Daily)
        if history.empty:
            self.Log(f"Not enough data for {symbol} yet")
            continue
        closes:pd.Series = history.loc[symbol].close
        for time, close in closes.items():
            self.data[symbol].update(close)
    
    if self.data[symbol].is_ready():
        LOW[stock] = self.data[symbol].get_latest_price() / self.data[symbol].minimum()
long:List[Fundamental] = []
short:List[Fundamental] = []
if len(LOW) >= self.quantile:
    # LOW sorting
    sorted_by_LOW:List[Fundamental] = sorted(LOW, key = LOW.get, reverse = True)
    quantile:int = int(len(sorted_by_LOW) / self.quantile)
    long = sorted_by_LOW[-quantile:]
    short = sorted_by_LOW[:len(sorted_by_LOW) - 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

class SymbolData():
def __init__(self, period: int):
self._price:RollingWindow = RollingWindow[float](period)

def update(self, price: float) -> None:
self._price.Add(price)

def is_ready(self) -> bool:
return self._price.IsReady
 
# Skip last month.
def minimum(self) -> float:
return min([x for x in self._price][4*5:])

def get_latest_price(self) -> float:
return self._price[0]
# Custom fee model.
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))