In This Page

Introduction

About the Provider

Getting Started

Data Summary

Example Applications

Data Point Attributes

Requesting Data

Accessing Data

Historical Data

Universe Selection

Universe History

Remove Subscriptions

Example Applications

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:

Select Language:

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)

_symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
_datasetSymbol = AddData<QuiverWallStreetBets>(_symbol).Symbol;

_universe = AddUniverse<QuiverWallStreetBetsUniverse>(UniverseSelection);

Data Summary

The following table describes the dataset properties:

Property	Value
Start Date	August 2018
Asset Coverage	6,000 US Equities
Data Density	Sparse
Resolution	Daily
Timezone	UTC

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

For more example algorithms, see Examples.

Data Point Attributes

The WallStreetBets dataset provides QuiverWallStreetBets and QuiverWallStreetBetsUniverse objects.

QuiverWallStreetBets Attributes

QuiverWallStreetBets objects have the following attributes:

QuiverWallStreetBets Select Language:

Date of the daily discussion thread

date: DateTime

The number of mentions on the given date

mentions: Int32

This ticker's rank on the given date (as determined by total number of mentions)

rank: Int32

Average sentiment of all comments containing the given ticker on this date. Sentiment is calculated using VADER sentiment analysis. The value can range between -1 and +1. Negative values imply negative sentiment, whereas positive values imply positive sentiment.

sentiment: decimal

The time the data point ends at and becomes available to the algorithm

end_time: DateTime

Market Data Type of this data - does it come in individual price packets or is it grouped into OHLC.

data_type: MarketDataType

True if this is a fill forward piece of data

is_fill_forward: bool

Current time marker of this data packet.

time: DateTime

Symbol representation for underlying Security

symbol: Symbol

Value representation of this data packet. All data requires a representative value for this moment in time. For streams of data this is the price now, for OHLC packets this is the closing price.

value: decimal

As this is a backtesting platform we'll provide an alias of value as price.

price: decimal

QuiverWallStreetBetsUniverse Attributes

QuiverWallStreetBetsUniverse objects have the following attributes:

QuiverWallStreetBetsUniverse Select Language:

The number of mentions on the given date

mentions: Int32

This ticker's rank on the given date (as determined by total number of mentions)

rank: Int32

Average sentiment of all comments containing the given ticker on this date. Sentiment is calculated using VADER sentiment analysis. The value can range between -1 and +1. Negative values imply negative sentiment, whereas positive values imply positive sentiment.

sentiment: decimal

The time the data point ends at and becomes available to the algorithm

end_time: DateTime

The associated underlying price data if any

underlying: BaseData

Gets or sets the contracts selected by the universe

filtered_contracts: HashSet<Symbol>

Gets the data list

data: List<BaseData>

Market Data Type of this data - does it come in individual price packets or is it grouped into OHLC.

data_type: MarketDataType

True if this is a fill forward piece of data

is_fill_forward: bool

Current time marker of this data packet.

time: DateTime

Symbol representation for underlying Security

symbol: Symbol

Value representation of this data packet. All data requires a representative value for this moment in time. For streams of data this is the price now, for OHLC packets this is the closing price.

value: decimal

As this is a backtesting platform we'll provide an alias of value as price.

price: decimal

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.

Select Language:

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.

Select Language:

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.

Select Language:

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.

Select Language:

# 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.

Select Language:

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.

Select Language:

var universeHistory = History(_universe, 30, Resolution.Daily);
foreach (var bets in universeHistory)
{
    foreach (QuiverWallStreetBetsUniverse bet in bets)
    {
        Log($"{bet.Symbol} mentions at {bet.EndTime}: {bet.Mentions}");
    }
}

# DataFrame example where the columns are the QuiverWallStreetBetsUniverse attributes: 
history_df = self.history(self._universe, 30, Resolution.DAILY, flatten=True)

# Series example where the values are lists of QuiverWallStreetBetsUniverse objects: 
universe_history = self.history(self._universe, 30, Resolution.DAILY)
for (univere_symbol, time), bets in universe_history.items():
    for bet in bets:
        self.log(f"{bet.symbol} mentions at {bet.end_time}: {bet.mentions}")

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.

Select Language:

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}");
    }
}

# DataFrame example where the columns are the QuiverWallStreetBetsUniverse attributes: 
history_df = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)

# Series example where the values are lists of QuiverWallStreetBetsUniverse objects: 
universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
for (univere_symbol, time), bets in universe_history.items():
    for bet in bets:
        print(f"{bet.symbol} rank at {bet.end_time}: {bet.rank}")

You can call the Historyhistory method in Research.

Remove Subscriptions

To remove a subscription, call the RemoveSecurityremove_security method.

Select Language:

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

For more example algorithms, see Examples.

You can also see our Videos. You can also get in touch with us via Discord.

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Hello

Quiver Quantitative

WallStreetBets

Introduction

About the Provider

Getting Started

Data Summary

Example Applications

Data Point Attributes

QuiverWallStreetBets Attributes

QuiverWallStreetBetsUniverse Attributes

Requesting Data

Accessing Data

Historical Data

Universe Selection

Universe History

Historical Universe Data in Algorithms

Historical Universe Data in Research

Remove Subscriptions

Example Applications

▶
Key Concepts

▶
Securities

▶
Portfolio

▶
Universes

▶
Datasets

▶
Importing Data

▶
Consolidating Data

▶
Historical Data

▶
Trading and Orders

▶
Reality Modeling

▶
Indicators

▶
Optimization

▶
Machine Learning

▶
Algorithm Framework

▶
Statistics

▶
Live Trading

▶
Migrations