Overall Statistics
Total Trades
0
Average Win
0%
Average Loss
0%
Compounding Annual Return
0%
Drawdown
0%
Expectancy
0
Net Profit
0%
Sharpe Ratio
0
Probabilistic Sharpe Ratio
0%
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0
Beta
0
Annual Standard Deviation
0
Annual Variance
0
Information Ratio
0
Tracking Error
0
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
 
#region imports
from AlgorithmImports import *
#endregion
# https://quantpedia.com/Screener/Details/26
from QuantConnect.Data.UniverseSelection import *
import math
import numpy as np
import pandas as pd
import scipy as sp

class BooktoMarketAnomaly(QCAlgorithm):

    def Initialize(self):

        self.SetStartDate(2018, 7, 1)   
        self.SetEndDate(2018, 7, 2)         
        self.SetCash(1000000)            
        self.UniverseSettings.Resolution = Resolution.Daily
        self.sorted_by_bm = None
        self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
       
        # Count the number of months that have passed since the algorithm starts
      
    def CoarseSelectionFunction(self, coarse):
        return [x.Symbol for x in coarse if x.HasFundamentalData and x.Price > 5]     

    def FineSelectionFunction(self, fine):
        top_market_cap = sorted(fine, key = lambda x:x.MarketCap, reverse=True)[:int(len([i for i in fine])*0.2)]
        self.sorted_by_mc = [i.Symbol for i in top_market_cap] 
        total_market_cap = np.sum([i.MarketCap for i in top_market_cap])
        self.weights = {}
        for i in top_market_cap:
            self.weights[str(i.Symbol)] = i.MarketCap/total_market_cap
        return self.sorted_by_mc

    

    def OnData(self, data):
        pass