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I'm new to Quantconnect and trying to implement a value investing old school strategy assisted by TA market timing, specifically using fast/slow EMA cross and RSI to pickup the value shares in the universe.
Flow:
- Create coarse universe from high dollar vol equities
- Slice fine universe with a couple of value investing requitements (only dividend bearing companies, p/e less than 15 etc.)
- Add to final universe if fast/slow EMA cross overs and RSI less than 25 .. ie. its uptrending but had a pull back
import talib
from Selection.EmaCrossUniverseSelectionModel import EmaCrossUniverseSelectionModel
class ModulatedOptimizedProcessor(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2016, 11, 30) # Set Start Date
self.SetCash(100000) # Set Strategy Cash
self.SetBenchmark("SPY")
#self.UniverseSettings.Resolution = Resolution.Daily
self.UniverseSettings.Leverage = 1
fastPeriod = 10
slowPeriod = 30
count = 10
#self.SetUniverseSelection(EmaCrossUniverseSelectionModel(fastPeriod, slowPeriod, count))
#self.AddUniverse(self.Universe.Index.QC500)
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
def CoarseSelectionFunction(self, coarse):
sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
filtered = [ x.Symbol for x in sortedByDollarVolume if x.HasFundamentalData ]
return filtered[:50]
def FineSelectionFunction(self, fine):
filtered_fine = [x for x in fine if x.EarningReports.TotalDividendPerShare.ThreeMonths > 0
and x.ValuationRatios.PriceChange1M > 0
and x.ValuationRatios.BookValuePerShare
and x.ValuationRatios.PERatio < 18
and x.ValuationRatios.FCFYield]
# sortedByfactor1 = sorted(filtered_fine, key=lambda x: x.EarningReports.TotalDividendPerShare.ThreeMonths, reverse=True)
# sortedByfactor2 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.PriceChange1M, reverse=False)
# sortedByfactor3 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.BookValuePerShare, reverse=True)
# sortedByfactor4 = sorted(filtered_fine, key=lambda x: x.ValuationRatios.FCFYield, reverse=True)
sortedByPeRatio = sorted(filtered_fine, key=lambda x: x.ValuationRatios.PERatio, reverse=False)
stocks = [ x.Symbol for x in sortedByPeRatio[:50] ]
return stocks
def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
Arguments:
data: Slice object keyed by symbol containing the stock 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 15% allocation in each security in our universe
for security in self._changes.AddedSecurities:
self.SetHoldings(security.Symbol, 0.15)
self._changes = None
#self.next_rebalance = Expiry.EndOfMonth(self.Time)
# this event fires whenever we have changes to our universe
def OnSecuritiesChanged(self, changes):
self._changes = changes
# if not self.Portfolio.Invested:
# self.SetHoldings("SPY", 1)
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