Overall Statistics |
Total Trades
144328
Average Win
0.01%
Average Loss
-0.01%
Compounding Annual Return
11.559%
Drawdown
33.000%
Expectancy
0.290
Net Profit
330.218%
Sharpe Ratio
0.649
Probabilistic Sharpe Ratio
5.072%
Loss Rate
42%
Win Rate
58%
Profit-Loss Ratio
1.22
Alpha
0.004
Beta
0.879
Annual Standard Deviation
0.136
Annual Variance
0.019
Information Ratio
-0.156
Tracking Error
0.049
Treynor Ratio
0.101
Total Fees
$3282.47
Estimated Strategy Capacity
$130000000.00
Lowest Capacity Asset
TEVIY R735QTJ8XC9X
Portfolio Turnover
6.45%
|
# https://quantpedia.com/strategies/trend-following-effect-in-stocks/ # # The investment universe consists of US-listed companies. A minimum stock price filter is used to avoid penny stocks, and a minimum # daily liquidity filter is used to avoid stocks that are not liquid enough. The entry signal occurs if today’s close is greater than # or equal to the highest close during the stock’s entire history. A 10-period average true range trailing stop is used as an exit # signal. The investor holds all stocks which satisfy entry criterion and are not stopped out. The portfolio is equally weighted and # rebalanced daily. Transaction costs of 0.5% round-turn are deducted from each trade to account for estimated commission and slippage. # # QC implementation: # - Universe consists of top 100 liquid US stocks. import numpy as np from AlgorithmImports import * class TrendFollowingStocks(QCAlgorithm): def Initialize(self): self.SetStartDate(2010, 1, 1) self.SetCash(100000) self.SetSecurityInitializer(lambda x: x.SetMarketPrice(self.GetLastKnownPrice(x))) self.course_count = 100 self.long = [] self.max_close = {} self.atr = {} self.sl_order = {} self.sl_price = {} self.selection = [] self.period = 10*12*21 self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.CoarseSelectionFunction) def OnSecuritiesChanged(self, changes): for security in changes.AddedSecurities: security.SetFeeModel(CustomFeeModel()) symbol = security.Symbol if symbol not in self.atr: self.atr[symbol] = self.ATR(symbol, 10, Resolution.Daily) if symbol not in self.max_close: hist = self.History([self.Symbol(symbol)], self.period, Resolution.Daily) if 'close' in hist.columns: closes = hist['close'] self.max_close[symbol] = max(closes) def CoarseSelectionFunction(self, coarse): if self.IsWarmingUp: return selected = sorted([x for x in coarse if x.HasFundamentalData and x.Price > 5], key=lambda x: x.DollarVolume, reverse=True) self.selection = [x.Symbol for x in selected[:self.course_count]] return self.selection def OnData(self, data): if self.IsWarmingUp: return for symbol in self.selection: if symbol in data.Bars: price = data[symbol].Value if symbol not in self.max_close: continue if price >= self.max_close[symbol]: self.max_close[symbol] = price self.long.append(symbol) stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested] count = len(self.long) + len(stocks_invested) if count == 0: return # Update stoploss orders for symbol in stocks_invested: if not self.Securities[symbol].IsTradable: self.Liquidate(symbol) if self.atr[symbol].Current.Value == 0: continue # Move SL if symbol not in self.sl_price: continue self.SetHoldings(symbol, 1 / count) new_sl = self.Securities[symbol].Price - self.atr[symbol].Current.Value if new_sl > self.sl_price[symbol]: update_order_fields = UpdateOrderFields() update_order_fields.StopPrice = new_sl # Update SL price quantity = self.CalculateOrderQuantity(symbol, (1 / count)) update_order_fields.Quantity = quantity # Update SL quantity self.sl_price[symbol] = new_sl self.sl_order[symbol].Update(update_order_fields) # self.Log('SL MOVED on ' + str(symbol) + ' to: ' + str(new_sl)) # Open new trades for symbol in self.long: if not self.Portfolio[symbol].Invested and self.atr[symbol].Current.Value != 0: price = data[symbol].Value if self.Securities[symbol].IsTradable: unit_size = self.CalculateOrderQuantity(symbol, (1 / count)) self.MarketOrder(symbol, unit_size) sl_price = price - self.atr[symbol].Current.Value self.sl_price[symbol] = sl_price if unit_size != 0: self.sl_order[symbol] = self.StopMarketOrder(symbol, -unit_size, sl_price, 'SL') # self.Log('SL SET on ' + str(symbol) + ' to: ' + str(sl_price)) self.long.clear() # Custom fee model. class CustomFeeModel(FeeModel): def GetOrderFee(self, parameters): fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005 return OrderFee(CashAmount(fee, "USD"))