| Overall Statistics |
|
Total Orders 4 Average Win 0% Average Loss -0.02% Compounding Annual Return -80.246% Drawdown 2.600% Expectancy -1 Start Equity 100000 End Equity 97369.19 Net Profit -2.631% Sharpe Ratio -5.306 Sortino Ratio -7.218 Probabilistic Sharpe Ratio 0.382% Loss Rate 100% Win Rate 0% Profit-Loss Ratio 0 Alpha -0.987 Beta -0.616 Annual Standard Deviation 0.142 Annual Variance 0.02 Information Ratio -1.172 Tracking Error 0.322 Treynor Ratio 1.224 Total Fees $3.33 Estimated Strategy Capacity $760000000.00 Lowest Capacity Asset SPY R735QTJ8XC9X Portfolio Turnover 16.66% |
from AlgorithmImports import *
from QuantConnect.DataSource import *
class RegalyticsDataAlgorithm(QCAlgorithm):
negative_sentiment_phrases = ["emergency rule", "proposed rule change", "development of rulemaking"]
def initialize(self) -> None:
self.set_start_date(2022, 7, 10)
self.set_end_date(2022, 7, 15)
self.set_cash(100000)
self.spy = self.add_equity("SPY", Resolution.DAILY).symbol
# Requesting data
self.regalytics_symbol = self.add_data(RegalyticsRegulatoryArticles, "REG").symbol
# Historical data
history = self.history(self.regalytics_symbol, 7, Resolution.DAILY)
self.debug(f"We got {len(history)} items from our history request")
def on_data(self, slice: Slice) -> None:
data = slice.Get(RegalyticsRegulatoryArticles)
if data:
for articles in data.values():
self.log(articles.to_string())
if any([p in article.title.lower() for p in self.negative_sentiment_phrases for article in articles]):
self.set_holdings(self.spy, -1)
else:
self.set_holdings(self.spy, 1)