Quiver Quantitative
WallStreetBets
Introduction
The WallStreetBets dataset by Quiver Quantitative tracks daily mentions of different equities on Reddit’s popular WallStreetBets forum. The data covers 6,000 Equities, starts in August 2018, and is delivered on a daily frequency. The dataset is created by scraping the daily discussion threads on r/WallStreetBets and parsing the comments for ticker mentions.
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.
For more information about the WallStreetBets dataset, including CLI commands and pricing, see the dataset listing.
About the Provider
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.
Getting Started
The following snippet demonstrates how to request data from the WallStreetBets dataset:
from QuantConnect.DataSource import *
self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
self.dataset_symbol = self.add_data(QuiverWallStreetBets, self.aapl).symbol
self._universe = self.add_universe(QuiverWallStreetBetsUniverse, self.universe_selection) using QuantConnect.DataSource;
_symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
_datasetSymbol = AddData<QuiverWallStreetBets>(_symbol).Symbol;
_universe = AddUniverse<QuiverWallStreetBetsUniverse>(UniverseSelection);
Example Applications
The WallStreetBets dataset enables you to create strategies using the latest activity on the WallStreetBets daily discussion thread. Examples include the following strategies:
- Trading any security that is being mentioned
- Trading securities that are receiving more/less mentions than they were previously
- Trading the security that is being mentioned the most/least for the day
Data Point Attributes
The WallStreetBets dataset provides QuiverWallStreetBets and QuiverWallStreetBetsUniverse objects.
QuiverWallStreetBets Attributes
QuiverWallStreetBets objects have the following attributes:
QuiverWallStreetBetsUniverse Attributes
QuiverWallStreetBetsUniverse objects have the following attributes:
Requesting Data
To add WallStreetBets data to your algorithm, call the AddDataadd_data method. Save a reference to the dataset Symbol so you can access the data later in your algorithm.
class QuiverWallStreetBetsDataAlgorithm(QCAlgorithm):
def initialize(self) -> None:
self.set_start_date(2019, 1, 1)
self.set_end_date(2020, 6, 1)
self.set_cash(100000)
self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
self.dataset_symbol = self.add_data(QuiverWallStreetBets, self.aapl).symbol namespace QuantConnect
{
public class QuiverWallStreetBetsDataAlgorithm : QCAlgorithm
{
private Symbol _symbol, _datasetSymbol;
public override void Initialize()
{
SetStartDate(2019, 1, 1);
SetEndDate(2020, 6, 1);
SetCash(100000);
_symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
_datasetSymbol = AddData<QuiverWallStreetBets>(_symbol).Symbol;
}
}
}
Accessing Data
To get the current WallStreetBets data, index the current Slice with the dataset Symbol. Slice objects deliver unique events to your algorithm as they happen, but the Slice may not contain data for your dataset at every time step. To avoid issues, check if the Slice contains the data you want before you index it.
def on_data(self, slice: Slice) -> None:
if slice.contains_key(self.dataset_symbol):
data_points = slice[self.dataset_symbol]
for data_point in data_points:
self.log(f"{self.dataset_symbol} mentions at {slice.time}: {data_point.mentions}") public override void OnData(Slice slice)
{
if (slice.ContainsKey(_datasetSymbol))
{
var dataPoints = slice[_datasetSymbol];
foreach (var dataPoint in dataPoints)
{
Log($"{_datasetSymbol} mentions at {slice.Time}: {dataPoint.Mentions}");
}
}
}
To iterate through all of the dataset objects in the current Slice, call the Getget method.
def on_data(self, slice: Slice) -> None:
for dataset_symbol, data_points in slice.get(QuiverWallStreetBets).items():
for data_point in data_points:
self.log(f"{dataset_symbol} mentions at {slice.time}: {data_point.mentions}") public override void OnData(Slice slice)
{
foreach (var kvp in slice.Get<QuiverWallStreetBets>())
{
var datasetSymbol = kvp.Key;
var dataPoints = kvp.Value;
foreach (var dataPoint in dataPoints)
{
Log($"{datasetSymbol} mentions at {slice.Time}: {dataPoint.Mentions}");
}
}
}
Historical Data
To get historical WallStreetBets data, call the Historyhistory method with the dataset Symbol. If there is no data in the period you request, the history result is empty.
# DataFrame history_df = self.history(self.dataset_symbol, 100, Resolution.DAILY) # Dataset objects history_bars = self.history[QuiverWallStreetBets](self.dataset_symbol, 100, Resolution.DAILY)
var history = History<QuiverWallStreetBets>(_datasetSymbol, 100, Resolution.Daily);
For more information about historical data, see History Requests.
Universe Selection
To select a dynamic universe of US Equities based on WallStreetBets data, call the AddUniverseadd_universe method with the QuiverWallStreetBetsUniverse class and a selection function.
def initialize(self) -> None:
self._universe = self.add_universe(QuiverWallStreetBetsUniverse, self.universe_selection)
def universe_selection(self, alt_coarse: List[QuiverWallStreetBetsUniverse]) -> List[Symbol]:
return [d.symbol for d in alt_coarse if d.mentions > 100 and d.rank < 100] private Universe _universe;
public override void Initialize()
{
_universe = AddUniverse<QuiverWallStreetBetsUniverse>(altCoarse =>
{
return from d in altCoarse.OfType<QuiverWallStreetBetsUniverse>()
where d.Mentions > 10 && d.Rank > 10 select d.Symbol;
});
}
For more information about dynamic universes, see Universes.
Universe History
You can get historical universe data in an algorithm and in the Research Environment.
Historical Universe Data in Algorithms
To get historical universe data in an algorithm, call the Historyhistory method with the Universe object and the lookback period. If there is no data in the period you request, the history result is empty.
var universeHistory = History(_universe, 30, Resolution.Daily);
foreach (var bets in universeHistory)
{
foreach (QuiverWallStreetBetsUniverse bet in bets)
{
Log($"{bet.Symbol} rank at {bet.EndTime}: {bet.Rank}");
}
} universe_history = self.history(self.universe, 30, Resolution.DAILY)
for (univere_symbol, time), pages in universe_history.items():
for page in pages:
self.log(f"{page.symbol} week percent change at {page.end_time}: {page.week_percent_change}")
Historical Universe Data in Research
To get historical universe data in research, call the UniverseHistoryuniverse_history method with the Universe object, a start date, and an end date. This method returns the filtered universe. If there is no data in the period you request, the history result is empty.
var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
{
foreach (QuiverWallStreetBetsUniverse bet in bets)
{
Log($"{bet.Symbol} rank at {bet.EndTime}: {bet.Rank}");
}
} universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
for (univere_symbol, time), pages in universe_history.items():
for page in pages:
print(f"{page.symbol} week percent change at {page.end_time}: {page.week_percent_change}")
You can call the History method in Research.
Remove Subscriptions
To remove a subscription, call the RemoveSecurityremove_security method.
self.remove_security(self.dataset_symbol)
RemoveSecurity(_datasetSymbol);
If you subscribe to WallStreetBets data for assets in a dynamic universe, remove the dataset subscription when the asset leaves your universe. To view a common design pattern, see Track Security Changes.
Example Applications
The WallStreetBets dataset enables you to create strategies using the latest activity on the WallStreetBets daily discussion thread. Examples include the following strategies:
- Trading any security that is being mentioned
- Trading securities that are receiving more/less mentions than they were previously
- Trading the security that is being mentioned the most/least for the day
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