| Overall Statistics |
|
Total Orders 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Start Equity 100000 End Equity 100000 Net Profit 0% Sharpe Ratio 0 Sortino Ratio 0 Probabilistic Sharpe Ratio 0% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio -0.655 Tracking Error 0.179 Treynor Ratio 0 Total Fees $0.00 Estimated Strategy Capacity $0 Lowest Capacity Asset Portfolio Turnover 0% |
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(2020, 1, 1)
#self.set_end_date(2023, 5, 10)
self.set_cash(100000)
self.spy = self.add_equity("SPY", Resolution.DAILY).symbol
# Requesting data
self.regalytics_symbol = self.add_data(RegalyticsRegulatoryArticles, "REG").symbol
self.cum_articles = 0
def on_data(self, slice: Slice) -> None:
data = slice.Get(RegalyticsRegulatoryArticles)
if data:
data_points = len(data.values()[0].Data)
self.Log(f"Articles data: {data_points}")
self.plot("Data points","Daily", data_points)
self.cum_articles += data_points
self.plot("Data points","Cummulative", self.cum_articles)
def on_end_of_algorithm(self):
self.log(f"Total Data Points: {self.cum_articles}")