About Wikipedia Page Views
The Wikipedia Page Views dataset by Quiver Quantitative tracks Wikipedia page views for US Equities. The data covers 1,300 US Equities, starts in October 2016, and is delivered on a daily frequency. This dataset is created by scraping the Wikipedia pages of companies.
This dataset depends on the US Equity Security Master dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.
About Quiver Quantitative
Quiver Quantitative was founded by two college students in February 2020 with the goal of bridging the information gap between Wall Street and non-professional investors. Quiver allows retail investors to tap into the power of big data and have access to actionable, easy to interpret data that hasn’t already been dissected by Wall Street.
About QuantConnect
QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
Algorithm Example
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
# Filter using QuiverWikipedia data
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
# Assuming the popularity of the company growed
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 of wikipedia view for estimating popularity
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]:
# Set threshold to be significant increase and material view number to ensure the growth is not random
return [d.Symbol for d in alt_coarse \
if d.page_views > 100 \
and d.week_percent_change < 0.2]
Example Applications
The Wikipedia Page Views dataset enables you to observe patterns in the traffic of company Wikipedia pages. Examples include the following strategies:
- Capitalizing on companies that have experienced a sharp increase in Wikipedia traffic on the premise that volatility in traffic will translate to volatility in price
- Mitigating risk by avoiding companies that have a decreasing web presence on the premise that a reduction in traffic will result in a reduction in price
Pricing
Cloud Access
Harness Wikipedia Page Views data in the QuantConnect Cloud for your backtesting and live trading purposes.
Download On Premise
Wikipedia Page Views archived in LEAN format for on premise backtesting and research. One file per ticker.
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