Tiingo

Tiingo was founded by Rishi Singh in 2014. Tiingo goes beyond traditional news sources and focuses on finding rich, quality content written by knowledgeable writers. Their proprietary algorithms scan unstructured, non-traditional news and other information sources while tagging companies, topics, and assets. This refined system is backed by over ten years of research and development, and is written by former institutional quant traders. Because of this dedicated approach, Tiingo's News API is a trusted tool used by quant funds, hedge funds, pension funds, social media companies, and tech companies around the world.

The following snippet demonstrates how to request data from the Tiingo News Feed dataset:

from QuantConnect.DataSource import *

self.aapl = self.add_equity("AAPL", Resolution.MINUTE).symbol
self.dataset_symbol = self.add_data(TiingoNews, self.symbol).symbol

using QuantConnect.DataSource;

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

The following table describes the dataset properties:

The Tiingo News Feed enables you to accurately design strategies harnessing news articles on the companies you're trading. Examples include the following strategies:

• Creating a dictionary of sentiment scores for various words and assigning a sentiment score to the content of each news release
• Calculating the sentiment of news releases with Natural Language Processing (NLP)
• Trading securities when their news releases are tagged by Tiingo with current buzzwords
• Detecting impactful news in ETF constituents

The Tiingo News Feed dataset provides TiingoNews objects, which have the following attributes:

To add Tiingo News Feed 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 TiingoNewsDataAlgorithm(QCAlgorithm):
    def initialize(self) -> None:
        self.set_start_date(2021, 1, 1)
        self.set_end_date(2021, 6, 1)
        self.set_cash(100000)

        self.aapl = self.add_equity("AAPL", Resolution.MINUTE).symbol
        self.dataset_symbol = self.add_data(TiingoNews, self.symbol).symbol

namespace QuantConnect.Algorithm.CSharp.AltData
{
    public class TiingoNewsDataAlgorithm : QCAlgorithm
    {
        private Symbol _symbol, _datasetSymbol;

        public override void Initialize()
        {
            SetStartDate(2021, 1, 1);
            SetEndDate(2021, 6, 1);
            SetCash(100000);

            _symbol = AddEquity("AAPL", Resolution.Minute).Symbol;
            _datasetSymbol = AddData<TiingoNews>(_symbol).Symbol;
        }
    }
}

To get the current Tiingo News Feed 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):
        article = slice[self.dataset_symbol]
        self.log(f"{self.dataset_symbol} article description at {slice.time}: {article.description}")

public override void OnData(Slice slice)
{
    if (slice.ContainsKey(_datasetSymbol))
    {
        var article = slice[_datasetSymbol];
        Log($"{_datasetSymbol} article description at {slice.Time}: {article.Description}");
    }
}

To iterate through all of the articles in the current Slice, call the Getget method.

def on_data(self, slice: Slice) -> None:
    for dataset_symbol, article in slice.get(TiingoNews).items():
        self.log(f"{dataset_symbol} article description at {slice.time}: {article.description}")

public override void OnData(Slice slice)
{
    foreach (var kvp in slice.Get<TiingoNews>())
    {
        var datasetSymbol = kvp.Key;
        var article = kvp.Value;
        Log($"{datasetSymbol} article description at {slice.Time}: {article.Description}");
    }
}

To get historical Tiingo News Feed 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
self.history[TiingoNews](self.dataset_symbol, 100, Resolution.DAILY)

var history = History<TiingoNews>(_datasetSymbol, 100, Resolution.Daily);

For more information about historical data, see History Requests.

To remove a subscription, call the RemoveSecurityremove_security method.

self.remove_security(self.dataset_symbol)

RemoveSecurity(_datasetSymbol);

If you subscribe to Tiingo News Feed 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.

The Tiingo News Feed enables you to accurately design strategies harnessing news articles on the companies you're trading. Examples include the following strategies:

• Creating a dictionary of sentiment scores for various words and assigning a sentiment score to the content of each news release
• Calculating the sentiment of news releases with Natural Language Processing (NLP)
• Trading securities when their news releases are tagged by Tiingo with current buzzwords
• Detecting impactful news in ETF constituents