Overall Statistics |
Total Trades
33960
Average Win
0.38%
Average Loss
-0.32%
Compounding Annual Return
19.136%
Drawdown
41.700%
Expectancy
0.076
Net Profit
6225.632%
Sharpe Ratio
0.72
Probabilistic Sharpe Ratio
2.405%
Loss Rate
51%
Win Rate
49%
Profit-Loss Ratio
1.19
Alpha
0.137
Beta
0.286
Annual Standard Deviation
0.215
Annual Variance
0.046
Information Ratio
0.395
Tracking Error
0.239
Treynor Ratio
0.54
Total Fees
$747361.20
Estimated Strategy Capacity
$54000000.00
Lowest Capacity Asset
SNY SFYYC8T8HEN9
Portfolio Turnover
38.87%
|
# https://quantpedia.com/strategies/short-term-reversal-in-stocks/ # # The investment universe consists of the 100 biggest companies by market capitalization. # The investor goes long on the ten stocks with the lowest performance in the previous week and # goes short on the ten stocks with the greatest performance of the prior month. The portfolio is rebalanced weekly. # # QC implementation changes: # - Instead of all listed stocks, we first select 500 most liquid stock from QC as a first filter due to time complexity issues tied to whole universe filtering. # - Then top 100 market cap stocks are used in momentum sorting. #region imports from AlgorithmImports import * from pandas.core.frame import DataFrame from typing import List, Dict #endregion class ShortTermReversalEffectinStocks(QCAlgorithm): def Initialize(self) -> None: self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.symbol:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol self.coarse_count:int = 500 self.stock_selection:int = 10 self.top_by_market_cap_count:int = 100 self.leverage:int = 5 self.period:int = 21 self.long:List[Symbol] = [] self.short:List[Symbol] = [] # daily close data self.data:Dict[Symbol, SymbolData] = {} self.day:int = 1 self.selection_flag:bool = False self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) self.Schedule.On(self.DateRules.EveryDay(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection) def OnSecuritiesChanged(self, changes:SecurityChanges) -> None: for security in changes.AddedSecurities: security.SetFeeModel(CustomFeeModel()) security.SetLeverage(self.leverage) def CoarseSelectionFunction(self, coarse:List[CoarseFundamental]) -> List[Symbol]: # update the rolling window every day for stock in coarse: symbol:Symbol = stock.Symbol # store monthly price if symbol in self.data: self.data[symbol].update(stock.AdjustedPrice) if not self.selection_flag: return Universe.Unchanged selected:List[CoarseFundamental] = sorted([x for x in coarse if x.HasFundamentalData and x.Market == 'usa' and x.Price > 1], key=lambda x: x.DollarVolume, reverse=True) selected:List[Symbol] = [x.Symbol for x in selected][:self.coarse_count] # warmup price rolling windows for symbol in selected: if symbol in self.data: continue self.data[symbol] = SymbolData(self.period+1) history:DataFrame = self.History(symbol, self.period+1, Resolution.Daily) if history.empty: self.Log(f"Not enough data for {symbol} yet") continue closes:pd.Series = history.loc[symbol] for time, row in closes.iterrows(): self.data[symbol].update(row['close']) return [x for x in selected if self.data[x].is_ready()] def FineSelectionFunction(self, fine:List[FineFundamental]) -> List[Symbol]: fine:List[FineFundamental] = [x for x in fine if x.MarketCap != 0] sorted_by_market_cap:List = sorted(fine, key = lambda x:x.MarketCap, reverse = True) top_by_market_cap:List[Symbol] = [x.Symbol for x in sorted_by_market_cap[:self.top_by_market_cap_count]] month_performances:Dict[Symbol, float] = {symbol : self.data[symbol].performance(self.period) for symbol in top_by_market_cap} week_performances:Dict[Symbol, float] = {symbol : self.data[symbol].performance(5) for symbol in top_by_market_cap} sorted_by_month_perf:List[Symbol] = [x[0] for x in sorted(month_performances.items(), key=lambda item: item[1], reverse=True)] sorted_by_week_perf:List[Symbol] = [x[0] for x in sorted(week_performances.items(), key=lambda item: item[1])] self.long = sorted_by_week_perf[:self.stock_selection] self.short = sorted_by_month_perf[:self.stock_selection] return self.long + self.short def OnData(self, data:Slice) -> None: if not self.selection_flag: return self.selection_flag = False invested:List[Symbol] = [x.Key for x in self.Portfolio if x.Value.Invested] for symbol in invested: if symbol not in self.long + self.short: self.Liquidate(symbol) # leveraged portfolio - 100% long, 100% short for symbol in self.long: if symbol in data and data[symbol]: self.SetHoldings(symbol, 1 / len(self.long)) for symbol in self.short: if symbol in data and data[symbol]: self.SetHoldings(symbol, -1 / len(self.short)) self.long.clear() self.short.clear() def Selection(self) -> None: if self.day == 5: self.selection_flag = True self.day += 1 if self.day > 5: self.day = 1 class SymbolData(): def __init__(self, period:float) -> None: self._daily_close = RollingWindow[float](period) def update(self, close:float) -> None: self._daily_close.Add(close) def is_ready(self) -> bool: return self._daily_close.IsReady def performance(self, period:int) -> float: return self._daily_close[0] / self._daily_close[period] - 1 # Custom fee model class CustomFeeModel(FeeModel): def GetOrderFee(self, parameters): fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD"))