| Overall Statistics |
|
Total Trades 48 Average Win 5.05% Average Loss -2.76% Compounding Annual Return 14.548% Drawdown 45.400% Expectancy 0.769 Net Profit 60.890% Sharpe Ratio 0.521 Loss Rate 38% Win Rate 62% Profit-Loss Ratio 1.83 Alpha 0.161 Beta 0.308 Annual Standard Deviation 0.387 Annual Variance 0.15 Information Ratio 0.175 Tracking Error 0.395 Treynor Ratio 0.655 Total Fees $465.51 |
from QuantConnect.Data.UniverseSelection import *
import math
import numpy as np
import pandas as pd
import scipy as sp
class PriceEarningsAnamoly(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2016, 1, 1)
self.SetEndDate(2019, 7, 1)
self.SetCash(100000)
self.SetBenchmark("SPY")
self.UniverseSettings.Resolution = Resolution.Daily
self.symbols = []
# record the year that have passed since the algorithm starts
self.year = -1
self._NumCoarseStocks = 200
self._NumStocksInPortfolio = 10
self.UniverseSettings.Leverage = 1.0
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
def CoarseSelectionFunction(self, coarse):
if self.Time.year == self.year:
return self.symbols
# drop stocks which have no fundamental data or have low price
CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData and x.Price > 5]
sortedByDollarVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=False)
return [i.Symbol for i in sortedByDollarVolume[:self._NumCoarseStocks]]
def FineSelectionFunction(self, fine):
if self.Time.year == self.year:
return self.symbols
self.year = self.Time.year
fine = [x for x in fine if x.ValuationRatios.PERatio > 0]
sortedPERatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio)
self.symbols = [i.Symbol for i in sortedPERatio[:self._NumStocksInPortfolio]]
return self.symbols
def OnSecuritiesChanged(self, change):
margin = self.Portfolio.TotalMarginUsed
self.Debug(str(self.Time) + " Current Margin Used post Liquidate: " + str(margin))
# liquidate securities that removed from the universe
for security in change.RemovedSecurities:
if self.Portfolio[security.Symbol].Invested:
self.Liquidate(security.Symbol)
count = len(change.AddedSecurities)
# evenly invest on securities that newly added to the universe
for security in change.AddedSecurities:
self.SetHoldings(security.Symbol, 1.0/count)