Overall Statistics |
Total Trades
4490
Average Win
1.06%
Average Loss
-1.16%
Compounding Annual Return
-1.963%
Drawdown
85.300%
Expectancy
-0.018
Net Profit
-35.779%
Sharpe Ratio
0.101
Probabilistic Sharpe Ratio
0.000%
Loss Rate
49%
Win Rate
51%
Profit-Loss Ratio
0.92
Alpha
0.039
Beta
-0.168
Annual Standard Deviation
0.289
Annual Variance
0.083
Information Ratio
-0.088
Tracking Error
0.343
Treynor Ratio
-0.174
Total Fees
$616.11
Estimated Strategy Capacity
$31000000.00
Lowest Capacity Asset
SAM R735QTJ8XC9X
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# https://quantpedia.com/strategies/consistent-momentum-strategy/ # # The investment universe consists of stocks listed at NYSE, AMEX, and NASDAQ, whose price data (at least for the past 7 months) are available # at the CRSP database. The investor creates a zero-investment portfolio at the end of the month t, longing stocks that are in the top decile # in terms of returns both in the period from t-7 to t-1 and from t-6 to t, while shorting stocks in the bottom decile in both periods (i.e. # longing consistent winners and shorting consistent losers). The stocks in the portfolio are weighted equally. The holding period is six months, # with no rebalancing during the period. There is a one-month skip between the formation and holding period. # # QC implementation changes: # - Universe consists of 500 most liquid stocks traded on NYSE, AMEX, or NASDAQ. class ConsistentMomentumStrategy(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.coarse_count = 500 self.long = [] self.short = [] self.data = {} self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol self.period = 7 * 21 self.months = 0 self.selection_flag = False self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Rebalance) def OnSecuritiesChanged(self, changes): for security in changes.AddedSecurities: symbol = security.Symbol 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'] 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.CompanyReference.IsREIT != 1 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.Symbol for x in sorted_by_market_cap[:self.coarse_count]] # else: # top_by_market_cap = [x.Symbol for x in fine] top_by_market_cap = [x.Symbol for x in fine] momentum_t71_t60 = { x : (self.data[x].performance_t7t1(), self.data[x].performance_t6t0()) for x in top_by_market_cap} # Momentum t-7 to t-1 sorting sorted_by_perf_t71 = sorted(momentum_t71_t60.items(), key = lambda x: x[1][0], reverse = True) decile = int(len(sorted_by_perf_t71) / 10) high_by_perf_t71 = [x[0] for x in sorted_by_perf_t71[:decile]] low_by_perf_t71 = [x[0] for x in sorted_by_perf_t71[-decile:]] # Momentum t-6 to t sorting sorted_by_perf_t60 = sorted(momentum_t71_t60.items(), key = lambda x: x[1][1], reverse = True) decile = int(len(sorted_by_perf_t60) / 10) high_by_perf_t60 = [x[0] for x in sorted_by_perf_t60[:decile]] low_by_perf_t60 = [x[0] for x in sorted_by_perf_t60[-decile:]] self.long = [x for x in high_by_perf_t71 if x in high_by_perf_t60] self.short = [x for x in low_by_perf_t71 if x in low_by_perf_t60] self.selection_flag = False return self.long + self.short def Rebalance(self): if self.months == 0: self.selection_flag = True self.months += 1 return if self.months == 1: # Trade execution and liquidation. invested = [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) long_count = len(self.long) short_count = len(self.short) for symbol in self.long: self.SetHoldings(symbol, 1/long_count) for symbol in self.short: self.SetHoldings(symbol, -1/short_count) self.months += 1 if self.months == 6: self.months = 0 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 def performance_t7t1(self): closes = [x for x in self.Price][21:] return (closes[0] / closes[-1] - 1) def performance_t6t0(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"))