| Overall Statistics |
|
Total Orders 615 Average Win 7.40% Average Loss -7.11% Compounding Annual Return 29.911% Drawdown 63.600% Expectancy 0.255 Start Equity 1000000 End Equity 43643639.72 Net Profit 4264.364% Sharpe Ratio 0.689 Sortino Ratio 0.545 Probabilistic Sharpe Ratio 3.981% Loss Rate 38% Win Rate 62% Profit-Loss Ratio 1.04 Alpha 0.161 Beta 2.198 Annual Standard Deviation 0.511 Annual Variance 0.261 Information Ratio 0.606 Tracking Error 0.438 Treynor Ratio 0.16 Total Fees $1459409.00 Estimated Strategy Capacity $11000000.00 Lowest Capacity Asset BGU U7EC123NWZTX Portfolio Turnover 21.81% |
# region imports
from AlgorithmImports import *
# endregion
class MomentumStrategy(QCAlgorithm):
def initialize(self):
self.set_start_date(2009, 12, 23)
self.set_cash(1000000)
self.spy = self.add_equity("SPXL", Resolution.DAILY).symbol
self.portfolio.MarginCallModel = MarginCallModel.Null
self.UniverseSettings.Leverage = 8
# Schedule recalibration at the start of each week
self.schedule.on(self.date_rules.every_day(), self.time_rules.at(15, 58), self.rebalance)
self.state = 0
chart = Chart("My Custom Chart")
self.add_chart(chart)
chart.AddSeries(Series("SPY Close", SeriesType.LINE, '$'))
chart.AddSeries(Series("Long_term_mean", SeriesType.LINE, '$'))
chart.AddSeries(Series("Short_term_mean", SeriesType.LINE, '$'))
chart.AddSeries(Series(name="Buy Signal", type=SeriesType.SCATTER, unit='$', color=Color.DarkGreen, symbol=ScatterMarkerSymbol.TRIANGLE))
chart.AddSeries(Series("Sell Signal", SeriesType.SCATTER, '$', Color.DarkRed, ScatterMarkerSymbol.TRIANGLE_DOWN))
chart.AddSeries(Series("Volatility Upper", SeriesType.Line, '$'))
chart.AddSeries(Series("Volatility Lower", SeriesType.Line, '$'))
self.volatility_period = 27 # Period for volatility calculation
self.rolling_window = RollingWindow[float](self.volatility_period)
def rebalance(self):
history = self.history(self.spy, self.volatility_period+1, Resolution.DAILY)
if history.empty or history.isnull().values.any():
raise ValueError("Empty History")
#self.Debug(history)
self.data = history['close'].unstack(level=0)
for i in range(len(self.data)):
self.rolling_window.add(self.data.iloc[i])
long_term_mean = float(self.data.mean().values) * float(self.data.std().values) * np.sqrt( 252 / len(self.data))
short_term_mean = float(self.data[:5].mean().values) * float(self.data[:5].std().values) * np.sqrt( 252 / 5)
self.Plot("My Custom Chart", "SPY Close", self.data.iloc[-1])
self.Plot("My Custom Chart", "Long_term_mean", long_term_mean)
self.Plot("My Custom Chart", "Short_term_mean", short_term_mean)
if self.rolling_window.IsReady:
returns = [self.rolling_window[i] / self.rolling_window[i+1] - 1 for i in range(self.volatility_period - 1)]
volatility = np.std(returns)*500
upper_bar = 100 + volatility / 2
lower_bar = 100 - volatility / 2
self.Plot("My Custom Chart", "Volatility Upper", upper_bar)
self.Plot("My Custom Chart", "Volatility Lower", lower_bar)
self.previous_state = self.state
if (float(self.data.std().values) * np.sqrt( 252 / len(self.data))) > 22.5:
self.debug(f"HIGH VOL {(float(self.data.std().values) * np.sqrt( 252 / len(self.data)))}")
self.liquidate()
return
if short_term_mean < long_term_mean and self.state != 1:
self.state = 1
elif short_term_mean > long_term_mean and self.state != -1:
self.state = -1
if self.previous_state != self.state:
self.trade()
def trade(self):
if self.state == 1:
self.plot("My Custom Chart", "Buy Signal", self.data.iloc[-1])
self.liquidate()
self.set_holdings("SPXL", 1.9)
if self.state == -1:
self.plot("My Custom Chart", "Sell Signal", self.data.iloc[-1])
self.liquidate()
# self.set_holdings("SPXL", -1.9)