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
-0.334
Tracking Error
0.139
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
Portfolio Turnover
0%
 
from AlgorithmImports import *
from QuantConnect.DataSource import *
import numpy as np

class ETFConstituentsDataAlgorithm(QCAlgorithm):

    def Initialize(self) -> None:
        self.SetStartDate(2016, 1, 1)
        self.SetEndDate(2016, 5, 1)
        self.SetCash(100000)

        res = Resolution.Minute
     
        self.spy = self.AddEquity("SPY",res).Symbol
        self.emn = self.AddEquity("EMN",res).Symbol

        self.Schedule.On(self.DateRules.MonthStart("SPY"),
                 self.TimeRules.AfterMarketOpen("SPY",30),
                 self.Rebalance)
        
        self.stock = Symbol.Create("EMN", SecurityType.Equity, Market.USA)

    def Rebalance(self) -> None:

        df = self.History(Fundamental, self.stock, 1000,Resolution.Daily).valuationratios.apply(lambda x: x.PBRatio)
        self.Debug(f'{self.Time} ratio = {df.iloc[-1]} --- {df.mean()}')