| Overall Statistics |
|
Total Orders 1960 Average Win 0.39% Average Loss -0.35% Compounding Annual Return 21.979% Drawdown 18.700% Expectancy 0.281 Start Equity 100000 End Equity 251424.38 Net Profit 151.424% Sharpe Ratio 0.937 Sortino Ratio 1.062 Probabilistic Sharpe Ratio 53.188% Loss Rate 40% Win Rate 60% Profit-Loss Ratio 1.13 Alpha 0.093 Beta 0.458 Annual Standard Deviation 0.143 Annual Variance 0.02 Information Ratio 0.291 Tracking Error 0.152 Treynor Ratio 0.293 Total Fees $4286.65 Estimated Strategy Capacity $2500000.00 Lowest Capacity Asset FNA XSOQ708JLBHH Portfolio Turnover 7.80% |
# region imports
from AlgorithmImports import *
# endregion
class LeveragedCopyCongressAlgorithm(QCAlgorithm):
def initialize(self):
self.set_start_date(2020, 1, 1)
self.set_cash(100000)
self._universe = self.add_universe(
QuiverQuantCongressUniverse,
lambda constituents: [c.symbol for c in constituents if c.transaction == OrderDirection.BUY]
)
spy = Symbol.create('SPY', SecurityType.EQUITY, Market.USA)
self.schedule.on(self.date_rules.week_start(spy), self.time_rules.after_market_open(spy, 30), self._trade)
def _trade(self):
if self._universe.selected is None: return
symbols = [s for s in self._universe.selected if s in self.securities and self.securities[s].price]
if len(symbols) == 0: return
inv_volatility_by_symbol = 1 / self.history(symbols, timedelta(6*30), Resolution.DAILY)['close'].unstack(0).pct_change().iloc[1:].std()
targets = [
PortfolioTarget(symbol, min(0.1, 1.5 * (inv_volatility_by_symbol[symbol] / inv_volatility_by_symbol.sum())) )
for symbol in symbols
]
self.set_holdings(targets, True)