| Overall Statistics |
|
Total Orders 2023 Average Win 0.30% Average Loss -0.28% Compounding Annual Return 25.081% Drawdown 25.500% Expectancy 0.129 Start Equity 1000000 End Equity 1406748.31 Net Profit 40.675% Sharpe Ratio 0.598 Sortino Ratio 0.755 Probabilistic Sharpe Ratio 34.755% Loss Rate 45% Win Rate 55% Profit-Loss Ratio 1.07 Alpha -0.009 Beta 1.422 Annual Standard Deviation 0.252 Annual Variance 0.064 Information Ratio 0.184 Tracking Error 0.209 Treynor Ratio 0.106 Total Fees $16517.37 Estimated Strategy Capacity $11000000.00 Lowest Capacity Asset GFS XT0JK4MKIDET Portfolio Turnover 26.65% |
from AlgorithmImports import *
class ShortingYesterdayTopDailyGainers(QCAlgorithm):
def initialize(self):
self.set_cash(1_000_000)
self.set_start_date(2023, 2, 13)
etf = self.add_equity("QQQ").symbol
self.add_universe(self.universe.etf(etf))
self.settings.rebalance_portfolio_on_insight_changes = False
self.universe_settings.schedule.on(self.date_rules.month_start())
self.schedule.on(self.date_rules.every_day(etf), self.time_rules.before_market_close(etf, 1), self.store_closes)
self.schedule.on(self.date_rules.every_day(etf), self.time_rules.after_market_open(etf, 1), self.gap_check)
self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
self.closes = {}
self.gap_size = float(self.get_parameter("gap_size", 5))/100
self.insight_period = int(float(self.get_parameter("insight_period", 10)))
def store_closes(self):
self.closes = {security : security.price for security in self.active_securities.values if security.price > 0}
def gap_check(self):
if not self.closes: return
gap = {security.symbol: ((security.price - close) / close) for (security, close) in self.closes.items() \
if ((security.price - close) / close) > self.gap_size}
gap_rank = list(dict(sorted(gap.items(), key=lambda item: item[1], reverse=True)).keys())[:10]
self.emit_insights([Insight.price(symbol, timedelta(self.insight_period), InsightDirection.UP) for symbol in gap_rank])