| Overall Statistics |
|
Total Trades 77 Average Win 1.84% Average Loss -2.07% Compounding Annual Return 3.376% Drawdown 15.800% Expectancy 0.056 Net Profit 5.244% Sharpe Ratio 0.223 Probabilistic Sharpe Ratio 12.263% Loss Rate 44% Win Rate 56% Profit-Loss Ratio 0.89 Alpha 0.037 Beta -0.417 Annual Standard Deviation 0.174 Annual Variance 0.03 Information Ratio 0.146 Tracking Error 0.295 Treynor Ratio -0.093 Total Fees $170.81 Estimated Strategy Capacity $690000000.00 Lowest Capacity Asset SPY R735QTJ8XC9X |
# region imports
from AlgorithmImports import *
# endregion
class HipsterFluorescentPinkMule(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2021, 7, 28) # Set Start Date
self.SetEndDate(2023, 2, 8)
self.SetCash(100000) # Set Strategy Cash
self.symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
self.dataset_symbol = self.AddData(RegalyticsRegulatoryArticles, "REG").Symbol
self.lookback_period = timedelta(days=90)
self.Train(self.DateRules.Every(DayOfWeek.Sunday), self.TimeRules.At(0,0), self.train_model)
self.train_model()
#self.SetBenchmark(self.symbol)
def train_model(self):
spy_history = self.History(self.symbol, self.lookback_period).loc[self.symbol]
daily_returns = spy_history['open'].pct_change()[1:]
reg_history = self.History[RegalyticsRegulatoryArticles](self.dataset_symbol, self.lookback_period)
returns_by_alert_type = {}
for articles in reg_history:
for article in articles:
future_spy_returns = daily_returns.loc[daily_returns.index > article.Time] # Is this time the same time we get in backtests?
if len(future_spy_returns) < 2:
continue
if article.AlertType not in returns_by_alert_type:
returns_by_alert_type[article.AlertType] = []
returns_by_alert_type[article.AlertType].append(future_spy_returns.iloc[1])
self.expected_return_by_alert_type = {
alert_type: sum(future_returns)/len(future_returns)
for alert_type, future_returns in returns_by_alert_type.items()
}
def OnData(self, slice: Slice) -> None:
# Parse articles
if not slice.ContainsKey(self.dataset_symbol):
return
articles = slice[self.dataset_symbol]
expected_returns = []
alert_types = set()
for article in articles:
alert_types.add(article.AlertType)
for alert_type in alert_types:
if alert_type in self.expected_return_by_alert_type:
expected_returns.append(self.expected_return_by_alert_type[alert_type])
if len(expected_returns) == 0:
return
expected_return = sum(expected_returns) / len(expected_returns)
if expected_return > 0 and not self.Portfolio[self.symbol].IsLong:
self.SetHoldings(self.symbol, 1)
elif expected_return < 0 and not self.Portfolio[self.symbol].IsShort:
self.SetHoldings(self.symbol, -1)