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I'm already aware of how to impliment a stop loss in Quanconnect. But i'd like a better way to impliment a trailing stop loss.
One way I did it, which I don't prefer is having a function that counts the number of days a security is in my holdings and then using that number to decide how many days to look back in history I want to check to compare if the price has dropped x % less than the maximum price in history.
For example:
The fucntion below keeps track of number of days in portfolio.
def position_check(self):
for stock in self.symbols:
shares_held = float(self.Portfolio[stock].Quantity)
if shares_held:
self.days_in_portfolio[stock]+=1
else:
self.days_in_portfolio[stock]=0
The funtion below checks to see if the current price is x % less than the price since I bought the security:
def stop_loss(self,stock):
hist = self.History([stock],self.days_in_portfolio[stock],Resolution.Daily)
curr_price = float(self.Portfolio[stock].Price)
if 'close' not in hist:
return 0
prev_max =hist['close'].max()
trail_stop= ((curr_price - prev_max) /prev_max) <= -self.stop_loss_value
return trail_stop
Thanks for any help in advance. :)
Derek Tishler
Maybe something like:
def Initialize(self): self.max_loss_frac = 0.03 self.asset_best_price = {} def OnData(self, data): # risk managment to limit per position loss to n% map(self.RiskManagement, self.universe) def RiskManagement(self, symbol): # https://github.com/QuantConnect/Lean/blob/24fcd239a702c391c26854601a99c514136eba7c/Common/Securities/SecurityHolding.cs#L79https://github.com/QuantConnect/Lean/blob/24fcd239a702c391c26854601a99c514136eba7c/Common/Securities/SecurityHolding.cs#L79 if self.Portfolio[symbol].HoldStock: # init the avg price as our current best price for the asset if symbol not in self.asset_best_price: self.asset_best_price[symbol] = float(self.Portfolio[symbol].AveragePrice) # For long positions if self.Portfolio[symbol].Quantity > 0: # update best price self.asset_best_price[symbol] = np.maximum(self.asset_best_price[symbol], float(self.Securities[symbol].Price)) # have we exceeded the target limits? if (float(self.Securities[symbol].Price)-self.asset_best_price[symbol])/self.asset_best_price[symbol] < -self.max_loss_frac: # cover the position self.Log("RM Exit of Long pos: %s"%symbol) self.Liquidate(symbol, tag="RM Long Cover") del self.asset_best_price[symbol] # For Short positions elif self.Portfolio[symbol].Quantity < 0: # update best price self.asset_best_price[symbol] = np.minimum(self.asset_best_price[symbol], float(self.Securities[symbol].Price)) # have we exceeded the target limits? if (float(self.Securities[symbol].Price)-self.asset_best_price[symbol])/self.asset_best_price[symbol] > self.max_loss_frac: # cover the position self.Log("RM Exit of Short pos: %s"%symbol) self.Liquidate(symbol, tag="RM Short Cover") del self.asset_best_price[symbol]Leigham Springer Sutton
Thanks :). I think this is probably something that I should submit a feature request for as I bet many community members want trailing stop orders as a built in order type.
Leigham Springer Sutton
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