| Overall Statistics |
|
Total Orders 2654 Average Win 0.12% Average Loss -0.09% Compounding Annual Return 52.610% Drawdown 10.800% Expectancy 0.607 Start Equity 100000 End Equity 200335.18 Net Profit 100.335% Sharpe Ratio 1.996 Sortino Ratio 2.653 Probabilistic Sharpe Ratio 94.259% Loss Rate 33% Win Rate 67% Profit-Loss Ratio 1.38 Alpha 0.134 Beta 1.207 Annual Standard Deviation 0.151 Annual Variance 0.023 Information Ratio 1.99 Tracking Error 0.082 Treynor Ratio 0.25 Total Fees $2686.61 Estimated Strategy Capacity $11000000.00 Lowest Capacity Asset CMB R735QTJ8XC9X Portfolio Turnover 4.65% |
from AlgorithmImports import *
from hmmlearn import hmm
class HMMRegimeDetection(QCAlgorithm):
def initialize(self):
self.set_start_date(2023, 1, 1)
self.add_universe(lambda fundamental: [f.symbol for f in sorted(fundamental, key=lambda x: x.market_cap, reverse=True)[:10]])
self.set_warm_up(timedelta(3))
def on_securities_changed(self, changes):
for added in changes.added_securities:
added.roc = RateOfChange(1)
added.roc.window.size = 150
added.model = hmm.GaussianHMM(n_components=3, n_iter=100, random_state=100)
added.model_month = -1
added.consolidator = TradeBarConsolidator(timedelta(minutes=5))
added.consolidator.data_consolidated += self.on_consolidated
self.subscription_manager.add_consolidator(added.symbol, added.consolidator)
def on_consolidated(self, _, bar):
security = self.securities[bar.symbol]
security.roc.update(bar.end_time, bar.price)
if security.roc.window.is_ready:
if security.model_month != bar.end_time.month:
security.model.fit(np.array([point.value for point in security.roc.window])[::-1].reshape(-1, 1))
security.model_month = bar.end_time.month
post_prob = security.model.predict_proba(np.array([security.roc.current.value]).reshape(1, -1)).flatten()
self.set_holdings(bar.symbol, 0.1 if post_prob[2] > post_prob [0] else -0.1)