| 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