trophy

Long Only, ETF Alpha Streams Competition with $27,500 Prize Pool

Back

Updating ADX in universe and label not in index issues

Thanks for the help of QC members. I could now do some meaningful tests. 
However, I still meet some problems. Could someone help, please? Thank you

1. How to update the ADX/ATR in the Fine Universe? I have searched some posts and found that Jared said universe data does not have OHLC and cannot update it. 

2. Runtime Error: Trying to retrieve an element from a collection using a key that does not exist in that collection throws a KeyError exception. To prevent the exception, ensure that the LEVI X305F7R1QVS5 key exist in the collection and/or that collection is not empty.
at FineSelectionFunction in main.py:line 61
KeyError : 'the label [LEVI X305F7R1QVS5] is not in the [index]'

 

from clr import AddReference
AddReference("System.Core")
AddReference("System.Collections")
AddReference("QuantConnect.Common")
AddReference("QuantConnect.Algorithm")

from System import *
from System.Collections.Generic import List
from QuantConnect import *
from QuantConnect.Algorithm import QCAlgorithm
from QuantConnect.Data.UniverseSelection import *
import numpy as np
class VerticalCalibratedRegulators(QCAlgorithm):

def Initialize(self):
self.SetStartDate(2019, 3, 1) # Set Start Date
self.SetEndDate(2019, 4, 1)
self.SetCash(100000) # Set Strategy Cash
# self.AddEquity("SPY", Resolution.Minute)
self.__numberOfSymbols = 100
self.__numberOfSymbolsFine = 5
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)

self.smav = {}
self.rsi = {}
#self.adx = {}
#self.atr = {}
self.sma = {}
self.selectedSymbols = []
self._changes = None

def OnData(self, data):
pass

# sort the data by daily dollar volume and take the top 'NumberOfSymbols'
def CoarseSelectionFunction(self, coarse):

filtered = [ x for x in coarse if x.HasFundamentalData and x.Price > 1 and x.DollarVolume > 2500000 ]
sortedByDollarVolume = sorted(filtered, key=lambda x: x.DollarVolume, reverse=True)

# return the symbol objects of the top entries from our sorted collection
return [ x.Symbol for x in sortedByDollarVolume[:self.__numberOfSymbols] ]


# sort the data by P/E ratio and take the top 'NumberOfSymbolsFine'
def FineSelectionFunction(self, fine):
# sort descending by P/E ratio
sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True)

## Retrieve 20 days of historical data for each symbol
symbols = [x.Symbol for x in sortedByPeRatio]
history = self.History(symbols, 150, Resolution.Daily)
## Iterate through symbols
for symbol in symbols:
## Find hsitory for specific symbol
symbolVolumeHistory = history.loc[str(symbol)]
if symbolVolumeHistory.empty:
self.Log("EMPTY dataframe!")

## Create SMA for symbol and register it with algorithm
else:
symbolSMAv = SimpleMovingAverage(50)
symbolRSI = RelativeStrengthIndex(3)
#symbolADX = AverageDirectionalIndex(7)
#symbolATR = AverageTrueRange(10)
symbolSMA = SimpleMovingAverage(150)

## Iterate through historical data
for tuple in symbolVolumeHistory.itertuples():
## Update SMA with data time and volume
symbolSMAv.Update(tuple.Index, tuple.volume)
#self.Debug(f'Updating {symbol.Value} SMA...')
symbolRSI.Update(tuple.Index, tuple.close)
#symbolADX.Update(bar)
#symbolATR.Update(bar)
symbolSMA.Update(tuple.Index, tuple.close)

## Add SMA to dictionary so you can access it later
self.smav[symbol] = symbolSMAv
self.rsi[symbol] = symbolRSI
#self.adx[symbol] = symbolADX
#self.atr[symbol] = symbolATR
self.sma[symbol] = symbolSMA
#self.Debug(f' {symbol.Value} Price ' + str(symbolVolumeHistory['close'][-1]) + 'SMA' + str(self.sma[symbol]))


## Perform SMA filtering conditions and select/return the symbols you want to add to your universe
## Fine Selection will use only these ones

self.selectedSymbols = [ x for x in symbols if self.smav[x].Current.Value > 500000 and self.rsi[x].Current.Value < 30 and history.loc[str(x)]['close'][-1] > self.sma[x].Current.Value ]

return self.selectedSymbols

def OnData(self, data):
# if we have no changes, do nothing
if self._changes is None: return

# liquidate removed securities
for security in self._changes.RemovedSecurities:
if security.Invested:
self.Liquidate(security.Symbol)

# we want 20% allocation in each security in our universe
for security in self._changes.AddedSecurities:
self.SetHoldings(security.Symbol, 0.1)

self._changes = None


# this event fires whenever we have changes to our universe
def OnSecuritiesChanged(self, changes):
self._changes = changes

 

Update Backtest







0

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.


Update Backtest





0

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.


Loading...

This discussion is closed