Overall Statistics
Total Orders
0
Average Win
0%
Average Loss
0%
Compounding Annual Return
0%
Drawdown
0%
Expectancy
0
Start Equity
100000
End Equity
100000
Net Profit
0%
Sharpe Ratio
0
Sortino 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
-2.502
Tracking Error
0.095
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
Portfolio Turnover
0%
 
# region imports
from AlgorithmImports import *
# endregion
class LiquidUniverseSelection(QCAlgorithm):
    
    filtered_by_price = None
    
    def initialize(self):
        self.set_start_date(2019, 1, 11)  
        self.set_end_date(2019, 7, 1) 
        self.set_cash(100000)  
        self.add_universe(self.coarse_selection_filter)
        # Ignore this for now, we'll cover it in the next task.
        self.universe_settings.resolution = Resolution.DAILY 

        self.SetWarmup(timedelta(days=30))  # Warm up with 30 days of data

    def coarse_selection_filter(self, coarse):
        sorted_by_dollar_volume = sorted(coarse, key=lambda x: x.dollar_volume, reverse=True) 
        filtered_by_price = [x.symbol for x in sorted_by_dollar_volume if x.has_fundamental_data and x.price > 10]
        return filtered_by_price[:8]
   
    def on_securities_changed(self, changes):
        self.changes = changes
        self.log(f"on_securities_changed({self.time}):: {changes}")
        
        #1. Liquidate removed securities
        for security in changes.removed_securities:
            if security.invested:
                self.liquidate(security.symbol)
        
        #2. We want 10% allocation in each security in our universe
        # for security in self.changes.added_securities:
        #     self.set_holdings(security.symbol, 0.1)

    def OnData(self, data):

        for symbol in set(data.Keys).intersection(self.changes.added_securities):
            self.set_holdings(security.symbol, 0.1)