Overall Statistics
Total Orders
815
Average Win
0.16%
Average Loss
-0.15%
Compounding Annual Return
3.003%
Drawdown
3.900%
Expectancy
0.076
Start Equity
100000
End Equity
104287.87
Net Profit
4.288%
Sharpe Ratio
-0.045
Sortino Ratio
-0.046
Probabilistic Sharpe Ratio
26.954%
Loss Rate
48%
Win Rate
52%
Profit-Loss Ratio
1.07
Alpha
-0
Beta
-0.013
Annual Standard Deviation
0.038
Annual Variance
0.001
Information Ratio
-0.531
Tracking Error
0.234
Treynor Ratio
0.13
Total Fees
$783.60
Estimated Strategy Capacity
$5300000.00
Lowest Capacity Asset
ENPH V5C9T324VA05
Portfolio Turnover
6.46%
 
from AlgorithmImports import *
from QuantConnect.DataSource import *

class KavoutCompositeFactorBundleAlgorithm(QCAlgorithm):

    def initialize(self) -> None:
        self.set_start_date(2019, 1, 1)
        self.set_end_date(2020, 6, 1)
        self.set_cash(100000)
        
        self.last_time = datetime.min
        
        self.add_universe(self.my_coarse_filter_function)
        self.universe_settings.resolution = Resolution.MINUTE
        
    def my_coarse_filter_function(self, coarse: List[CoarseFundamental]) -> List[Symbol]:
        sorted_by_dollar_volume = sorted([x for x in coarse if x.has_fundamental_data], 
                                key=lambda x: x.dollar_volume, reverse=True)
        selected = [x.symbol for x in sorted_by_dollar_volume[:100]]
        return selected

    def on_data(self, slice: Slice) -> None:
        if self.last_time > self.time: return
    
        # Accessing Data
        points = slice.Get(KavoutCompositeFactorBundle)            
        sorted_by_score = sorted(points.items(), key=self.total_score)
        long_symbols = [x[0].underlying for x in sorted_by_score[-10:]]
        short_symbols = [x[0].underlying for x in sorted_by_score[:10]]
        
        for symbol in [x.symbol for x in self.portfolio.Values if x.invested]:
            if symbol not in long_symbols + short_symbols:
                self.liquidate(symbol)
        
        long_targets = [PortfolioTarget(symbol, 0.05) for symbol in long_symbols]
        short_targets = [PortfolioTarget(symbol, -0.05) for symbol in short_symbols]
        self.set_holdings(long_targets + short_targets)
        
        self.last_time = Expiry.END_OF_DAY(self.time)
        
    def on_securities_changed(self, changes: SecurityChanges) -> None:
        for security in changes.added_securities:
            # Requesting Data
            kavout_composite_factor_bundle_symbol= self.add_data(KavoutCompositeFactorBundle, security.symbol).symbol
            
            # Historical Data
            history = self.history(kavout_composite_factor_bundle_symbol, 2, Resolution.DAILY)
            self.debug(f"We got {len(history)} items from our history request")
            
    def total_score(self, value: Tuple[Symbol, KavoutCompositeFactorBundle]) -> float:
        '''Return the total score to integrate overall likelihood to outcompete, take equal weighting for each factor'''
        value = value[1]
        return value.growth + value.low_volatility + value.momentum + value.quality + value.value_factor