| Overall Statistics |
|
Total Orders 6604 Average Win 4.68% Average Loss -3.78% Compounding Annual Return 18.179% Drawdown 46.300% Expectancy 0.149 Start Equity 100000 End Equity 126747.78 Net Profit 26.748% Sharpe Ratio 0.509 Sortino Ratio 0.478 Probabilistic Sharpe Ratio 23.247% Loss Rate 49% Win Rate 51% Profit-Loss Ratio 1.24 Alpha 0.143 Beta 0.999 Annual Standard Deviation 0.521 Annual Variance 0.271 Information Ratio 0.304 Tracking Error 0.468 Treynor Ratio 0.265 Total Fees $3838.34 Estimated Strategy Capacity $170000.00 Lowest Capacity Asset ACEL X9PDY4DEHQ3P Portfolio Turnover 20.70% |
from AlgorithmImports import *
from QuantConnect.DataSource import *
class QuiverWikipediaDataAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2019, 1, 1)
self.set_end_date(2020, 6, 1)
self.set_cash(100000)
self.universe_settings.resolution = Resolution.DAILY
self._universe = self.add_universe(QuiverWikipediaUniverse, self.universe_selection)
def on_data(self, slice: Slice) -> None:
points = slice.Get(QuiverWikipedia)
for point in points.Values:
symbol = point.symbol.underlying
# Buy if the company's Wikipedia page views have increased over the last week and month
if point.month_percent_change > 0:
self.set_holdings(symbol, 1)
# Sell our holdings if the company's Wikipedia page views have not increased over the last month
else:
self.set_holdings(symbol, 0)
def on_securities_changed(self, changes: SecurityChanges) -> None:
for added in changes.added_securities:
# Requesting data
quiver_wiki_symbol = self.add_data(QuiverWikipedia, added.symbol).symbol
# Historical data
history = self.history(QuiverWikipedia, quiver_wiki_symbol, 60, Resolution.DAILY)
self.debug(f"We got {len(history)} items from our history request for Quiver Wikipedia data")
def universe_selection(self, alt_coarse: List[QuiverWikipediaUniverse]) -> List[Symbol]:
return [d.Symbol for d in alt_coarse \
if d.page_views > 100 \
and d.week_percent_change < 0.2]