Overall Statistics |
Total Trades 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Net Profit 0% Sharpe Ratio 0 Probabilistic Sharpe Ratio 0% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio -1.891 Tracking Error 0.104 Treynor Ratio 0 Total Fees $0.00 |
from clr import AddReference AddReference("System") AddReference("QuantConnect.Algorithm") AddReference("QuantConnect.Indicators") AddReference("QuantConnect.Common") AddReference("NodaTime") from System import * from QuantConnect import * from QuantConnect.Data import * from QuantConnect.Data.Market import * from QuantConnect.Data.Consolidators import * from QuantConnect.Algorithm import * from QuantConnect.Indicators import * from QuantConnect.Securities import * from QuantConnect.Orders import * from datetime import datetime from System.Drawing import Color from NodaTime import DateTimeZone from QuantConnect.Brokerages import * from QuantConnect.Data.Market import * from QuantConnect import * import decimal as d import numpy as np class BBTrend(QCAlgorithm): def Initialize(self): # configuration parameters (configurable inputs into the algorithm) DEBUG_LOG = False MINUTES_AFTER_OPEN = 0 MINUTES_BEFORE_CLOSE = 1 SYMBOL = "SPY" BBLENGTH = 20 BBDEV = 2 self.ORDER_MAP = ["Market", "Limit", "StopMarket", "StopLimit", "MarketOnOpen", "MarketOnClose", "OptionExercise"] self.DEBUG = DEBUG_LOG # initialization self.SetStartDate(2020, 9, 28) self.SetEndDate(2020, 9, 30) self.SetCash(100000) #self.stock = self.AddEquity(SYMBOL, Resolution.Second, extendedMarketHours = True) self.stock = self.AddEquity(SYMBOL, Resolution.Second, Market.USA, True, 1, True) self.SetTimeZone(TimeZones.Chicago) # Assigning Interactive Brokerage as our brokerage model self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin) # Assigning securities custom slippage models: # self.Securities[SYMBOL].SetSlippageModel(CustomSlippageModel(self)) self.Schedule.On(self.DateRules.EveryDay(self.stock.Symbol), self.TimeRules.AfterMarketOpen(self.stock.Symbol, MINUTES_AFTER_OPEN), self.OnMarketOpen) self.Schedule.On(self.DateRules.EveryDay(self.stock.Symbol), self.TimeRules.BeforeMarketClose(self.stock.Symbol, MINUTES_BEFORE_CLOSE), self.OnMarketClose) # set the trade flag to False. we'll only start trading when the flag flips to True (after the market open event) self.tradeFlag = False self.bb = BollingerBands(BBLENGTH, 2, MovingAverageType.Exponential) self.lastPrice = 0.0 self.pl = 0.0 self._lo = None # self.SetWarmUp(BBLENGTH * 5, Resolution.Minute) self.Consolidate(SYMBOL, timedelta(minutes=5), self.OnStockBarConsolidated) # OnMarketOpen event, callback from our TimeRules.AfterMarketOpen initialization def OnMarketOpen(self): # start trading! self.tradeFlag = True # OnMarketClose event, callback from our TimeRules.BeforeMarketClose initialization def OnMarketClose(self): # liquidate all holdings if self.stock.Invested: self.Liquidate(self.stock.Symbol, "EOD Liquidate") else: self.Transactions.CancelOpenOrders() # reset trade flag for following day self.tradeFlag = False if self.DEBUG: self.Debug("Profit/Loss as of " + str(self.Time) + ": " + str(self.pl) + " | Portfolio Value: " + str(self.Portfolio.TotalPortfolioValue)) def OnStockBarConsolidated(self, consolidated): self.bb.Update(consolidated.EndTime, consolidated.Close) self.Plot("IsReady", "Val", int(self.bb.IsReady)) if self.IsWarmingUp or not self.tradeFlag: return