Overall Statistics
Total Orders
6598
Average Win
0.05%
Average Loss
-0.05%
Compounding Annual Return
-16.423%
Drawdown
25.800%
Expectancy
-0.160
Start Equity
100000
End Equity
76363.75
Net Profit
-23.636%
Sharpe Ratio
-2.142
Sortino Ratio
-2.896
Probabilistic Sharpe Ratio
0.009%
Loss Rate
57%
Win Rate
43%
Profit-Loss Ratio
0.96
Alpha
-0.11
Beta
-0.582
Annual Standard Deviation
0.078
Annual Variance
0.006
Information Ratio
-1.549
Tracking Error
0.172
Treynor Ratio
0.288
Total Fees
$6598.00
Estimated Strategy Capacity
$4700000.00
Lowest Capacity Asset
CNTE SAI0XJNH6IJP
Portfolio Turnover
11.11%
 
from AlgorithmImports import *

class ShortingYesterdayTopDailyGainers(QCAlgorithm):

    def initialize(self):
        self.set_start_date(2023, 2, 13)
        self.universe_settings.schedule.on(self.date_rules.week_start())
        self._universe = self.add_universe(self.selection)
        self.schedule.on(self.date_rules.week_start(), self.time_rules.at(9, 31), self.rebalance)
        self.set_execution(SpreadExecutionModel(0.01))
        self.add_risk_management(MaximumUnrealizedProfitPercentPerSecurity(0.05))
        self.add_risk_management(MaximumDrawdownPercentPerSecurity(0.05))
        self.last_prices = None

    def selection(self, fundamental):
        selected = []
        prices = pd.DataFrame([[f.symbol, f.adjusted_price, f.market_cap] for f in fundamental], columns=["Symbol", "Price", "Cap"]).set_index("Symbol")
        prices = prices[(prices["Cap"] >= 1e10) & (prices["Price"] >= 5)]
        if isinstance(self.last_prices, pd.DataFrame) and not self.last_prices.empty:
            pct_chg = ((prices - self.last_prices) / self.last_prices).dropna()
            selected = list(pct_chg.nlargest(50, 'Price').index)
        self.last_prices = prices
        return selected

    def rebalance(self):
        self.set_holdings([PortfolioTarget(symbol, -0.01 if symbol in self._universe.members.keys else 0) for symbol in self.securities.keys()])