Hi,

I think the code is doing something wrong because is not doing what I need. First I want to calculate the number of shares to buy in order to place the stop loss and Take profit and win or lose the same amount, in this case, $1000.

The code is not calculating well the # of shares and it seems the algo is doing the order, then the stop loss and then the take profit, this is wrong because what I need is one or the other, not the 2. Meaning either Stop Loss or Take Profit for any given order. Hope someone can help.

Is just a simple mean-reverting strategy.

import clr clr.AddReference("System") clr.AddReference("QuantConnect.Algorithm") clr.AddReference("QuantConnect.Indicators") clr.AddReference("QuantConnect.Common") from System import * from QuantConnect import * from QuantConnect.Algorithm import * from QuantConnect.Indicators import * class KeltnerMeanReversionAlgorithm(QCAlgorithm): def Initialize(self): '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.''' self.SetStartDate(2018, 1, 1) #Set Start Date #self.SetEndDate(2015, 1, 1) #Set End Date self.SetCash(100000) #Set Strategy Cash # Find more symbols here: http://quantconnect.com/data self.AddEquity("SPY") # create the 20 EMA self.mean = self.EMA("SPY", 20, Resolution.Daily) # create the ATR self.atr = self.ATR("SPY", 20, Resolution.Daily) self.previous = None def OnData(self, data): '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.''' # a couple things to notice in this method: # 1. We never need to 'update' our indicators with the data, the engine takes care of this for us # 2. We can use indicators directly in math expressions # 3. We can easily plot many indicators at the same time # wait for our ema to fully initialize if not self.mean.IsReady: return # only once per day if self.previous is not None and self.previous.date() == self.Time.date(): return risk_per_trade = 100 holdings = self.Portfolio["SPY"].Quantity # If there is no trades if holdings == 0: quantity = round(risk_per_trade / self.atr.Current.Value) # Mean reversion when price is too low if self.Securities["SPY"].Price < self.mean.Current.Value - 2 * self.atr.Current.Value: self.Log("BUY >> {0}".format(self.Securities["SPY"].Price)) marketTicket = self.MarketOrder("SPY", quantity) limitTicket = self.LimitOrder("SPY", -self.Portfolio['SPY'].Quantity, self.Securities["SPY"].Price + self.atr.Current.Value) stopTicket = self.StopMarketOrder("SPY", -self.Portfolio['SPY'].Quantity, self.Securities["SPY"].Price - self.atr.Current.Value) # Mean reversion when price is too high if self.Securities["SPY"].Price > self.mean.Current.Value + 2 * self.atr.Current.Value: self.Log("SELL >> {0}".format(self.Securities["SPY"].Price)) marketTicket = self.MarketOrder("SPY", -quantity) limitTicket = self.LimitOrder("SPY", self.Portfolio['SPY'].Quantity, self.Securities["SPY"].Price - self.atr.Current.Value) stopTicket = self.StopMarketOrder("SPY", self.Portfolio['SPY'].Quantity, self.Securities["SPY"].Price + self.atr.Current.Value) self.previous = self.Time