Overall Statistics
Total Trades
9744
Average Win
0.01%
Average Loss
0.00%
Compounding Annual Return
1.479%
Drawdown
2.100%
Expectancy
0.483
Net Profit
9.206%
Sharpe Ratio
0.764
Probabilistic Sharpe Ratio
12.849%
Loss Rate
53%
Win Rate
47%
Profit-Loss Ratio
2.13
Alpha
0.01
Beta
0.011
Annual Standard Deviation
0.013
Annual Variance
0
Information Ratio
-0.174
Tracking Error
0.174
Treynor Ratio
0.907
Total Fees
$1548786.21
Estimated Strategy Capacity
$5000.00
Lowest Capacity Asset
ADGE R735QTJ8XC9X
 
#region imports
from AlgorithmImports import *
#endregion
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(1998, 1, 2)   
        # self.SetEndDate(1981, 1, 1)      
        self.year = -1
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.CoarseSelectionFunction)
        self.SetCash(100000000) 
        
    def CoarseSelectionFunction(self, coarse):
        if self.Time.year == self.year:
            return []
        self.year = self.Time.year
        
        CoarseWithFundamental = [x for x in coarse if x.HasFundamentalData]
        sortedByDollarVolume = sorted(CoarseWithFundamental, key=lambda x: x.DollarVolume, reverse=False)
        return [i.Symbol for i in sortedByDollarVolume[:1000]]
    
    def OnSecuritiesChanged(self, change):
        # 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)