Overall Statistics |
Total Trades
142232
Average Win
0.01%
Average Loss
-0.01%
Compounding Annual Return
10.964%
Drawdown
45.400%
Expectancy
0.295
Net Profit
997.146%
Sharpe Ratio
0.707
Probabilistic Sharpe Ratio
3.664%
Loss Rate
22%
Win Rate
78%
Profit-Loss Ratio
0.66
Alpha
0.046
Beta
0.639
Annual Standard Deviation
0.116
Annual Variance
0.013
Information Ratio
0.33
Tracking Error
0.078
Treynor Ratio
0.128
Total Fees
$2174.17
Estimated Strategy Capacity
$37000.00
Lowest Capacity Asset
LGV XOD5K8FVI44L
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#region imports from AlgorithmImports import * #endregion # https://quantpedia.com/strategies/low-volatility-factor-effect-in-stocks-long-only-version/ # # The investment universe consists of global large-cap stocks (or US large-cap stocks). At the end of each month, the investor constructs # equally weighted decile portfolios by ranking the stocks on the past three-year volatility of weekly returns. The investor goes long # stocks in the top decile (stocks with the lowest volatility). # # QC implementation changes: # - Top quartile (stocks with the lowest volatility) is selected instead of decile. import numpy as np class LowVolatilityFactorEffectStocks(QCAlgorithm): def Initialize(self): self.SetStartDate(2000, 1, 1) self.SetCash(100000) self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol self.period = 12*21 self.coarse_count = 3000 self.last_coarse = [] self.data = {} self.long = [] self.selection_flag = True 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.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 # Store daily 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(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] # market cap sorting if len(fine) > self.coarse_count: sorted_by_market_cap = sorted(fine, key = lambda x: x.MarketCap, reverse=True) fine = sorted_by_market_cap[:self.coarse_count] weekly_vol = {x.Symbol : self.data[x.Symbol].volatility() for x in fine} # volatility sorting sorted_by_vol = sorted(weekly_vol.items(), key = lambda x: x[1], reverse = True) quartile = int(len(sorted_by_vol) / 4) self.long = [x[0] for x in sorted_by_vol[-quartile:]] return self.long 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.Liquidate(symbol) for symbol in self.long: if symbol in data and data[symbol]: self.SetHoldings(symbol, 1 / len(self.long)) self.long.clear() def Selection(self): self.selection_flag = True class SymbolData(): def __init__(self, period): self.price = RollingWindow[float](period) def update(self, value): self.price.Add(value) def is_ready(self) -> bool: return self.price.IsReady def volatility(self) -> float: closes = [x for x in self.price] # Weekly volatility calc. separete_weeks = [closes[x:x+5] for x in range(0, len(closes), 5)] weekly_returns = [(x[0] - x[-1]) / x[-1] for x in separete_weeks] return np.std(weekly_returns) # Custom fee model. class CustomFeeModel(FeeModel): def GetOrderFee(self, parameters): fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD"))