'''
For notes, etc.
Full API (C# only): https://www.quantconnect.com/lean/docs
Python API with examples (doesn't include every method but does include most): https://www.quantconnect.com/docs/algorithm-reference/overview
Time example: https://www.quantconnect.com/forum/discussion/142/how-to-get-simulated-time-and-date
Crisis Events: https://www.quantconnect.com/blog/leans-tear-sheet-the-lean-report-creator/
Bracket Orders https://www.quantconnect.com/forum/discussion/3328/futures-quot-bracket-order-quot-example/p1
To do:
- Universe Selection
- Option intergration
- Going back certain number of periods using slices to get indicator values prior to our current period
- Trailing Stop Loss
'''
class MultipleSymbolConsolidationAlgorithm(QCAlgorithm):
# Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
def Initialize(self):
#Initial investment and backtest period
self.SetStartDate(2015, 1, 1)
self.SetEndDate(datetime.now().date() - timedelta(1))
self.SetCash(20000)
#Brokerage Model
self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage)
# This is the period of bars we'll be creating
BarPeriod = TimeSpan.FromMinutes(20)
# This is the period of our rsi indicators
RSIPeriod = 30
# This is the period of our vwap indicators
VWAPPeriod = 10
# This is the period of our volume moving average
TEMAvolumePeriod = 8
# This is the the volume of the last bar
SMAvolumeOnePeriod = 1
# This is the period of our sma indicators
SimpleMovingAveragePeriod = 30
# This is the period of our last price
SimpleMovingAverageonePeriod = 1
# This is the period of our ema indicators
ExponentialMovingAveragePeriod = 8
# This is the period of our tema indicators
TripleExponentialMovingAveragePeriod = 8
# This is the number of consolidated bars we'll hold in symbol data for reference
RollingWindowSize = 30
# Holds all of our data keyed by each symbol
self.Data = {}
# Contains all of our equity symbols
EquitySymbols = ["AAPL"]
# initialize our equity data
for symbol in EquitySymbols:
equity = self.AddEquity(symbol)
self.Data[symbol] = SymbolData(equity.Symbol, BarPeriod, RollingWindowSize)
# loop through all our symbols and request data subscriptions and initialize indicator
for symbol, symbolData in self.Data.items():
# define the indicator
symbolData.SMA = SimpleMovingAverage(self.CreateIndicatorName(symbol, "SMA" + str(SimpleMovingAveragePeriod), Resolution.Minute), SimpleMovingAveragePeriod)
# define the indicator
symbolData.RSI = RelativeStrengthIndex(self.CreateIndicatorName(symbol, "RSI" + str(RSIPeriod), Resolution.Minute), RSIPeriod, MovingAverageType.Simple)
# define the indicator
symbolData.SMAone = SimpleMovingAverage(self.CreateIndicatorName(symbol, "SMA" + str(SimpleMovingAverageonePeriod), Resolution.Minute), SimpleMovingAverageonePeriod)
# define a consolidator to consolidate data for this symbol on the requested period
consolidator = TradeBarConsolidator(BarPeriod)
# write up our consolidator to update the indicator
consolidator.DataConsolidated += self.OnDataConsolidated
# we need to add this consolidator so it gets auto updates
self.SubscriptionManager.AddConsolidator(symbolData.Symbol, consolidator)
def OnDataConsolidated(self, sender, bar):
self.Data[bar.Symbol.Value].SMA.Update(bar.Time, bar.Close)
self.Data[bar.Symbol.Value].SMAone.Update(bar.Time, bar.Close)
self.Data[bar.Symbol.Value].RSI.Update(bar.Time, bar.Close)
self.Data[bar.Symbol.Value].Bars.Add(bar)
# OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
# Argument "data": Slice object, dictionary object with your stock data
def OnData(self,data):
# loop through each symbol in our structure
for symbol in self.Data.keys():
symbolData = self.Data[symbol]
stopLossPercent = .99
profitTargetPercent = 1.02
# this check proves that this symbol was JUST updated prior to this OnData function being called
if symbolData.IsReady() and symbolData.WasJustUpdated(self.Time):
#----TIME VARIABLES----
# Set up pre-market close time
CloseTimeString = "15:44:00"
CloseOutTime = datetime.strptime(CloseTimeString, "%H:%M:%S")
CloseTime = datetime.time(CloseOutTime)
#self.Debug(str(CloseTime) + " CLOSETIME ")
# Get symbol trade time
DataSymbolTime = data[symbol].Time
DataTime = datetime.time(DataSymbolTime)
#self.Debug(str(DataTime) + " DATATIME ")
#----TIME VARIABLES----
# Check to see if we are near market close
if DataTime < CloseTime:
#if we do not own this symbol
if not self.Portfolio[symbol].Invested:
#if RSI value is less than 'x'
if float(symbolData.RSI.Current.Value) < 35:
#Liquidate opposite symbol
self.Liquidate()
self.Debug("LIQUIDATING in loop...")
#Cancel open ordersif there are any
openOrders = self.Transactions.GetOpenOrders()
if len(openOrders)> 0:
for x in openOrders:
self.Transactions.CancelOrder(x.Id)
# Set Position Size
posSize = self.CalculateOrderQuantity(symbol, 1)
# Market Order to buy
self.MarketOrder(symbol, posSize)
# Stop Loss/Profit Taker
self.StopLimitOrder(symbol, -posSize, float(symbolData.SMAone.Current.Value) * stopLossPercent, float(symbolData.SMAone.Current.Value) * profitTargetPercent)
# End of a trading day event handler. This method is called at the end of the algorithm day (or multiple times if trading multiple assets).
# Method is called 10 minutes before closing to allow user to close out position.
def OnEndOfDay(self):
#Liquidate all symbol
self.Liquidate()
self.Debug("LIQUIDATING at end of day...")
'''
#Cancel open ordersif there are any
openOrders = self.Transactions.GetOpenOrders()
if len(openOrders)> 0:
for x in openOrders:
self.Transactions.CancelOrder(x.Id)
'''
'''
i = 0
for symbol in sorted(self.Data.keys()):
symbolData = self.Data[symbol]
# we have too many symbols to plot them all, so plot every other
i += 1
if symbolData.IsReady() and i%2 == 0:
self.Plot(symbol, symbol, symbolData.SMA.Current.Value)
'''
class SymbolData(object):
def __init__(self, symbol, barPeriod, windowSize):
self.Symbol = symbol
# The period used when population the Bars rolling window
self.BarPeriod = barPeriod
# A rolling window of data, data needs to be pumped into Bars by using Bars.Update( tradeBar ) and can be accessed like:
# mySymbolData.Bars[0] - most first recent piece of data
# mySymbolData.Bars[5] - the sixth most recent piece of data (zero based indexing)
self.Bars = RollingWindow[IBaseDataBar](windowSize)
# The simple moving average indicator for our symbol
self.SMA = None
# Returns true if all the data in this instance is ready (indicators, rolling windows, ect...)
def IsReady(self):
return self.Bars.IsReady and self.SMA.IsReady
# Returns true if the most recent trade bar time matches the current time minus the bar's period, this
# indicates that update was just called on this instance
def WasJustUpdated(self, current):
return self.Bars.Count > 0 and self.Bars[0].Time == current - self.BarPeriod
