The strategy invests in stocks near their 52-week lows, going long on the bottom 5% and shorting the remaining 95%. The portfolio is value-weighted, held for one month
ASSET CLASS: stocks | REGION: United States | FREQUENCY:
Monthly | MARKET: equities | KEYWORD: Nearness, 52-Week, Low, Strategy
I. 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.
II. 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.
III. SOURCE PAPER
Nearness to the 52-Week High and Low Prices, Past Returns, and Average Stock Returns [Click to Open PDF]
Chen, National Chung Cheng University; Yu, National University of Kaohsiung
<Abstract>
This study examines the interactions between trading strategies based on the nearness to the 52-week high, the nearness to the 52-week low, and past returns. We offer evidence that the nearness to the 52-week low has predictive power for future average returns. Our results also reveal that the nearness to the 52-week high as well as to the 52-week low and past returns each have certain exclusive unpriced information content in the cross-sectional pricing of stocks. Moreover, a trading strategy based on the nearness to the 52-week low provides an excellent hedge for the momentum strategy, thereby nearly doubling the Sharpe ratio of the momentum strategy.
IV. BACKTEST PERFORMANCE
| Annualised Return | 7.67% |
| Volatility | 11.44% |
| Beta | 0.272 |
| Sharpe Ratio | 0.67 |
| Sortino Ratio | 0.095 |
| Maximum Drawdown | N/A |
| Win Rate | 30% |
V. 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"))VI. Backtest Performance
