Overall Statistics |
Total Trades
50371
Average Win
0.10%
Average Loss
-0.11%
Compounding Annual Return
-1.663%
Drawdown
82.100%
Expectancy
-0.027
Net Profit
-29.802%
Sharpe Ratio
0.062
Probabilistic Sharpe Ratio
0.000%
Loss Rate
49%
Win Rate
51%
Profit-Loss Ratio
0.91
Alpha
0.015
Beta
-0.009
Annual Standard Deviation
0.238
Annual Variance
0.057
Information Ratio
-0.183
Tracking Error
0.298
Treynor Ratio
-1.594
Total Fees
$1198.31
|
# https://quantpedia.com/strategies/momentum-factor-effect-in-stocks/ # # The investment universe consists of NYSE, AMEX, and NASDAQ stocks. We define momentum as the past 12-month return, skipping the most # recent month’s return (to avoid microstructure and liquidity biases). To capture “momentum”, UMD portfolio goes long stocks that have # high relative past one-year returns and short stocks that have low relative past one-year returns. # # QC implementation changes: # - Instead of all listed stock, we select top 500 stocks by market cap from QC stock universe. class MomentumFactorEffectinStocks(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol self.long = [] self.short = [] self.data = {} self.period = 12 * 21 self.coarse_count = 500 self.selection_flag = False self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) self.Schedule.On(self.DateRules.MonthStart(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection) def OnSecuritiesChanged(self, changes): for security in changes.AddedSecurities: security.SetFeeModel(CustomFeeModel(self)) security.SetLeverage(10) def CoarseSelectionFunction(self, coarse): # Update the rolling window every day. for stock in coarse: 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 = [x.Symbol for x in coarse if x.HasFundamentalData and x.Market == 'usa' and x.Price > 5] selected = [x.Symbol for x in sorted([x for x in coarse if x.HasFundamentalData and x.Market == 'usa'], key = lambda x: x.DollarVolume, reverse = True)[:self.coarse_count]] # Warmup price rolling windows. for symbol in selected: if symbol in self.data: continue self.data[symbol] = SymbolData(symbol, self.period) history = self.History(symbol, self.period, Resolution.Daily) if history.empty: self.Log(f"Not enough data for {symbol} yet") continue closes = history.loc[symbol].close for time, close in closes.iteritems(): self.data[symbol].update(close) return [x for x in selected if self.data[x].is_ready()] def FineSelectionFunction(self, fine): fine = [x for x in fine if x.MarketCap != 0 and \ ((x.SecurityReference.ExchangeId == "NYS") or (x.SecurityReference.ExchangeId == "NAS") or (x.SecurityReference.ExchangeId == "ASE"))] # if len(fine) > self.coarse_count: # sorted_by_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True) # top_by_market_cap = [x for x in sorted_by_market_cap[:self.coarse_count]] # else: # top_by_market_cap = fine perf = {x.Symbol : self.data[x.Symbol].performance() for x in fine} sorted_by_perf = sorted(perf.items(), key = lambda x:x[1], reverse=True) quintile = int(len(sorted_by_perf) / 5) self.long = [x[0] for x in sorted_by_perf[:quintile]] self.short = [x[0] for x in sorted_by_perf[-quintile:]] return self.long + self.short def OnData(self, data): if not self.selection_flag: return self.selection_flag = False # Trade execution. long_count = len(self.long) short_count = len(self.short) stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested] for symbol in stocks_invested: if symbol not in self.long + self.short: self.Liquidate(symbol) for symbol in self.long: if self.Securities[symbol].Price != 0 and self.Securities[symbol].IsTradable: self.SetHoldings(symbol, 1 / long_count) for symbol in self.short: if self.Securities[symbol].Price != 0 and self.Securities[symbol].IsTradable: self.SetHoldings(symbol, -1 / short_count) self.long.clear() self.short.clear() def Selection(self): self.selection_flag = True class SymbolData(): def __init__(self, symbol, period): self.Symbol = symbol self.Price = RollingWindow[float](period) def update(self, value): self.Price.Add(value) def is_ready(self): return self.Price.IsReady # Yearly performance, one month skipped. def performance(self): closes = [x for x in self.Price][21:] return (closes[0] / closes[-1] - 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"))