Tiingo
Tiingo News Feed
Introduction
The Tiingo News Feed dataset by Tiingo tracks US Equity news releases. The data covers 10,000 US Equities, starts in January 2014, and is delivered on a second frequency. This dataset is creating by Tiingo integrating over 120 different news providers into their platform.
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 Tiingo News Feed dataset, including CLI commands and pricing, see the dataset listing.
About the Provider
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.
Example Applications
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
For more example algorithms, see Examples.
Requesting Data
To add Tiingo News Feed data to your algorithm, call the add_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.aapl).symbol
Accessing Data
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}")
To iterate through all of the articles in the current Slice
, call the get
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}")
Historical Data
To get historical Tiingo News Feed data, call the history
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)
For more information about historical data, see History Requests.
Remove Subscriptions
To remove a subscription, call the remove_security
method.
self.remove_security(self.dataset_symbol)
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.
Example Applications
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
For more example algorithms, see Examples.