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

CNBC Trading

Introduction

The CNBC Trading dataset by Quiver Quantitative tracks the recommendations made by media personalities on CNBC and their historical performance. The data covers over 1,500 US Equities, starts in December 2020, and is delivered on a daily frequency. This dataset covers recommendations made on Mad Money, Halftime Report, and Fast Money.

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 CNBC Trading 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 CNBC Trading dataset:

Select Language:

self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
self.dataset_symbol = self.add_data(QuiverCNBCs, self.aapl).symbol

self._universe = self.add_universe(QuiverCNBCsUniverse, self.universe_selection)

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

_universe = AddUniverse<QuiverCNBCsUniverse>(UniverseSelection);

Data Summary

The following table describes the dataset properties:

Property	Value
Start Date	December 25, 2020
Asset Coverage	1,515 US Equities
Data Density	Sparse
Resolution	Daily
Timezone	UTC

Example Applications

The Quiver Quantitative CNBC Trading dataset enables you to create strategies using the latest recommendations made by media personalities on CNBC. Examples include the following strategies:

Taking short positions in securities that were mentioned by Jim Cramer (CNBC commentator) in the last week
Trading securities that were most/least discussed across CNBC programs over the last year

For more example algorithms, see Examples.

Data Point Attributes

The Quiver Quantitative CNBC Trading dataset provides QuiverCNBCs, QuiverCNBC, and QuiverCNBCsUniverse objects.

QuiverCNBCs

QuiverCNBCs objects have the following attributes:

QuiverCNBCs Select Language:

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>

Gets or sets the end time of this data

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

QuiverCNBC

QuiverCNBC objects have the following attributes:

QuiverCNBC Select Language:

Contract description

notes: string

Direction of trade

direction: OrderDirection

Individual Name

traders: string

Time the data became available

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

QuiverCNBCsUniverse

QuiverCNBCsUniverse objects have the following attributes:

QuiverCNBCsUniverse Select Language:

Extra Information

notes: string

Direction of trade

direction: OrderDirection

Individual Name

traders: string

Time the data became available

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 CNBC Trading 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 QuiverCNBCDataAlgorithm(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(QuiverCNBCs, self.aapl).symbol

namespace QuantConnect.Algorithm.CSharp.AltData
{
    public class QuiverCNBCDataAlgorithm: 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<QuiverCNBCs>(_symbol).Symbol;
        }
    }
}

Accessing Data

To get the current CNBC Trading 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} direction at {slice.time}: {data_point.direction}")

public override void OnData(Slice slice)
{
    if (slice.ContainsKey(_datasetSymbol))
    {
        var dataPoints = slice[_datasetSymbol];
        foreach (var dataPoint in dataPoints)
        {
            Log($"{_datasetSymbol} direction at {slice.Time}: {dataPoint.Direction}");
        }
    }
}

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(QuiverCNBCs).items():
        for data_point in data_points:
            self.log(f"{dataset_symbol} direction at {slice.time}: {data_point.direction}")

public override void OnData(Slice slice)
{
    foreach (var kvp in slice.Get<QuiverCNBCs>())
    {
        var datasetSymbol = kvp.Key;
        var dataPoints = kvp.Value;
        foreach(var dataPoint in dataPoints)
        {
            Log($"{datasetSymbol} direction at {slice.Time}: {dataPoint.Direction}");
        }
    }
}

Historical Data

To get historical CNBC Trading 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[QuiverCNBCs](self.dataset_symbol, 100, Resolution.DAILY)

var history = History<QuiverCNBCs>(_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 CNBC Trading data, call the AddUniverseadd_universe method with the QuiverCNBCsUniverse class and a selection function.

Select Language:

def initialize(self):
    self._uinverse = self.add_universe(QuiverCNBCsUniverse, self.universe_selection)

def universe_selection(self, alt_coarse: List[QuiverCNBCsUniverse]) -> List[Symbol]:
    cnbc_data_by_symbol = {}

    for datum in alt_coarse:
        symbol = datum.symbol
        
        if symbol not in cnbc_data_by_symbol:
            cnbc_data_by_symbol[symbol] = []
        cnbc_data_by_symbol[symbol].append(datum)
    
    # define our selection criteria
    return [symbol for symbol, d in cnbc_data_by_symbol.items()
            if len([x for x in d if x.direction == OrderDirection.BUY]) >= 3]

private Universe _universe;
public override void Initialize()
{
    _universe = AddUniverse<QuiverCNBCsUniverse>(altCoarse =>
    {
        var cnbcDataBySymbol = new Dictionary<Symbol, List<QuiverCNBCsUniverse>>();

        foreach (var datum in altCoarse.OfType<QuiverCNBCsUniverse>())
        {
            var symbol = datum.Symbol;

            if (!cnbcDataBySymbol.ContainsKey(symbol))
            {
                cnbcDataBySymbol.Add(symbol, new List<QuiverCNBCsUniverse>());
            }
            cnbcDataBySymbol[symbol].Add(datum);
        }

        // define our selection criteria
        return from kvp in cnbcDataBySymbol
            where kvp.Value.Where(x => x.Direction == OrderDirection.Buy) >= 3
            select kvp.Key;
    });
}

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 cnbcs in universeHistory)
{
    foreach (QuiverCNBCsUniverse cnbc in cnbcs)
    {
        Log($"{cnbc.Symbol} traders at {cnbc.EndTime}: {cnbc.Traders}");
    }
}

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

# Series example where the values are lists of QuiverCNBCsUniverse objects: 
universe_history = self.history(self._universe, 30, Resolution.DAILY)
for (_, time), cbncs in universe_history.items():
    for cbnc in cbncs:
        self.log(f"{cbnc.symbol} traders at {cbnc.end_time}: {cbnc.traders}")

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 (var cnbcs in universeHistory)
{
    foreach (QuiverCNBCsUniverse cnbc in cnbcs)
    {
        Console.WriteLine($"{cnbc.Symbol} traders at {cnbc.EndTime}: {cnbc.Traders}");
    }
}

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

# Series example where the values are lists of QuiverCNBCsUniverse objects: 
universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
for (_, time), cbncs in universe_history.items():
    for cbnc in cbncs:
        print(f"{cbnc.symbol} traders at {cbnc.end_time}: {cbnc.traders}")

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 CNBC Trading 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 Quiver Quantitative CNBC Trading dataset enables you to create strategies using the latest recommendations made by media personalities on CNBC. Examples include the following strategies:

Taking short positions in securities that were mentioned by Jim Cramer (CNBC commentator) in the last week
Trading securities that were most/least discussed across CNBC programs over the last year

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

CNBC Trading

Introduction

About the Provider

Getting Started

Data Summary

Example Applications

Data Point Attributes

QuiverCNBCs

QuiverCNBC

QuiverCNBCsUniverse

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