Overall Statistics |
Total Trades
2312
Average Win
0.73%
Average Loss
-0.91%
Compounding Annual Return
-6.789%
Drawdown
88.100%
Expectancy
-0.099
Net Profit
-58.714%
Sharpe Ratio
-0.074
Probabilistic Sharpe Ratio
0.000%
Loss Rate
50%
Win Rate
50%
Profit-Loss Ratio
0.80
Alpha
-0.036
Beta
0.183
Annual Standard Deviation
0.244
Annual Variance
0.06
Information Ratio
-0.438
Tracking Error
0.27
Treynor Ratio
-0.099
Total Fees
$335.39
Estimated Strategy Capacity
$280000000.00
Lowest Capacity Asset
SEAS VFUDGZIY8ZMT
|
# https://quantpedia.com/strategies/momentum-effect-in-stocks-in-small-portfolios/ # # The investment universe consists of all UK listed companies (this is the investment universe used in the source academic study, and it could be easily # changed into any other market – see Ammann, Moellenbeck, Schmid: Feasible Momentum Strategies in the US Stock Market). Stocks with the lowest market # capitalization (25% of the universe) are excluded due to liquidity reasons. Momentum profits are calculated by ranking companies based on their stock # market performance over the previous 12 months (the rank period). The investor goes long in the ten stocks with the highest performance and goes short # in the ten stocks with the lowest performance. The portfolio is equally weighted and rebalanced yearly. We assume the investor has an account size of # 10 000 pounds. # # QC implementation changes: # - Universe consists of top 500 US stock by market cap. # - Instead of 10 000 pounds we use 100 000 dollars. # - Decile is used instead of 10 stocks. from AlgorithmImports import * class MomentumEffectinStocksinSmallPortfolios(QCAlgorithm): def Initialize(self): self.SetStartDate(2010, 1, 1) self.SetCash(100000) self.coarse_count = 500 self.long = [] self.short = [] # Daily data. self.data = {} self.period = 12 * 21 self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol self.selection_flag = True self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction) self.month = 11 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()) security.SetLeverage(10) def CoarseSelectionFunction(self, coarse): # Update the rolling window every day. for stock in coarse: symbol = stock.Symbol if symbol in self.data: # Store daily price. self.data[symbol].update(stock.AdjustedPrice) # Selection once a month. 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].Price.Add(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] # if len(fine) > self.coarse_count: # sorted_by_market_cap = sorted(fine, key = lambda x: x.MarketCap, reverse=True) # top_by_market_cap = sorted_by_market_cap[:self.coarse_count] # else: # top_by_market_cap = fine # Performance sorting. performance = {x.Symbol : self.data[x.Symbol].performance() for x in fine} if len(performance) >= 10: decile = int(len(performance) / 10) sorted_by_perf = sorted(performance.items(), key = lambda x: x[1], reverse = True) self.long = [x[0] for x in sorted_by_perf[:decile]] self.short = [x[0] for x in sorted_by_perf[-decile:]] 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) 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) for symbol in self.long: self.SetHoldings(symbol, 1 / long_count) for symbol in self.short: self.SetHoldings(symbol, -1 / short_count) def Selection(self): # Rebalance every 12 months. if self.month == 12: self.selection_flag = True self.month += 1 if self.month > 12: self.month = 1 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(self): closes = [x for x in self.Price] 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"))