Overall Statistics |
Total Trades
7751
Average Win
0.96%
Average Loss
-1.00%
Compounding Annual Return
-2.142%
Drawdown
83.500%
Expectancy
-0.001
Net Profit
-39.365%
Sharpe Ratio
0.066
Probabilistic Sharpe Ratio
0.000%
Loss Rate
49%
Win Rate
51%
Profit-Loss Ratio
0.96
Alpha
0.029
Beta
-0.207
Annual Standard Deviation
0.25
Annual Variance
0.062
Information Ratio
-0.131
Tracking Error
0.315
Treynor Ratio
-0.08
Total Fees
$3276.26
Estimated Strategy Capacity
$15000000.00
Lowest Capacity Asset
AZPN R735QTJ8XC9X
|
# https://quantpedia.com/strategies/momentum-factor-combined-with-asset-growth-effect/ # # The investment universe consists of NYSE, AMEX and NASDAQ stocks (data for the backtest in the source paper are from Compustat). # Stocks with a market capitalization less than the 20th NYSE percentile (smallest stocks) are removed. The asset growth variable # is defined as the yearly percentage change in balance sheet total assets. Data from year t-2 to t-1 are used to calculate asset # growth, and July is the cut-off month. Every month, stocks are then sorted into deciles based on asset growth and only stocks # with the highest asset growth are used. The next step is to sort stocks from the highest asset growth decile into quintiles, # based on their past 11-month return (with the last month’s performance skipped in the calculation). The investor then goes long # on stocks with the strongest momentum and short on stocks with the weakest momentum. The portfolio is equally weighted and is # rebalanced monthly. The investor holds long-short portfolios only during February-December -> January is excluded as this month # has been repeatedly documented as a negative month for a momentum strategy (see “January Effect Filter and Momentum in Stocks”). # # QC implementation changes: # - Universe consists of 500 most liquid stocks traded on NYSE, AMEX, or NASDAQ. from AlgorithmImports import * class MomentumFactorAssetGrowthEffect(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) # Monthly close data. self.data = {} self.period = 13 self.total_assets_history_period = 2 self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol self.spy_consolidator = TradeBarConsolidator(timedelta(days=21)) self.spy_consolidator.DataConsolidated += self.CustomHandler self.SubscriptionManager.AddConsolidator(self.symbol, self.spy_consolidator) self.data[self.symbol] = SymbolData(self.symbol, self.period, self.total_assets_history_period) # Warmup market history. history = self.History(self.symbol, self.period, Resolution.Daily) if not history.empty: closes = history.loc[self.symbol].close closes_len = len(closes.keys()) # Find monthly closes. for index, time_close in enumerate(closes.iteritems()): # index out of bounds check. if index + 1 < closes_len: date_month = time_close[0].date().month next_date_month = closes.keys()[index + 1].month # Found last day of month. if date_month != next_date_month: self.data[self.symbol].update(time_close[1]) self.coarse_count = 500 self.long = [] self.short = [] 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.BeforeMarketClose(self.symbol), self.Selection) def CustomHandler(self, sender, consolidated): self.data[self.symbol].update(consolidated.Close) def OnSecuritiesChanged(self, changes): for security in changes.AddedSecurities: security.SetFeeModel(CustomFeeModel()) security.SetLeverage(5) def CoarseSelectionFunction(self, coarse): if not self.selection_flag: return Universe.Unchanged # Update the rolling window every month. for stock in coarse: symbol = stock.Symbol # Store monthly price. if symbol in self.data: self.data[symbol].update(stock.AdjustedPrice) # 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, self.total_assets_history_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 closes_len = len(closes.keys()) # Find monthly closes. for index, time_close in enumerate(closes.iteritems()): # index out of bounds check. if index + 1 < closes_len: date_month = time_close[0].date().month next_date_month = closes.keys()[index + 1].month # Found last day of month. if date_month != next_date_month: self.data[symbol].update(time_close[1]) return [x for x in selected if self.data[x].is_ready()] def FineSelectionFunction(self, fine): fine = [x for x in fine if x.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths > 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 = sorted_by_market_cap[:self.coarse_count] # else: # top_by_market_cap = fine top_by_market_cap = fine # Asset growth calc. asset_growth = {} for stock in top_by_market_cap: symbol = stock.Symbol if self.data[symbol].asset_data_is_ready(): asset_growth[symbol] = self.data[symbol].asset_growth() self.data[symbol].update_assets(stock.FinancialStatements.BalanceSheet.TotalAssets.TwelveMonths) sorted_by_growth = sorted(asset_growth.items(), key = lambda x: x[1], reverse = True) decile = int(len(sorted_by_growth) / 10) top_by_growth = [x[0] for x in sorted_by_growth][:decile] performance = { x : self.data[x].performance(1) for x in top_by_growth} sorted_by_performance = sorted(performance.items(), key = lambda x: x[1], reverse = True) quintile = int(len(sorted_by_performance) / 5) self.long = [x[0] for x in sorted_by_performance][:quintile] self.short = [x[0] for x in sorted_by_performance][-quintile:] return self.long + self.short def OnData(self, data): if not self.selection_flag: return self.selection_flag = False # Trade execution. 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: self.SetHoldings(symbol, 1 / len(self.long)) for symbol in self.short: self.SetHoldings(symbol, -1 / len(self.short)) self.long.clear() self.short.clear() def Selection(self): # Exclude January trading. if self.Time.month != 12: self.selection_flag = True else: self.Liquidate() class SymbolData(): def __init__(self, symbol, period, total_assets_history_period): self.Symbol = symbol self.Price = RollingWindow[float](period) self.TotalAssets = RollingWindow[float](total_assets_history_period) def update(self, value): self.Price.Add(value) def update_assets(self, assets_value): self.TotalAssets.Add(assets_value) def asset_data_is_ready(self) -> bool: return self.TotalAssets.IsReady def asset_growth(self) -> float: asset_values = [x for x in self.TotalAssets] return (asset_values[0] - asset_values[1]) / asset_values[1] def is_ready(self) -> bool: return self.Price.IsReady # Performance, one month skipped. def performance(self, values_to_skip = 0) -> float: closes = [x for x in self.Price][values_to_skip:] 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"))