I've been trying to figure out how to trade symbols that are the results of universes w/o the use of securities and can't figure it out and I can't find anything about it, I've tried setting it as a variable and using it
import numpy as np
class MyCoarseUniverseAlgorithm(QCAlgorithm):
def Initialize(self):
self.AddUniverse(self.MyCoarseFilterFunction)
self.UniverseSettings.Resolution = Resolution.Daily
self.SetStartDate(2020, 6, 10)
self.SetCash(100000)
self.lookback = 20
self.ceiling, self.floor = 30, 10
self.initialStopRisk = 0.98
self.trailingStopRisk = 0.9
self.Schedule.On(self.DateRules.EveryDay(self.symbol), \
self.TimeRules.AfterMarketOpen(self.symbol, 20), \
Action(self.EveryMarketOpen))
def MyCoarseFilterFunction(self, coarse):
pass
class SelectionData(object):
def __init__(self, symbol, period):
self.volume = 0
self.symbol = symbol
self.ema = ExponentialMovingAverage(period)
self.is_above_ema = False
def update(self, time, price, volume):
self.volume = volume
if self.ema.Update(time, price):
self.is_above_ema = price > ema
def MyCoarseFilterFunction(self, coarse):
for c in coarse:
if c.Symbol not in self.stateData:
self.stateData[c.Symbol] = SelectionData(c.Symbol, 200)
avg = self.stateData[c.Symbol]
avg.update(c.EndTime, c.AdjustedPrice, c.DollarVolume)
values = [x for x in self.stateData.values() if x.is_above_ema and x.volume > 10000000]
values.sort(key=lambda x: x.volume, reverse=True)
return [ x.symbol for x in values[:10] ]
def OnData(self, data):
self.Plot("Data Chart", self.symbol, self.Securities[self.symbol].Close)
def EveryMarketOpen(self):
close = self.History(self.symbol, 31, Resolution.Daily)["close"]
todayvol = np.std(close[1:31])
yesterdayvol = np.std(close[0:30])
deltavol = (todayvol - yesterdayvol) / todayvol
self.lookback = round(self.lookback * (1 + deltavol))
if self.lookback > self.ceiling:
self.lookback = self.ceiling
elif self.lookback < self.floor:
self.lookback = self.floor
self.high = self.History(self.symbol, self.lookback, Resolution.Daily)["high"]
if not self.Securities[self.symbol].Invested and \
self.Securities[self.symbol].Close >= max(self.high[:-1]):
self.SetHoldings(self.symbol, 1)
self.breakoutlvl = max(self.high[:-1])
self.highestPrice = self.breakoutlvl
if self.Securities[self.symbol].Invested:
if not self.Transactions.GetOpenOrders(self.symbol):
self.stopMarketTicket = self.StopMarketOrder(self.symbol, \
-self.Portfolio[self.symbol].Quantity, \
self.initialStopRisk * self.breakoutlvl)
if self.Securities[self.symbol].Close > self.highestPrice and \
self.initialStopRisk * self.breakoutlvl < self.Securities[self.symbol].Close * self.trailingStopRisk:
self.highestPrice = self.Securities[self.symbol].Close
updateFields = UpdateOrderFields()
updateFields.StopPrice = self.Securities[self.symbol].Close * self.trailingStopRisk
self.stopMarketTicket.Update(updateFields)
self.Debug(updateFields.StopPrice)
self.Plot("Data Chart", "Stop Price", self.stopMarketTicket.Get(OrderField.StopPrice))
in the self.AddEquity and it won't work, any1 know how I would go about doing it?
Shile Wen
Hi Caio,
We can iterate through ActiveSecurities.Keys, which are the Symbols created by the Universe Selection, though it also includes securities that were removed but that are still invested, which means we need to liquidate them before using ActiveSecurities. I've shown an example in the attached backtest.
Best,
Shile Wen
Hussain
What if we're using the algorithm framework i.e. implementations of AlphaModel and PortfolioConstructionModel?
Would you advise we use algorithm.ActiveSecurities.Keys in the AlphaModel's Update(algorithm, data)?
My guess is while PortfolioConstructionModel discards insights pertaining to removed securities, you wouldn't want to be producing fresh insights for these in the AlphaModel implementation.
Therefore we should probably detect and liquidate removed securities in the AlphaModel's OnSecuritiesChanged, which presumably gets called prior to the AlphaModel's Update().
What do you think?
Shile Wen
Hi Hussain,
We typically have a SymbolData/SelectionData dictionary pattern shown in this BootCamp when using an Alpha Model, which means we can iterate over the keys of the dictionary instead of the ActiveSecurities when generating insights. Inside the Alpha Model's OnSecuritiesChanged, we typically remove keys for removed securities.
Best,
Shile Wen
Caio Navarro
The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.
To unlock posting to the community forums please complete at least 30% of Boot Camp.
You can continue your Boot Camp training progress from the terminal. We hope to see you in the community soon!